[ { "path": ".gitignore", "content": "data/\ndata\nwork_dirs/\nlocal_scripts/\nsync_files.sh\n*.mp4\n*draft*\n*.zip\n*.tar\n*.gz\n\n# media\n*.mp4\n*.png\n*.jpg\n*.html\n\n\nckpts/\n# wandb\nwandb/\n\n# work in progress\n*wip*\n\n*results*\n*debug*\ntmp*\n\n# caches\n*.pyc\n*.swp\n\n\n__pycache__\n\n\n# comming soon\n*viewer*\nthird_party/flow3d_viewer/\nthird_party/viser_extras/" }, { "path": ".vscode/settings.json", "content": "{\n // Automatically format using Black on save.\n \"editor.formatOnSave\": true,\n // Draw a ruler at Black's column width.\n \"editor.rulers\": [\n 100,\n ],\n // Hide non-code files.\n \"files.exclude\": {\n \"**/.git\": true,\n \"**/.svn\": true,\n \"**/.hg\": true,\n \"**/CVS\": true,\n \"**/.DS_Store\": true,\n \"**/Thumbs.db\": true,\n \"**/__pycache__\": true,\n \"**/venv\": true\n },\n \"[python]\": {\n \"editor.defaultFormatter\": \"ms-python.black-formatter\",\n },\n \"debug.focusWindowOnBreak\": false,\n \"files.watcherExclude\": {\n \"**/.git/**\": true,\n \"**/checkpoints/**\": true,\n \"**/data/**\": true,\n \"**/work_dirs/**\": true,\n \"**/lightning_logs/**\": true,\n \"**/outputs/**\": true,\n \"**/dataset_cache/**\": true,\n \"**/.ruff_cache/**\": true,\n \"**/venv/**\": true,\n \"**/data\": true\n },\n \"editor.mouseWheelZoom\": true,\n \"terminal.integrated.mouseWheelZoom\": true\n}" }, { "path": "LICENSE", "content": "NVIDIA License\n\n1. 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\n\n# **STORM** \n### Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenes \n\n[Project Page](https://jiawei-yang.github.io/STORM/) • [arXiv Paper](https://arxiv.org/abs/2501.00602)\n
\n\n---\n\n## Highlights\n* **Fast, feed-forward, and self-supervised** dynamic scene reconstruction from sparse multi-view sequences\n* Learns **3D Gaussian** *and* **scene flow** jointly; supports real-time rendering (once Gaussians are generated) and motion segmentation\n* Outperforms per‑scene optimization and other generalizable models by **+4 dB PSNR** on dynamic regions while being significantly faster\n\n---\n\n## Installation\n> Tested with **CUDA 12.1**, **PyTorch 2.3** and an NVIDIA **A100** \n> Replace the CUDA/PyTorch versions as needed for your environment\n\n```bash\n# clone project\ngit clone https://github.com/NVlabs/GaussianSTORM.git\ncd GaussianSTORM\n\n# create conda environment\nconda create -n storm python=3.10 -y\nconda activate storm\n\n# install python dependencies\npip install -r requirements.txt\n\n# install gsplat (for batch-wise rendering support)\npip install git+https://github.com/nerfstudio-project/gsplat.git@2b0de894232d21e8963179a7bbbd315f27c52c9c\n# └─ if the above fails, drop the commit hash:\n# pip install git+https://github.com/nerfstudio-project/gsplat.git\n```\n> Note: installing `gsplat` can be machine-dependent and sometimes be tricky. if you encounter issues, please refer to the original `gsplat` repository for troubleshooting.\n\n## Quick Start (Playground)\n\nWe provide a tiny subset of Waymo Open Dataset (3 sequences) for quick experimentation:\n\n```bash\n# download dataset subset (≈ 600 MB)\ngdown 14fapsAGoMCQ5Ky82cg2X6bk-mLQ7fdCF\ntar -xf STORM_subset.tar.gz\n```\n\n```bash\n# run single-GPU inference demo\npython inference.py \\\n --project storm_playground --exp_name visualization \\\n --data_root data/STORM_subset \\\n --model STORM-B/8 --num_motion_tokens 16 \\\n --use_sky_token --use_affine_token \\\n --load_depth --load_flow --load_ground \\\n --load_from $CKPT_PTH\n```\n> `CKPT_PTH` refers to the checkpoint. We cannot share an official checkpoint at this moment. Please refer to the issue page for an unofficial checkpoint.\n\n## Dataset Preparation\n\n### Waymo Dataset\n- To prepare the Waymo Open Dataset, please refer to [Waymo Data](docs/WAYMO.md)\n\n### Other datasets\nWe haven't included instructions for preparing NuScenes and Argoverse2 datasets. We might include these based on the capacity.\n\n## Training\n\nMulti-GPU example that reproduces the paper's STORM-B/8 model:\n\n```bash\n# with a global batch size= num_gpus * batch_size = 8 * 4 = 32 (We used 64 global batch size for main experiments)\ntorchrun --nproc_per_node=8 main_storm.py \\\n --project 0504_storm \\\n --exp_name 0504_pixel_storm \\\n --data_root ../storm2.3/data/STORM2 \\ # replace this with your data root.\n --batch_size 4 --num_iterations 100000 --lr_sched constant \\\n --model STORM-B/8 --num_motion_tokens 16 \\\n --use_sky_token --use_affine_token \\\n --load_depth --load_flow --load_ground \\\n --enable_depth_loss --enable_flow_reg_loss --flow_reg_coeff 0.005 --enable_sky_opacity_loss \\\n --enable_perceptual_loss --perceptual_loss_start_iter 5000 \\\n --enable_wandb \\\n --auto_resume\n```\n\n> **Tips:**\n> - Checkpoints and logs are saved at `work_dirs//`\n> - `batch_size` is per-GPU; global batch = batch_size × #GPUs × #nodes\n> - For additional arguments, see `main_storm.py`\n\n## Evaluation\n\n```bash\ntorchrun --nproc_per_node=8 main_storm.py \\\n --project 0504_storm \\\n --exp_name 0504_pixel_storm \\\n --data_root ../storm2.3/data/STORM2 \\ # replace this with your data root.\n --batch_size 4 --num_iterations 100000 --lr_sched constant \\\n --model STORM-B/8 --num_motion_tokens 16 \\\n --use_sky_token --use_affine_token \\\n --load_depth --load_flow --load_ground \\\n --enable_depth_loss --enable_flow_reg_loss --flow_reg_coeff 0.005 --enable_sky_opacity_loss \\\n --enable_perceptual_loss --perceptual_loss_start_iter 5000 \\\n --auto_resume \\\n --evaluate # this parameter specifies the evaluation mode\n```\n## TODO\n[ ] Viewers.\n\n\n## Citation\n\n```bibtex\n@inproceedings{yang2025storm,\n title = {STORM: Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenes},\n author = {Jiawei Yang and Jiahui Huang and Yuxiao Chen and Yan Wang and Boyi Li and Yurong You and Maximilian Igl and Apoorva Sharma and Peter Karkus and Danfei Xu and Boris Ivanovic and Yue Wang and Marco Pavone},\n booktitle = {ICLR},\n year = {2025}\n}\n```\n\n## License\n\nThis project is licensed under the **NVIDIA License**. See the [LICENSE](LICENSE) file for details.\n\n## Acknowledgements\n\nOur implementation builds upon **gsplat**.\nWe thank the respective authors for open‑sourcing their excellent work.\n\n\n" }, { "path": "docs/WAYMO.md", "content": "# Preparing Waymo Dataset\n> Note: This document is modified from [OmniRe](https://github.com/ziyc/drivestudio/blob/main/docs/Waymo.md) and [EmerNeRF](https://github.com/NVlabs/EmerNeRF/blob/main/docs/NOTR.md)\n\n## 1. Register on Waymo Open Dataset\n\n#### Sign Up for a Waymo Open Dataset Account and Install gcloud SDK\n\nTo download the Waymo dataset, you need to register an account at [Waymo Open Dataset](https://waymo.com/open/). You also need to install gcloud SDK and authenticate your account. Please refer to [this page](https://cloud.google.com/sdk/docs/install) for more details.\n\n#### Set Up the Data Directory\n\nOnce you've registered and installed the gcloud SDK, create a directory to house the raw data:\n\n```bash\n# create the data directory or create a symbolic link to the data directory\nmkdir -p ./data/waymo/raw \n```\n\n## 2. Environment\n\nWe highly recommend setting up another environment for data processing as the TensorFlow dependencies often conflict with our main environment.\n```bash\nconda create -n storm_data python=3.10\nconda activate storm_data\npip install -r requirements_data_preprocess.txt\n```\n\n## 3. Download the Raw Data\nFor the Waymo Open Dataset, we first organize the scene names alphabetically and store them in `data/dataset_scene_list/waymo_train_list.txt` and `data/dataset_scene_list/waymo_val_list.txt`. The scene index is then determined by the line number minus one.\n\nFor example, you can download 3 sequences from the dataset by:\n\n```bash\npython preproc/waymo_download.py \\\n --target_dir ./data/waymo/raw/training \\\n --split_file data/dataset_scene_list/waymo_train_list.txt \\\n --scene_ids 700 754 23\n```\nIf you wish to run experiments on different scenes, please specify your own list of scenes.\n\nYou can also omit the `scene_ids` to download all scenes specified in the `split_file`:\n\n```bash\n# training set\npython preproc/waymo_download.py \\\n --target_dir ./data/waymo/raw/training \\\n --split_file data/dataset_scene_list/waymo_train_list.txt\n\n# validation set\npython preproc/waymo_download.py \\\n --target_dir ./data/waymo/raw/validation \\\n --split_file data/dataset_scene_list/waymo_val_list.txt\n```\n\n
\nIf this script doesn't work due to network issues, consider manual download:\n\nDownload the [scene flow version](https://console.cloud.google.com/storage/browser/waymo_open_dataset_scene_flow;tab=objects?prefix=&forceOnObjectsSortingFiltering=false) of Waymo.\n\n![Waymo Dataset Download Page](https://github.com/user-attachments/assets/a1737699-e792-4fa0-bb68-0ab1813f1088)\n\n> **Note**: Ensure you're downloading the scene flow version to avoid errors.\n\n
\n\n## 4. Preprocess the Data\nAfter downloading the raw dataset, you'll need to preprocess this compressed data to extract and organize various components.\n\n#### Run the Preprocessing Script\nTo preprocess specific scenes of the dataset, use the following command:\n```bash\npython preprocess.py \\\n --data_root data/waymo/raw/ \\\n --target_dir data/waymo/processed \\\n --dataset waymo \\\n --split training \\\n --scene_list_file data/dataset_scene_list/waymo_train_list.txt \\\n --scene_ids 700 754 23 \\\n --num_workers 8 \\\n --process_keys images lidar calib pose dynamic_masks ground \\\n --json_folder_to_save data/STORM_data/annotations/waymo \n```\nAlternatively, preprocess a batch of scenes by providing the split file:\n```bash\n# training set\npython preprocess.py \\\n --data_root data/waymo/raw/ \\\n --target_dir data/waymo/processed \\\n --dataset waymo \\\n --split training \\\n --scene_list_file data/dataset_scene_list/waymo_train_list.txt \\\n --num_workers 8 \\\n --process_keys images lidar calib pose dynamic_masks ground \\\n --json_folder_to_save data/STORM_data/annotations/waymo \n\n# validation set\npython preprocess.py \\\n --data_root data/waymo/raw/ \\\n --target_dir data/waymo/processed \\\n --dataset waymo \\\n --split validation \\\n --scene_list_file data/dataset_scene_list/waymo_val_list.txt \\\n --num_workers 8 \\\n --process_keys images lidar calib pose dynamic_masks ground \\\n --json_folder_to_save data/STORM_data/annotations/waymo \n```\nThe extracted data will be stored in the `data/waymo/processed` directory.\n\n## 5. Extract Sky Masks\n\n- We originally used `ViT-adapater`, following [EmerNeRF](https://github.com/NVlabs/EmerNeRF/blob/main/docs/NOTR.md#sky-mask), to extract sky masks. We later realized the non-trivial dependency of installing `mmcv` properly.\n\n- Therefore, we recommend using `DepthAnything-v2` to extract the sky masks. Specifically, the predicted depth map from DepthAnything-v2 is a relative depth map, where lower values indicate farther regions, and zero values indicate infinitely far regions, which represent the sky.\n\n- To do this:\n\n 1. Use whatever command you prefer to generate the file list to all image files, e.g., `find data/waymo/processed/training/*/images -name \"*.jpg\" > file_list.txt`\n 2. Download DpethAnything-v2 checkpoint, e.g., `mkdir ckpts && wget https://huggingface.co/depth-anything/Depth-Anything-V2-Large/resolve/main/depth_anything_v2_vitl.pth -O ckpts/depth_anything_v2_vitl.pth`\n 2. Use `extract_sky_mask.py` to extract sky masks for these images by `python extract_sky.py --file_list ./file_list.txt`\n\n## 6. Data Structure\nAfter completing all preprocessing steps, the project files should be organized according to the following structure:\n```bash\nProjectPath/data/\n └── waymo/\n ├── raw/\n │ ├── segment-454855130179746819_4580_000_4600_000_with_camera_labels.tfrecord\n │ └── ...\n └── processed/\n └──training/\n ├── 000/\n │ ├──cam_to_ego/ # camera to ego-vehicle transformations: {cam_id}.txt\n │ ├──cam_to_world/ # camera to world transformations: {timestep:03d}_{cam_id}.txt\n │ ├──depth_flows_4/ # downsampled (1/4) depth flow maps: {timestep:03d}_{cam_id}.npy\n │ ├──dynamic_masks/ # bounding-box-generated dynamic masks: {timestep:03d}_{cam_id}.png\n │ ├──ego_to_world/ # ego-vehicle to world transformations: {timestep:03d}.txt\n │ ├──ground_label_4/ # downsampled (1/4) ground labels extracted from point cloud, used for flow evaluation only: {timestep:03d}.txt\n │ ├──images/ # original camera images: {timestep:03d}_{cam_id}.jpg\n │ ├──images_4/ # downsampled (1/4) camera images: {timestep:03d}_{cam_id}.jpg\n │ ├──intrinsics/ # camera intrinsics: {cam_id}.txt\n │ ├──lidar/ # lidar data: {timestep:03d}.bin\n │ ├──sky_masks/ # sky masks: {timestep:03d}_{cam_id}.png\n ├── 001/\n ├── ...\n```" }, { "path": "docs/example_data/segment-990914685337955114_980_000_1000_000_with_camera_labels.json", "content": "{\"dataset\": \"waymo\", \"scene_id\": 796, \"scene_name\": \"segment-990914685337955114_980_000_1000_000_with_camera_labels\", \"num_timesteps\": 198, \"camera_list\": [\"0\", \"1\", \"2\", \"3\", \"4\"], \"normalized_time\": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4.0, 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5.0, 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, 7.0, 7.1, 7.2, 7.3, 7.4, 7.5, 7.6, 7.7, 7.8, 7.9, 8.0, 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7, 8.8, 8.9, 9.0, 9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8, 9.9, 10.0, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9, 11.0, 11.1, 11.2, 11.3, 11.4, 11.5, 11.6, 11.7, 11.8, 11.9, 12.0, 12.1, 12.2, 12.3, 12.4, 12.5, 12.6, 12.7, 12.8, 12.9, 13.0, 13.1, 13.2, 13.3, 13.4, 13.5, 13.6, 13.7, 13.8, 13.9, 14.0, 14.1, 14.2, 14.3, 14.4, 14.5, 14.6, 14.7, 14.8, 14.9, 15.0, 15.1, 15.2, 15.3, 15.4, 15.5, 15.6, 15.7, 15.8, 15.9, 16.0, 16.1, 16.2, 16.3, 16.4, 16.5, 16.6, 16.7, 16.8, 16.9, 17.0, 17.1, 17.2, 17.3, 17.4, 17.5, 17.6, 17.7, 17.8, 17.9, 18.0, 18.1, 18.2, 18.3, 18.4, 18.5, 18.6, 18.7, 18.8, 18.9, 19.0, 19.1, 19.2, 19.3, 19.4, 19.5, 19.6, 19.7], \"normalized_intrinsics\": {\"0\": [1.0760852812653638, 1.6141279218980458, 0.4971965468813711, 0.5072499859713261], \"1\": [1.0714336048044208, 1.6071504072066312, 0.5112488553103808, 0.5005822834492333], \"2\": [1.067119656762737, 1.6006794851441053, 0.49226577388679255, 0.4929978469775671], \"3\": [1.0787773443175446, 2.337756773238923, 0.502880797268437, 0.2763063252339018], \"4\": [1.0748925209023337, 2.329338194280452, 0.5118216810459222, 0.25624083627281696]}, \"camera_to_ego\": {\"0\": [[0.9998842873191989, -0.0024402666225806988, 0.015015227969911943, 1.5389990840669499], [0.002560104988162815, 0.9999649851086498, -0.007967083475600458, -0.023573164304480632], [-0.014995260405451157, 0.008004602143037503, 0.9998555238182686, 2.115384290448616], [0.0, 0.0, 0.0, 1.0]], \"1\": [[0.718026611157686, -0.6959597957041816, 0.008817507176236696, 1.492768092127657], [0.6959181048595154, 0.7180804580838422, 0.007645066830298392, 0.09188386825351309], [-0.011652338741628897, 0.000646901455438632, 0.9999318999413697, 2.1150114403293925], [0.0, 0.0, 0.0, 1.0]], \"2\": [[0.7164452330835464, 0.6976372776735257, -0.00290805641879582, 1.4905599043950848], [-0.6976364192340506, 0.7164504681797242, 0.001467379755150582, -0.09390150748070432], [0.0031071772004358147, 0.0009774688362383767, 0.999994694988188, 2.1157584813144563], 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\"training/796/images/161_4.jpg\", \"training/796/images/162_4.jpg\", \"training/796/images/163_4.jpg\", \"training/796/images/164_4.jpg\", \"training/796/images/165_4.jpg\", \"training/796/images/166_4.jpg\", \"training/796/images/167_4.jpg\", \"training/796/images/168_4.jpg\", \"training/796/images/169_4.jpg\", \"training/796/images/170_4.jpg\", \"training/796/images/171_4.jpg\", \"training/796/images/172_4.jpg\", \"training/796/images/173_4.jpg\", \"training/796/images/174_4.jpg\", \"training/796/images/175_4.jpg\", \"training/796/images/176_4.jpg\", \"training/796/images/177_4.jpg\", \"training/796/images/178_4.jpg\", \"training/796/images/179_4.jpg\", \"training/796/images/180_4.jpg\", \"training/796/images/181_4.jpg\", \"training/796/images/182_4.jpg\", \"training/796/images/183_4.jpg\", \"training/796/images/184_4.jpg\", \"training/796/images/185_4.jpg\", \"training/796/images/186_4.jpg\", \"training/796/images/187_4.jpg\", \"training/796/images/188_4.jpg\", \"training/796/images/189_4.jpg\", \"training/796/images/190_4.jpg\", \"training/796/images/191_4.jpg\", \"training/796/images/192_4.jpg\", \"training/796/images/193_4.jpg\", \"training/796/images/194_4.jpg\", \"training/796/images/195_4.jpg\", \"training/796/images/196_4.jpg\", \"training/796/images/197_4.jpg\"]}, \"fps\": 10}" }, { "path": "docs/example_data/waymo_train.txt", "content": "annotations/waymo/training/segment-10231929575853664160_1160_000_1180_000_with_camera_labels.json\nannotations/waymo/training/segment-10391312872392849784_4099_400_4119_400_with_camera_labels.json\nannotations/waymo/training/segment-12339284075576056695_1920_000_1940_000_with_camera_labels.json\nannotations/waymo/training/segment-14810689888487451189_720_000_740_000_with_camera_labels.json\nannotations/waymo/training/segment-7670103006580549715_360_000_380_000_with_camera_labels.json\nannotations/waymo/training/segment-8822503619482926605_1080_000_1100_000_with_camera_labels.json\nannotations/waymo/training/segment-9907794657177651763_1126_570_1146_570_with_camera_labels.json\nannotations/waymo/training/segment-990914685337955114_980_000_1000_000_with_camera_labels.json\n" }, { "path": "engine_storm.py", "content": "import datetime\nimport logging\nimport os\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom skimage.metrics import structural_similarity as ssim\nfrom tqdm import tqdm\n\nimport storm.utils.distributed as distributed\nfrom storm.dataset.constants import MEAN, STD\nfrom storm.dataset.data_utils import prepare_inputs_and_targets\nfrom storm.utils.losses import compute_scene_flow_metrics\nfrom storm.visualization.video_maker import make_video\n\nlogger = logging.getLogger(\"STORM\")\n\n\n@torch.no_grad()\ndef visualize(args, model, dset_train, step, train_vis_id, device, dset_val=None, val_vis_id=None):\n model.eval()\n global_rank = distributed.get_global_rank()\n split = \"train\"\n for vis_id, dataset in zip([train_vis_id, val_vis_id], [dset_train, dset_val]):\n if vis_id is None or dataset is None: # sometimes there is no validation set\n continue\n\n sample_id = global_rank * 80 + vis_id\n out_pth = f\"{args.video_dir}/step{step}-rank{global_rank}-sample{sample_id}-{split}.mp4\"\n\n logger.info(f\"saving video to {out_pth}\")\n make_video(\n dataset,\n model,\n device,\n output_filename=out_pth,\n scene_id=sample_id,\n skip_plot_gt_depth_and_flow=False,\n )\n\n logger.info(f\"saved video to {out_pth}\")\n split = \"val\"\n\n torch.cuda.empty_cache()\n return train_vis_id + 1, val_vis_id + 1 if val_vis_id is not None else None\n\n\n@torch.no_grad()\ndef evaluate(dataloader, model, args, name_str=None):\n torch.cuda.empty_cache()\n model.eval()\n device = next(model.parameters()).device\n mean = torch.tensor(MEAN).to(device)\n std = torch.tensor(STD).to(device)\n\n eval_result_dir = os.path.join(args.log_dir, \"eval_results\")\n os.makedirs(eval_result_dir, exist_ok=True)\n logger.info(f\"Saving evaluation results to {eval_result_dir}\")\n # use yr-mo-dy-hr-min\n if name_str is None:\n name_str = datetime.datetime.now().strftime(\"%y-%m-%d-%H-%M\")\n\n def get_numpy(tensor):\n return tensor.squeeze().detach().cpu().numpy()\n\n # Initialize running sums and counts\n total_samples, total_dynamic_samples, total_valid_dynamic_depth_samples = 0, 0, 0\n total_psnr, total_ssim, total_depth_rmse = 0.0, 0.0, 0.0\n total_occupied_psnr, total_occupied_ssim = 0.0, 0.0\n total_dynamic_psnr, total_dynamic_ssim, total_dynamic_rmse = 0.0, 0.0, 0.0\n # test_indices = [1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19]\n printed = False\n pbar = tqdm(dataloader, desc=\"Evaluating\")\n for data_dict in pbar:\n input_dict, target_dict = prepare_inputs_and_targets(data_dict, device)\n input_indices = input_dict[\"context_frame_idx\"][0].cpu().numpy().tolist()\n input_indice_start = input_indices[0]\n input_indices = [idx - input_indice_start for idx in input_indices]\n # target indices include all frames between t+0 to t+19, including the input frames\n target_indices = target_dict[\"target_frame_idx\"][0].cpu().numpy().tolist()\n # remove the input frames from target indices to get test indices\n target_indices = [idx - input_indice_start for idx in target_indices]\n test_indices = [idx for idx in target_indices if idx not in input_indices]\n if not printed:\n logger.info(f\"Input indices: {input_indices}\")\n logger.info(f\"Test indices: {test_indices}\")\n printed = True\n pred_dict = model(input_dict)\n # evaluate on real target images:\n # b, t, v, c, h, w\n gt_rgb = target_dict[\"target_image\"][:, test_indices]\n # b, t, v, h, w, c\n gt_rgb = gt_rgb.permute(0, 1, 2, 4, 5, 3) * std + mean # transform from [-1, 1] to [0, 1]\n\n height, width = gt_rgb.shape[-3], gt_rgb.shape[-2]\n # btv, h, w, c\n gt_rgb = gt_rgb.reshape(-1, height, width, 3)\n gt_depth = target_dict[\"target_depth\"][:, test_indices].view(-1, height, width)\n gt_sky_mask = target_dict[\"target_sky_masks\"][:, test_indices].view(-1, height, width)\n occupied_mask = (gt_sky_mask == 0).bool()\n if \"target_dynamic_masks\" in target_dict:\n gt_dynamic_mask = target_dict[\"target_dynamic_masks\"][:, test_indices]\n gt_dynamic_mask = gt_dynamic_mask.view(-1, height, width)\n dynamic_mask = gt_dynamic_mask.bool()\n else:\n dynamic_mask = torch.ones_like(occupied_mask)\n valid_depth_mask = gt_depth > 0.0\n\n rendered_results = pred_dict[\"render_results\"]\n pred_rgb = rendered_results[rendered_results[\"rgb_key\"]][:, test_indices] * std + mean\n pred_rgb = pred_rgb.reshape(-1, height, width, 3).detach()\n pred_rgb = torch.clamp(pred_rgb, 0, 1)\n if rendered_results[\"decoder_depth_key\"] is None:\n pred_depth = rendered_results[rendered_results[\"depth_key\"]][:, test_indices].view(\n -1, height, width\n )\n else:\n pred_depth = rendered_results[rendered_results[\"decoder_depth_key\"]][\n :, test_indices\n ].view(-1, height, width)\n psnrs, ssim_scores, depth_rmses = [], [], []\n occupied_ssims, occupied_psnrs = [], []\n dynamic_ssims, dynamic_psnrs, dynamic_depth_rmses = [], [], []\n for i in range(len(gt_rgb)):\n ssim_score = ssim(\n get_numpy(pred_rgb[i]),\n get_numpy(gt_rgb[i]),\n data_range=1.0,\n channel_axis=-1,\n )\n ssim_scores.append(ssim_score)\n occupied_ssims.append(\n ssim(\n get_numpy(pred_rgb[i]),\n get_numpy(gt_rgb[i]),\n data_range=1.0,\n channel_axis=-1,\n full=True,\n )[1][get_numpy(occupied_mask[i])].mean()\n )\n psnrs.append(\n -10\n * torch.log10(\n F.mse_loss(\n pred_rgb[i],\n gt_rgb[i],\n )\n ).item()\n )\n occupied_psnrs.append(\n -10\n * torch.log10(\n F.mse_loss(\n pred_rgb[i][occupied_mask[i]],\n gt_rgb[i][occupied_mask[i]],\n )\n ).item()\n )\n depth_rms = torch.sqrt(\n F.mse_loss(\n pred_depth[i][valid_depth_mask[i]],\n gt_depth[i][valid_depth_mask[i]],\n )\n ).item()\n depth_rmses.append(depth_rms)\n if dynamic_mask[i].sum() == 0:\n continue\n dynamic_ssims.append(\n ssim(\n get_numpy(pred_rgb[i]),\n get_numpy(gt_rgb[i]),\n data_range=1.0,\n channel_axis=-1,\n full=True,\n )[1][get_numpy(dynamic_mask[i])].mean()\n )\n dynamic_psnrs.append(\n -10\n * torch.log10(\n F.mse_loss(\n pred_rgb[i][dynamic_mask[i]],\n gt_rgb[i][dynamic_mask[i]],\n )\n ).item()\n )\n\n total_dynamic_samples += 1\n _valid_depth_mask = dynamic_mask[i] & valid_depth_mask[i]\n if _valid_depth_mask.sum() == 0:\n continue\n dynamic_depth_rms = torch.sqrt(\n F.mse_loss(\n pred_depth[i][dynamic_mask[i] & valid_depth_mask[i]],\n gt_depth[i][dynamic_mask[i] & valid_depth_mask[i]],\n )\n ).item()\n dynamic_depth_rmses.append(dynamic_depth_rms)\n total_valid_dynamic_depth_samples += 1\n\n psnr_sum = np.sum(psnrs)\n ssim_sum = np.sum(ssim_scores)\n depth_rmse_sum = np.sum(depth_rmses)\n occupied_ssim_sum = np.sum(occupied_ssims)\n occupied_psnr_sum = np.sum(occupied_psnrs)\n dynamic_ssim_sum = np.sum(dynamic_ssims)\n dynamic_psnr_sum = np.sum(dynamic_psnrs)\n dynamic_depth_rmse_sum = np.sum(dynamic_depth_rmses)\n batch_size = len(gt_rgb)\n # Update running sums and counts\n total_psnr += psnr_sum\n total_ssim += ssim_sum\n total_depth_rmse += depth_rmse_sum\n total_occupied_psnr += occupied_psnr_sum\n total_occupied_ssim += occupied_ssim_sum\n total_dynamic_psnr += dynamic_psnr_sum\n total_dynamic_ssim += dynamic_ssim_sum\n total_dynamic_rmse += dynamic_depth_rmse_sum\n total_samples += batch_size\n pbar.set_postfix(\n psnr=psnr_sum / batch_size,\n ssim=ssim_sum / batch_size,\n depth_rmse=depth_rmse_sum / batch_size,\n avg_psnr=total_psnr / total_samples,\n avg_depth_rmse=total_depth_rmse / total_samples,\n avg_dynamic_psnr=total_dynamic_psnr / total_dynamic_samples,\n avg_dynamic_depth_rmse=total_dynamic_rmse / total_valid_dynamic_depth_samples,\n )\n\n # Create tensors for sums and counts\n total_psnr_tensor = torch.tensor(total_psnr, device=device)\n total_ssim_tensor = torch.tensor(total_ssim, device=device)\n total_depth_rmse_tensor = torch.tensor(total_depth_rmse, device=device)\n total_occupied_psnr_tensor = torch.tensor(total_occupied_psnr, device=device)\n total_occupied_ssim_tensor = torch.tensor(total_occupied_ssim, device=device)\n total_dynamic_psnr_tensor = torch.tensor(total_dynamic_psnr, device=device)\n total_dynamic_ssim_tensor = torch.tensor(total_dynamic_ssim, device=device)\n total_dynamic_rmse_tensor = torch.tensor(total_dynamic_rmse, device=device)\n total_samples_tensor = torch.tensor(total_samples, device=device)\n total_dynamic_samples_tensor = torch.tensor(total_dynamic_samples, device=device)\n total_valid_dynamic_depth_samples_tensor = torch.tensor(\n total_valid_dynamic_depth_samples, device=device\n )\n\n torch.cuda.synchronize()\n\n if distributed.is_enabled():\n # Aggregate sums across all processes\n torch.distributed.all_reduce(total_psnr_tensor)\n torch.distributed.all_reduce(total_ssim_tensor)\n torch.distributed.all_reduce(total_depth_rmse_tensor)\n torch.distributed.all_reduce(total_occupied_psnr_tensor)\n torch.distributed.all_reduce(total_occupied_ssim_tensor)\n torch.distributed.all_reduce(total_dynamic_psnr_tensor)\n torch.distributed.all_reduce(total_dynamic_ssim_tensor)\n torch.distributed.all_reduce(total_dynamic_rmse_tensor)\n torch.distributed.all_reduce(total_samples_tensor)\n torch.distributed.all_reduce(total_dynamic_samples_tensor)\n torch.distributed.all_reduce(total_valid_dynamic_depth_samples_tensor)\n result = None\n if distributed.is_main_process():\n avg_psnr = total_psnr_tensor.item() / total_samples_tensor.item()\n avg_ssim = total_ssim_tensor.item() / total_samples_tensor.item()\n avg_depth_rmse = total_depth_rmse_tensor.item() / total_samples_tensor.item()\n avg_occupied_psnr = total_occupied_psnr_tensor.item() / total_samples_tensor.item()\n avg_occupied_ssim = total_occupied_ssim_tensor.item() / total_samples_tensor.item()\n avg_dynamic_psnr = total_dynamic_psnr_tensor.item() / total_dynamic_samples_tensor.item()\n avg_dynamic_ssim = total_dynamic_ssim_tensor.item() / total_dynamic_samples_tensor.item()\n avg_dynamic_rmse = (\n total_dynamic_rmse_tensor.item() / total_valid_dynamic_depth_samples_tensor.item()\n )\n with open(os.path.join(eval_result_dir, f\"eval_{name_str}.txt\"), \"w\") as f:\n f.write(f\"Average PSNR: {avg_psnr:.4f}\\n\")\n f.write(f\"Average SSIM: {avg_ssim:.4f}\\n\")\n f.write(f\"Average Depth RMSE: {avg_depth_rmse:.4f}\\n\")\n f.write(f\"Average Occupied PSNR: {avg_occupied_psnr:.4f}\\n\")\n f.write(f\"Average Occupied SSIM: {avg_occupied_ssim:.4f}\\n\")\n f.write(f\"Average Dynamic PSNR: {avg_dynamic_psnr:.4f}\\n\")\n f.write(f\"Average Dynamic SSIM: {avg_dynamic_ssim:.4f}\\n\")\n f.write(f\"Average Dynamic Depth RMSE: {avg_dynamic_rmse:.4f}\\n\")\n logger.info(\"Evaluation results saved.\")\n logger.info(f\"Evaluated on {total_samples_tensor.item()} samples.\")\n logger.info(\n f\"Average PSNR: {avg_psnr:.4f}, Average SSIM: {avg_ssim:.4f}, Average Depth RMSE: {avg_depth_rmse:.4f}\"\n )\n logger.info(\n f\"Average Occupied PSNR: {avg_occupied_psnr:.4f}, Average Occupied SSIM: {avg_occupied_ssim:.4f}\"\n )\n logger.info(\n f\"Average Dynamic PSNR: {avg_dynamic_psnr:.4f}, Average Dynamic SSIM: {avg_dynamic_ssim:.4f}, Average Dynamic Depth RMSE: {avg_dynamic_rmse:.4f}\"\n )\n result = {\n \"psnr\": avg_psnr,\n \"ssim\": avg_ssim,\n \"depth_rmse\": avg_depth_rmse,\n \"occupied_psnr\": avg_occupied_psnr,\n \"occupied_ssim\": avg_occupied_ssim,\n \"dynamic_psnr\": avg_dynamic_psnr,\n \"dynamic_ssim\": avg_dynamic_ssim,\n \"dynamic_depth_rmse\": avg_dynamic_rmse,\n }\n torch.cuda.empty_cache()\n return result\n\n\n@torch.no_grad()\ndef evaluate_flow(dataloader, model, args, name_str=None):\n torch.cuda.empty_cache()\n model.eval()\n device = next(model.parameters()).device\n eval_result_dir = os.path.join(args.log_dir, \"eval_results\")\n os.makedirs(eval_result_dir, exist_ok=True)\n logger.info(f\"Saving evaluation results to {eval_result_dir}\")\n # use yr-mo-dy-hr-min\n if name_str is None:\n name_str = datetime.datetime.now().strftime(\"%y-%m-%d-%H-%M\")\n\n (\n total_flow_epes,\n total_flow_accs_strict,\n total_flow_accs_relax,\n total_flow_angles,\n total_flow_rmse,\n total_numb_flow_samples,\n ) = (0.0, 0.0, 0.0, 0.0, 0.0, 0.0)\n pbar = tqdm(dataloader, desc=\"Evaluating\")\n for data_dict in pbar:\n input_dict, target_dict = prepare_inputs_and_targets(data_dict, device)\n pred_dict = model(input_dict)\n # evaluate on real target images:\n # b, t, v, c, h, w\n b, t, v, height, width = target_dict[\"target_depth\"].shape\n gt_depth = target_dict[\"target_depth\"].view(b * t, -1, height, width)\n num_imgs = gt_depth.shape[0]\n valid_depth_mask = gt_depth > 0.0\n rendered_results = pred_dict[\"render_results\"]\n if args.load_ground:\n gt_ground_mask = target_dict[\"target_ground_masks\"].view(b * t, -1, height, width)\n gt_ground_mask = gt_ground_mask.bool()\n num_valid_samples = 0\n eval_flow = (\n \"rendered_flow\" in rendered_results\n and args.decoder_type == \"dummy\"\n and args.load_flow\n and \"target_flow\" in target_dict\n )\n if eval_flow:\n gt_flow = target_dict[\"target_flow\"].view(b * t, -1, height, width, 3)\n pred_flow = rendered_results[\"rendered_flow\"].view(b * t, -1, height, width, 3)\n # pred_flow = gt_flow.clone() + torch.rand_like(gt_flow) * 0.05\n flow_epes, flow_accs_strict, flow_accs_relax, flow_angles = [], [], [], []\n flow_rmse = []\n\n for i in range(num_imgs):\n if torch.max(gt_flow.norm(dim=-1)) > 1.0:\n if args.load_ground:\n non_ground_gt_flow = gt_flow[i][~gt_ground_mask[i] & valid_depth_mask[i]]\n non_ground_pred_flow = pred_flow[i][\n ~gt_ground_mask[i] & valid_depth_mask[i]\n ]\n else:\n non_ground_gt_flow = gt_flow[i][valid_depth_mask[i]]\n non_ground_pred_flow = pred_flow[i][valid_depth_mask[i]]\n flow_metrics = compute_scene_flow_metrics(\n non_ground_pred_flow, non_ground_gt_flow\n )\n flow_epes.append(flow_metrics[\"EPE3D\"])\n flow_accs_strict.append(flow_metrics[\"acc3d_strict\"] * 100)\n flow_accs_relax.append(flow_metrics[\"acc3d_relax\"] * 100)\n flow_angles.append(flow_metrics[\"angle_error\"])\n flow_rmse.append(\n torch.sqrt(\n F.mse_loss(\n pred_flow[i][valid_depth_mask[i]],\n gt_flow[i][valid_depth_mask[i]],\n )\n ).item()\n )\n num_valid_samples += 1\n\n flow_epe_sum = np.sum(flow_epes)\n flow_acc_strict_sum = np.sum(flow_accs_strict)\n flow_acc_relax_sum = np.sum(flow_accs_relax)\n flow_angle_sum = np.sum(flow_angles)\n flow_rmse_sum = np.sum(flow_rmse)\n valid_flow_samples = num_valid_samples\n\n # Update running sums and counts\n total_flow_epes += flow_epe_sum\n total_flow_accs_strict += flow_acc_strict_sum\n total_flow_accs_relax += flow_acc_relax_sum\n total_flow_angles += flow_angle_sum\n total_flow_rmse += flow_rmse_sum\n total_numb_flow_samples += valid_flow_samples\n\n pbar.set_postfix(\n avg_flow_epe=total_flow_epes / total_numb_flow_samples,\n avg_flow_acc_relax=total_flow_accs_relax / total_numb_flow_samples,\n avg_flow_acc_strict=total_flow_accs_strict / total_numb_flow_samples,\n avg_flow_angle=total_flow_angles / total_numb_flow_samples,\n avg_flow_rmse=total_flow_rmse / total_numb_flow_samples,\n )\n\n # Create tensors for sums and counts\n result = None\n if eval_flow:\n total_flow_epes_tensor = torch.tensor(total_flow_epes, device=device)\n total_flow_accs_strict_tensor = torch.tensor(total_flow_accs_strict, device=device)\n total_flow_accs_relax_tensor = torch.tensor(total_flow_accs_relax, device=device)\n total_flow_angles_tensor = torch.tensor(total_flow_angles, device=device)\n total_flow_rmse_tensor = torch.tensor(total_flow_rmse, device=device)\n total_numb_flow_samples_tensor = torch.tensor(total_numb_flow_samples, device=device)\n\n torch.cuda.synchronize()\n\n if distributed.is_enabled():\n # Aggregate sums across all processes\n torch.distributed.all_reduce(total_flow_epes_tensor)\n torch.distributed.all_reduce(total_flow_accs_strict_tensor)\n torch.distributed.all_reduce(total_flow_accs_relax_tensor)\n torch.distributed.all_reduce(total_flow_angles_tensor)\n torch.distributed.all_reduce(total_flow_rmse_tensor)\n torch.distributed.all_reduce(total_numb_flow_samples_tensor)\n if distributed.is_main_process() and total_numb_flow_samples_tensor.item() > 0:\n avg_flow_epe = total_flow_epes_tensor.item() / total_numb_flow_samples_tensor.item()\n avg_flow_acc_strict = (\n total_flow_accs_strict_tensor.item() / total_numb_flow_samples_tensor.item()\n )\n avg_flow_acc_relax = (\n total_flow_accs_relax_tensor.item() / total_numb_flow_samples_tensor.item()\n )\n avg_flow_angle = total_flow_angles_tensor.item() / total_numb_flow_samples_tensor.item()\n avg_flow_rmse = total_flow_rmse_tensor.item() / total_numb_flow_samples_tensor.item()\n with open(os.path.join(eval_result_dir, f\"eval_{name_str}_flow.txt\"), \"w\") as f:\n f.write(f\"Average Flow EPE: {avg_flow_epe:.4f}\\n\")\n f.write(f\"Average Flow Acc Strict: {avg_flow_acc_strict:.4f}\\n\")\n f.write(f\"Average Flow Acc Relax: {avg_flow_acc_relax:.4f}\\n\")\n f.write(f\"Average Flow Angle: {avg_flow_angle:.4f}\\n\")\n f.write(f\"Average Flow RMSE: {avg_flow_rmse:.4f}\\n\")\n logger.info(\"Evaluation results saved.\")\n logger.info(f\"Evaluated on {total_numb_flow_samples_tensor.item()} samples.\")\n logger.info(\n f\"Average Flow EPE: {avg_flow_epe:.4f}, Average Flow Acc Strict: {avg_flow_acc_strict:.4f}, Average Flow Acc Relax: {avg_flow_acc_relax:.4f}, Average Flow Angle: {avg_flow_angle:.4f}\"\n )\n logger.info(f\"Average Flow RMSE: {avg_flow_rmse:.4f}\")\n result = {\n \"flow_epe\": avg_flow_epe,\n \"flow_acc_strict\": avg_flow_acc_strict,\n \"flow_acc_relax\": avg_flow_acc_relax,\n \"flow_angle\": avg_flow_angle,\n \"flow_rmse\": avg_flow_rmse,\n }\n torch.cuda.empty_cache()\n return result\n" }, { "path": "extract_sky.py", "content": "import argparse\nimport os\nfrom typing import Optional, Tuple\n\nimport numpy as np\nimport torch\nimport torch.utils.data\nimport torchvision.transforms as transforms\nfrom PIL import Image\nfrom tqdm import tqdm\nfrom torchvision.datasets.folder import default_loader\nfrom storm.dataset.constants import IMGNET_MEAN, IMGNET_STD\nfrom third_party.depth_anything_v2.dpt import DepthAnythingV2\n\nclass ListDataset:\n \"\"\"Dataset class that loads images from a list of file paths.\"\"\"\n \n def __init__(\n self,\n data_list: str,\n transform: Optional[transforms.Compose] = None,\n return_path: bool = False,\n ):\n \"\"\"\n Initialize the dataset.\n \n Args:\n data_list: Path to text file containing image paths\n transform: Optional transforms to apply to images\n return_path: Whether to return image paths along with images\n \"\"\"\n self.transform = transform\n self.return_path = return_path\n self.loader = default_loader\n self.samples = self._load_samples(data_list)\n\n def _load_samples(self, data_list: str) -> list:\n \"\"\"Load image paths from the data list file.\"\"\"\n samples = []\n with open(data_list, \"r\") as f:\n for line in f:\n file_path = line.strip()\n samples.append(file_path)\n return samples\n\n def __getitem__(self, index: int) -> Tuple[torch.Tensor, str]:\n \"\"\"Get an image and optionally its path.\"\"\"\n img_pth = self.samples[index]\n try:\n img = self.loader(img_pth)\n except Exception as e:\n print(f\"Error loading '{img_pth}': {e}\")\n return self.__getitem__((index + 1) % len(self.samples))\n\n if self.transform is not None:\n img = self.transform(img)\n \n to_return = [img]\n if self.return_path:\n to_return.append(img_pth)\n return tuple(to_return) if len(to_return) > 1 else to_return[0]\n\n def __len__(self) -> int:\n return len(self.samples)\n\ndef get_args_parser() -> argparse.ArgumentParser:\n \"\"\"Get command line argument parser.\"\"\"\n parser = argparse.ArgumentParser(\"extract sky masks\", add_help=False)\n parser.add_argument(\"--eval_batch_size\", type=int, default=16)\n parser.add_argument(\"--num_workers\", default=10, type=int)\n parser.add_argument(\"--file_list\", type=str, default=\"file_list.txt\")\n parser.add_argument(\"--depth_ckpt\", type=str, default=\"ckpts/depth_anything_v2_vitl.pth\")\n\n return parser\n\n\ndef setup_model(device: torch.device, depth_ckpt: str) -> DepthAnythingV2:\n \"\"\"Initialize and setup the depth estimation model.\"\"\"\n depthv2 = DepthAnythingV2(encoder=\"vitl\", features=256, out_channels=[256, 512, 1024, 1024])\n depthv2.load_state_dict(torch.load(depth_ckpt, map_location=\"cpu\"))\n depthv2 = depthv2.eval().to(device)\n for param in depthv2.parameters():\n param.requires_grad = False\n return depthv2\n\n@torch.no_grad()\ndef get_sky_mask(dataloader: torch.utils.data.DataLoader, depthv2: DepthAnythingV2) -> None:\n \"\"\"\n Extract sky masks from images using depth estimation.\n \n Args:\n dataloader: DataLoader containing images\n depthv2: Depth estimation model\n \"\"\"\n torch.cuda.empty_cache()\n device = next(depthv2.parameters()).device\n \n pbar = tqdm(dataloader, desc=f\"Extracting sky masks\")\n for samples, paths in pbar:\n samples = samples.to(device)\n # predict depth using the model\n with torch.autocast(device.type, dtype=torch.bfloat16):\n outputs = depthv2(samples)\n # identify sky regions (depth = 0)\n sky_masks = (outputs == 0).float()\n sky_masks = sky_masks.cpu().numpy()\n # save masks\n for i in range(len(sky_masks)):\n mask = sky_masks[i]\n mask = (mask * 255).astype(np.uint8)\n raise NotImplementedError(\"please specify the target path by yourself and comment this line\")\n \n # e.g:\n tgt_pth = paths[i].replace(\"images\", \"sky_masks_518\")\n tgt_pth = tgt_pth.replace(\"jpg\", \"png\")\n \n # ensure directory exists and save mask\n os.makedirs(os.path.dirname(tgt_pth), exist_ok=True)\n Image.fromarray(mask).save(tgt_pth)\n\ndef main(args: argparse.Namespace) -> None:\n # setup device\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n img_transformation = transforms.Compose([\n transforms.Resize([518, 518], interpolation=Image.BICUBIC, antialias=True),\n transforms.ToTensor(),\n transforms.Normalize(mean=IMGNET_MEAN, std=IMGNET_STD),\n ])\n # setup dataloaders\n dataset = ListDataset(data_list=args.file_list, transform=img_transformation, return_path=True)\n data_loader = torch.utils.data.DataLoader(\n dataset,\n batch_size=args.eval_batch_size,\n num_workers=args.num_workers,\n shuffle=False,\n drop_last=False,\n )\n # setup model\n depthv2 = setup_model(device, args.depth_ckpt)\n get_sky_mask(data_loader, depthv2)\n\nif __name__ == \"__main__\":\n args = get_args_parser()\n args = args.parse_args()\n main(args)\n" }, { "path": "inference.py", "content": "import argparse\nimport copy\nimport datetime\nimport json\nimport logging\nimport math\nimport os\nimport time\n\nimport numpy as np\nimport torch\nimport torch.backends.cudnn as cudnn\nimport torch.utils.data\n\nimport storm.models as models\nimport storm.utils.misc as misc\nfrom storm.dataset.constants import DATASET_DICT\nfrom storm.dataset.data_utils import to_batch_tensor\nfrom storm.dataset.storm_dataset import SingleSequenceDataset\nfrom storm.utils.logging import setup_logging\nfrom storm.visualization.video_maker import make_video\n\ntorch.backends.cuda.matmul.allow_tf32 = True\ntorch.backends.cudnn.allow_tf32 = True\n\n\ndef get_args_parser():\n parser = argparse.ArgumentParser(\"STORM training\", add_help=False)\n\n # =============== Model parameters ================= #\n parser.add_argument(\"--model\", default=\"STORM-B/8\", type=str)\n parser.add_argument(\"--num_context_timesteps\", default=4, type=int)\n parser.add_argument(\"--num_target_timesteps\", default=4, type=int)\n parser.add_argument(\"--gs_dim\", default=3, type=int, help=\"Number of gs dimensions\")\n parser.add_argument(\"--use_sky_token\", action=\"store_true\")\n parser.add_argument(\"--use_affine_token\", action=\"store_true\")\n parser.add_argument(\"--use_latest_gsplat\", action=\"store_true\")\n parser.add_argument(\n \"--decoder_type\",\n type=str,\n choices=[\"dummy\", \"conv\"],\n default=\"dummy\",\n help=\"STORM or LatentSTORM\",\n )\n parser.add_argument(\"--num_motion_tokens\", default=16, type=int, help=\"Number of motion tokens\")\n\n # ============= Checkpoint parameters ============= #\n parser.add_argument(\"--auto_resume\", action=\"store_true\")\n parser.add_argument(\"--resume_from\", default=None, help=\"resume from checkpoint\")\n parser.add_argument(\"--load_from\", type=str, default=None)\n\n # ============= Dataset parameters ============= #\n parser.add_argument(\"--data_root\", default=\"./data/STORM2\", type=str, help=\"dataset path\")\n parser.add_argument(\"--overwrite_train_ctx_view_with\", default=None, type=int)\n parser.add_argument(\"--overwrite_train_tgt_view_with\", default=None, type=int)\n parser.add_argument(\"--overwrite_test_ctx_view_with\", default=None, type=int)\n parser.add_argument(\"--input_size\", default=(160, 240), type=int, nargs=2)\n parser.add_argument(\"--num_max_cameras\", type=int, default=3)\n parser.add_argument(\"--timespan\", type=float, default=2.0)\n parser.add_argument(\"--load_ground\", action=\"store_true\")\n parser.add_argument(\"--load_depth\", action=\"store_true\")\n parser.add_argument(\"--load_flow\", action=\"store_true\")\n parser.add_argument(\"--dataset\", default=\"waymo\", type=str, choices=DATASET_DICT.keys())\n parser.add_argument(\"--skip_sky_mask\", action=\"store_true\", help=\"skip sky mask loading\")\n # ============= Logging ============= #\n parser.add_argument(\"--output_dir\", default=\"./work_dirs\")\n parser.add_argument(\"--num_vis_samples\", type=int, default=1)\n\n # ============= Miscellaneous ============= #\n parser.add_argument(\"--seed\", default=1, type=int)\n parser.add_argument(\"--device\", default=\"cuda\", help=\"device to use for training / testing\")\n\n # ============= WandB ============= #\n parser.add_argument(\"--project\", default=\"debug\", type=str)\n parser.add_argument(\"--exp_name\", default=None, type=str)\n\n return parser\n\n\ndef main(args):\n global logger\n args.exp_name = args.model.replace(\"/\", \"-\") if args.exp_name is None else args.exp_name\n log_dir = os.path.join(args.output_dir, args.project, args.exp_name)\n checkpoint_dir = os.path.join(log_dir, \"checkpoints\")\n video_dir = os.path.join(log_dir, \"videos\")\n args.log_dir, args.ckpt_dir, args.video_dir = log_dir, checkpoint_dir, video_dir\n\n device = torch.device(args.device)\n seed = args.seed\n misc.fix_random_seeds(seed)\n cudnn.benchmark = True\n\n # set up logging\n setup_logging(output=log_dir, level=logging.INFO)\n logger = logging.getLogger(\"STORM\")\n logger.info(f\"hostname: {os.uname().nodename}\\n\")\n logger.info(f\"job dir: {os.path.dirname(os.path.realpath(__file__))}\")\n logger.info(f\"Logging to {log_dir}\")\n logger.info(json.dumps(args.__dict__, indent=4, sort_keys=True))\n with open(os.path.join(log_dir, \"args.json\"), \"w\") as f:\n json.dump(args.__dict__, f, indent=4)\n dataset_meta = DATASET_DICT[args.dataset]\n train_annotation = dataset_meta[\"annotation_txt_file_train\"]\n val_annotation = dataset_meta[\"annotation_txt_file_val\"]\n if train_annotation is not None:\n if args.dataset == \"nuscenes\":\n train_annotation = f\"data/dataset_scene_list/nuscenes_train.txt\"\n else:\n train_annotation = f\"{args.data_root}/{train_annotation}\"\n if val_annotation is not None:\n if args.dataset == \"nuscenes\":\n val_annotation = f\"data/dataset_scene_list/nuscenes_val.txt\"\n else:\n val_annotation = f\"{args.data_root}/{val_annotation}\"\n if not os.path.exists(val_annotation):\n val_annotation = None\n num_context_timesteps = dataset_meta[\"num_context_timesteps\"]\n num_target_timesteps = dataset_meta[\"num_target_timesteps\"]\n if args.overwrite_train_ctx_view_with is not None:\n num_context_timesteps = args.overwrite_train_ctx_view_with\n if args.overwrite_test_ctx_view_with is not None:\n num_context_timesteps = args.overwrite_test_ctx_view_with\n if args.overwrite_train_tgt_view_with is not None:\n num_target_timesteps = args.overwrite_train_tgt_view_with\n input_size = dataset_meta[\"size\"]\n logger.info(f\"Dataset: {args.dataset}\")\n logger.info(f\"annotation_txt_file_list_train: {train_annotation}\")\n\n if args.model in models.STORM_models:\n model = models.STORM_models[args.model](\n img_size=args.input_size,\n gs_dim=args.gs_dim,\n decoder_type=args.decoder_type,\n use_sky_token=args.use_sky_token,\n use_affine_token=args.use_affine_token,\n num_motion_tokens=args.num_motion_tokens,\n use_latest_gsplat=args.use_latest_gsplat,\n )\n else:\n raise ValueError(f\"Invalid model name: {args.model}\")\n\n logger.info(f\"Model = {str(model)}\")\n n_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n logger.info(f\"{args.model} Parameters: {n_params / 1e6:.2f}M ({n_params:,})\")\n model.to(device)\n dataset = SingleSequenceDataset(\n data_root=args.data_root,\n annotation_txt_file_list=train_annotation,\n target_size=input_size,\n num_context_timesteps=num_context_timesteps,\n num_target_timesteps=num_target_timesteps,\n timespan=args.timespan,\n num_max_cams=args.num_max_cameras,\n load_depth=args.load_depth,\n load_flow=args.load_flow,\n )\n\n logger.info(f\"Dataset contains {len(dataset):,} sequences using {train_annotation}.\")\n misc.load_model(args, model)\n num_trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n logger.info(f\"{args.model} Trainable Parameters: {num_trainable_params / 1e6:.2f}M\")\n model.eval().cuda()\n logger.info(f\"Preparing data... (This may take a while)\")\n data_dict_list = dataset.__getitem__(index=0, start_index=0, end_index=60)\n data_dict_list = to_batch_tensor(data_dict_list)\n logger.info(f\"Done preparing data.\")\n for i in range(len(data_dict_list)):\n data_dict = data_dict_list[i]\n output_name = f\"test_{i}.mp4\"\n make_video(\n dataset=None,\n model=model,\n device=device,\n output_filename=output_name,\n data_dict=data_dict,\n )\n print(f\"Saved video to {output_name}\")\n\n\nif __name__ == \"__main__\":\n args = get_args_parser().parse_args()\n main(args)\n" }, { "path": "main_storm.py", "content": "import argparse\nimport copy\nimport datetime\nimport json\nimport logging\nimport math\nimport os\nimport sys\nimport time\n\nimport numpy as np\nimport timm.optim.optim_factory as optim_factory\nimport torch\nimport torch.backends.cudnn as cudnn\nimport torch.distributed\nimport torch.utils.data\n\n# STORM imports\nimport storm.models as models\nimport storm.utils.distributed as distributed\nimport storm.utils.misc as misc\nfrom engine_storm import evaluate, evaluate_flow, visualize\nfrom storm.dataset.constants import DATASET_DICT\nfrom storm.dataset.data_utils import prepare_inputs_and_targets\nfrom storm.dataset.samplers import InfiniteSampler, NoPaddingDistributedSampler\nfrom storm.dataset.storm_dataset import STORMDataset, STORMDatasetEval\nfrom storm.utils.logging import MetricLogger, WandbLogger, setup_logging\nfrom storm.utils.losses import compute_loss\nfrom storm.utils.lpips_loss import RGBLpipsLoss\nfrom storm.utils.misc import NativeScalerWithGradNormCount as NativeScaler\n\ntorch.backends.cuda.matmul.allow_tf32 = True\ntorch.backends.cudnn.allow_tf32 = True\ncudnn.benchmark = True\n\n\ndef get_args_parser():\n parser = argparse.ArgumentParser(\"STORM training\", add_help=False)\n\n # =============== Model parameters ================= #\n parser.add_argument(\"--model\", default=\"STORM-B/8\", type=str)\n parser.add_argument(\"--num_context_timesteps\", default=4, type=int)\n parser.add_argument(\"--num_target_timesteps\", default=4, type=int)\n parser.add_argument(\"--gs_dim\", default=3, type=int, help=\"Number of gs dimensions\")\n parser.add_argument(\"--use_sky_token\", action=\"store_true\")\n parser.add_argument(\"--use_affine_token\", action=\"store_true\")\n parser.add_argument(\"--use_latest_gsplat\", action=\"store_true\")\n parser.add_argument(\n \"--decoder_type\",\n type=str,\n choices=[\"dummy\", \"conv\"],\n default=\"dummy\",\n help=\"STORM or LatentSTORM\",\n )\n parser.add_argument(\"--num_motion_tokens\", default=16, type=int, help=\"Number of motion tokens\")\n\n # =============== Losses =============== #\n parser.add_argument(\"--enable_depth_loss\", action=\"store_true\")\n\n # Option 1: push the sky depth to a fixed value\n parser.add_argument(\"--enable_sky_depth_loss\", action=\"store_true\")\n parser.add_argument(\"--sky_depth\", type=float, default=300.0)\n # Option 2: make sky gaussians transparent and use a sky token to represent sky\n parser.add_argument(\"--enable_sky_opacity_loss\", action=\"store_true\")\n parser.add_argument(\"--sky_opacity_loss_coeff\", type=float, default=0.1)\n\n # flow regularization loss\n parser.add_argument(\"--enable_flow_reg_loss\", action=\"store_true\")\n parser.add_argument(\"--flow_reg_coeff\", type=float, default=0.005)\n\n # perceptual loss\n parser.add_argument(\"--enable_perceptual_loss\", action=\"store_true\")\n parser.add_argument(\"--perceptual_weight\", default=0.05, type=float, help=\"LPIPS weight\")\n parser.add_argument(\"--perceptual_loss_start_iter\", default=5000, type=int)\n\n # ============= Optimizer and LR parameters ============= #\n parser.add_argument(\"--lr\", type=float, default=4e-4, help=\"learning rate (absolute lr)\")\n parser.add_argument(\"--blr\", type=float, default=8e-4, help=\"base learning rate\")\n parser.add_argument(\"--min_lr\", type=float, default=0.0)\n parser.add_argument(\"--lr_sched\", type=str, default=\"cosine\", choices=[\"constant\", \"cosine\"])\n parser.add_argument(\"--warmup_iters\", type=int, default=5000, help=\"iters to warmup LR\")\n parser.add_argument(\"--weight_decay\", type=float, default=0.05)\n parser.add_argument(\"--grad_clip\", type=float, default=3.0, help=\"Gradient clip\")\n parser.add_argument(\"--disable_grad_checkpointing\", action=\"store_true\")\n\n parser.add_argument(\"--start_iteration\", default=0, type=int, help=\"start iteration\")\n parser.add_argument(\"--num_iterations\", default=200_000, type=int, help=\"num of iterations\")\n parser.add_argument(\"--resume_from\", default=None, help=\"resume from checkpoint\")\n parser.add_argument(\"--auto_resume\", action=\"store_true\")\n parser.add_argument(\"--load_from\", type=str, default=None)\n\n # ============= Dataset parameters ============= #\n parser.add_argument(\"--data_root\", default=\"./data/STORM2\", type=str, help=\"dataset path\")\n parser.add_argument(\"--batch_size\", default=8, type=int, help=\"Batch size per GPU\")\n parser.add_argument(\"--eval_batch_size\", type=int, default=1)\n parser.add_argument(\"--input_size\", default=(160, 240), type=int, nargs=2)\n parser.add_argument(\"--num_max_cameras\", type=int, default=3)\n parser.add_argument(\"--timespan\", type=float, default=2.0)\n parser.add_argument(\"--load_ground\", action=\"store_true\")\n parser.add_argument(\"--load_depth\", action=\"store_true\")\n parser.add_argument(\"--load_flow\", action=\"store_true\")\n parser.add_argument(\"--dataset\", default=\"waymo\", type=str, choices=DATASET_DICT.keys())\n parser.add_argument(\"--subset_ratio\", default=1.0, type=float)\n parser.add_argument(\"--num_workers\", default=16, type=int)\n parser.add_argument(\"--skip_sky_mask\", action=\"store_true\", help=\"skip sky mask loading\")\n # ============= Logging ============= #\n parser.add_argument(\"--output_dir\", default=\"./work_dirs\")\n parser.add_argument(\"--num_vis_samples\", type=int, default=1)\n parser.add_argument(\"--log_every_n_iters\", type=int, default=50)\n parser.add_argument(\"--vis_every_n_iters\", type=int, default=5000)\n parser.add_argument(\"--ckpt_every_n_iters\", type=int, default=5000)\n parser.add_argument(\"--eval_every_n_iters\", type=int, default=50000)\n parser.add_argument(\"--total_elapsed_time\", type=float, default=0.0, help=\"total time elapsed\")\n parser.add_argument(\"--keep_n_ckpts\", default=1, type=int)\n\n # ============= Miscellaneous ============= #\n parser.add_argument(\"--seed\", default=1, type=int)\n parser.add_argument(\"--device\", default=\"cuda\", help=\"device to use for training / testing\")\n parser.add_argument(\"--visualization_only\", action=\"store_true\")\n parser.add_argument(\"--evaluate\", action=\"store_true\")\n\n # ============= WandB ============= #\n parser.add_argument(\"--enable_wandb\", action=\"store_true\")\n parser.add_argument(\"--project\", default=\"debug\", type=str)\n parser.add_argument(\"--entity\", default=\"YOUR_ENTITY\", type=str)\n parser.add_argument(\"--exp_name\", default=None, type=str)\n parser.add_argument(\"--overwrite_wandb\", action=\"store_true\")\n\n return parser\n\n\ndef main(args):\n # Prepare distributed training\n distributed.enable(overwrite=True)\n\n global logger\n args.exp_name = args.model.replace(\"/\", \"-\") if args.exp_name is None else args.exp_name\n log_dir = os.path.join(args.output_dir, args.project, args.exp_name)\n checkpoint_dir = os.path.join(log_dir, \"checkpoints\")\n video_dir = os.path.join(log_dir, \"videos\")\n args.log_dir, args.ckpt_dir, args.video_dir = log_dir, checkpoint_dir, video_dir\n\n device = torch.device(args.device)\n world_size, global_rank = distributed.get_world_size(), distributed.get_global_rank()\n seed = args.seed + global_rank\n misc.fix_random_seeds(seed)\n\n log_writer = None\n if global_rank == 0:\n [os.makedirs(d, exist_ok=True) for d in [log_dir, checkpoint_dir, video_dir]]\n if args.enable_wandb:\n run_id_path, run_id = os.path.join(log_dir, \"wandb_run_id.txt\"), None\n if os.path.exists(run_id_path) and not args.overwrite_wandb:\n with open(run_id_path, \"r\") as f:\n run_id = f.readlines()[-1].strip()\n log_writer = WandbLogger(args=args, resume=\"must\", id=run_id)\n if run_id is None:\n with open(run_id_path, \"a\") as f:\n f.write(log_writer.run_id + \"\\n\")\n\n # set up logging\n setup_logging(output=log_dir, level=logging.INFO)\n logger = logging.getLogger(\"STORM\")\n logger.info(f\"hostname: {os.uname().nodename}\\n\")\n logger.info(f\"job dir: {os.path.dirname(os.path.realpath(__file__))}\")\n logger.info(f\"Logging to {log_dir}\")\n logger.info(json.dumps(args.__dict__, indent=4, sort_keys=True))\n if global_rank == 0:\n with open(os.path.join(log_dir, \"args.json\"), \"w\") as f:\n json.dump(args.__dict__, f, indent=4)\n\n dataset_meta = DATASET_DICT[args.dataset]\n train_annotation = dataset_meta[\"annotation_txt_file_train\"]\n val_annotation = dataset_meta[\"annotation_txt_file_val\"]\n if train_annotation is not None:\n if args.dataset == \"nuscenes\":\n train_annotation = f\"data/dataset_scene_list/nuscenes_train.txt\"\n else:\n train_annotation = f\"{args.data_root}/{train_annotation}\"\n if val_annotation is not None:\n if args.dataset == \"nuscenes\":\n val_annotation = f\"data/dataset_scene_list/nuscenes_val.txt\"\n else:\n val_annotation = f\"{args.data_root}/{val_annotation}\"\n if not os.path.exists(val_annotation):\n val_annotation = None\n\n dataset_train = STORMDataset(\n data_root=args.data_root,\n annotation_txt_file_list=train_annotation,\n target_size=args.input_size,\n num_context_timesteps=args.num_context_timesteps,\n num_target_timesteps=args.num_target_timesteps,\n timespan=args.timespan,\n num_max_cams=args.num_max_cameras,\n load_depth=args.load_depth,\n load_flow=args.load_flow,\n skip_sky_mask=args.skip_sky_mask,\n )\n sampler_train = InfiniteSampler(sample_count=len(dataset_train), shuffle=True, seed=seed)\n data_loader_train = torch.utils.data.DataLoader(\n dataset_train,\n sampler=sampler_train,\n batch_size=args.batch_size,\n num_workers=args.num_workers,\n pin_memory=False,\n persistent_workers=True,\n drop_last=True,\n )\n\n if val_annotation is not None:\n dataset_val = STORMDataset(\n data_root=args.data_root,\n annotation_txt_file_list=val_annotation,\n target_size=args.input_size,\n num_context_timesteps=args.num_context_timesteps,\n num_target_timesteps=args.num_target_timesteps,\n timespan=args.timespan,\n num_max_cams=args.num_max_cameras,\n load_depth=args.load_depth,\n load_flow=args.load_flow,\n skip_sky_mask=args.skip_sky_mask,\n )\n dataset_eval = STORMDatasetEval(\n data_root=args.data_root,\n annotation_txt_file_list=val_annotation,\n target_size=args.input_size,\n num_context_timesteps=args.num_context_timesteps,\n num_target_timesteps=args.num_target_timesteps,\n timespan=args.timespan,\n num_max_cams=args.num_max_cameras,\n load_depth=args.load_depth,\n load_flow=args.load_flow,\n load_dynamic_mask=True,\n load_ground_label=args.load_ground,\n skip_sky_mask=args.skip_sky_mask,\n )\n dataset_eval_flow = STORMDatasetEval(\n data_root=args.data_root,\n annotation_txt_file_list=val_annotation,\n target_size=args.input_size,\n num_context_timesteps=args.num_context_timesteps,\n num_target_timesteps=args.num_target_timesteps,\n timespan=args.timespan,\n num_max_cams=args.num_max_cameras,\n load_depth=args.load_depth,\n load_flow=args.load_flow,\n load_dynamic_mask=False,\n load_ground_label=args.load_ground,\n return_context_as_target=True,\n skip_sky_mask=args.skip_sky_mask,\n )\n sampler = NoPaddingDistributedSampler(\n dataset_eval,\n num_replicas=world_size,\n rank=global_rank,\n shuffle=False,\n )\n data_loader_eval = torch.utils.data.DataLoader(\n dataset_eval,\n batch_size=args.eval_batch_size,\n num_workers=args.num_workers,\n sampler=sampler,\n pin_memory=False,\n persistent_workers=True,\n shuffle=False,\n drop_last=False,\n )\n data_loader_eval_flow = torch.utils.data.DataLoader(\n dataset_eval_flow,\n batch_size=args.eval_batch_size,\n num_workers=args.num_workers,\n sampler=sampler,\n pin_memory=False,\n persistent_workers=True,\n shuffle=False,\n drop_last=False,\n )\n else:\n dataset_val = None\n dataset_eval = None\n dataset_eval_flow = None\n data_loader_eval = None\n data_loader_eval_flow = None\n\n logger.info(f\"Dataset: {args.dataset}, train: {train_annotation}, val: {val_annotation}\")\n logger.info(f\"Dataset contains {len(dataset_train):,} sequences using {train_annotation}.\")\n\n if args.model in models.STORM_models:\n model = models.STORM_models[args.model](\n img_size=args.input_size,\n gs_dim=args.gs_dim,\n decoder_type=args.decoder_type,\n grad_checkpointing=not args.disable_grad_checkpointing,\n use_sky_token=args.use_sky_token,\n use_affine_token=args.use_affine_token,\n num_motion_tokens=args.num_motion_tokens,\n use_latest_gsplat=args.use_latest_gsplat,\n )\n else:\n raise ValueError(f\"Invalid model name: {args.model}\")\n\n logger.info(f\"Model = {str(model)}\")\n n_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n logger.info(f\"{args.model} Parameters: {n_params / 1e6:.2f}M ({n_params:,})\")\n model.to(device)\n model_without_ddp = model\n\n if distributed.is_enabled():\n model = torch.nn.parallel.DistributedDataParallel(model)\n model_without_ddp = model.module\n global_batch_size = args.batch_size * world_size\n if args.lr is None: # only base_lr is specified\n args.lr = args.blr * global_batch_size / 256\n logger.info(\"Global batch size: %d\" % global_batch_size)\n logger.info(f\"Base lr: {args.lr * 256 / global_batch_size:.2e}, Actual lr: {args.lr:.2e}\")\n\n param_groups = optim_factory.param_groups_weight_decay(model_without_ddp, args.weight_decay)\n optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95))\n loss_scaler = NativeScaler()\n\n logger.info(f\"Optimizer = {optimizer}\")\n logger.info(f\"Loss Scaler = {loss_scaler}\")\n\n # Load checkpoint or resume training\n logger.info(f\"Original start Iteration: {args.start_iteration}\")\n vis_slice_id = misc.load_model(args, model_without_ddp, optimizer, loss_scaler)\n logger.info(f\"New start iteration {args.start_iteration}\")\n\n num_trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n logger.info(f\"{args.model} Trainable Parameters: {num_trainable_params / 1e6:.2f}M\")\n logger.info(f\"Training with {world_size} GPUs\")\n\n data_iter_step = args.start_iteration\n if log_writer is not None:\n log_writer.set_step(data_iter_step)\n\n if args.evaluate:\n eval_result = evaluate(data_loader_eval, model_without_ddp, args)\n if log_writer is not None and eval_result is not None:\n eval_result = {f\"eval/{k}\": v for k, v in eval_result.items()}\n log_writer.update(eval_result)\n if args.dataset == \"waymo\":\n if args.decoder_type != \"conv\":\n flow_eval_result = evaluate_flow(data_loader_eval_flow, model_without_ddp, args)\n if log_writer is not None and flow_eval_result is not None:\n flow_eval_result = {f\"eval/{k}\": v for k, v in flow_eval_result.items()}\n log_writer.update(flow_eval_result)\n logger.info(\"Evaluation done, exiting.\")\n exit()\n\n valid_slice_id = copy.deepcopy(vis_slice_id)\n if dataset_val is not None and valid_slice_id >= len(dataset_val):\n valid_slice_id = 0\n for _ in range(args.num_vis_samples):\n vis_slice_id, valid_slice_id = visualize(\n args=args,\n model=model_without_ddp,\n dset_train=dataset_train,\n step=data_iter_step,\n train_vis_id=vis_slice_id,\n device=device,\n dset_val=dataset_val,\n val_vis_id=valid_slice_id,\n )\n if args.visualization_only:\n logger.info(\"Visualization done, exiting.\")\n exit()\n\n rgb_and_lpips_loss = RGBLpipsLoss(\n perceptual_weight=args.perceptual_weight,\n enable_perceptual_loss=args.enable_perceptual_loss,\n ).to(device)\n # will turn on perceptual loss after a certain number of iterations\n rgb_and_lpips_loss.set_perceptual_loss(False)\n\n logger.info(f\"Starting training from iteration {args.start_iteration} to {args.num_iterations}\")\n metrics_file = os.path.join(args.log_dir, \"training_metrics.json\")\n metric_logger = MetricLogger(delimiter=\" \", output_file=metrics_file)\n start_time = time.time()\n num_tokens_printed = False\n\n for data_dict in metric_logger.log_every(\n data_loader_train,\n print_freq=args.log_every_n_iters,\n header=\"Training\",\n n_iterations=args.num_iterations,\n start_iteration=args.start_iteration,\n ):\n if data_iter_step > args.num_iterations:\n break\n if log_writer is not None:\n log_writer.set_step(data_iter_step)\n if args.enable_perceptual_loss and data_iter_step >= args.perceptual_loss_start_iter:\n rgb_and_lpips_loss.set_perceptual_loss(True)\n\n model.train()\n misc.adjust_learning_rate(optimizer, data_iter_step, args)\n with torch.autocast(\"cuda\", dtype=torch.bfloat16):\n input_dict, target_dict = prepare_inputs_and_targets(data_dict, device)\n pred_dict = model(input_dict)\n loss_dict = compute_loss(pred_dict, target_dict, args, rgb_and_lpips_loss)\n\n loss_value = sum(loss for k, loss in loss_dict.items() if \"loss\" in k)\n\n if not math.isfinite(loss_value):\n logger.info(\"NaN detected\")\n raise AssertionError\n\n grad_norm = loss_scaler(\n loss_value,\n optimizer,\n parameters=model.parameters(),\n clip_grad=args.grad_clip,\n )\n optimizer.zero_grad()\n torch.cuda.synchronize()\n if world_size > 1:\n [torch.distributed.all_reduce(v) for v in loss_dict.values()]\n loss_dict_reduced = {k: v.item() / world_size for k, v in loss_dict.items()}\n total_loss_reduced = sum(loss for k, loss in loss_dict_reduced.items() if \"loss\" in k)\n lr = optimizer.param_groups[0][\"lr\"]\n psnr = -10 * np.log10(loss_dict_reduced[\"rgb_loss\"])\n metric_logger.update(lr=lr, psnr=psnr, loss=total_loss_reduced, **loss_dict_reduced)\n metric_logger.update(grad_norm=grad_norm)\n\n if \"num_tokens\" in pred_dict and not num_tokens_printed:\n logger.info(f\"num_tokens: {pred_dict['num_tokens']}\")\n num_tokens_printed = True\n\n if log_writer is not None:\n log_writer.update(\n {\n \"psnr\": psnr,\n \"loss\": total_loss_reduced,\n **loss_dict_reduced,\n \"lr\": lr,\n \"grad_norm\": grad_norm,\n }\n )\n log_writer.set_step()\n\n if (data_iter_step + 1) % args.ckpt_every_n_iters == 0:\n if distributed.is_main_process():\n elapsed_t = time.time() - start_time + args.total_elapsed_time\n checkpoint = {\n \"model\": model_without_ddp.state_dict(),\n \"optimizer\": optimizer.state_dict(),\n \"loss_scaler\": loss_scaler.state_dict(),\n \"latest_step\": data_iter_step,\n \"vis_slice_id\": vis_slice_id,\n \"args\": args,\n \"total_elapsed_time\": elapsed_t,\n }\n checkpoint_path = os.path.join(args.ckpt_dir, f\"ckpt_{data_iter_step:06d}.pth\")\n torch.save(checkpoint, checkpoint_path)\n misc.cleanup_checkpoints(args.ckpt_dir, keep_num=args.keep_n_ckpts)\n logger.info(f\"Saved checkpoint to {checkpoint_path}\")\n\n torch.distributed.barrier()\n torch.cuda.empty_cache()\n\n if (data_iter_step + 1) % args.vis_every_n_iters == 0:\n for _ in range(args.num_vis_samples):\n vis_slice_id, valid_slice_id = visualize(\n args=args,\n model=model_without_ddp,\n dset_train=dataset_train,\n step=data_iter_step,\n train_vis_id=vis_slice_id,\n device=device,\n dset_val=dataset_val,\n val_vis_id=valid_slice_id,\n )\n torch.distributed.barrier()\n torch.cuda.empty_cache()\n\n if (data_iter_step + 1) % args.eval_every_n_iters == 0 and (\n data_iter_step + 1\n ) != args.num_iterations:\n eval_result = evaluate(data_loader_eval, model_without_ddp, args, f\"{data_iter_step}\")\n if log_writer is not None and eval_result is not None:\n log_writer.update({f\"eval/{k}\": v for k, v in eval_result.items()})\n if args.decoder_type != \"conv\" and args.dataset == \"waymo\":\n flow_eval_result = evaluate_flow(\n data_loader_eval_flow,\n model_without_ddp,\n args,\n name_str=f\"{data_iter_step}\",\n )\n if log_writer is not None and flow_eval_result is not None:\n log_writer.update({f\"eval/{k}\": v for k, v in flow_eval_result.items()})\n torch.distributed.barrier()\n torch.cuda.empty_cache()\n\n data_iter_step += 1\n\n metric_logger.synchronize_between_processes()\n\n total_time = time.time() - start_time + args.total_elapsed_time\n total_time_str = str(datetime.timedelta(seconds=int(total_time)))\n logger.info(\"Training time {}\".format(total_time_str))\n eval_result = evaluate(data_loader_eval, model_without_ddp, args)\n if log_writer is not None and eval_result is not None:\n log_writer.update({f\"eval/{k}\": v for k, v in eval_result.items()})\n if args.decoder_type != \"conv\" and args.dataset == \"waymo\":\n flow_eval_result = evaluate_flow(data_loader_eval_flow, model_without_ddp, args)\n if log_writer is not None and flow_eval_result is not None:\n log_writer.update({f\"eval/{k}\": v for k, v in flow_eval_result.items()})\n logger.info(\"Done!\")\n\n\n\nif __name__ == \"__main__\":\n args = get_args_parser().parse_args()\n main(args)\n" }, { "path": "preproc/utils.py", "content": "# To track process without installing mmcv\n# Code is modified from mmcv.utils\nimport sys\nimport time\nfrom collections.abc import Iterable\nfrom multiprocessing import Pool\nfrom shutil import get_terminal_size\n\nimport numpy as np\n\n\nclass ProgressBar:\n \"\"\"A progress bar which can print the progress.\"\"\"\n\n def __init__(self, task_num=0, bar_width=50, start=True, file=sys.stdout):\n self.task_num = task_num\n self.bar_width = bar_width\n self.completed = 0\n self.file = file\n if start:\n self.start()\n\n @property\n def terminal_width(self):\n width, _ = get_terminal_size()\n return width\n\n def start(self):\n if self.task_num > 0:\n self.file.write(f'[{\" \" * self.bar_width}] 0/{self.task_num}, ' \"elapsed: 0s, ETA:\")\n else:\n self.file.write(\"completed: 0, elapsed: 0s\")\n self.file.flush()\n self.start_time = time.time()\n\n def update(self, num_tasks=1):\n assert num_tasks > 0\n self.completed += num_tasks\n elapsed = time.time() - self.start_time\n if elapsed > 0:\n fps = self.completed / elapsed\n else:\n fps = float(\"inf\")\n if self.task_num > 0:\n percentage = self.completed / float(self.task_num)\n eta = int(elapsed * (1 - percentage) / percentage + 0.5)\n msg = (\n f\"\\r[{{}}] {self.completed}/{self.task_num}, \"\n f\"{fps:.1f} task/s, elapsed: {int(elapsed + 0.5)}s, \"\n f\"ETA: {eta:5}s\"\n )\n\n bar_width = min(\n self.bar_width,\n int(self.terminal_width - len(msg)) + 2,\n int(self.terminal_width * 0.6),\n )\n bar_width = max(2, bar_width)\n mark_width = int(bar_width * percentage)\n bar_chars = \">\" * mark_width + \" \" * (bar_width - mark_width)\n self.file.write(msg.format(bar_chars))\n else:\n self.file.write(\n f\"completed: {self.completed}, elapsed: {int(elapsed + 0.5)}s,\"\n f\" {fps:.1f} tasks/s\"\n )\n self.file.flush()\n\n\ndef init_pool(process_num, initializer=None, initargs=None):\n if initializer is None:\n return Pool(process_num)\n elif initargs is None:\n return Pool(process_num, initializer)\n else:\n if not isinstance(initargs, tuple):\n raise TypeError('\"initargs\" must be a tuple')\n return Pool(process_num, initializer, initargs)\n\n\ndef track_parallel_progress(\n func,\n tasks,\n nproc,\n initializer=None,\n initargs=None,\n bar_width=50,\n chunksize=1,\n skip_first=False,\n keep_order=True,\n file=sys.stdout,\n):\n \"\"\"Track the progress of parallel task execution with a progress bar.\n\n The built-in :mod:`multiprocessing` module is used for process pools and\n tasks are done with :func:`Pool.map` or :func:`Pool.imap_unordered`.\n\n Args:\n func (callable): The function to be applied to each task.\n tasks (list or tuple[Iterable, int]): A list of tasks or\n (tasks, total num).\n nproc (int): Process (worker) number.\n initializer (None or callable): Refer to :class:`multiprocessing.Pool`\n for details.\n initargs (None or tuple): Refer to :class:`multiprocessing.Pool` for\n details.\n chunksize (int): Refer to :class:`multiprocessing.Pool` for details.\n bar_width (int): Width of progress bar.\n skip_first (bool): Whether to skip the first sample for each worker\n when estimating fps, since the initialization step may takes\n longer.\n keep_order (bool): If True, :func:`Pool.imap` is used, otherwise\n :func:`Pool.imap_unordered` is used.\n\n Returns:\n list: The task results.\n \"\"\"\n if isinstance(tasks, tuple):\n assert len(tasks) == 2\n assert isinstance(tasks[0], Iterable)\n assert isinstance(tasks[1], int)\n task_num = tasks[1]\n tasks = tasks[0]\n elif isinstance(tasks, Iterable):\n task_num = len(tasks)\n else:\n raise TypeError('\"tasks\" must be an iterable object or a (iterator, int) tuple')\n pool = init_pool(nproc, initializer, initargs)\n start = not skip_first\n task_num -= nproc * chunksize * int(skip_first)\n prog_bar = ProgressBar(task_num, bar_width, start, file=file)\n results = []\n if keep_order:\n gen = pool.imap(func, tasks, chunksize)\n else:\n gen = pool.imap_unordered(func, tasks, chunksize)\n for result in gen:\n results.append(result)\n if skip_first:\n if len(results) < nproc * chunksize:\n continue\n elif len(results) == nproc * chunksize:\n prog_bar.start()\n continue\n prog_bar.update()\n prog_bar.file.write(\"\\n\")\n pool.close()\n pool.join()\n return results\n" }, { "path": "preproc/waymo_download.py", "content": "import argparse\nimport os\nimport subprocess\nfrom concurrent.futures import ThreadPoolExecutor\nfrom typing import List\n\n\ndef download_file(filename, target_dir, source):\n result = subprocess.run(\n [\n \"gsutil\",\n \"cp\",\n \"-n\",\n f\"{source}/{filename}.tfrecord\",\n target_dir,\n ],\n capture_output=True,\n text=True,\n )\n\n if result.returncode != 0:\n raise Exception(result.stderr)\n\n\ndef download_files(\n file_names: List[str],\n target_dir: str,\n source: str,\n max_workers: int = 10,\n):\n total_files = len(file_names)\n\n with ThreadPoolExecutor(max_workers=max_workers) as executor:\n futures = [\n executor.submit(download_file, filename, target_dir, source) for filename in file_names\n ]\n\n for counter, future in enumerate(futures, start=1):\n try:\n future.result()\n print(f\"[{counter}/{total_files}] Downloaded successfully!\")\n except Exception as e:\n print(f\"[{counter}/{total_files}] Failed to download. Error: {e}\")\n\n\nif __name__ == \"__main__\":\n print(\"note: `gcloud auth login` is required before running this script\")\n print(\"Downloading Waymo dataset from Google Cloud Storage...\")\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--target_dir\",\n type=str,\n default=\"data/waymo/raw\",\n help=\"Path to the target directory\",\n )\n parser.add_argument(\n \"--split\",\n type=str,\n default=\"train\",\n )\n parser.add_argument(\n \"--scene_ids\", type=int, nargs=\"+\", help=\"scene ids to download\", default=None\n )\n parser.add_argument(\n \"--split_file\",\n type=str,\n default=\"data/dataset_scene_list/waymo_train_list.txt\",\n help=\"\",\n )\n parser.add_argument(\n \"--max_workers\",\n type=int,\n default=10,\n help=\"Number of threads to use for downloading\",\n )\n args = parser.parse_args()\n os.makedirs(args.target_dir, exist_ok=True)\n total_list = open(args.split_file, \"r\").readlines()\n total_list = [x.strip() for x in total_list]\n if args.scene_ids is not None:\n file_names = [total_list[i] for i in args.scene_ids]\n else:\n file_names = total_list\n download_files(\n file_names,\n args.target_dir,\n source=f\"gs://waymo_open_dataset_scene_flow/{args.split}\",\n max_workers=args.max_workers,\n )\n" }, { "path": "preproc/waymo_preprocess.py", "content": "import json\nimport os\nimport time\n\nimport imageio\nimport imageio_ffmpeg as imageio_ffmpeg # Ensure ffmpeg is installed\nimport numpy as np\nimport tensorflow as tf\nfrom PIL import Image\nfrom tqdm import tqdm, trange\nfrom waymo_open_dataset.utils.frame_utils import parse_range_image_and_camera_projection\n\nfrom storm.visualization.visualization_tools import depth_visualizer, scene_flow_to_rgb\n\nfrom .utils import track_parallel_progress\n\nORIGINAL_SIZE = {\n \"0\": (1280, 1920),\n \"1\": (1280, 1920),\n \"2\": (1280, 1920),\n \"3\": (886, 1920),\n \"4\": (886, 1920),\n}\nOPENCV2DATASET = np.array([[0, 0, 1, 0], [-1, 0, 0, 0], [0, -1, 0, 0], [0, 0, 0, 1]])\n\n# Acknowledgement:\n# 1. https://github.com/open-mmlab/mmdetection3d/blob/main/tools/dataset_converters/waymo_converter.py\n# 2. https://github.com/leolyj/DCA-SRSFE/blob/main/data_preprocess/Waymo/generate_flow.py\ntry:\n from waymo_open_dataset import dataset_pb2\nexcept ImportError:\n raise ImportError(\n 'Please run \"pip install waymo-open-dataset-tf-2-6-0\" '\n \">1.4.5 to install the official devkit first.\"\n )\n\nimport numpy as np\nimport tensorflow as tf\nfrom waymo_open_dataset.utils import box_utils, range_image_utils, transform_utils\nfrom waymo_open_dataset.utils.frame_utils import parse_range_image_and_camera_projection\nfrom waymo_open_dataset.wdl_limited.camera.ops import py_camera_model_ops\n\n##### Very important to set memory growth for GPU to avoid OOM errors\ngpu_devices = tf.config.experimental.list_physical_devices(\"GPU\")\nfor device in gpu_devices:\n tf.config.experimental.set_memory_growth(device, True)\n\n\ndef project_vehicle_to_image(vehicle_pose, calibration, points):\n \"\"\"Projects from vehicle coordinate system to image with global shutter.\n\n Arguments:\n vehicle_pose: Vehicle pose transform from vehicle into world coordinate\n system.\n calibration: Camera calibration details (including intrinsics/extrinsics).\n points: Points to project of shape [N, 3] in vehicle coordinate system.\n\n Returns:\n Array of shape [N, 3], with the latter dimension composed of (u, v, ok).\n \"\"\"\n # Transform points from vehicle to world coordinate system (can be\n # vectorized).\n pose_matrix = np.array(vehicle_pose.transform).reshape(4, 4)\n world_points = np.zeros_like(points)\n for i, point in enumerate(points):\n cx, cy, cz, _ = np.matmul(pose_matrix, [*point, 1])\n world_points[i] = (cx, cy, cz)\n\n # Populate camera image metadata. Velocity and latency stats are filled with\n # zeroes.\n extrinsic = tf.reshape(\n tf.constant(list(calibration.extrinsic.transform), dtype=tf.float32), [4, 4]\n )\n intrinsic = tf.constant(list(calibration.intrinsic), dtype=tf.float32)\n metadata = tf.constant(\n [\n calibration.width,\n calibration.height,\n dataset_pb2.CameraCalibration.GLOBAL_SHUTTER,\n ],\n dtype=tf.int32,\n )\n camera_image_metadata = list(vehicle_pose.transform) + [0.0] * 10\n\n # Perform projection and return projected image coordinates (u, v, ok).\n return py_camera_model_ops.world_to_image(\n extrinsic, intrinsic, metadata, camera_image_metadata, world_points\n ).numpy()\n\n\ndef get_ground_np(pts):\n \"\"\"\n This function performs ground removal on a point cloud.\n Modified from https://github.com/tusen-ai/LiDAR_SOT/blob/main/waymo_data/data_preprocessing/ground_removal.py\n\n Args:\n pts (numpy.ndarray): The input point cloud.\n\n Returns:\n numpy.ndarray: A boolean array indicating whether each point is ground or not.\n \"\"\"\n th_seeds_ = 1.2\n num_lpr_ = 20\n n_iter = 10\n th_dist_ = 0.3\n pts_sort = pts[pts[:, 2].argsort(), :]\n lpr = np.mean(pts_sort[:num_lpr_, 2])\n pts_g = pts_sort[pts_sort[:, 2] < lpr + th_seeds_, :]\n normal_ = np.zeros(3)\n for i in range(n_iter):\n mean = np.mean(pts_g, axis=0)[:3]\n xx = np.mean((pts_g[:, 0] - mean[0]) * (pts_g[:, 0] - mean[0]))\n xy = np.mean((pts_g[:, 0] - mean[0]) * (pts_g[:, 1] - mean[1]))\n xz = np.mean((pts_g[:, 0] - mean[0]) * (pts_g[:, 2] - mean[2]))\n yy = np.mean((pts_g[:, 1] - mean[1]) * (pts_g[:, 1] - mean[1]))\n yz = np.mean((pts_g[:, 1] - mean[1]) * (pts_g[:, 2] - mean[2]))\n zz = np.mean((pts_g[:, 2] - mean[2]) * (pts_g[:, 2] - mean[2]))\n cov = np.array(\n [[xx, xy, xz], [xy, yy, yz], [xz, yz, zz]],\n dtype=np.float32,\n )\n U, S, V = np.linalg.svd(cov)\n normal_ = U[:, 2]\n d_ = -normal_.dot(mean)\n th_dist_d_ = th_dist_ - d_\n result = pts[:, :3] @ normal_[..., np.newaxis]\n pts_g = pts[result.squeeze(-1) < th_dist_d_]\n ground_label = result < th_dist_d_\n return ground_label\n\n\ndef compute_range_image_cartesian(\n range_image_polar,\n extrinsic,\n pixel_pose=None,\n frame_pose=None,\n dtype=tf.float32,\n scope=None,\n):\n \"\"\"Computes range image cartesian coordinates from polar ones.\n\n Args:\n range_image_polar: [B, H, W, 3] float tensor. Lidar range image in polar\n coordinate in sensor frame.\n extrinsic: [B, 4, 4] float tensor. Lidar extrinsic.\n pixel_pose: [B, H, W, 4, 4] float tensor. If not None, it sets pose for each\n range image pixel.\n frame_pose: [B, 4, 4] float tensor. This must be set when pixel_pose is set.\n It decides the vehicle frame at which the cartesian points are computed.\n dtype: float type to use internally. This is needed as extrinsic and\n inclination sometimes have higher resolution than range_image.\n scope: the name scope.\n\n Returns:\n range_image_cartesian: [B, H, W, 3] cartesian coordinates.\n \"\"\"\n range_image_polar_dtype = range_image_polar.dtype\n range_image_polar = tf.cast(range_image_polar, dtype=dtype)\n extrinsic = tf.cast(extrinsic, dtype=dtype)\n if pixel_pose is not None:\n pixel_pose = tf.cast(pixel_pose, dtype=dtype)\n if frame_pose is not None:\n frame_pose = tf.cast(frame_pose, dtype=dtype)\n\n with tf.compat.v1.name_scope(\n scope,\n \"ComputeRangeImageCartesian\",\n [range_image_polar, extrinsic, pixel_pose, frame_pose],\n ):\n azimuth, inclination, range_image_range = tf.unstack(range_image_polar, axis=-1)\n\n cos_azimuth = tf.cos(azimuth)\n sin_azimuth = tf.sin(azimuth)\n cos_incl = tf.cos(inclination)\n sin_incl = tf.sin(inclination)\n\n # [B, H, W].\n x = cos_azimuth * cos_incl * range_image_range\n y = sin_azimuth * cos_incl * range_image_range\n z = sin_incl * range_image_range\n\n # [B, H, W, 3]\n range_image_points = tf.stack([x, y, z], -1)\n range_image_origins = tf.zeros_like(range_image_points)\n # [B, 3, 3]\n rotation = extrinsic[..., 0:3, 0:3]\n # translation [B, 1, 3]\n translation = tf.expand_dims(tf.expand_dims(extrinsic[..., 0:3, 3], 1), 1)\n\n # To vehicle frame.\n # [B, H, W, 3]\n range_image_points = tf.einsum(\"bkr,bijr->bijk\", rotation, range_image_points) + translation\n range_image_origins = (\n tf.einsum(\"bkr,bijr->bijk\", rotation, range_image_origins) + translation\n )\n if pixel_pose is not None:\n # To global frame.\n # [B, H, W, 3, 3]\n pixel_pose_rotation = pixel_pose[..., 0:3, 0:3]\n # [B, H, W, 3]\n pixel_pose_translation = pixel_pose[..., 0:3, 3]\n # [B, H, W, 3]\n range_image_points = (\n tf.einsum(\"bhwij,bhwj->bhwi\", pixel_pose_rotation, range_image_points)\n + pixel_pose_translation\n )\n range_image_origins = (\n tf.einsum(\"bhwij,bhwj->bhwi\", pixel_pose_rotation, range_image_origins)\n + pixel_pose_translation\n )\n\n if frame_pose is None:\n raise ValueError(\"frame_pose must be set when pixel_pose is set.\")\n # To vehicle frame corresponding to the given frame_pose\n # [B, 4, 4]\n world_to_vehicle = tf.linalg.inv(frame_pose)\n world_to_vehicle_rotation = world_to_vehicle[:, 0:3, 0:3]\n world_to_vehicle_translation = world_to_vehicle[:, 0:3, 3]\n # [B, H, W, 3]\n range_image_points = (\n tf.einsum(\"bij,bhwj->bhwi\", world_to_vehicle_rotation, range_image_points)\n + world_to_vehicle_translation[:, tf.newaxis, tf.newaxis, :]\n )\n range_image_origins = (\n tf.einsum(\"bij,bhwj->bhwi\", world_to_vehicle_rotation, range_image_origins)\n + world_to_vehicle_translation[:, tf.newaxis, tf.newaxis, :]\n )\n\n range_image_points = tf.cast(range_image_points, dtype=range_image_polar_dtype)\n range_image_origins = tf.cast(range_image_origins, dtype=range_image_polar_dtype)\n return range_image_points, range_image_origins\n\n\ndef extract_point_cloud_from_range_image(\n range_image,\n extrinsic,\n inclination,\n pixel_pose=None,\n frame_pose=None,\n dtype=tf.float32,\n scope=None,\n):\n \"\"\"Extracts point cloud from range image.\n\n Args:\n range_image: [B, H, W] tensor. Lidar range images.\n extrinsic: [B, 4, 4] tensor. Lidar extrinsic.\n inclination: [B, H] tensor. Inclination for each row of the range image.\n 0-th entry corresponds to the 0-th row of the range image.\n pixel_pose: [B, H, W, 4, 4] tensor. If not None, it sets pose for each range\n image pixel.\n frame_pose: [B, 4, 4] tensor. This must be set when pixel_pose is set. It\n decides the vehicle frame at which the cartesian points are computed.\n dtype: float type to use internally. This is needed as extrinsic and\n inclination sometimes have higher resolution than range_image.\n scope: the name scope.\n\n Returns:\n range_image_points: [B, H, W, 3] with {x, y, z} as inner dims in vehicle frame.\n range_image_origins: [B, H, W, 3] with {x, y, z}, the origin of the range image\n \"\"\"\n with tf.compat.v1.name_scope(\n scope,\n \"ExtractPointCloudFromRangeImage\",\n [range_image, extrinsic, inclination, pixel_pose, frame_pose],\n ):\n range_image_polar = range_image_utils.compute_range_image_polar(\n range_image, extrinsic, inclination, dtype=dtype\n )\n (\n range_image_points_cartesian,\n range_image_origins_cartesian,\n ) = compute_range_image_cartesian(\n range_image_polar,\n extrinsic,\n pixel_pose=pixel_pose,\n frame_pose=frame_pose,\n dtype=dtype,\n )\n return range_image_origins_cartesian, range_image_points_cartesian\n\n\ndef parse_range_image_flow_and_camera_projection(frame):\n range_images = {}\n camera_projections = {}\n range_image_top_pose = None\n for laser in frame.lasers:\n if (\n len(laser.ri_return1.range_image_flow_compressed) > 0\n ): # pylint: disable=g-explicit-length-test\n range_image_str_tensor = tf.io.decode_compressed(\n laser.ri_return1.range_image_flow_compressed, \"ZLIB\"\n )\n ri = dataset_pb2.MatrixFloat()\n ri.ParseFromString(bytearray(range_image_str_tensor.numpy()))\n range_images[laser.name] = [ri]\n\n if laser.name == dataset_pb2.LaserName.TOP:\n range_image_top_pose_str_tensor = tf.io.decode_compressed(\n laser.ri_return1.range_image_pose_compressed, \"ZLIB\"\n )\n range_image_top_pose = dataset_pb2.MatrixFloat()\n range_image_top_pose.ParseFromString(\n bytearray(range_image_top_pose_str_tensor.numpy())\n )\n\n camera_projection_str_tensor = tf.io.decode_compressed(\n laser.ri_return1.camera_projection_compressed, \"ZLIB\"\n )\n cp = dataset_pb2.MatrixInt32()\n cp.ParseFromString(bytearray(camera_projection_str_tensor.numpy()))\n camera_projections[laser.name] = [cp]\n if (\n len(laser.ri_return2.range_image_flow_compressed) > 0\n ): # pylint: disable=g-explicit-length-test\n range_image_str_tensor = tf.io.decode_compressed(\n laser.ri_return2.range_image_flow_compressed, \"ZLIB\"\n )\n ri = dataset_pb2.MatrixFloat()\n ri.ParseFromString(bytearray(range_image_str_tensor.numpy()))\n range_images[laser.name].append(ri)\n\n camera_projection_str_tensor = tf.io.decode_compressed(\n laser.ri_return2.camera_projection_compressed, \"ZLIB\"\n )\n cp = dataset_pb2.MatrixInt32()\n cp.ParseFromString(bytearray(camera_projection_str_tensor.numpy()))\n camera_projections[laser.name].append(cp)\n return range_images, camera_projections, range_image_top_pose\n\n\ndef convert_range_image_to_point_cloud_flow(\n frame,\n range_images,\n range_images_flow,\n camera_projections,\n range_image_top_pose,\n ri_index=0,\n):\n \"\"\"\n Modified from the codes of Waymo Open Dataset.\n Convert range images to point cloud.\n Convert range images flow to scene flow.\n Args:\n frame: open dataset frame\n range_images: A dict of {laser_name, [range_image_first_return, range_image_second_return]}.\n range_imaages_flow: A dict similar to range_images.\n camera_projections: A dict of {laser_name,\n [camera_projection_from_first_return, camera_projection_from_second_return]}.\n range_image_top_pose: range image pixel pose for top lidar.\n ri_index: 0 for the first return, 1 for the second return.\n\n Returns:\n points: {[N, 3]} list of 3d lidar points of length 5 (number of lidars).\n points_flow: {[N, 3]} list of scene flow vector of each point.\n cp_points: {[N, 6]} list of camera projections of length 5 (number of lidars).\n \"\"\"\n calibrations = sorted(frame.context.laser_calibrations, key=lambda c: c.name)\n origins, points, cp_points = [], [], []\n points_intensity = []\n points_elongation = []\n points_flow = []\n laser_ids = []\n\n frame_pose = tf.convert_to_tensor(np.reshape(np.array(frame.pose.transform), [4, 4]))\n # [H, W, 6]\n range_image_top_pose_tensor = tf.reshape(\n tf.convert_to_tensor(range_image_top_pose.data), range_image_top_pose.shape.dims\n )\n # [H, W, 3, 3]\n range_image_top_pose_tensor_rotation = transform_utils.get_rotation_matrix(\n range_image_top_pose_tensor[..., 0],\n range_image_top_pose_tensor[..., 1],\n range_image_top_pose_tensor[..., 2],\n )\n range_image_top_pose_tensor_translation = range_image_top_pose_tensor[..., 3:]\n range_image_top_pose_tensor = transform_utils.get_transform(\n range_image_top_pose_tensor_rotation, range_image_top_pose_tensor_translation\n )\n for c in calibrations:\n range_image = range_images[c.name][ri_index]\n range_image_flow = range_images_flow[c.name][ri_index]\n if len(c.beam_inclinations) == 0: # pylint: disable=g-explicit-length-test\n beam_inclinations = range_image_utils.compute_inclination(\n tf.constant([c.beam_inclination_min, c.beam_inclination_max]),\n height=range_image.shape.dims[0],\n )\n else:\n beam_inclinations = tf.constant(c.beam_inclinations)\n\n beam_inclinations = tf.reverse(beam_inclinations, axis=[-1])\n extrinsic = np.reshape(np.array(c.extrinsic.transform), [4, 4])\n\n range_image_tensor = tf.reshape(\n tf.convert_to_tensor(range_image.data), range_image.shape.dims\n )\n range_image_flow_tensor = tf.reshape(\n tf.convert_to_tensor(range_image_flow.data), range_image_flow.shape.dims\n )\n pixel_pose_local = None\n frame_pose_local = None\n if c.name == dataset_pb2.LaserName.TOP:\n pixel_pose_local = range_image_top_pose_tensor\n pixel_pose_local = tf.expand_dims(pixel_pose_local, axis=0)\n frame_pose_local = tf.expand_dims(frame_pose, axis=0)\n range_image_mask = range_image_tensor[..., 0] > 0\n range_image_intensity = range_image_tensor[..., 1]\n range_image_elongation = range_image_tensor[..., 2]\n\n flow_x = range_image_flow_tensor[..., 0]\n flow_y = range_image_flow_tensor[..., 1]\n flow_z = range_image_flow_tensor[..., 2]\n flow_class = range_image_flow_tensor[..., 3]\n\n mask_index = tf.where(range_image_mask)\n\n (origins_cartesian, points_cartesian,) = extract_point_cloud_from_range_image(\n tf.expand_dims(range_image_tensor[..., 0], axis=0),\n tf.expand_dims(extrinsic, axis=0),\n tf.expand_dims(tf.convert_to_tensor(beam_inclinations), axis=0),\n pixel_pose=pixel_pose_local,\n frame_pose=frame_pose_local,\n )\n origins_cartesian = tf.squeeze(origins_cartesian, axis=0)\n points_cartesian = tf.squeeze(points_cartesian, axis=0)\n\n origins_tensor = tf.gather_nd(origins_cartesian, mask_index)\n points_tensor = tf.gather_nd(points_cartesian, mask_index)\n\n points_intensity_tensor = tf.gather_nd(range_image_intensity, mask_index)\n points_elongation_tensor = tf.gather_nd(range_image_elongation, mask_index)\n\n points_flow_x_tensor = tf.expand_dims(tf.gather_nd(flow_x, mask_index), axis=1)\n points_flow_y_tensor = tf.expand_dims(tf.gather_nd(flow_y, mask_index), axis=1)\n points_flow_z_tensor = tf.expand_dims(tf.gather_nd(flow_z, mask_index), axis=1)\n points_flow_class_tensor = tf.expand_dims(tf.gather_nd(flow_class, mask_index), axis=1)\n\n origins.append(origins_tensor.numpy())\n points.append(points_tensor.numpy())\n points_intensity.append(points_intensity_tensor.numpy())\n points_elongation.append(points_elongation_tensor.numpy())\n laser_ids.append(np.full_like(points_intensity_tensor.numpy(), c.name - 1))\n\n points_flow.append(\n tf.concat(\n [\n points_flow_x_tensor,\n points_flow_y_tensor,\n points_flow_z_tensor,\n points_flow_class_tensor,\n ],\n axis=-1,\n ).numpy()\n )\n\n return (\n origins,\n points,\n points_flow,\n cp_points,\n points_intensity,\n points_elongation,\n laser_ids,\n )\n\n\nclass WaymoProcessor:\n def __init__(\n self,\n load_dir,\n save_dir,\n scene_lists,\n prefix,\n downsample_factors=[4],\n process_keys=[\"images\", \"lidar\", \"calib\", \"pose\", \"ground\", \"dynamic_masks\"],\n json_folder_to_save=None,\n num_workers=64,\n overwrite=False,\n ):\n self.overwrite = overwrite\n self.process_keys = process_keys\n self.json_folder_to_save = f\"{json_folder_to_save}/{prefix}\"\n os.makedirs(self.json_folder_to_save, exist_ok=True)\n print(\"will process keys: \", self.process_keys)\n\n self.load_dir = f\"{load_dir}/{prefix}\"\n self.save_dir = f\"{save_dir}/{prefix}\"\n self.prefix = prefix\n self.downsample_factors = downsample_factors\n self.num_workers = int(num_workers)\n self.scene_ids = [s[0] for s in scene_lists]\n self.scene_names = [s[1] for s in scene_lists]\n self.create_folder()\n\n def convert(self):\n \"\"\"Convert action.\"\"\"\n print(\"Start converting ...\")\n track_parallel_progress(self.convert_one, range(len(self.scene_ids)), self.num_workers)\n print(\"\\nFinished ...\")\n\n def convert_one(self, file_id):\n \"\"\"Convert action for single file.\n\n Args:\n file_id (int): Index of the file to be converted.\n \"\"\"\n scene_id = self.scene_ids[file_id]\n scene_name = self.scene_names[file_id]\n tfrecord_path = f\"{self.load_dir}/{scene_name}.tfrecord\"\n dataset = tf.data.TFRecordDataset(tfrecord_path, compression_type=\"\")\n num_frames = sum(1 for _ in dataset)\n\n for frame_id, data in enumerate(\n tqdm(dataset, desc=f\"File {file_id}\", total=num_frames, dynamic_ncols=True)\n ):\n frame = dataset_pb2.Frame()\n frame.ParseFromString(bytearray(data.numpy()))\n if \"images\" in self.process_keys:\n self.save_image(frame, file_id, frame_id)\n if \"calib\" in self.process_keys:\n self.save_calib(frame, file_id, frame_id)\n if \"pose\" in self.process_keys:\n self.save_pose(frame, file_id, frame_id)\n if \"lidar\" in self.process_keys and \"depth\" not in self.process_keys:\n self.save_lidar(frame, file_id, frame_id)\n if \"depth\" in self.process_keys and \"lidar\" not in self.process_keys:\n self.save_depth(frame, file_id, frame_id)\n if \"dynamic_masks\" in self.process_keys:\n self.save_dynamic_mask(frame, file_id, frame_id)\n if \"ground\" in self.process_keys:\n self.save_ground(frame, file_id, frame_id)\n self.make_json(file_id)\n # save a video\n downsample_factor = self.downsample_factors[-1]\n scene_id = f\"{scene_id:03d}\"\n video_path = f\"{self.json_folder_to_save.replace('annotations', 'video_preview')}/{scene_id}-{self.scene_names[file_id].split('segment-')[1].split('_')[0]}.mp4\"\n os.makedirs(os.path.dirname(video_path), exist_ok=True)\n scene_path = f\"{self.save_dir}/{scene_id}\"\n video_frames = []\n for frame_id in trange(num_frames, desc=f\"Making video {scene_id}\", dynamic_ncols=True):\n frame = []\n for cam_id in [3, 1, 0, 2, 4]:\n image_path = (\n f\"{scene_path}/images_{downsample_factor}/\"\n + f\"{str(frame_id).zfill(3)}_{str(cam_id)}.jpg\"\n )\n image = Image.open(image_path).convert(\"RGB\")\n depth_flows_path = (\n f\"{scene_path}/depth_flows_{downsample_factor}/\"\n + f\"{str(frame_id).zfill(3)}_{str(cam_id)}.npy\"\n )\n ground_path = image_path.replace(\"images\", \"ground_label\").replace(\"jpg\", \"png\")\n ground_mask = np.array(Image.open(ground_path))\n\n depth_flows = np.load(depth_flows_path)\n depth_image = depth_flows[:, :, 0]\n flow_image = depth_flows[:, :, 1:4]\n depth_img = depth_visualizer(depth_image, depth_image > 0)\n flow_img = scene_flow_to_rgb(flow_image, flow_max_radius=15)\n\n img_array = np.array(image) / 255.0\n # Prepare the ground mask and image for blending\n ground_mask = (\n np.expand_dims(ground_mask, axis=-1) / 255.0\n ) # Normalize mask to [0, 1]\n\n # Ensure ground_mask is binary (0 or 1), just in case the values are not exactly 0 or 255\n ground_mask = np.where(ground_mask > 0.5, 1, 0) # Threshold the mask at 0.5\n\n # Blending the image using the ground mask\n # If ground_mask == 1 -> blend with 30% gray\n # If ground_mask == 0 -> keep the original image\n gray_value = 0.3 # Gray color value (30%)\n img_array = img_array * (1 - ground_mask) + ground_mask * gray_value\n\n depth_img = np.array(depth_img)\n flow_img = np.array(flow_img)\n if img_array.shape[0] != 160:\n _img_array = np.zeros((160, 240, 3))\n _img_array[50:] = img_array\n img_array = _img_array\n\n _depth_img = np.zeros((160, 240, 3))\n _depth_img[50:] = depth_img\n depth_img = _depth_img\n\n _flow_img = np.zeros((160, 240, 3))\n _flow_img[50:] = flow_img\n flow_img = _flow_img\n\n img_array = np.concatenate([img_array, depth_img, flow_img], axis=0)\n\n frame.append((img_array * 255).astype(np.uint8))\n frame = np.concatenate(frame, axis=1)\n video_frames.append(frame)\n video_frames = np.stack(video_frames)\n # imageio.mimwrite(video_path, video_frames, fps=10)\n imageio.mimwrite(video_path, video_frames, fps=10, codec=\"libx264\", quality=4)\n print(f\"Scene {scene_id} video saved to {video_path}\")\n\n def make_json(self, file_id):\n scene_id = self.scene_ids[file_id]\n scene_name = self.scene_names[file_id]\n file_folder = os.path.join(self.save_dir, f\"{scene_id:03d}\")\n if os.path.exists(f\"{self.json_folder_to_save}/{scene_name}.json\") and not self.overwrite:\n print(\n f\"Scene {scene_id} json already exists at {self.json_folder_to_save}/{scene_name}.json\"\n )\n return\n else:\n print(\"start overwriting\")\n num_timesteps = len(os.listdir(f\"{file_folder}/ego_to_world\"))\n camera_list = [\"0\", \"1\", \"2\", \"3\", \"4\"]\n scene_dict = {\n \"dataset\": \"waymo\",\n \"scene_id\": int(scene_id),\n \"scene_name\": scene_name,\n \"num_timesteps\": num_timesteps,\n # 0: front, 1: left, 2: right, 3: front_left, 4: front_right\n \"camera_list\": camera_list,\n # list for synchronized timestamps, dict for unsynchronized, measured in seconds\n \"normalized_time\": [],\n # camera_name: [fx, fy, cx, cy] (real_fx = fx * width, real_fy = fy * height)\n \"normalized_intrinsics\": {cam: [] for cam in camera_list},\n # camera_name: 3x4 matrix\n \"camera_to_ego\": {cam: [] for cam in camera_list},\n # list of 4x4 matrices\n \"ego_to_world\": [],\n \"camera_to_world\": {cam: [] for cam in camera_list},\n # camera_name: [height, width]\n \"original_image_size\": {\n \"0\": [1280, 1920],\n \"1\": [1280, 1920],\n \"2\": [1280, 1920],\n \"3\": [886, 1920],\n \"4\": [886, 1920],\n },\n # camera_name: relative path to the image\n \"relative_image_path\": {},\n \"fps\": 10, # assume all cameras have the same fps\n }\n\n for t in range(num_timesteps):\n ego_pose = np.loadtxt(f\"{file_folder}/ego_to_world/{t:03d}.txt\")\n scene_dict[\"ego_to_world\"].append(ego_pose.tolist())\n scene_dict[\"normalized_time\"].append(t / scene_dict[\"fps\"])\n for cam_id in range(5):\n cam_to_world = np.loadtxt(f\"{file_folder}/cam_to_world/{t:03d}_{cam_id}.txt\")\n scene_dict[\"camera_to_world\"][str(cam_id)].append(cam_to_world.tolist())\n\n for cam_id in range(5):\n fx, fy, cx, cy = np.loadtxt(f\"{file_folder}/intrinsics/{cam_id}.txt\")[:4]\n extrinsics = np.loadtxt(f\"{file_folder}/cam_to_ego/{cam_id}.txt\")\n height = scene_dict[\"original_image_size\"][str(cam_id)][0]\n width = scene_dict[\"original_image_size\"][str(cam_id)][1]\n normalized_fx = fx / width\n normalized_fy = fy / height\n normalized_cx = cx / width\n normalized_cy = cy / height\n scene_dict[\"normalized_intrinsics\"][str(cam_id)] = [\n normalized_fx,\n normalized_fy,\n normalized_cx,\n normalized_cy,\n ]\n scene_dict[\"camera_to_ego\"][str(cam_id)] = extrinsics.tolist()\n\n for cam_id in range(5):\n scene_dict[\"relative_image_path\"][str(cam_id)] = []\n for t in range(num_timesteps):\n scene_dict[\"relative_image_path\"][str(cam_id)].append(\n f\"{self.prefix}/{scene_id:03d}/images/{t:03d}_{cam_id}.jpg\"\n )\n with open(f\"{self.json_folder_to_save}/{scene_name}.json\", \"w\") as f:\n json.dump(scene_dict, f)\n\n def __len__(self):\n \"\"\"Length of the filename list.\"\"\"\n return len(self.scene_ids)\n\n def save_image(self, frame, file_id, frame_id):\n \"\"\"Parse and save the images in jpg format.\n\n Args:\n frame (:obj:`Frame`): Open dataset frame proto.\n file_id (int): Current file index.\n frame_id (int): Current frame index.\n \"\"\"\n scene_id = self.scene_ids[file_id]\n for img in frame.images:\n img_path = f\"{self.save_dir}/{str(scene_id).zfill(3)}/images/{str(frame_id).zfill(3)}_{img.name - 1}.jpg\"\n if not os.path.exists(img_path) or self.overwrite:\n with open(img_path, \"wb\") as f:\n f.write(img.image)\n for downsample_factor in self.downsample_factors:\n if downsample_factor == 1:\n continue\n else:\n postfix = f\"_{downsample_factor}\"\n downsampled_img_path = (\n f\"{self.save_dir}/{str(scene_id).zfill(3)}/images{postfix}/\"\n + f\"{str(frame_id).zfill(3)}_{img.name - 1}.jpg\"\n )\n if not os.path.exists(downsampled_img_path) or self.overwrite:\n image = Image.open(img_path)\n new_size = (\n image.width // downsample_factor,\n image.height // downsample_factor,\n )\n downsampled_image = image.resize(new_size)\n downsampled_image.save(downsampled_img_path, format=\"JPEG\")\n\n def save_calib(self, frame, file_id, frame_id):\n \"\"\"Parse and save the calibration data.\n\n Args:\n file_id (int): Current file index.\n frame_id (int): Current frame index.\n \"\"\"\n scene_id = self.scene_ids[file_id]\n if frame_id != 0:\n # only save the calibration data for the first frame\n # because the calibration data is the same for all frames\n return\n extrinsics = []\n intrinsics = []\n for camera in frame.context.camera_calibrations:\n # extrinsic parameters\n extrinsic = np.array(camera.extrinsic.transform).reshape(4, 4)\n intrinsic = list(camera.intrinsic)\n extrinsics.append(extrinsic)\n intrinsics.append(intrinsic)\n # all camera ids are saved as id-1 in the result because\n # camera 0 is unknown in the proto\n for i in range(5):\n np.savetxt(\n f\"{self.save_dir}/{str(scene_id).zfill(3)}/cam_to_ego/\" + f\"{str(i)}.txt\",\n extrinsics[i],\n )\n np.savetxt(\n f\"{self.save_dir}/{str(scene_id).zfill(3)}/intrinsics/\" + f\"{str(i)}.txt\",\n intrinsics[i],\n )\n\n def save_lidar(self, frame, file_id, frame_id):\n \"\"\"Parse and save the lidar data in psd format.\n\n Args:\n file_id (int): Current file index.\n frame_id (int): Current frame index.\n \"\"\"\n scene_id = self.scene_ids[file_id]\n scene_path = f\"{self.save_dir}/{str(scene_id).zfill(3)}\"\n pc_path = f\"{scene_path}/lidar/{str(frame_id).zfill(3)}.bin\"\n if not os.path.exists(pc_path) or self.overwrite:\n (\n range_images,\n camera_projections,\n seg_labels,\n range_image_top_pose,\n ) = parse_range_image_and_camera_projection(frame)\n # https://github.com/waymo-research/waymo-open-dataset/blob/master/src/waymo_open_dataset/protos/segmentation.proto\n if range_image_top_pose is None:\n # the camera only split doesn't contain lidar points.\n return\n # collect first return only\n range_images_flow, _, _ = parse_range_image_flow_and_camera_projection(frame)\n (\n origins,\n points,\n flows,\n cp_points,\n intensity,\n elongation,\n laser_ids,\n ) = convert_range_image_to_point_cloud_flow(\n frame,\n range_images,\n range_images_flow,\n camera_projections,\n range_image_top_pose,\n ri_index=0,\n )\n origins = np.concatenate(origins, axis=0)\n points = np.concatenate(points, axis=0)\n # -1: no-flow-label, the point has no flow information.\n # 0: unlabeled or \"background,\", i.e., the point is not contained in a\n # bounding box.\n # 1: vehicle, i.e., the point corresponds to a vehicle label box.\n # 2: pedestrian, i.e., the point corresponds to a pedestrian label box.\n # 3: sign, i.e., the point corresponds to a sign label box.\n # 4: cyclist, i.e., the point corresponds to a cyclist label box.\n flows = np.concatenate(flows, axis=0)\n point_cloud = np.column_stack((origins, points, flows))\n point_cloud.astype(np.float32).tofile(pc_path)\n else:\n point_cloud = np.fromfile(pc_path, dtype=np.float32).reshape(-1, 10)\n\n for cam_id in range(5):\n world_to_cam = None\n\n for downsample_factor in self.downsample_factors:\n if downsample_factor == 1:\n continue\n scale_postfix = f\"_{downsample_factor}\"\n depth_path = f\"{scene_path}/depth_flows{scale_postfix}/{str(frame_id).zfill(3)}_{str(cam_id)}.npy\"\n if not os.path.exists(depth_path) or self.overwrite:\n points = point_cloud[:, 3:6].astype(np.float32)\n flows = point_cloud[:, 6:9].astype(np.float32)\n flow_class = point_cloud[:, 9].astype(np.int32)\n if world_to_cam is None:\n intrinsics = np.loadtxt(\n f\"{scene_path}/intrinsics/{str(cam_id)}.txt\",\n dtype=np.float32,\n )\n fx, fy, cx, cy = intrinsics[:4]\n intrinsics = np.array(\n [\n [fx, 0, cx, 0],\n [0, fy, cy, 0],\n [0, 0, 1, 0],\n [0, 0, 0, 1],\n ],\n dtype=np.float32,\n )\n extrinsic = np.loadtxt(\n f\"{scene_path}/cam_to_ego/{str(cam_id)}.txt\",\n dtype=np.float32,\n )\n world_to_cam = np.linalg.inv(extrinsic @ OPENCV2DATASET)\n target_size = (\n ORIGINAL_SIZE[str(cam_id)][0] // downsample_factor,\n ORIGINAL_SIZE[str(cam_id)][1] // downsample_factor,\n )\n _intrinsics = intrinsics.copy()\n _intrinsics[0, 0] /= ORIGINAL_SIZE[str(cam_id)][1] / target_size[1]\n _intrinsics[1, 1] /= ORIGINAL_SIZE[str(cam_id)][0] / target_size[0]\n _intrinsics[0, 2] /= ORIGINAL_SIZE[str(cam_id)][1] / target_size[1]\n _intrinsics[1, 2] /= ORIGINAL_SIZE[str(cam_id)][0] / target_size[0]\n lidar2img = _intrinsics @ world_to_cam\n points_2d = (np.dot(lidar2img[:3, :3], points.T) + lidar2img[:3, 3:4]).T\n depth_2d = points_2d[:, 2]\n cam_coords = points_2d[:, :2] / (depth_2d[:, None] + 1e-6)\n valid_mask = (\n (cam_coords[:, 0] >= 0)\n & (cam_coords[:, 0] < target_size[1])\n & (cam_coords[:, 1] >= 0)\n & (cam_coords[:, 1] < target_size[0])\n & (depth_2d > 0)\n )\n # Get valid depth points and corresponding coordinates\n valid_depth_points = depth_2d[valid_mask]\n valid_cam_coords = cam_coords[valid_mask]\n # Convert coordinates to integer indices\n x_indices = valid_cam_coords[:, 0].astype(np.int32)\n y_indices = valid_cam_coords[:, 1].astype(np.int32)\n # Initialize arrays to accumulate depth sums and counts\n depth_sums = np.zeros(target_size)\n depth_counts = np.zeros(target_size)\n\n np.add.at(depth_sums, (y_indices, x_indices), valid_depth_points)\n np.add.at(depth_counts, (y_indices, x_indices), 1)\n\n depth_image = np.divide(depth_sums, depth_counts, where=depth_counts > 0)\n\n depth_image[depth_counts == 0] = 0\n\n valid_flow = flows[valid_mask] * (flow_class[valid_mask][:, None] >= 0)\n flow_sums = np.zeros((target_size[0], target_size[1], 3))\n flow_counts = np.zeros((target_size[0], target_size[1], 3))\n np.add.at(flow_sums, (y_indices, x_indices), valid_flow)\n np.add.at(flow_counts, (y_indices, x_indices), 1)\n flow_image = np.divide(flow_sums, flow_counts, where=flow_counts > 0)\n flow_image[flow_counts == 0] = 0\n flow_image[np.linalg.norm(flow_image, axis=-1) < 0.5] = 0\n\n concate_image = np.concatenate(\n [depth_image[:, :, None], flow_image], axis=-1\n ).astype(np.float32)\n path = f\"{scene_path}/depth_flows{scale_postfix}/{str(frame_id).zfill(3)}_{str(cam_id)}.npy\"\n os.makedirs(os.path.dirname(path), exist_ok=True)\n np.save(path, concate_image)\n\n def save_ground(self, frame, file_id, frame_id):\n \"\"\"Parse and save the lidar data in psd format.\n\n Args:\n file_id (int): Current file index.\n frame_id (int): Current frame index.\n \"\"\"\n scene_id = self.scene_ids[file_id]\n scene_path = f\"{self.save_dir}/{str(scene_id).zfill(3)}\"\n pc_path = f\"{scene_path}/lidar/{str(frame_id).zfill(3)}.bin\"\n point_cloud = np.fromfile(pc_path, dtype=np.float32).reshape(-1, 10)\n\n for cam_id in range(5):\n world_to_cam = None\n\n for downsample_factor in self.downsample_factors:\n if downsample_factor == 1:\n continue\n scale_postfix = f\"_{downsample_factor}\"\n ground_label_path = f\"{scene_path}/ground_label{scale_postfix}/{str(frame_id).zfill(3)}_{str(cam_id)}.png\"\n if not os.path.exists(ground_label_path) or self.overwrite:\n points = point_cloud[:, 3:6].astype(np.float32)\n ground_label = get_ground_np(points).reshape(-1)\n\n if world_to_cam is None:\n intrinsics = np.loadtxt(\n f\"{scene_path}/intrinsics/{str(cam_id)}.txt\",\n dtype=np.float32,\n )\n fx, fy, cx, cy = intrinsics[:4]\n intrinsics = np.array(\n [\n [fx, 0, cx, 0],\n [0, fy, cy, 0],\n [0, 0, 1, 0],\n [0, 0, 0, 1],\n ],\n dtype=np.float32,\n )\n extrinsic = np.loadtxt(\n f\"{scene_path}/cam_to_ego/{str(cam_id)}.txt\",\n dtype=np.float32,\n )\n world_to_cam = np.linalg.inv(extrinsic @ OPENCV2DATASET)\n target_size = (\n ORIGINAL_SIZE[str(cam_id)][0] // downsample_factor,\n ORIGINAL_SIZE[str(cam_id)][1] // downsample_factor,\n )\n _intrinsics = intrinsics.copy()\n _intrinsics[0, 0] /= ORIGINAL_SIZE[str(cam_id)][1] / target_size[1]\n _intrinsics[1, 1] /= ORIGINAL_SIZE[str(cam_id)][0] / target_size[0]\n _intrinsics[0, 2] /= ORIGINAL_SIZE[str(cam_id)][1] / target_size[1]\n _intrinsics[1, 2] /= ORIGINAL_SIZE[str(cam_id)][0] / target_size[0]\n lidar2img = _intrinsics @ world_to_cam\n points_2d = (np.dot(lidar2img[:3, :3], points.T) + lidar2img[:3, 3:4]).T\n depth_2d = points_2d[:, 2]\n cam_coords = points_2d[:, :2] / (depth_2d[:, None] + 1e-6)\n valid_mask = (\n (cam_coords[:, 0] >= 0)\n & (cam_coords[:, 0] < target_size[1])\n & (cam_coords[:, 1] >= 0)\n & (cam_coords[:, 1] < target_size[0])\n & (depth_2d > 0)\n )\n # Get valid depth points and corresponding coordinates\n valid_cam_coords = cam_coords[valid_mask]\n # Convert coordinates to integer indices\n x_indices = valid_cam_coords[:, 0].astype(np.int32)\n y_indices = valid_cam_coords[:, 1].astype(np.int32)\n # Initialize arrays to accumulate depth sums and counts\n depth_image = np.zeros(target_size)\n depth_image[y_indices, x_indices] = ground_label[valid_mask]\n imageio.imwrite(ground_label_path, (depth_image * 255).astype(np.uint8))\n\n def save_pose(self, frame, file_id, frame_id):\n \"\"\"Parse and save the pose data.\n\n Note that SDC's own pose is not included in the regular training\n of KITTI dataset. KITTI raw dataset contains ego motion files\n but are not often used. Pose is important for algorithms that\n take advantage of the temporal information.\n\n Args:\n frame (:obj:`Frame`): Open dataset frame proto.\n file_id (int): Current file index.\n frame_id (int): Current frame index.\n \"\"\"\n scene_id = self.scene_ids[file_id]\n scene_path = f\"{self.save_dir}/{str(scene_id).zfill(3)}\"\n ego_to_world = np.array(frame.pose.transform).reshape(4, 4)\n np.savetxt(\n f\"{self.save_dir}/{str(scene_id).zfill(3)}/ego_to_world/\"\n + f\"{str(frame_id).zfill(3)}.txt\",\n ego_to_world,\n )\n for cam_id in range(5):\n cam_to_world_path = (\n f\"{scene_path}/cam_to_world/\" + f\"{str(frame_id).zfill(3)}_{str(cam_id)}.txt\"\n )\n if not os.path.exists(cam_to_world_path):\n cam_to_ego = np.loadtxt(\n f\"{scene_path}/cam_to_ego/\" + f\"{str(cam_id)}.txt\",\n dtype=np.float32,\n )\n cam_to_world = ego_to_world @ cam_to_ego\n np.savetxt(cam_to_world_path, cam_to_world)\n\n def save_dynamic_mask(self, frame, file_id, frame_id):\n \"\"\"Parse and save the segmentation data.\n\n Args:\n frame (:obj:`Frame`): Open dataset frame proto.\n file_idx (int): Current file index.\n frame_idx (int): Current frame index.\n \"\"\"\n scene_id = self.scene_ids[file_id]\n scene_path = f\"{self.save_dir}/{str(scene_id).zfill(3)}\"\n for img in frame.images:\n # dynamic_mask\n img_path = f\"{scene_path}/images/\" + f\"{str(frame_id).zfill(3)}_{str(img.name - 1)}.jpg\"\n img_shape = np.array(Image.open(img_path))\n dynamic_mask = np.zeros_like(img_shape, dtype=np.float32)[..., 0]\n\n filter_available = any(\n [label.num_top_lidar_points_in_box > 0 for label in frame.laser_labels]\n )\n calibration = next(\n cc for cc in frame.context.camera_calibrations if cc.name == img.name\n )\n for label in frame.laser_labels:\n # camera_synced_box is not available for the data with flow.\n # box = label.camera_synced_box\n box = label.box\n meta = label.metadata\n speed = np.linalg.norm([meta.speed_x, meta.speed_y])\n if not box.ByteSize():\n continue # Filter out labels that do not have a camera_synced_box.\n if (filter_available and not label.num_top_lidar_points_in_box) or (\n not filter_available and not label.num_lidar_points_in_box\n ):\n continue # Filter out likely occluded objects.\n\n # Retrieve upright 3D box corners.\n box_coords = np.array(\n [\n [\n box.center_x,\n box.center_y,\n box.center_z,\n box.length,\n box.width,\n box.height,\n box.heading,\n ]\n ]\n )\n corners = box_utils.get_upright_3d_box_corners(box_coords)[0].numpy() # [8, 3]\n\n # Project box corners from vehicle coordinates onto the image.\n projected_corners = project_vehicle_to_image(frame.pose, calibration, corners)\n u, v, ok = projected_corners.transpose()\n ok = ok.astype(bool)\n\n # Skip object if any corner projection failed. Note that this is very\n # strict and can lead to exclusion of some partially visible objects.\n if not all(ok):\n continue\n u = u[ok]\n v = v[ok]\n\n # Clip box to image bounds.\n u = np.clip(u, 0, calibration.width)\n v = np.clip(v, 0, calibration.height)\n\n if u.max() - u.min() == 0 or v.max() - v.min() == 0:\n continue\n\n # Draw projected 2D box onto the image.\n xy = (u.min(), v.min())\n width = u.max() - u.min()\n height = v.max() - v.min()\n # max pooling\n dynamic_mask[\n int(xy[1]) : int(xy[1] + height),\n int(xy[0]) : int(xy[0] + width),\n ] = np.maximum(\n dynamic_mask[\n int(xy[1]) : int(xy[1] + height),\n int(xy[0]) : int(xy[0] + width),\n ],\n speed,\n )\n # thresholding, use 1.0 m/s to determine whether the pixel is moving\n dynamic_mask = np.clip((dynamic_mask > 1.0) * 255, 0, 255).astype(np.uint8)\n dynamic_mask = Image.fromarray(dynamic_mask, \"L\")\n dynamic_mask_path = (\n f\"{scene_path}/dynamic_masks/\" + f\"{str(frame_id).zfill(3)}_{str(img.name - 1)}.png\"\n )\n dynamic_mask.save(dynamic_mask_path)\n\n def create_folder(self):\n \"\"\"Create folder for data preprocessing.\"\"\"\n for i in self.scene_ids:\n scene_path = f\"{self.save_dir}/{str(i).zfill(3)}\"\n os.makedirs(f\"{scene_path}/images\", exist_ok=True)\n os.makedirs(f\"{scene_path}/dynamic_masks\", exist_ok=True)\n for downsample_factor in self.downsample_factors:\n postfix = \"\" if downsample_factor == 1 else f\"_{downsample_factor}\"\n os.makedirs(f\"{scene_path}/images{postfix}\", exist_ok=True)\n os.makedirs(f\"{scene_path}/depth_flows{postfix}\", exist_ok=True)\n os.makedirs(f\"{scene_path}/ground_label{postfix}\", exist_ok=True)\n os.makedirs(f\"{scene_path}/ego_to_world\", exist_ok=True)\n os.makedirs(f\"{scene_path}/cam_to_world\", exist_ok=True)\n os.makedirs(f\"{scene_path}/cam_to_ego\", exist_ok=True)\n os.makedirs(f\"{scene_path}/intrinsics\", exist_ok=True)\n os.makedirs(f\"{scene_path}/lidar\", exist_ok=True)\n" }, { "path": "preprocess.py", "content": "import argparse\nimport os\n\nimport numpy as np\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=\"Data converter arg parser\")\n parser.add_argument(\"--data_root\", type=str, required=True, help=\"root path of dataset\")\n parser.add_argument(\"--dataset\", type=str, default=\"waymo\", help=\"dataset name\")\n parser.add_argument(\"--scene_list_file\", type=str, default=None)\n parser.add_argument(\n \"--split\",\n type=str,\n default=\"training\",\n help=\"split of the dataset, e.g. training, validation, testing, please specify the split name for different dataset\",\n )\n parser.add_argument(\n \"--target_dir\",\n type=str,\n required=True,\n help=\"output directory of processed data\",\n )\n parser.add_argument(\n \"--json_folder_to_save\",\n type=str,\n required=True,\n help=\"to save the json files\",\n )\n parser.add_argument(\n \"--num_workers\",\n type=int,\n default=4,\n help=\"number of threads to be used\",\n )\n # priority: scene_ids > start_idx + num_scenes\n parser.add_argument(\n \"--scene_ids\",\n default=None,\n type=int,\n nargs=\"+\",\n help=\"scene ids to be processed, a list of integers separated by space. Range: [0, 798] for training, [0, 202] for validation\",\n )\n parser.add_argument(\n \"--start_idx\",\n type=int,\n default=0,\n help=\"If no scene id is given, use start_idx and num_scenes to generate scene_ids\",\n )\n parser.add_argument(\n \"--num_scenes\",\n type=int,\n default=200,\n help=\"number of scenes to be processed\",\n )\n parser.add_argument(\n \"--interpolate_N\",\n type=int,\n default=0,\n help=\"Interpolate to get frames at higher frequency, this is only used for nuscene dataset\",\n )\n parser.add_argument(\n \"--overwrite\",\n action=\"store_true\",\n help=\"overwrite the existing files\",\n )\n parser.add_argument(\n \"--process_keys\",\n nargs=\"+\",\n default=[\"images\", \"lidar\", \"calib\", \"pose\", \"ground\", \"dynamic_masks\"],\n )\n args = parser.parse_args()\n if args.dataset != \"nuscenes\" and args.interpolate_N > 0:\n parser.error(\"interpolate_N > 0 is only allowed when dataset is 'nuscenes'\")\n os.makedirs(args.target_dir, exist_ok=True)\n\n if args.scene_ids is not None:\n scene_ids = args.scene_ids\n else:\n scene_ids = np.arange(args.start_idx, args.start_idx + args.num_scenes)\n\n if args.dataset == \"nuscenes\":\n scene_lists = scene_ids\n else:\n if args.scene_list_file is None:\n raise ValueError(\"scene_list_file is required for non-nuscenes dataset\")\n if not os.path.exists(args.scene_list_file):\n raise ValueError(f\"scene_list_file {args.scene_list_file} does not exist\")\n scene_lists = open(args.scene_list_file, \"r\").read().splitlines()\n if np.max(scene_ids) >= len(scene_lists):\n scene_ids = [scene_id for scene_id in scene_ids if scene_id < len(scene_lists)]\n scene_lists = [(i, scene_lists[i]) for i in scene_ids]\n print(f\"scene_lists: {scene_lists}\")\n\n if args.dataset == \"waymo\":\n from preproc.waymo_preprocess import WaymoProcessor\n\n dataset_processor = WaymoProcessor(\n load_dir=args.data_root,\n save_dir=args.target_dir,\n scene_lists=scene_lists,\n prefix=args.split,\n process_keys=args.process_keys,\n json_folder_to_save=args.json_folder_to_save,\n num_workers=args.num_workers,\n overwrite=args.overwrite,\n )\n elif args.dataset == \"argoverse\":\n raise NotImplementedError(\"ArgoverseProcessor is not implemented yet\")\n from preproc.argoverse_preprocess import ArgoVerseProcessor\n\n scene_ids = [int(scene_id) for scene_id in scene_ids]\n dataset_processor = ArgoVerseProcessor(\n load_dir=args.data_root,\n save_dir=args.target_dir,\n process_keys=args.process_keys,\n scene_lists=scene_lists,\n prefix=args.split,\n num_workers=args.num_workers,\n json_folder_to_save=args.json_folder_to_save,\n )\n elif args.dataset == \"nuscenes\":\n raise NotImplementedError(\"NuScenesProcessor is not implemented yet\")\n from preproc.nuscenes_preprocess import NuScenesProcessor\n\n scene_ids = [f\"{scene_id:03d}\" for scene_id in scene_ids]\n dataset_processor = NuScenesProcessor(\n load_dir=args.data_root,\n save_dir=args.target_dir,\n split=args.split,\n interpolate_N=args.interpolate_N,\n process_keys=args.process_keys,\n scene_lists=scene_ids,\n num_workers=args.num_workers,\n overwrite=args.overwrite,\n json_folder_to_save=args.json_folder_to_save,\n )\n else:\n raise ValueError(\n f\"Unknown dataset {args.dataset}, please choose from waymo, pandaset, argoverse, nuscenes, kitti, nuplan\"\n )\n\n if args.scene_ids is not None and args.num_workers <= 1:\n for idx in range(len(args.scene_ids)):\n dataset_processor.convert_one(idx)\n else:\n dataset_processor.convert()\n" }, { "path": "requirements.txt", "content": "--extra-index-url https://download.pytorch.org/whl/cu121\ntorch==2.3.1\ntorchvision\ntimm\neinops\njaxtyping\nopencv-python\nmatplotlib==3.7.3\nimageio\nimageio-ffmpeg\ngdown\n\n\nwandb\ntensorboard\nisort\npre-commit\nblack==22.6.0\nflake8==5.0.4\npylint==2.15.0\npandas\n\n# viewer:\nnerfview\nviser\nsplines\nloguru\n\ntorch_kmeans\npynvml\n" }, { "path": "requirements_data_preprocess.txt", "content": "--extra-index-url https://download.pytorch.org/whl/cu112\ntorch\nwaymo-open-dataset-tf-2-11-0==1.6.1\ntensorflow-gpu==2.11.0\nnuscenes-devkit\ntqdm\nimageio\nimageio[ffmpeg]\nscipy\ngdown\ngsutil\n" }, { "path": "storm/dataset/constants.py", "content": "import numpy as np\n\nMEAN = [0.5, 0.5, 0.5]\nSTD = [0.5, 0.5, 0.5]\n\n\nIMGNET_MEAN = [0.485, 0.456, 0.406]\nIMGNET_STD = [0.229, 0.224, 0.225]\n\nopencv2waymo = np.array([[0, 0, 1, 0], [-1, 0, 0, 0], [0, -1, 0, 0], [0, 0, 0, 1]])\n\nDATASETS = {\n \"waymo\": {\"opencv2dataset\": opencv2waymo, \"canonical_to_flu\": np.eye(4)},\n \"nuscenes\": {\"opencv2dataset\": np.eye(4), \"canonical_to_flu\": opencv2waymo},\n \"argoverse2\": {\"opencv2dataset\": np.eye(4), \"canonical_to_flu\": opencv2waymo},\n}\n\nDATASET_DICT = {\n \"waymo\": {\n \"size\": [160, 240],\n \"temporal\": True,\n \"num_context_timesteps\": 4,\n \"num_target_timesteps\": 4,\n \"annotation_txt_file_train\": \"scene_list/waymo_train.txt\",\n \"annotation_txt_file_val\": \"scene_list/waymo_val.txt\",\n \"camera_list\": {\n 1: [\"0\"],\n 3: [\"1\", \"0\", \"2\"],\n 5: [\"3\", \"1\", \"0\", \"2\", \"4\"],\n 6: [\"3\", \"1\", \"0\", \"2\", \"4\"], # capped at 5\n 7: [\"3\", \"1\", \"0\", \"2\", \"4\"],\n },\n \"ref_camera\": \"0\",\n },\n \"nuscenes\": {\n \"size\": [160, 288],\n \"temporal\": True,\n \"num_context_timesteps\": 4,\n \"num_target_timesteps\": 4,\n \"annotation_txt_file_train\": \"scene_list/nuscenes_train.txt\",\n \"annotation_txt_file_val\": \"scene_list/nuscenes_val.txt\",\n \"camera_list\": {\n 1: [\"CAM_FRONT\"],\n 3: [\"CAM_FRONT_LEFT\", \"CAM_FRONT\", \"CAM_FRONT_RIGHT\"],\n 5: [\n \"CAM_FRONT_LEFT\",\n \"CAM_FRONT\",\n \"CAM_FRONT_RIGHT\",\n \"CAM_BACK_RIGHT\",\n \"CAM_BACK\",\n \"CAM_BACK_LEFT\",\n ],\n 6: [\n \"CAM_FRONT_LEFT\",\n \"CAM_FRONT\",\n \"CAM_FRONT_RIGHT\",\n \"CAM_BACK_RIGHT\",\n \"CAM_BACK\",\n \"CAM_BACK_LEFT\",\n ],\n 7: [\n \"CAM_FRONT_LEFT\",\n \"CAM_FRONT\",\n \"CAM_FRONT_RIGHT\",\n \"CAM_BACK_RIGHT\",\n \"CAM_BACK\",\n \"CAM_BACK_LEFT\",\n ],\n },\n \"ref_camera\": \"CAM_FRONT\",\n },\n \"argoverse2\": {\n \"size\": [192, 256],\n \"temporal\": True,\n \"num_context_timesteps\": 4,\n \"num_target_timesteps\": 4,\n \"annotation_txt_file_train\": \"scene_list/argoverse2_train.txt\",\n \"annotation_txt_file_val\": \"scene_list/argoverse2_val.txt\",\n \"camera_list\": {\n 1: [\"0\"],\n 3: [\"1\", \"0\", \"2\"],\n 5: [\"3\", \"1\", \"0\", \"2\", \"4\"],\n 6: [\"3\", \"1\", \"0\", \"2\", \"4\"],\n 7: [\"5\", \"3\", \"1\", \"0\", \"2\", \"4\", \"6\"],\n },\n \"ref_camera\": \"0\",\n },\n \"rel10k\": {\n \"size\": [160, 296],\n \"temporal\": False,\n \"num_context_timesteps\": 2,\n \"num_target_timesteps\": 8,\n \"annotation_txt_file_train\": \"scene_list/rel10k_train.txt\",\n \"annotation_txt_file_val\": \"scene_list/rel10k_val.txt\",\n \"batch_size_scale\": 6,\n \"camera_list\": {\n 1: [\"0\"],\n 3: [\"0\"],\n 5: [\"0\"],\n 6: [\"0\"],\n 7: [\"0\"],\n },\n },\n \"dl3dv\": {\n \"size\": [160, 288],\n \"temporal\": False,\n \"num_context_timesteps\": 2,\n \"num_target_timesteps\": 8,\n \"annotation_txt_file_train\": \"scene_list/dl3dv_train.txt\",\n \"annotation_txt_file_val\": \"scene_list/dl3dv_val.txt\",\n \"batch_size_scale\": 6,\n \"camera_list\": {\n 1: [\"0\"],\n 3: [\"0\"],\n 5: [\"0\"],\n 6: [\"0\"],\n 7: [\"0\"],\n },\n },\n}\n" }, { "path": "storm/dataset/data_utils.py", "content": "from typing import List, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom scipy import interpolate\nfrom scipy.spatial.transform import Rotation as R\nfrom scipy.spatial.transform import Slerp\n\n\ndef forward_pose(pose: torch.tensor, transform: torch.tensor, inv=False) -> torch.tensor:\n rotation = pose[..., :3, :3]\n translation = pose[..., :3, 3]\n if inv:\n return (torch.transpose(rotation, -1, -2) * (transform - translation).unsqueeze(-2)).sum(-1)\n else:\n return (rotation * transform.unsqueeze(-2)).sum(-1) + translation\n\n\ndef get_path_front_left_lift_then_spiral_forward(\n pose_ref: np.ndarray, # [N0,4,4], Original ego pose; will generate trajectories along this\n num_frames: int, # Number of frames / waypoints to generate\n duration_frames: int = 100, # Number of frames per round / cycle\n # Spiral configs\n up_max: float = 0.3,\n up_min: float = -0.3,\n left_max: float = 2.0,\n left_min: float = -2.0,\n elongation: float = 1.0,\n first_with_forward=False,\n # Frontal direction vector of OpenCV\n forward_vec: np.ndarray = np.array([0.0, 0.0, 1.0]),\n # Frontal direction vector of OpenCV\n up_vec: np.ndarray = np.array([0.0, -1.0, 0.0]),\n # Frontal direction vector of OpenCV\n left_vec: np.ndarray = np.array([-1.0, 0.0, 0.0]),\n) -> np.ndarray:\n \"\"\"\n First lift ego in the front left direction, then do spiral forward\n \"\"\"\n pose_ref = torch.tensor(pose_ref, dtype=torch.float32, device=\"cpu\")\n n_rots = num_frames / duration_frames\n track_ref = pose_ref[..., :3, 3]\n rot_ref = pose_ref[..., :3, :3]\n # NOTE: Convert from observer's coords dir to world coords dir;\n # If the pose_ref is the pose of camera, `forward/up/left_vec` can be used as-is;\n # (since the cameras are already OpenCV cameras if dataio's xxx_dataset.py is correctly implemented)\n # otherwise, you should specify the coords dir vector of your given pose_ref.\n forward_vecs_ref = (\n forward_pose(pose_ref, torch.tensor(forward_vec, dtype=torch.float32)) - track_ref\n )\n up_vecs_ref = forward_pose(pose_ref, torch.tensor(up_vec, dtype=torch.float32)) - track_ref\n left_vecs_ref = forward_pose(pose_ref, torch.tensor(left_vec, dtype=torch.float32)) - track_ref\n\n forward_vecs_ref = forward_vecs_ref.numpy()\n up_vecs_ref = up_vecs_ref.numpy()\n left_vecs_ref = left_vecs_ref.numpy()\n track_ref = track_ref.numpy()\n rot_ref = rot_ref.numpy()\n\n nvs_seqs = []\n\n verti_radius = (up_max - up_min) / 2.0\n up_offset = (up_max + up_min) / 2.0\n horiz_radius = (left_max - left_min) / 2.0\n left_offset = (left_max + left_min) / 2.0\n assert (verti_radius >= -1e-5) and (horiz_radius >= -1e-5)\n\n # ----------------------------------------\n # ---- First: lift up & left\n # ----------------------------------------\n first_frames = int(0.1 * duration_frames) + 1\n remain_frames = num_frames - first_frames\n pace = np.linalg.norm(track_ref[0] - track_ref[-1]) * elongation\n forward_1st = ((first_frames / num_frames) * pace) if first_with_forward else 0\n\n track = (\n track_ref[0]\n + np.linspace(0, verti_radius + up_offset, first_frames)[..., None] * up_vecs_ref[0]\n + np.linspace(0, horiz_radius + left_offset, first_frames)[..., None] * left_vecs_ref[0]\n + np.linspace(0, forward_1st, first_frames)[..., None] * forward_vecs_ref[0]\n )\n pose = np.eye(4)[None, ...].repeat(first_frames, 0)\n pose[:, :3, 3] = track\n pose[:, :3, :3] = rot_ref[0]\n nvs_seqs.append(pose)\n\n # ----------------------------------------\n # ---- Then: Sprial forward\n # ----------------------------------------\n w = np.linspace(0, 1, remain_frames) # [0->1], data key time\n t = np.arange(len(track_ref)) / (\n len(track_ref) - 1\n ) # [0->1], render key time (could be extended)\n track_interp = interpolate.interp1d(t, track_ref, axis=0, fill_value=\"extrapolate\")\n up_vec_interp = interpolate.interp1d(t, up_vecs_ref, axis=0, fill_value=\"extrapolate\")\n left_vec_interp = interpolate.interp1d(t, left_vecs_ref, axis=0, fill_value=\"extrapolate\")\n\n up_vecs_all = up_vec_interp(w * elongation)\n left_vecs_all = left_vec_interp(w * elongation)\n # ---- Base: left * [1], up * [1]\n track_base_all = (\n track_interp(w * elongation)\n + (up_offset + verti_radius) * up_vecs_all\n + (left_offset + horiz_radius) * left_vecs_all\n + forward_1st * forward_vecs_ref[None, 0]\n )\n\n # up_vecs_all = np.percentile(up_vecs_ref, w*100, 0)\n # left_vecs_all = np.percentile(left_vecs_ref, w*100, 0)\n # track_base_all = np.percentile(track_ref, w*100, 0) + (up_max+up_offset) * up_vecs_all + (left_max+left_offset) * left_vecs_all\n\n key_rots = R.from_matrix(rot_ref)\n rot_slerp = Slerp(t, key_rots)\n if elongation > 1:\n mask = (w * elongation) < 1.0\n rot_base_all = np.eye(3)[None, ...].repeat(remain_frames, 0)\n rot_base_all[mask] = rot_slerp(w[mask]).as_matrix()\n rot_base_all[~mask] = rot_ref[-1]\n else:\n rot_base_all = rot_slerp(w).as_matrix()\n\n rads = np.linspace(0, remain_frames / duration_frames * np.pi * 2.0, remain_frames)\n # ---- Spiral:\n # left: [0, -1, -2, -1, 0] + base: [1] -> [1, 0, -1, 0, 1]\n # up: [0, 1, 0, -1, 0] + base [1] -> [1, 2, 1, 0, 1]\n track = (\n track_base_all\n + (np.cos(rads) - 1)[..., None] * horiz_radius * left_vecs_all\n + (np.sin(rads))[..., None] * verti_radius * up_vecs_all\n )\n pose = np.eye(4)[None, ...].repeat(remain_frames, 0)\n pose[:, :3, 3] = track\n pose[:, :3, :3] = rot_base_all\n nvs_seqs.append(pose)\n\n render_pose_all = np.concatenate(nvs_seqs, 0)\n\n return render_pose_all\n\n\ndef to_tensor(x: Union[np.ndarray, List, Tuple]) -> torch.Tensor:\n if isinstance(x, (list, tuple)):\n x = np.array(x)\n if isinstance(x, np.ndarray):\n x = torch.from_numpy(x).float()\n return x\n\n\ndef to_float_tensor(d):\n if isinstance(d, dict):\n return {k: to_float_tensor(v) for k, v in d.items()}\n elif isinstance(d, list):\n return [to_float_tensor(v) for v in d]\n elif isinstance(d, torch.Tensor):\n return d.float()\n elif isinstance(d, np.ndarray):\n return torch.from_numpy(d).float()\n else:\n return d\n\n\ndef to_batch_tensor(d):\n if isinstance(d, dict):\n return {k: to_batch_tensor(v) for k, v in d.items()}\n elif isinstance(d, list):\n return [to_batch_tensor(v) for v in d]\n elif isinstance(d, torch.Tensor):\n return d.unsqueeze(0)\n else:\n return d\n\n\ndef resize_depth(depth, target_size):\n height, width = depth.shape[-2:]\n if (height, width) == target_size:\n return depth\n if len(depth.shape) == 2:\n depth = depth[None, None, ...]\n elif len(depth.shape) == 3:\n depth = depth[None, ...]\n target_height, target_width = target_size\n kernel_size_h = height // target_height\n kernel_size_w = width // target_width\n\n if kernel_size_h > 0 and kernel_size_w > 0:\n depth = F.max_pool2d(\n depth,\n kernel_size=(kernel_size_h, kernel_size_w),\n )\n depth = F.interpolate(depth, size=target_size, mode=\"nearest\")\n return depth.squeeze()\n\n\ndef resize_flow(flow, target_size):\n height, width = flow.shape[-3:-1]\n if (height, width) == target_size:\n return flow\n if len(flow.shape) == 3:\n flow = flow[None, ...]\n target_height, target_width = target_size\n kernel_size_h = height // target_height\n kernel_size_w = width // target_width\n # flows have direction, so we can't just use max_pool2d.\n # otherwise the direction will be wrong, e.g., max_pool2d([0, -1]) = [0]\n # previous_valid_flow = (torch.norm(flow, p=2, dim=-1) > 0.5).float().sum()\n flow[torch.norm(flow, p=2, dim=-1) < 0.5] = -100000\n if kernel_size_h > 0 and kernel_size_w > 0:\n flow = F.max_pool2d(\n flow.permute(0, 3, 1, 2),\n kernel_size=(kernel_size_h, kernel_size_w),\n )\n flow = F.interpolate(flow, size=target_size, mode=\"nearest\")\n else:\n flow = F.interpolate(flow.permute(0, 3, 1, 2), size=target_size, mode=\"nearest\")\n flow = flow.permute(0, 2, 3, 1)\n flow[torch.norm(flow, p=2, dim=-1) > 1000] = 0\n # new_valid_flow = (torch.norm(flow, p=2, dim=-1) > 0.5).float().sum()\n return flow.squeeze()\n\n\ndef prepare_inputs_and_targets(data_dict, device=torch.device(\"cuda\"), v=3, timespan=2.0):\n assert data_dict[\"context\"][\"image\"].dim() == 5, \"need to be b, tv, c, h, w\"\n b, tv, c, h, w = data_dict[\"context\"][\"image\"].shape\n context_t = tv // v\n target_t = data_dict[\"target\"][\"image\"].shape[1] // v\n input_dict = {\n \"context_image\": data_dict[\"context\"][\"image\"].reshape(b, context_t, v, c, h, w),\n # targets to render\n \"context_camtoworlds\": data_dict[\"context\"][\"camtoworld\"].reshape(b, context_t, v, 4, 4),\n \"context_intrinsics\": data_dict[\"context\"][\"intrinsics\"].reshape(b, context_t, v, 3, 3),\n \"target_camtoworlds\": data_dict[\"target\"][\"camtoworld\"].reshape(b, target_t, v, 4, 4),\n \"target_intrinsics\": data_dict[\"target\"][\"intrinsics\"].reshape(b, target_t, v, 3, 3),\n }\n if \"depth\" in data_dict[\"context\"]:\n depth_h, depth_w = data_dict[\"context\"][\"depth\"].shape[-2:]\n input_dict[\"context_depth\"] = data_dict[\"context\"][\"depth\"].reshape(\n b, context_t, v, depth_h, depth_w\n )\n if \"flow\" in data_dict[\"context\"]:\n flow_h, flow_w = data_dict[\"context\"][\"flow\"].shape[-3:-1]\n input_dict[\"context_flow\"] = data_dict[\"context\"][\"flow\"].reshape(\n b, context_t, v, flow_h, flow_w, 3\n )\n if \"time\" in data_dict[\"context\"]:\n input_dict[\"context_time\"] = (\n data_dict[\"context\"][\"time\"].reshape(b, context_t, v) / timespan\n )\n if \"time\" in data_dict[\"target\"]:\n input_dict[\"target_time\"] = data_dict[\"target\"][\"time\"].reshape(b, target_t, v) / timespan\n if \"sky_masks\" in data_dict[\"context\"]:\n input_dict[\"context_sky_masks\"] = data_dict[\"context\"][\"sky_masks\"].reshape(\n b, context_t, v, h, w\n )\n target_dict = {\n \"target_image\": data_dict[\"target\"][\"image\"].reshape(b, target_t, v, 3, h, w),\n }\n\n if \"depth\" in data_dict[\"target\"]:\n target_dict[\"target_depth\"] = data_dict[\"target\"][\"depth\"].reshape(\n b, target_t, v, depth_h, depth_w\n )\n if \"flow\" in data_dict[\"target\"]:\n target_dict[\"target_flow\"] = data_dict[\"target\"][\"flow\"].reshape(\n b, target_t, v, depth_h, depth_w, 3\n )\n target_dict[\"context_flow\"] = data_dict[\"context\"][\"flow\"].reshape(b, context_t, v, h, w, 3)\n if \"flow\" in data_dict[\"context\"]:\n target_dict[\"context_flow\"] = data_dict[\"context\"][\"flow\"].reshape(\n b, context_t, v, depth_h, depth_w, 3\n )\n if \"sky_masks\" in data_dict[\"target\"]:\n target_dict[\"target_sky_masks\"] = data_dict[\"target\"][\"sky_masks\"].reshape(\n b, target_t, v, h, w\n )\n if \"sky_masks\" in data_dict[\"context\"]:\n target_dict[\"context_sky_masks\"] = data_dict[\"context\"][\"sky_masks\"].reshape(\n b, context_t, v, h, w\n )\n if \"dynamic_masks\" in data_dict[\"target\"]:\n target_dict[\"target_dynamic_masks\"] = data_dict[\"target\"][\"dynamic_masks\"].reshape(\n b, target_t, v, h, w\n )\n if \"ground_masks\" in data_dict[\"target\"]:\n target_dict[\"target_ground_masks\"] = data_dict[\"target\"][\"ground_masks\"].reshape(\n b, target_t, v, h, w\n )\n\n input_dict[\"context_frame_idx\"] = data_dict[\"context\"][\"frame_idx\"]\n target_dict[\"target_frame_idx\"] = data_dict[\"target\"][\"frame_idx\"]\n input_dict = {k: v.to(device) for k, v in input_dict.items()}\n target_dict = {k: v.to(device) for k, v in target_dict.items()}\n input_dict[\"timespan\"] = timespan\n input_dict[\"scene_id\"] = data_dict[\"scene_id\"]\n input_dict[\"scene_name\"] = data_dict[\"scene_name\"]\n input_dict[\"height\"], input_dict[\"width\"] = h, w\n return input_dict, target_dict\n\n\ndef prepare_inputs_and_targets_novel_view(data_dict, device=torch.device(\"cpu\")):\n raise NotImplementedError(\"Legacy code, not tested for a while. But you can use it as a reference.\")\n\n assert data_dict[\"context\"][\"image\"].dim() == 5, \"need to be b, tv, c, h, w\"\n b, tv, c, h, w = data_dict[\"context\"][\"image\"].shape\n v = int(data_dict[\"context\"][\"num_views\"].flatten()[0].item())\n context_t = tv // v\n target_t = data_dict[\"target\"][\"image\"].shape[1] // v\n # move the camera to right by 1 meter:\n # freeze time\n # cam_to_worlds = data_dict[\"target\"][\"camtoworld\"].view(b, target_t, v, 4, 4)\n # cam_to_worlds0 = cam_to_worlds[:, 0:1]\n # cam_to_worlds = cam_to_worlds0.expand(-1, target_t, -1, -1, -1)\n # data_dict[\"target\"][\"camtoworld\"] = cam_to_worlds.reshape(b, target_t * v, 4, 4)\n ### novel view\n cam_to_worlds = data_dict[\"target\"][\"camtoworld\"].view(b, target_t, v, 4, 4)\n left_cam_to_worlds = cam_to_worlds[:, :, 0].numpy()\n center_cam_to_worlds = cam_to_worlds[:, :, 1].numpy()\n right_cam_to_worlds = cam_to_worlds[:, :, 2].numpy()\n new_left_cam_to_worlds = []\n new_center_cam_to_worlds = []\n new_right_cam_to_worlds = []\n for bix in range(left_cam_to_worlds.shape[0]):\n new_path = get_path_front_left_lift_then_spiral_forward(\n left_cam_to_worlds[bix],\n num_frames=target_t,\n duration_frames=20,\n )\n new_left_cam_to_worlds.append(torch.from_numpy(new_path))\n new_path = get_path_front_left_lift_then_spiral_forward(\n center_cam_to_worlds[bix],\n num_frames=target_t,\n duration_frames=20,\n )\n new_center_cam_to_worlds.append(torch.from_numpy(new_path))\n new_path = get_path_front_left_lift_then_spiral_forward(\n right_cam_to_worlds[bix],\n num_frames=target_t,\n duration_frames=20,\n )\n new_right_cam_to_worlds.append(torch.from_numpy(new_path))\n new_left_cam_to_worlds = torch.stack(new_left_cam_to_worlds, dim=0)\n new_center_cam_to_worlds = torch.stack(new_center_cam_to_worlds, dim=0)\n new_right_cam_to_worlds = torch.stack(new_right_cam_to_worlds, dim=0)\n new_cam_to_worlds = torch.stack(\n [new_left_cam_to_worlds, new_center_cam_to_worlds, new_right_cam_to_worlds],\n dim=2,\n )\n data_dict[\"target\"][\"camtoworld\"] = new_cam_to_worlds.reshape(b, target_t * v, 4, 4).to(\n data_dict[\"target\"][\"camtoworld\"]\n )\n ### novel view\n input_dict = {\n \"context_image\": data_dict[\"context\"][\"image\"].reshape(b, context_t, v, c, h, w),\n # targets to render\n \"context_camtoworlds\": data_dict[\"context\"][\"camtoworld\"].reshape(b, context_t, v, 4, 4),\n \"context_intrinsics\": data_dict[\"context\"][\"intrinsics\"].reshape(b, context_t, v, 3, 3),\n \"target_camtoworlds\": data_dict[\"target\"][\"camtoworld\"].reshape(b, target_t, v, 4, 4),\n \"target_intrinsics\": data_dict[\"target\"][\"intrinsics\"].reshape(b, target_t, v, 3, 3),\n }\n if \"depth\" in data_dict[\"context\"]:\n input_dict[\"context_depth\"] = data_dict[\"context\"][\"depth\"].reshape(b, context_t, v, h, w)\n if \"flow\" in data_dict[\"context\"]:\n input_dict[\"context_flow\"] = data_dict[\"context\"][\"flow\"].reshape(b, context_t, v, h, w, 3)\n if \"time\" in data_dict[\"context\"]:\n input_dict[\"context_time\"] = data_dict[\"context\"][\"time\"].reshape(b, context_t)\n if \"time\" in data_dict[\"target\"]:\n input_dict[\"target_time\"] = data_dict[\"target\"][\"time\"].reshape(b, target_t)\n if \"sky_masks\" in data_dict[\"context\"]:\n input_dict[\"context_sky_masks\"] = data_dict[\"context\"][\"sky_masks\"].reshape(\n b, context_t, v, h, w\n )\n\n target_dict = {\n \"target_image\": data_dict[\"target\"][\"image\"].reshape(b, target_t, v, 3, h, w),\n }\n\n if \"depth\" in data_dict[\"target\"]:\n target_dict[\"target_depth\"] = data_dict[\"target\"][\"depth\"].reshape(b, target_t, v, h, w)\n if \"depth\" in data_dict[\"context\"]:\n target_dict[\"context_depth\"] = data_dict[\"context\"][\"depth\"].reshape(b, context_t, v, h, w)\n if \"flow\" in data_dict[\"target\"]:\n target_dict[\"target_flow\"] = data_dict[\"target\"][\"flow\"].reshape(b, target_t, v, h, w, 3)\n if \"sky_masks\" in data_dict[\"target\"]:\n target_dict[\"target_sky_masks\"] = data_dict[\"target\"][\"sky_masks\"].reshape(\n b, target_t, v, h, w\n )\n if \"sky_masks\" in data_dict[\"context\"]:\n target_dict[\"context_sky_masks\"] = data_dict[\"context\"][\"sky_masks\"].reshape(\n b, context_t, v, h, w\n )\n if \"dynamic_masks\" in data_dict[\"target\"]:\n target_dict[\"target_dynamic_masks\"] = data_dict[\"target\"][\"dynamic_masks\"].reshape(\n b, target_t, v, h, w\n )\n if \"ground_masks\" in data_dict[\"target\"]:\n target_dict[\"target_ground_masks\"] = data_dict[\"target\"][\"ground_masks\"].reshape(\n b, target_t, v, h, w\n )\n\n target_dict[\"target_frame_idx\"] = data_dict[\"target\"][\"frame_idx\"]\n input_dict = {k: v.to(device) for k, v in input_dict.items()}\n target_dict = {k: v.to(device) for k, v in target_dict.items()}\n try:\n input_dict[\"height\"] = data_dict[\"height\"][0].item()\n input_dict[\"width\"] = data_dict[\"width\"][0].item()\n if \"timespan\" in data_dict:\n input_dict[\"timespan\"] = data_dict[\"timespan\"].flatten()[0].item()\n except:\n input_dict[\"height\"] = data_dict[\"height\"]\n input_dict[\"width\"] = data_dict[\"width\"]\n if \"timespan\" in data_dict:\n input_dict[\"timespan\"] = data_dict[\"timespan\"]\n input_dict[\"context_frame_idx\"] = data_dict[\"context\"][\"frame_idx\"]\n input_dict[\"scene_id\"] = data_dict[\"scene_id\"]\n input_dict[\"scene_name\"] = data_dict[\"scene_name\"]\n return input_dict, target_dict\n" }, { "path": "storm/dataset/samplers.py", "content": "import itertools\nfrom typing import Any, Optional\n\nimport torch\nfrom torch.utils.data.sampler import Sampler\n\nimport storm.utils.distributed as distributed\n\n\ndef _get_torch_dtype(size: int) -> Any:\n \"\"\"Return the appropriate PyTorch dtype based on size.\"\"\"\n return torch.int32 if size <= 2**31 else torch.int64\n\n\ndef _generate_randperm_indices(*, size: int, generator: torch.Generator):\n \"\"\"\n Generate the indices of a random permutation.\n\n This matches PyTorch's CPU implementation.\n See: https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/TensorFactories.cpp#L900-L921\n \"\"\"\n dtype = _get_torch_dtype(size)\n perm = torch.arange(size, dtype=dtype)\n for i in range(size):\n j = torch.randint(i, size, size=(1,), generator=generator).item()\n # Always swap even if no-op\n value = perm[j].item()\n perm[j] = perm[i].item()\n perm[i] = value\n yield value\n\n\nclass InfiniteSampler(Sampler):\n def __init__(\n self,\n sample_count: int,\n shuffle: bool = False,\n seed: int = 0,\n start: Optional[int] = None,\n step: Optional[int] = None,\n advance: int = 0,\n ):\n \"\"\"\n A sampler that infinitely yields indices for a dataset.\n\n Args:\n sample_count (int): Number of samples in the dataset.\n shuffle (bool): Whether to shuffle indices.\n seed (int): Seed for random generator.\n start (Optional[int]): Starting index for sampling.\n step (Optional[int]): Step size for sampling.\n advance (int): Number of indices to skip at the start.\n \"\"\"\n self._sample_count = sample_count\n self._seed = seed\n self._shuffle = shuffle\n self._start = distributed.get_global_rank() if start is None else start\n self._step = distributed.get_world_size() if step is None else step\n self._advance = advance\n\n def __iter__(self):\n \"\"\"Yield indices based on the specified configuration.\"\"\"\n iterator = self._shuffled_iterator() if self._shuffle else self._iterator()\n yield from itertools.islice(iterator, self._advance, None)\n\n def _iterator(self):\n \"\"\"Generate indices sequentially.\"\"\"\n assert not self._shuffle\n while True:\n iterable = range(self._sample_count)\n yield from itertools.islice(iterable, self._start, None, self._step)\n\n def _shuffled_iterator(self):\n \"\"\"Generate shuffled indices.\"\"\"\n assert self._shuffle\n generator = torch.Generator().manual_seed(self._seed)\n while True:\n iterable = _generate_randperm_indices(size=self._sample_count, generator=generator)\n yield from itertools.islice(iterable, self._start, None, self._step)\n\n\nclass NoPaddingDistributedSampler(Sampler):\n def __init__(self, dataset, num_replicas=None, rank=None, shuffle=False):\n \"\"\"\n A distributed sampler without padding.\n Used for distributed evaluation, i.e., when the dataset is not divisible by the number of\n replicas but we still want to evaluate on the full dataset.\n\n Args:\n dataset: The dataset to sample from.\n num_replicas (int): Number of replicas in distributed setting.\n rank (int): Rank of the current process.\n shuffle (bool): Whether to shuffle indices.\n \"\"\"\n self.dataset = dataset\n self.num_replicas = distributed.get_world_size() if num_replicas is None else num_replicas\n self.rank = distributed.get_global_rank() if rank is None else rank\n self.shuffle = shuffle\n self.num_samples = len(self.dataset) // self.num_replicas\n self.total_size = self.num_samples * self.num_replicas\n self.rank_start = self.rank * self.num_samples\n self.rank_end = (\n (self.rank + 1) * self.num_samples\n if self.rank < self.num_replicas - 1\n else len(self.dataset)\n )\n\n def __iter__(self):\n \"\"\"Yield indices for the current rank.\"\"\"\n indices = list(range(len(self.dataset)))\n if self.shuffle:\n g = torch.Generator()\n g.manual_seed(0)\n indices = torch.randperm(len(indices), generator=g).tolist()\n indices = indices[self.rank_start : self.rank_end]\n return iter(indices)\n\n def __len__(self):\n \"\"\"Return the number of samples for the current rank.\"\"\"\n return self.num_samples\n" }, { "path": "storm/dataset/storm_dataset.py", "content": "import json\nimport logging\nimport os\nfrom typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nimport torchvision.transforms as transforms\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nfrom torch.utils.data.dataloader import default_collate\nfrom tqdm import trange\n\nfrom .constants import DATASET_DICT, DATASETS, MEAN, STD\nfrom .data_utils import resize_depth, resize_flow, to_float_tensor, to_tensor\n\nlogger = logging.getLogger(\"STORM\")\n\n\nclass STORMDataset(Dataset):\n def __init__(\n self,\n data_root: str,\n annotation_txt_file_list: Union[str, List[str]],\n target_size: Tuple[int, int] = (160, 240),\n num_context_timesteps: int = 4,\n num_target_timesteps: int = 4,\n num_max_cams: Literal[1, 3, 5, 6, 7] = 3,\n timespan: float = 2.0, # 2.0 seconds\n subset_indices: Optional[List[int]] = None,\n num_replicas: int = 1,\n equispaced: bool = True,\n load_depth: bool = True,\n load_flow: bool = False,\n load_dynamic_mask: bool = False,\n load_ground_label: bool = False,\n return_context_as_target: bool = False,\n skip_sky_mask: bool = False,\n ):\n super().__init__()\n self.data_root = data_root\n self.target_size = target_size\n self.num_context_timesteps = num_context_timesteps\n self.num_target_timesteps = num_target_timesteps\n self.num_max_cams = num_max_cams\n self.timespan = timespan\n self.load_depth = load_depth\n self.load_flow = load_flow\n self.load_dynamic_mask = load_dynamic_mask\n self.load_ground_label = load_ground_label\n self.skip_sky_mask = skip_sky_mask\n if isinstance(annotation_txt_file_list, str):\n annotation_txt_file_list = [annotation_txt_file_list]\n scene_list = []\n for annotation_txt_file in annotation_txt_file_list:\n with open(annotation_txt_file, \"r\") as f:\n scene_list += f.readlines()\n annotation_paths = [line.strip() for line in scene_list]\n if subset_indices is not None:\n annotation_paths = [annotation_paths[i] for i in subset_indices]\n self.annotations = []\n for annotation_path in annotation_paths:\n with open(os.path.join(data_root, annotation_path), \"r\") as f:\n self.annotations.append(json.load(f))\n logger.info(f\"Loaded {len(self.annotations)} annotations.\")\n self.num_replicas = num_replicas\n if self.num_replicas > 1:\n self.annotations *= self.num_replicas\n self.equispaced = equispaced\n self.return_context_as_target = return_context_as_target\n self.img_transformation = transforms.Compose(\n [\n transforms.Resize(target_size, interpolation=Image.BICUBIC, antialias=True),\n transforms.ToTensor(),\n transforms.Normalize(mean=MEAN, std=STD),\n ]\n )\n\n def __len__(self) -> int:\n return len(self.annotations)\n\n def get_frame(\n self,\n scene_json: Dict[str, Any],\n frame_idx: int,\n source_frame_idx: int = -1,\n ) -> Dict[str, Any]:\n \"\"\"Retrieve a single frame from the dataset.\"\"\"\n normalized_intrinsics = scene_json[\"normalized_intrinsics\"]\n dataset_name = scene_json[\"dataset\"]\n cam_to_world = scene_json[\"camera_to_world\"]\n\n images, depths, sky_masks, flows = [], [], [], []\n camtoworlds, intrinsics = [], []\n dynamic_masks, ground_masks = [], []\n\n if source_frame_idx < 0:\n source_frame_idx = frame_idx\n\n camera_list = DATASET_DICT[dataset_name][\"camera_list\"][self.num_max_cams]\n ref_camera_name = DATASET_DICT[dataset_name][\"ref_camera\"]\n world_to_canonical = np.linalg.inv(cam_to_world[ref_camera_name][source_frame_idx])\n\n for camera in camera_list:\n img_relative_path = scene_json[\"relative_image_path\"][camera][frame_idx]\n if dataset_name in [\"waymo\", \"nuscenes\", \"argoverse2\", \"argoverse\"]:\n img_relative_path = img_relative_path.replace(\"images\", f\"images_4\")\n img_relative_path = img_relative_path.replace(\"sweeps\", f\"sweeps_4\")\n img_relative_path = img_relative_path.replace(\"samples\", f\"samples_4\")\n\n # Get RGB\n img_path = os.path.join(self.data_root, \"datasets\", dataset_name, img_relative_path)\n img = Image.open(img_path).convert(\"RGB\")\n img = self.img_transformation(img)\n images.append(img)\n\n # Get sky mask\n if dataset_name in [\"waymo\", \"nuscenes\", \"argoverse2\"]:\n # if dataset_name in [\"waymo\", \"nuscenes\"]:\n if dataset_name == \"nuscenes\":\n sky_path = img_path.replace(\"samples\", \"samples_sky_mask\")\n sky_path = sky_path.replace(\"sweeps\", \"sweeps_sky_mask\")\n elif dataset_name == \"waymo\":\n sky_path = img_path.replace(\"images\", \"sky_masks\")\n elif dataset_name == \"argoverse2\":\n sky_path = img_path.replace(\"images_4\", \"sky_masks_512\")\n sky_path = sky_path.replace(\"jpg\", \"png\")\n if self.skip_sky_mask:\n sky = torch.zeros(self.target_size[0], self.target_size[1]).float()\n else:\n try:\n new_sky_path = sky_path.replace(\"STORM2\", \"STORM_masks\")\n sky = Image.open(new_sky_path).convert(\"L\").resize(self.target_size[::-1])\n except FileNotFoundError:\n sky = Image.open(sky_path).convert(\"L\").resize(self.target_size[::-1])\n sky = to_tensor(np.array(sky) > 0).float()\n sky_masks.append(sky)\n\n # Get dynamic mask, this is for dynamic region evaluation.\n if dataset_name in [\"waymo\"] and self.load_dynamic_mask:\n dynamic_path = img_path.replace(\"images_8\", \"dynamic_masks/all\")\n dynamic_path = dynamic_path.replace(\"images_4\", \"dynamic_masks/all\")\n dynamic_path = dynamic_path.replace(\"jpg\", \"png\")\n if not os.path.exists(dynamic_path):\n dynamic_path = dynamic_path.replace(\"STORM2\", \"STORM\")\n dynamic_mask = Image.open(dynamic_path).convert(\"L\").resize(self.target_size[::-1])\n dynamic_mask = to_tensor(np.array(dynamic_mask) > 0).float()\n dynamic_masks.append(dynamic_mask)\n\n # Get ground label, this is for flow evaluation, i.e., we use this to exclude the ground lidar points.\n if dataset_name in [\"waymo\"] and self.load_ground_label:\n ground_path = img_path.replace(\"images\", \"ground_label\")\n ground_path = ground_path.replace(\"jpg\", \"png\")\n ground = Image.open(ground_path).convert(\"L\").resize(self.target_size[::-1])\n ground = to_tensor(np.array(ground) > 0).float()\n ground_masks.append(ground)\n\n camtoworld = (\n DATASETS[dataset_name][\"canonical_to_flu\"]\n @ world_to_canonical\n @ cam_to_world[camera][frame_idx]\n @ DATASETS[dataset_name][\"opencv2dataset\"]\n )\n\n camtoworld = to_tensor(camtoworld)\n camtoworlds.append(camtoworld)\n\n # intrinsics\n fx, fy, cx, cy = np.array(normalized_intrinsics[camera])\n fx = fx * self.target_size[1]\n fy = fy * self.target_size[0]\n cx = cx * self.target_size[1]\n cy = cy * self.target_size[0]\n intrinsics.append(\n torch.tensor(\n [\n [fx, 0.0, cx],\n [0.0, fy, cy],\n [0.0, 0.0, 1.0],\n ]\n ).float()\n )\n\n if self.load_depth or self.load_flow:\n if dataset_name == \"waymo\":\n depth_path = img_path.replace(\"images\", \"depth_flows\").replace(\"jpg\", \"npy\")\n depth_and_flow = np.load(depth_path)\n if self.load_depth:\n depth = depth_and_flow[..., 0]\n depth = torch.tensor(depth).float()\n depth = resize_depth(depth, self.target_size)\n depths.append(depth)\n if self.load_flow:\n flow = depth_and_flow[..., 1:]\n flow = torch.tensor(flow).float()\n flow = resize_flow(flow, self.target_size)\n # there must be a better way to do this: rotate the flow to the canonical view\n flow = (\n flow\n @ torch.tensor(\n (\n world_to_canonical\n @ cam_to_world[camera][frame_idx]\n @ np.linalg.inv(scene_json[\"camera_to_ego\"][camera])\n )\n )\n .float()[:3, :3]\n .T\n )\n flows.append(flow)\n if dataset_name == \"nuscenes\":\n depth_path = img_path.replace(\"samples\", \"samples_depth\")\n depth_path = depth_path.replace(\"sweeps\", \"sweeps_depth\")\n depth_path = depth_path.replace(\"jpg\", \"npy\")\n depth = np.load(depth_path)\n depth = torch.tensor(depth).float()\n depth = resize_depth(depth, self.target_size)\n depths.append(depth)\n\n if dataset_name == \"argoverse2\":\n depth_path = img_path.replace(\"images\", \"depths\")\n depth_path = depth_path.replace(\"jpg\", \"npy\")\n depth = np.load(depth_path)\n depth = torch.tensor(depth).float()\n depth = resize_depth(depth, self.target_size)\n depths.append(depth)\n\n frame_images = torch.stack(images)\n frame_depths = torch.stack(depths) if len(depths) > 0 else None\n frame_sky_masks = torch.stack(sky_masks) if len(sky_masks) > 0 else None\n frame_flows = torch.stack(flows) if len(flows) > 0 else None\n frame_dynamic_masks = torch.stack(dynamic_masks) if len(dynamic_masks) > 0 else None\n frame_camtoworlds = torch.stack(camtoworlds)\n frame_intrinsics = torch.stack(intrinsics)\n ground_masks = torch.stack(ground_masks) if len(ground_masks) > 0 else None\n data_dict = {\n \"image\": frame_images,\n \"camtoworld\": frame_camtoworlds,\n \"intrinsics\": frame_intrinsics,\n \"frame_idx\": frame_idx,\n \"depth\": frame_depths,\n \"sky_masks\": frame_sky_masks,\n \"flow\": frame_flows,\n \"dynamic_masks\": frame_dynamic_masks,\n \"ground_masks\": ground_masks,\n }\n return {k: v for k, v in data_dict.items() if v is not None}\n\n def __getitem__(\n self, index: int, context_frame_idx: int = -1, return_all=False\n ) -> Dict[str, Any]:\n try:\n scene_json = self.annotations[index % len(self.annotations)]\n scene_id = scene_json[\"scene_id\"]\n num_timesteps = scene_json[\"num_timesteps\"]\n fps = scene_json[\"fps\"]\n num_max_future_frames = int(self.timespan * fps)\n if num_max_future_frames > num_timesteps:\n num_max_future_frames = int(fps) # make it 1 seconds\n time_in_seconds = scene_json[\"normalized_time\"]\n if context_frame_idx < 0:\n context_frame_idx = np.random.randint(0, num_timesteps - num_max_future_frames)\n if context_frame_idx + num_max_future_frames >= num_timesteps:\n context_frame_idx = np.random.randint(0, num_timesteps - num_max_future_frames)\n assert (\n context_frame_idx + num_max_future_frames < num_timesteps\n ), f\"scene_id: {scene_id}, context_frame_idx: {context_frame_idx}, num_timesteps: {num_timesteps}, num_max_future_frames: {num_max_future_frames}\"\n\n if self.equispaced:\n context_frame_idx = np.arange(\n context_frame_idx,\n context_frame_idx + num_max_future_frames,\n num_max_future_frames // self.num_context_timesteps,\n )\n else:\n context_frame_idx = np.random.choice(\n np.arange(\n context_frame_idx,\n context_frame_idx + num_max_future_frames,\n ),\n size=self.num_context_timesteps,\n replace=False,\n )\n context_frame_idx = sorted(context_frame_idx)\n\n if return_all:\n # return all frames between context_frame_idx and context_frame_idx + num_max_future_frames\n target_frame_idx = np.arange(\n context_frame_idx[0],\n context_frame_idx[0] + num_max_future_frames,\n )\n else:\n # randomly sample \"num_target_timesteps\" frames\n target_frame_idx = np.random.choice(\n np.arange(\n context_frame_idx[0],\n context_frame_idx[0] + num_max_future_frames,\n ),\n self.num_target_timesteps,\n replace=False,\n )\n target_frame_idx = [min(idx, num_timesteps - 1) for idx in target_frame_idx]\n target_frame_idx = sorted(target_frame_idx)\n\n # get context\n context_dict_list = []\n for ctx_id in context_frame_idx:\n context_dict = self.get_frame(\n scene_json=scene_json,\n frame_idx=ctx_id,\n source_frame_idx=context_frame_idx[0],\n )\n context_dict[\"time\"] = torch.tensor(\n [time_in_seconds[ctx_id] - time_in_seconds[context_frame_idx[0]]]\n * self.num_max_cams\n )\n context_dict_list.append(context_dict)\n if self.return_context_as_target:\n target_frame_idx = context_frame_idx\n target_dict_list = []\n for target_id in target_frame_idx:\n target_dict = self.get_frame(\n scene_json=scene_json,\n frame_idx=target_id,\n source_frame_idx=context_frame_idx[0],\n )\n target_dict[\"time\"] = torch.tensor(\n [time_in_seconds[target_id] - time_in_seconds[context_frame_idx[0]]]\n * self.num_max_cams\n )\n target_dict_list.append(target_dict)\n context_dict = default_collate(context_dict_list)\n target_dict = default_collate(target_dict_list)\n\n for k, v in context_dict.items():\n if isinstance(v, torch.Tensor) and len(v.shape) >= 2:\n context_dict[k] = torch.cat([d for d in v], dim=0)\n for k, v in target_dict.items():\n if isinstance(v, torch.Tensor) and len(v.shape) >= 2:\n target_dict[k] = torch.cat([d for d in v], dim=0)\n sample = {\n \"context\": context_dict,\n \"target\": target_dict,\n \"scene_id\": scene_id,\n \"scene_name\": scene_json[\"scene_name\"],\n \"width\": self.target_size[1],\n \"height\": self.target_size[0],\n \"fps\": fps,\n \"timespan\": self.timespan,\n }\n return to_float_tensor(sample)\n except Exception as e:\n logger.info(\n f\"Error in scene_id: {scene_id}, context_frame_idx: {context_frame_idx}, scene_name: {scene_json['scene_name']}\"\n )\n logger.info(e)\n try:\n return self.__getitem__(index + 1, 0, return_all)\n except Exception as e:\n logger.info(e)\n return self.__getitem__(index + 1)\n\n\nclass STORMDatasetEval(STORMDataset):\n def __init__(\n self,\n data_root: str,\n annotation_txt_file_list: Union[str, List[str]],\n target_size: Tuple[int, int] = (225, 400),\n num_context_timesteps: int = 4,\n num_target_timesteps: int = 4,\n num_max_cams: Literal[1, 3, 5, 6, 7] = 3,\n timespan: float = 2.0, # how many seconds\n subset_indices: Optional[List[int]] = None,\n num_replicas: int = 1,\n equispaced: bool = True,\n load_depth: bool = True,\n load_flow: bool = False,\n load_dynamic_mask: bool = False,\n load_ground_label: bool = False,\n return_context_as_target: bool = False,\n skip_sky_mask: bool = False,\n scene_id_list: Optional[List[int]] = None,\n ):\n super(STORMDatasetEval, self).__init__(\n data_root=data_root,\n annotation_txt_file_list=annotation_txt_file_list,\n target_size=target_size,\n num_context_timesteps=num_context_timesteps,\n num_target_timesteps=num_target_timesteps,\n num_max_cams=num_max_cams,\n timespan=timespan,\n subset_indices=subset_indices,\n num_replicas=num_replicas,\n equispaced=equispaced,\n load_depth=load_depth,\n load_flow=load_flow,\n load_dynamic_mask=load_dynamic_mask,\n load_ground_label=load_ground_label,\n return_context_as_target=return_context_as_target,\n skip_sky_mask=skip_sky_mask,\n )\n val_sample_list = []\n if scene_id_list is None:\n scene_id_list = list(range(len(self.annotations)))\n for scene_id in scene_id_list:\n for start_id in range(0, 200, 20):\n if scene_id == 63 and start_id == 0:\n continue\n val_sample_list.append((scene_id, start_id))\n self.val_sample_list = val_sample_list\n\n def __len__(self) -> int:\n return len(self.val_sample_list)\n\n def __getitem__(self, index: int):\n return super(STORMDatasetEval, self).__getitem__(\n self.val_sample_list[index][0],\n self.val_sample_list[index][1],\n return_all=True,\n )\n\n\nclass SingleSequenceDataset(Dataset):\n def __init__(\n self,\n data_root: str,\n annotation_txt_file_list: Union[str, List[str]],\n target_size: Tuple[int, int] = (225, 400),\n num_context_timesteps: int = 4,\n num_target_timesteps: int = 4,\n num_max_cams: Literal[1, 3, 5, 6, 7] = 3,\n timespan: float = 2.0, # how many seconds\n subset_indices: Optional[List[int]] = None,\n num_replicas: int = 1,\n equispaced: bool = True,\n load_depth: bool = True,\n load_flow: bool = False,\n load_dynamic_mask: bool = False,\n load_ground_label: bool = False,\n load_scale=\"4\",\n ):\n super().__init__()\n self.data_root = data_root\n self.target_size = target_size\n self.timespan = timespan\n self.num_context_timesteps = num_context_timesteps\n self.num_target_timesteps = num_target_timesteps\n self.num_max_cams = num_max_cams\n self.load_depth = load_depth\n self.load_flow = load_flow\n self.load_dynamic_mask = load_dynamic_mask\n self.load_ground_label = load_ground_label\n self.load_scale = load_scale\n if isinstance(annotation_txt_file_list, str):\n annotation_txt_file_list = [annotation_txt_file_list]\n scene_list = []\n for annotation_txt_file in annotation_txt_file_list:\n with open(annotation_txt_file, \"r\") as f:\n scene_list += f.readlines()\n annotation_paths = [line.strip() for line in scene_list]\n if subset_indices is not None:\n annotation_paths = [annotation_paths[i] for i in subset_indices]\n self.annotations = []\n for annotation_path in annotation_paths:\n with open(os.path.join(data_root, annotation_path), \"r\") as f:\n self.annotations.append(json.load(f))\n logger.info(f\"Loaded {len(self.annotations)} annotations.\")\n self.num_replicas = num_replicas\n if self.num_replicas > 1:\n self.annotations *= self.num_replicas\n self.equispaced = equispaced\n self.img_transformation = transforms.Compose(\n [\n transforms.Resize(target_size, interpolation=Image.BICUBIC, antialias=True),\n transforms.ToTensor(),\n transforms.Normalize(mean=MEAN, std=STD),\n ]\n )\n\n def __len__(self) -> int:\n return len(self.annotations)\n\n def get_frame(\n self,\n scene_json: Dict[str, Any],\n frame_idx: int,\n source_frame_idx: int = -1,\n ) -> Dict[str, Any]:\n normalized_intrinsics = scene_json[\"normalized_intrinsics\"]\n dataset_name = scene_json[\"dataset\"]\n cam_to_world = scene_json[\"camera_to_world\"]\n\n images, depths, sky_masks, flows = [], [], [], []\n camtoworlds, intrinsics = [], []\n dynamic_masks, ground_masks = [], []\n\n if source_frame_idx < 0:\n source_frame_idx = frame_idx\n\n camera_list = DATASET_DICT[dataset_name][\"camera_list\"][self.num_max_cams]\n ref_camera_name = DATASET_DICT[dataset_name][\"ref_camera\"]\n world_to_canonical = np.linalg.inv(cam_to_world[ref_camera_name][source_frame_idx])\n\n for camera in camera_list:\n img_relative_path = scene_json[\"relative_image_path\"][camera][frame_idx]\n if dataset_name in [\"waymo\", \"nuscenes\", \"argoverse2\", \"argoverse\"]:\n if self.load_scale == \"4\":\n img_relative_path = img_relative_path.replace(\"images\", f\"images_4\")\n img_relative_path = img_relative_path.replace(\"sweeps\", f\"sweeps_4\")\n img_relative_path = img_relative_path.replace(\"samples\", f\"samples_4\")\n\n # Get RGB\n img_path = os.path.join(self.data_root, \"datasets\", dataset_name, img_relative_path)\n img = Image.open(img_path).convert(\"RGB\")\n img = self.img_transformation(img)\n images.append(img)\n\n # Get sky mask\n if dataset_name in [\"waymo\", \"nuscenes\", \"argoverse2\"]:\n # if dataset_name in [\"waymo\", \"nuscenes\"]:\n if dataset_name == \"nuscenes\":\n sky_path = img_path.replace(\"samples\", \"samples_sky_mask\")\n sky_path = sky_path.replace(\"sweeps\", \"sweeps_sky_mask\")\n elif dataset_name == \"waymo\":\n sky_path = img_path.replace(\"images\", \"sky_masks\")\n elif dataset_name == \"argoverse2\":\n sky_path = img_path.replace(\"images_4\", \"sky_masks_512\")\n sky_path = sky_path.replace(\"jpg\", \"png\")\n try:\n new_sky_path = sky_path.replace(\"STORM2\", \"STORM_masks\")\n sky = Image.open(new_sky_path).convert(\"L\").resize(self.target_size[::-1])\n except FileNotFoundError:\n sky = Image.open(sky_path).convert(\"L\").resize(self.target_size[::-1])\n sky = to_tensor(np.array(sky) > 0).float()\n sky_masks.append(sky)\n\n # Get dynamic mask, this is for dynamic region evaluation.\n if dataset_name in [\"waymo\"] and self.load_dynamic_mask:\n dynamic_path = dynamic_path.replace(\"images_4\", \"dynamic_masks/all\")\n dynamic_path = dynamic_path.replace(\"jpg\", \"png\")\n if not os.path.exists(dynamic_path):\n dynamic_path = dynamic_path.replace(\"STORM2\", \"STORM\")\n dynamic_mask = Image.open(dynamic_path).convert(\"L\").resize(self.target_size[::-1])\n dynamic_mask = to_tensor(np.array(dynamic_mask) > 0).float()\n dynamic_masks.append(dynamic_mask)\n\n # Get ground label, this is for flow evaluation, i.e., we use this to exclude the ground lidar points.\n if dataset_name in [\"waymo\"] and self.load_ground_label:\n ground_path = img_path.replace(\"images\", \"ground_label\")\n ground_path = ground_path.replace(\"jpg\", \"png\")\n ground = Image.open(ground_path).convert(\"L\").resize(self.target_size[::-1])\n ground = to_tensor(np.array(ground) > 0).float()\n ground_masks.append(ground)\n\n camtoworld = (\n DATASETS[dataset_name][\"canonical_to_flu\"]\n @ world_to_canonical\n @ cam_to_world[camera][frame_idx]\n @ DATASETS[dataset_name][\"opencv2dataset\"]\n )\n\n camtoworld = to_tensor(camtoworld)\n camtoworlds.append(camtoworld)\n\n # intrinsics\n fx, fy, cx, cy = np.array(normalized_intrinsics[camera])\n fx = fx * self.target_size[1]\n fy = fy * self.target_size[0]\n cx = cx * self.target_size[1]\n cy = cy * self.target_size[0]\n intrinsics.append(\n torch.tensor(\n [\n [fx, 0.0, cx],\n [0.0, fy, cy],\n [0.0, 0.0, 1.0],\n ]\n ).float()\n )\n\n if self.load_depth or self.load_flow:\n if dataset_name == \"waymo\":\n depth_path = img_path.replace(\"images\", \"depth_flows\").replace(\"jpg\", \"npy\")\n depth_and_flow = np.load(depth_path)\n if self.load_depth:\n depth = depth_and_flow[..., 0]\n depth = torch.tensor(depth).float()\n depth = resize_depth(depth, self.target_size)\n depths.append(depth)\n if self.load_flow:\n flow = depth_and_flow[..., 1:]\n flow = torch.tensor(flow).float()\n flow = resize_flow(flow, self.target_size)\n # there must be a better way to do this: rotate the flow to the canonical view\n flow = (\n flow\n @ torch.tensor(\n (\n world_to_canonical\n @ cam_to_world[camera][frame_idx]\n @ np.linalg.inv(scene_json[\"camera_to_ego\"][camera])\n )\n )\n .float()[:3, :3]\n .T\n )\n flows.append(flow)\n if dataset_name == \"nuscenes\":\n depth_path = img_path.replace(\"samples\", \"samples_depth\")\n depth_path = depth_path.replace(\"sweeps\", \"sweeps_depth\")\n depth_path = depth_path.replace(\"jpg\", \"npy\")\n depth = np.load(depth_path)\n depth = torch.tensor(depth).float()\n depth = resize_depth(depth, self.target_size)\n depths.append(depth)\n\n if dataset_name == \"argoverse2\":\n depth_path = img_path.replace(\"images\", \"depths\")\n depth_path = depth_path.replace(\"jpg\", \"npy\")\n depth = np.load(depth_path)\n depth = torch.tensor(depth).float()\n depth = resize_depth(depth, self.target_size)\n depths.append(depth)\n\n frame_images = torch.stack(images)\n frame_depths = torch.stack(depths) if len(depths) > 0 else None\n frame_sky_masks = torch.stack(sky_masks) if len(sky_masks) > 0 else None\n frame_flows = torch.stack(flows) if len(flows) > 0 else None\n frame_dynamic_masks = torch.stack(dynamic_masks) if len(dynamic_masks) > 0 else None\n frame_camtoworlds = torch.stack(camtoworlds)\n frame_intrinsics = torch.stack(intrinsics)\n ground_masks = torch.stack(ground_masks) if len(ground_masks) > 0 else None\n data_dict = {\n \"image\": frame_images,\n \"camtoworld\": frame_camtoworlds,\n \"intrinsics\": frame_intrinsics,\n \"frame_idx\": frame_idx,\n \"depth\": frame_depths,\n \"sky_masks\": frame_sky_masks,\n \"flow\": frame_flows,\n \"dynamic_masks\": frame_dynamic_masks,\n \"ground_masks\": ground_masks,\n }\n return {k: v for k, v in data_dict.items() if v is not None}\n\n def get_segment(self, index: int, context_frame_idx: int = -1, return_all=False):\n scene_json = self.annotations[index % len(self.annotations)]\n scene_id = scene_json[\"scene_id\"]\n num_timesteps = scene_json[\"num_timesteps\"]\n fps = scene_json[\"fps\"]\n num_max_future_frames = int(self.timespan * fps)\n if num_max_future_frames > num_timesteps:\n num_max_future_frames = int(fps) # make it 1 seconds\n time_in_seconds = scene_json[\"normalized_time\"]\n if context_frame_idx < 0:\n context_frame_idx = np.random.randint(0, num_timesteps - num_max_future_frames)\n if context_frame_idx + num_max_future_frames >= num_timesteps:\n context_frame_idx = np.random.randint(0, num_timesteps - num_max_future_frames)\n assert (\n context_frame_idx + num_max_future_frames < num_timesteps\n ), f\"scene_id: {scene_id}, context_frame_idx: {context_frame_idx}, num_timesteps: {num_timesteps}, num_max_future_frames: {num_max_future_frames}\"\n\n if self.equispaced:\n context_frame_idx = np.arange(\n context_frame_idx,\n context_frame_idx + num_max_future_frames,\n num_max_future_frames // self.num_context_timesteps,\n )\n else:\n context_frame_idx = np.random.choice(\n np.arange(\n context_frame_idx,\n context_frame_idx + num_max_future_frames,\n ),\n size=self.num_context_timesteps,\n replace=False,\n )\n context_frame_idx = sorted(context_frame_idx)\n\n if return_all:\n # return all frames between context_frame_idx and context_frame_idx + num_max_future_frames\n target_frame_idx = np.arange(\n context_frame_idx[0],\n context_frame_idx[0] + num_max_future_frames,\n )\n else:\n # randomly sample \"num_target_timesteps\" frames\n target_frame_idx = np.random.choice(\n np.arange(\n context_frame_idx[0],\n context_frame_idx[0] + num_max_future_frames,\n ),\n self.num_target_timesteps,\n replace=False,\n )\n target_frame_idx = [min(idx, num_timesteps - 1) for idx in target_frame_idx]\n target_frame_idx = sorted(target_frame_idx)\n\n # get context\n context_dict_list = []\n for ctx_id in context_frame_idx:\n context_dict = self.get_frame(\n scene_json=scene_json,\n frame_idx=ctx_id,\n source_frame_idx=context_frame_idx[0],\n )\n context_dict[\"time\"] = torch.tensor(\n [time_in_seconds[ctx_id] - time_in_seconds[context_frame_idx[0]]]\n * self.num_max_cams\n )\n context_dict_list.append(context_dict)\n target_dict_list = []\n for target_id in target_frame_idx:\n target_dict = self.get_frame(\n scene_json=scene_json,\n frame_idx=target_id,\n source_frame_idx=context_frame_idx[0],\n )\n target_dict[\"time\"] = torch.tensor(\n [time_in_seconds[target_id] - time_in_seconds[context_frame_idx[0]]]\n * self.num_max_cams\n )\n target_dict_list.append(target_dict)\n context_dict = default_collate(context_dict_list)\n target_dict = default_collate(target_dict_list)\n\n for k, v in context_dict.items():\n if isinstance(v, torch.Tensor) and len(v.shape) >= 2:\n context_dict[k] = torch.cat([d for d in v], dim=0)\n for k, v in target_dict.items():\n if isinstance(v, torch.Tensor) and len(v.shape) >= 2:\n target_dict[k] = torch.cat([d for d in v], dim=0)\n sample = {\n \"context\": context_dict,\n \"target\": target_dict,\n \"scene_id\": scene_id,\n \"scene_name\": scene_json[\"scene_name\"],\n \"width\": self.target_size[1],\n \"height\": self.target_size[0],\n \"fps\": fps,\n \"timespan\": self.timespan,\n }\n return to_float_tensor(sample)\n\n def __getitem__(self, index: int, start_index: int = 0, end_index: int = -1) -> Dict[str, Any]:\n scene_json = self.annotations[index % len(self.annotations)]\n num_timesteps = scene_json[\"num_timesteps\"]\n if end_index < 0:\n end_index = num_timesteps\n segment_data = []\n cam_to_world = scene_json[\"camera_to_world\"]\n ref_camera_name = DATASET_DICT[scene_json[\"dataset\"]][\"ref_camera\"]\n world_to_canonical = np.linalg.inv(cam_to_world[ref_camera_name][start_index])\n for start_id in trange(start_index, end_index, 20):\n segment_dict = self.get_segment(\n index=index, context_frame_idx=start_id, return_all=True\n )\n # compute the relative transformation from a start_id to the first frame\n current_world = cam_to_world[ref_camera_name][start_id]\n segment_to_ref = world_to_canonical @ current_world\n segment_dict[\"segment_to_ref\"] = to_float_tensor(segment_to_ref)\n segment_data.append(segment_dict)\n return segment_data\n" }, { "path": "storm/models/__init__.py", "content": "from .storm import STORM_models\nfrom .vit import ViT_models\n" }, { "path": "storm/models/decoder.py", "content": "import logging\nfrom typing import Dict\n\nimport torch\nimport torch.nn as nn\nfrom einops import rearrange, repeat\nfrom torch import Tensor\nfrom torch.utils.checkpoint import checkpoint\n\nfrom .layers import GroupNorm, NonLocalBlock, ResidualBlock, Swish, UpSampleBlock, modulate\n\nlogger = logging.getLogger(\"STORM\")\n\n\ndef check_results(result_dict) -> bool:\n assert \"rgb_key\" in result_dict, \"rgb_key not found in result_dict\"\n assert \"depth_key\" in result_dict, \"depth_key not found in result_dict\"\n assert \"alpha_key\" in result_dict, \"alpha_key not found in result_dict\"\n assert \"flow_key\" in result_dict, \"flow_key not found in result_dict\"\n assert \"decoder_depth_key\" in result_dict, \"decoder_depth_key not found in result_dict\"\n assert \"decoder_alpha_key\" in result_dict, \"decoder_alpha_key not found in result_dict\"\n assert \"decoder_flow_key\" in result_dict, \"decoder_flow_key not found in result_dict\"\n return True\n\n\nclass Decoder(nn.Module):\n def __init__(\n self,\n latent_dim,\n channels=[512, 256, 256, 128, 128],\n num_res_blocks=3,\n ):\n super(Decoder, self).__init__()\n\n in_channels = channels[0]\n layers = [\n nn.Conv2d(latent_dim, in_channels, 3, 1, 1),\n ResidualBlock(in_channels, in_channels),\n NonLocalBlock(in_channels),\n ResidualBlock(in_channels, in_channels),\n ]\n\n for i in range(len(channels)):\n out_channels = channels[i]\n for j in range(num_res_blocks):\n layers.append(ResidualBlock(in_channels, out_channels))\n in_channels = out_channels\n if i != 0:\n layers.append(UpSampleBlock(in_channels))\n\n layers.append(GroupNorm(in_channels))\n layers.append(Swish())\n # rgb-d\n layers.append(nn.Conv2d(in_channels, 4, 3, 1, 1))\n self.model = nn.Sequential(*layers)\n\n def forward(self, render_results):\n x = render_results[\"rendered_image\"]\n b, t, v, h, w, c = x.shape\n x = rearrange(x, \"b t v h w c -> (b t v) c h w\")\n x = self.model(x)\n x = rearrange(x, \"(b t v) c h w -> b t v h w c\", b=b, t=t, v=v)\n decoder_dict = {}\n decoder_dict[\"decoded_image\"] = x[..., :3]\n decoder_dict[\"decoded_depth\"] = x[..., 3]\n decoder_dict[\"rgb_key\"] = \"decoded_image\"\n decoder_dict[\"decoder_depth_key\"] = \"decoded_depth\"\n render_results.update(decoder_dict)\n if not check_results(render_results):\n raise ValueError(\"Invalid result dict\")\n return render_results\n\n\nclass ConvDecoder(nn.Module):\n def __init__(\n self,\n latent_dim,\n out_channels=3,\n channels=[512, 256, 256, 128, 128],\n num_res_blocks=3,\n grad_checkpointing=False,\n ):\n super(ConvDecoder, self).__init__()\n in_channels = channels[0]\n self.input_projection_layer = nn.Conv2d(latent_dim, in_channels, 3, 1, 1)\n layers = [\n ResidualBlock(in_channels, in_channels),\n NonLocalBlock(in_channels),\n ResidualBlock(in_channels, in_channels),\n ]\n\n for i in range(len(channels)):\n out_chans = channels[i]\n for j in range(num_res_blocks):\n layers.append(ResidualBlock(in_channels, out_chans))\n in_channels = out_chans\n if i != 0:\n layers.append(UpSampleBlock(in_channels))\n\n layers.append(GroupNorm(in_channels))\n layers.append(Swish())\n # rgb-d\n layers.append(nn.Conv2d(in_channels, 4, 3, 1, 1))\n self.layers = nn.ModuleList(layers)\n self.out_channels = out_channels\n self.grad_checkpointing = grad_checkpointing\n logger.info(f\"ConvDecoder: grad_checkpointing: {grad_checkpointing}\")\n self.mask_token = nn.Parameter(torch.randn(channels[0]) * 0.02)\n\n def forward(self, render_results) -> Dict[str, Tensor]:\n x, opacity = render_results[\"rendered_image\"], render_results[\"rendered_alpha\"]\n b, t, v, h, w, c = x.shape\n x = rearrange(x, \"b t v h w c -> (b t v) c h w\")\n opacity = rearrange(opacity, \"b t v h w -> (b t v) h w\")\n x = self.input_projection_layer(x)\n mask_token = repeat(\n self.mask_token,\n \"d -> b d h w\",\n b=x.shape[0],\n h=x.shape[-2],\n w=x.shape[-1],\n )\n x = x * opacity.unsqueeze(1) + mask_token * (1 - opacity.unsqueeze(1))\n # chunk to avoid OOM\n for layer in self.layers:\n if self.grad_checkpointing and not torch.jit.is_scripting():\n x = checkpoint(layer, x)\n else:\n x = layer(x)\n x = rearrange(x, \"(b t v) c h w -> b t v h w c\", b=b, t=t, v=v)\n decoder_dict = {}\n if self.out_channels == 3:\n decoder_dict[\"decoded_image\"] = x[..., :3]\n decoder_dict[\"rgb_key\"] = \"decoded_image\"\n elif self.out_channels == 4:\n decoder_dict[\"decoded_image\"] = x[..., :3]\n decoder_dict[\"decoded_depth\"] = x[..., 3]\n decoder_dict[\"rgb_key\"] = \"decoded_image\"\n decoder_dict[\"decoder_depth_key\"] = \"decoded_depth\"\n else:\n # TODO\n raise ValueError(\"Invalid out_channels\")\n render_results.update(decoder_dict)\n if not check_results(render_results):\n raise ValueError(\"Invalid result dict\")\n return render_results\n\n\nclass ModulatedLinearLayer(nn.Module):\n def __init__(self, in_channels, hidden_channels=64, condition_channels=768, out_channels=3):\n super().__init__()\n self.linear = nn.Linear(in_channels, hidden_channels)\n self.norm = nn.LayerNorm(hidden_channels, elementwise_affine=False, eps=1e-6)\n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(), nn.Linear(hidden_channels, 2 * hidden_channels, bias=True)\n )\n self.condition_mapping = nn.Linear(condition_channels, hidden_channels)\n self.output = nn.Linear(hidden_channels, out_channels)\n\n def forward(self, x, c):\n x = self.linear(x)\n c = self.condition_mapping(c.squeeze(1))\n shift, scale = self.adaLN_modulation(c).chunk(2, dim=-1)\n x_shape = x.shape\n x = modulate(self.norm(x.reshape(x_shape[0], -1, x.shape[-1])), shift, scale)\n x = self.output(x)\n x = x.reshape(*x_shape[:-1], -1)\n return x\n\n\nclass ModulatedMLP(nn.Module):\n def __init__(\n self,\n in_channels,\n hidden_channels=64,\n condition_channels=768,\n out_channels=3,\n num_layers=1,\n ):\n super().__init__()\n self.linear = nn.Linear(in_channels, hidden_channels)\n self.norm = nn.LayerNorm(hidden_channels, elementwise_affine=False, eps=1e-6)\n self.adaLN_modulation = nn.Sequential(\n nn.SiLU(), nn.Linear(hidden_channels, 2 * hidden_channels, bias=True)\n )\n self.condition_mapping = nn.Linear(condition_channels, hidden_channels)\n output_layers = []\n for i in range(num_layers):\n if i < num_layers - 1:\n output_layers.append(nn.Linear(hidden_channels, hidden_channels))\n output_layers.append(nn.SiLU())\n else:\n output_layers.append(nn.Linear(hidden_channels, out_channels))\n self.output = nn.Sequential(*output_layers)\n\n def forward(self, x, c):\n x = self.linear(x)\n c = self.condition_mapping(c.squeeze(1))\n shift, scale = self.adaLN_modulation(c).chunk(2, dim=-1)\n x_shape = x.shape\n x = modulate(self.norm(x.reshape(x_shape[0], -1, x.shape[-1])), shift, scale)\n x = self.output(x)\n x = x.reshape(*x_shape[:-1], -1)\n return x\n\n\nclass DummyDecoder(nn.Module):\n def __init__(self, **kwargs):\n super(DummyDecoder, self).__init__()\n\n def forward(self, render_results):\n if not check_results(render_results):\n raise ValueError(\"Invalid result dict\")\n return render_results\n" }, { "path": "storm/models/embedders.py", "content": "# --------------------------------------------------------\n# References:\n# timm: https://github.com/rwightman/pytorch-image-models/tree/master/timm\n# --------------------------------------------------------\n\nimport math\nfrom typing import Callable, Literal, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange\nfrom torch import Tensor\n\nfrom storm.utils.misc import to_2tuple\n\n\nclass PatchEmbed(nn.Module):\n \"\"\"2D Image to Patch Embedding\"\"\"\n\n dynamic_img_pad: torch.jit.Final[bool]\n\n def __init__(\n self,\n img_size: Optional[int] = 224,\n patch_size: int = 16,\n in_chans: int = 3,\n embed_dim: int = 768,\n norm_layer: Optional[Callable] = None,\n output_fmt: Literal[\"NCHW\", \"NHWC\", \"NLC\", \"NCL\"] = \"NCHW\",\n bias: bool = True,\n dynamic_img_pad: bool = False,\n ):\n super().__init__()\n self.patch_size = to_2tuple(patch_size)\n if img_size is not None:\n self.img_size = to_2tuple(img_size)\n self.grid_size = tuple([s // p for s, p in zip(self.img_size, self.patch_size)])\n self.num_patches = self.grid_size[0] * self.grid_size[1]\n else:\n self.img_size = None\n self.grid_size = None\n self.num_patches = None\n\n self.output_fmt = output_fmt\n self.dynamic_img_pad = dynamic_img_pad\n\n self.proj = nn.Conv2d(in_chans, embed_dim, patch_size, stride=patch_size, bias=bias)\n self.norm = norm_layer(embed_dim) if norm_layer else None\n\n def feat_ratio(self, as_scalar=True) -> Union[Tuple[int, int], int]:\n if as_scalar:\n return max(self.patch_size)\n else:\n return self.patch_size\n\n def dynamic_feat_size(self, img_size: Tuple[int, int]) -> Tuple[int, int]:\n \"\"\"Get grid (feature) size for given image size taking account of dynamic padding.\n NOTE: must be torchscript compatible so using fixed tuple indexing\n \"\"\"\n if self.dynamic_img_pad:\n return math.ceil(img_size[0] / self.patch_size[0]), math.ceil(\n img_size[1] / self.patch_size[1]\n )\n else:\n return img_size[0] // self.patch_size[0], img_size[1] // self.patch_size[1]\n\n def forward(self, x: Tensor) -> Tensor:\n B, C, H, W = x.shape\n if self.dynamic_img_pad:\n pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]\n pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]\n x = F.pad(x, (0, pad_w, 0, pad_h))\n # defaut output format is NCHW\n x = self.proj(x)\n if self.output_fmt == \"NHWC\":\n x = rearrange(x, \"B C H W -> B H W C\")\n elif self.output_fmt == \"NLC\":\n x = rearrange(x, \"B C H W -> B (H W) C\")\n elif self.output_fmt == \"NCL\":\n x = rearrange(x, \"B C H W -> B C (H W)\")\n if self.norm:\n x = self.norm(x)\n return x\n\n\nclass PluckerEmbedder(nn.Module):\n \"\"\"\n Convert rays to plucker embedding\n \"\"\"\n\n def __init__(\n self,\n img_size: Optional[int] = 224,\n patch_size: int = 1,\n ):\n super().__init__()\n self.patch_size = to_2tuple(patch_size)\n self.img_size = to_2tuple(img_size)\n self.grid_size = tuple([s // p for s, p in zip(self.img_size, self.patch_size)])\n\n x, y = torch.meshgrid(\n torch.arange(self.grid_size[1]),\n torch.arange(self.grid_size[0]),\n indexing=\"xy\",\n )\n x = x.float().reshape(1, -1) + 0.5\n y = y.float().reshape(1, -1) + 0.5\n self.register_buffer(\"x\", x)\n self.register_buffer(\"y\", y)\n\n def forward(\n self,\n intrinsics: Tensor,\n camtoworlds: Tensor,\n image_size: Optional[Union[int, Tuple[int, int]]] = None,\n patch_size: Optional[Union[int, Tuple[int, int]]] = None,\n ) -> Tensor:\n assert intrinsics.shape[-2:] == (3, 3), \"intrinsics should be (B, 3, 3)\"\n assert camtoworlds.shape[-2:] == (4, 4), \"camtoworlds should be (B, 4, 4)\"\n intrinsics_shape = intrinsics.shape\n intrinsics = intrinsics.reshape(-1, 3, 3)\n camtoworlds = camtoworlds.reshape(-1, 4, 4)\n if image_size is not None:\n image_size = to_2tuple(image_size)\n else:\n image_size = self.img_size\n if patch_size is not None:\n patch_size = to_2tuple(patch_size)\n else:\n patch_size = self.patch_size\n\n grid_size = tuple([s // p for s, p in zip(image_size, patch_size)])\n\n if grid_size != self.grid_size:\n grid_size = tuple([s // p for s, p in zip(image_size, patch_size)])\n x, y = torch.meshgrid(\n torch.arange(grid_size[1]),\n torch.arange(grid_size[0]),\n indexing=\"xy\",\n )\n x = x.float().reshape(1, -1) + 0.5\n y = y.float().reshape(1, -1) + 0.5\n x = x.to(intrinsics.device)\n y = y.to(intrinsics.device)\n intrinsics = intrinsics.clone()\n # intrinsics should be scaled to the grid size\n intrinsics[..., 0, 0] = intrinsics[..., 0, 0] / patch_size[1]\n intrinsics[..., 0, 2] = intrinsics[..., 0, 2] / patch_size[1]\n intrinsics[..., 1, 1] = intrinsics[..., 1, 1] / patch_size[0]\n intrinsics[..., 1, 2] = intrinsics[..., 1, 2] / patch_size[0]\n else:\n x, y = self.x, self.y\n\n x = x.repeat(intrinsics.size(0), 1)\n y = y.repeat(intrinsics.size(0), 1)\n camera_dirs = torch.nn.functional.pad(\n torch.stack(\n [\n (x - intrinsics[:, 0, 2][..., None] + 0.5) / intrinsics[:, 0, 0][..., None],\n (y - intrinsics[:, 1, 2][..., None] + 0.5) / intrinsics[:, 1, 1][..., None],\n ],\n dim=-1,\n ),\n (0, 1),\n value=1.0,\n )\n directions = torch.sum(camera_dirs[:, :, None, :] * camtoworlds[:, None, :3, :3], dim=-1)\n origins = torch.broadcast_to(camtoworlds[:, :3, -1].unsqueeze(1), directions.shape)\n direction_norm = torch.linalg.norm(directions, dim=-1, keepdims=True)\n viewdirs = directions / (direction_norm + 1e-8)\n cross_prod = torch.cross(origins, viewdirs, dim=-1)\n plucker = torch.cat((cross_prod, viewdirs), dim=-1)\n origins = rearrange(origins, \"b (h w) c -> b h w c\", h=grid_size[0])\n viewdirs = rearrange(viewdirs, \"b (h w) c -> b h w c\", h=grid_size[0])\n directions = rearrange(directions, \"b (h w) c -> b h w c\", h=grid_size[0])\n plucker = rearrange(plucker, \"b (h w) c -> b h w c\", h=grid_size[0])\n return {\n \"origins\": origins.view(*intrinsics_shape[:-2], *grid_size, 3),\n \"viewdirs\": viewdirs.view(*intrinsics_shape[:-2], *grid_size, 3),\n \"dirs\": directions.view(*intrinsics_shape[:-2], *grid_size, 3),\n \"plucker\": plucker.view(*intrinsics_shape[:-2], *grid_size, 6),\n }\n\n\nclass TimestepEmbedder(nn.Module):\n \"\"\"\n From DiT\n Embeds scalar timesteps into vector representations.\n \"\"\"\n\n def __init__(self, hidden_size, frequency_embedding_size=256):\n super().__init__()\n self.mlp = nn.Sequential(\n nn.Linear(frequency_embedding_size, hidden_size, bias=True),\n nn.SiLU(inplace=True),\n nn.Linear(hidden_size, hidden_size, bias=True),\n )\n self.frequency_embedding_size = frequency_embedding_size\n\n @staticmethod\n def timestep_embedding(t, dim, max_period=10000):\n \"\"\"\n Create sinusoidal timestep embeddings.\n :param t: a 1-D Tensor of N indices, one per batch element.\n These may be fractional.\n :param dim: the dimension of the output.\n :param max_period: controls the minimum frequency of the embeddings.\n :return: an (N, D) Tensor of positional embeddings.\n \"\"\"\n # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py\n half = dim // 2\n freqs = torch.exp(\n -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half\n ).to(device=t.device)\n args = t[:, None].float() * freqs[None]\n embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)\n if dim % 2:\n embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)\n return embedding\n\n def forward(self, t):\n t_freq = self.timestep_embedding(t, self.frequency_embedding_size)\n t_emb = self.mlp(t_freq)\n return t_emb\n\n\nclass NeRFPosEmbedder(nn.Module):\n def __init__(\n self,\n in_dim: int,\n num_frequencies: int,\n min_freq_exp: int = 0,\n max_freq_exp: int = 8,\n include_input: bool = False,\n ) -> None:\n super().__init__()\n self.in_dim = in_dim\n self.num_frequencies = num_frequencies\n self.min_freq = min_freq_exp\n self.max_freq = max_freq_exp\n self.include_input = include_input\n freqs = 2 ** torch.linspace(self.min_freq, self.max_freq, self.num_frequencies)\n self.register_buffer(\"freqs\", freqs)\n\n def get_out_dim(self) -> int:\n out_dim = self.in_dim * self.num_frequencies * 2\n if self.include_input:\n out_dim += self.in_dim\n return out_dim\n\n def forward(self, in_tensor: Tensor) -> Tensor:\n scaled_in_tensor = 2 * torch.pi * in_tensor # scale to [0, 2pi]\n scaled_inputs = scaled_in_tensor[..., None] * self.freqs\n scaled_inputs = scaled_inputs.view(*scaled_inputs.shape[:-2], -1)\n encoded_inputs = torch.sin(\n torch.cat([scaled_inputs, scaled_inputs + torch.pi / 2.0], dim=-1)\n )\n if self.include_input:\n encoded_inputs = torch.cat([encoded_inputs, in_tensor], dim=-1)\n return encoded_inputs\n" }, { "path": "storm/models/layers.py", "content": "import logging\nimport math\nfrom functools import partial\nfrom typing import List, Optional\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange\nfrom torch import Tensor\nfrom torch.utils.checkpoint import checkpoint\n\nlogger = logging.getLogger(\"STORM\")\n\n\ndef modulate(x, shift=None, scale=None):\n if shift is None and scale is None:\n return x\n return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)\n\n\nclass Mlp(nn.Module):\n def __init__(self, in_dim: int, hidden_dim=None, out_dim=None, act_layer=nn.GELU):\n super().__init__()\n out_dim = out_dim or in_dim\n hidden_dim = hidden_dim or in_dim\n self.fc1 = nn.Linear(in_dim, hidden_dim)\n self.act = act_layer()\n self.fc2 = nn.Linear(hidden_dim, out_dim)\n\n def forward(self, x):\n return self.fc2(self.act(self.fc1(x)))\n\n\nclass Attention(nn.Module):\n _logged = False\n\n def __init__(\n self,\n dim: int,\n num_heads: int = 8,\n qk_norm: bool = False,\n norm_layer: nn.Module = nn.LayerNorm,\n is_cross_attn: bool = False,\n ) -> None:\n super().__init__()\n assert dim % num_heads == 0, f\"dim % num_heads !=0, got {dim} and {num_heads}\"\n self.num_heads = num_heads\n self.head_dim = dim // num_heads\n self.scale = self.head_dim**-0.5\n self.is_cross_attn = is_cross_attn\n\n self.fused_attn = hasattr(torch.nn.functional, \"scaled_dot_product_attention\")\n if self.fused_attn and not Attention._logged:\n Attention._logged = True\n logger.info(f\"[Attention]: Using {torch.__version__} Fused Attention\")\n\n if is_cross_attn:\n self.c_q = nn.Linear(dim, dim) # context to q\n self.c_kv = nn.Linear(dim, dim * 2) # context to kv\n else:\n self.qkv = nn.Linear(dim, dim * 3)\n self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()\n self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()\n self.proj = nn.Linear(dim, dim)\n\n def forward(self, x: Tensor, data: Tensor = None) -> Tensor:\n bs, n_ctx, C = x.shape\n if self.is_cross_attn:\n assert data is not None, \"data should not be None for cross attn\"\n q = self.c_q(x)\n kv = self.c_kv(data)\n _, n_data, _ = kv.shape\n q = q.view(bs, n_ctx, self.num_heads, self.head_dim).permute(0, 2, 1, 3)\n kv = kv.view(bs, n_data, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)\n k, v = kv.unbind(dim=0)\n else:\n qkv = (\n self.qkv(x)\n .reshape(bs, n_ctx, 3, self.num_heads, self.head_dim)\n .permute(2, 0, 3, 1, 4)\n )\n q, k, v = qkv.unbind(dim=0)\n q, k = self.q_norm(q), self.k_norm(k)\n\n if self.fused_attn:\n x = F.scaled_dot_product_attention(q, k, v)\n else:\n q = q * self.scale\n attn = q @ k.transpose(-2, -1)\n attn = attn.softmax(dim=-1)\n x = attn @ v\n\n x = x.transpose(1, 2).reshape(bs, n_ctx, C)\n x = self.proj(x)\n return x\n\n\nclass Block(nn.Module):\n _logged = False\n\n def __init__(\n self,\n dim: int,\n num_heads: int,\n mlp_ratio: float = 4.0,\n qk_norm: bool = False,\n act_layer: nn.Module = nn.GELU,\n norm_layer: nn.Module = partial(nn.LayerNorm, eps=1e-6),\n use_cross_attn: bool = False,\n ) -> None:\n super().__init__()\n self.norm1 = norm_layer(dim)\n self.use_cross_attn = use_cross_attn\n self.attn = Attention(\n dim,\n num_heads=num_heads,\n qk_norm=qk_norm,\n norm_layer=norm_layer,\n is_cross_attn=use_cross_attn,\n )\n self.data_norm = norm_layer(dim) if self.use_cross_attn else None\n self.norm2 = norm_layer(dim)\n self.mlp = Mlp(in_dim=dim, hidden_dim=int(dim * mlp_ratio), act_layer=act_layer)\n\n def forward(self, x: Tensor, data: Tensor = None) -> Tensor:\n if self.use_cross_attn:\n x = x + self.attn(self.norm1(x), self.data_norm(data))\n else:\n x = x + self.attn(self.norm1(x))\n return x + self.mlp(self.norm2(x))\n\n\nclass Transformer(nn.Module):\n def __init__(\n self,\n embed_dim: int = 768,\n depth: int = 12,\n num_heads: int = 12,\n mlp_ratio: float = 4.0,\n qk_norm: bool = False,\n norm_layer: nn.Module = partial(nn.LayerNorm, eps=1e-6),\n grad_checkpointing: bool = False,\n ):\n super().__init__()\n self.embed_dim = embed_dim\n self.depth = depth\n self.blocks = nn.ModuleList(\n [\n Block(\n dim=embed_dim,\n num_heads=num_heads,\n mlp_ratio=mlp_ratio,\n qk_norm=qk_norm,\n norm_layer=norm_layer,\n )\n for _ in range(depth)\n ]\n )\n self.grad_checkpointing = grad_checkpointing\n logger.info(f\"[Transformer]: grad_checkpointing={grad_checkpointing}\")\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n for block in self.blocks:\n if self.grad_checkpointing and self.training:\n x = checkpoint(block, x)\n else:\n x = block(x)\n return x\n\n\n######## Conv Layers ########\nclass GroupNorm(nn.Module):\n def __init__(self, channels):\n super(GroupNorm, self).__init__()\n self.gn = nn.GroupNorm(num_groups=32, num_channels=channels, eps=1e-6, affine=True)\n\n def forward(self, x):\n return self.gn(x)\n\n\nclass Swish(nn.Module):\n def forward(self, x):\n return x * torch.sigmoid(x)\n\n\nclass ResidualBlock(nn.Module):\n def __init__(self, in_channels, out_channels):\n super(ResidualBlock, self).__init__()\n self.in_channels = in_channels\n self.out_channels = out_channels\n self.block = nn.Sequential(\n GroupNorm(in_channels),\n Swish(),\n nn.Conv2d(in_channels, out_channels, 3, 1, 1),\n GroupNorm(out_channels),\n Swish(),\n nn.Conv2d(out_channels, out_channels, 3, 1, 1),\n )\n\n if in_channels != out_channels:\n self.channel_up = nn.Conv2d(in_channels, out_channels, 1, 1, 0)\n\n def forward(self, x):\n if self.in_channels != self.out_channels:\n return self.channel_up(x) + self.block(x)\n else:\n return x + self.block(x)\n\n\nclass UpSampleBlock(nn.Module):\n def __init__(self, channels):\n super(UpSampleBlock, self).__init__()\n self.conv = nn.Conv2d(channels, channels, 3, 1, 1)\n\n def forward(self, x):\n x = F.interpolate(x, scale_factor=2.0)\n return self.conv(x)\n\n\nclass NonLocalBlock(nn.Module):\n def __init__(self, channels):\n super(NonLocalBlock, self).__init__()\n self.in_channels = channels\n assert channels % 8 == 0, \"channels must be divisible by 8\"\n self.gn = GroupNorm(channels)\n self.attention = Attention(dim=channels, num_heads=8)\n\n def forward(self, x):\n h = self.gn(x)\n h = rearrange(h, \"b c h w -> b (h w) c\")\n h = self.attention(h)\n h = rearrange(h, \"b (h w) c -> b c h w\", h=x.shape[-2], w=x.shape[-1])\n return h + x\n\n\nclass LayerNorm2d(nn.Module):\n def __init__(self, num_channels: int, eps: float = 1e-6) -> None:\n super().__init__()\n self.weight = nn.Parameter(torch.ones(num_channels))\n self.bias = nn.Parameter(torch.zeros(num_channels))\n self.eps = eps\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n u = x.mean(1, keepdim=True)\n s = (x - u).pow(2).mean(1, keepdim=True)\n x = (x - u) / torch.sqrt(s + self.eps)\n x = self.weight[:, None, None] * x + self.bias[:, None, None]\n return x\n\n\ndef pos_enc(x, min_deg=0, max_deg=10, append_identity=True):\n \"\"\"The positional encoding used by the original NeRF paper.\"\"\"\n scales = 2 ** torch.arange(min_deg, max_deg).float()\n scales = scales.to(x.device)\n shape = x.shape[:-1] + (-1,)\n scaled_x = torch.reshape((x[..., None, :] * scales[:, None]), shape)\n # Note that we're not using safe_sin, unlike IPE.\n four_feat = torch.sin(torch.concat([scaled_x, scaled_x + 0.5 * np.pi], dim=-1))\n if append_identity:\n return torch.concat([x] + [four_feat], dim=-1)\n else:\n return four_feat\n\n\ndef get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, num_extra_tokens=1):\n \"\"\"\n grid_size: int of the grid height and width\n return:\n pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)\n \"\"\"\n if isinstance(grid_size, int):\n grid_size = [grid_size, grid_size]\n grid_h = np.arange(grid_size[0], dtype=np.float32)\n grid_w = np.arange(grid_size[1], dtype=np.float32)\n grid = np.meshgrid(grid_w, grid_h) # here w goes first\n grid = np.stack(grid, axis=0)\n\n grid = grid.reshape([2, 1, grid_size[0], grid_size[1]])\n pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)\n if cls_token and num_extra_tokens > 0:\n pos_embed = np.concatenate([np.zeros([num_extra_tokens, embed_dim]), pos_embed], axis=0)\n return pos_embed\n\n\ndef get_2d_sincos_pos_embed_from_grid(embed_dim, grid):\n assert embed_dim % 2 == 0\n\n # use half of dimensions to encode grid_h\n emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)\n emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)\n\n emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)\n return emb\n\n\ndef get_1d_sincos_pos_embed_from_grid(embed_dim, pos):\n \"\"\"\n embed_dim: output dimension for each position\n pos: a list of positions to be encoded: size (M,)\n out: (M, D)\n \"\"\"\n assert embed_dim % 2 == 0\n omega = np.arange(embed_dim // 2, dtype=np.float64)\n omega /= embed_dim / 2.0\n omega = 1.0 / 10000**omega # (D/2,)\n\n pos = pos.reshape(-1) # (M,)\n out = np.einsum(\"m,d->md\", pos, omega) # (M, D/2), outer product\n\n emb_sin = np.sin(out) # (M, D/2)\n emb_cos = np.cos(out) # (M, D/2)\n\n emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)\n return emb\n\n\ndef resample_abs_pos_embed(\n posemb,\n new_size: List[int],\n old_size: Optional[List[int]] = None,\n n_prefix_tokens: int = 1,\n interpolation: str = \"bicubic\", # bicubic is better.\n antialias: bool = True, # antialias is important.\n verbose: bool = False,\n):\n # sort out sizes, assume square if old size not provided\n num_pos_tokens = posemb.shape[1]\n num_new_tokens = new_size[0] * new_size[1] + n_prefix_tokens\n if num_new_tokens == num_pos_tokens and new_size[0] == new_size[1]:\n return posemb\n\n if old_size is None:\n hw = int(math.sqrt(num_pos_tokens - n_prefix_tokens))\n old_size = [hw, hw]\n\n if n_prefix_tokens:\n posemb_prefix, posemb = (\n posemb[:, :n_prefix_tokens],\n posemb[:, n_prefix_tokens:],\n )\n else:\n posemb_prefix, posemb = None, posemb\n\n # do the interpolation\n embed_dim = posemb.shape[-1]\n orig_dtype = posemb.dtype\n posemb = posemb.float() # interpolate needs float32\n posemb = posemb.reshape(1, old_size[0], old_size[1], -1).permute(0, 3, 1, 2)\n posemb = torch.nn.functional.interpolate(\n posemb, size=new_size, mode=interpolation, antialias=antialias\n )\n posemb = posemb.permute(0, 2, 3, 1).reshape(1, -1, embed_dim)\n posemb = posemb.to(orig_dtype)\n\n # add back extra (class, etc) prefix tokens\n if posemb_prefix is not None:\n posemb = torch.cat([posemb_prefix, posemb], dim=1)\n\n if not torch.jit.is_scripting() and verbose:\n logger.info(f\"Resized position embedding: {old_size} to {new_size}.\")\n\n return posemb\n" }, { "path": "storm/models/storm.py", "content": "import torch\nimport torch.nn as nn\nfrom einops import rearrange, repeat\nfrom gsplat.rendering import rasterization\nfrom torch import Tensor\nfrom torch.utils.checkpoint import checkpoint\n\nfrom .decoder import ConvDecoder, DummyDecoder, ModulatedLinearLayer\nfrom .embedders import PluckerEmbedder, TimestepEmbedder\nfrom .layers import LayerNorm2d, Mlp\nfrom .vit import VisionTransformer as ViT\n\n\nclass STORM(ViT):\n def __init__(\n self,\n img_size=224,\n in_chans=9,\n gs_dim=3,\n decoder_type=\"dummy\",\n near=0.2,\n far=400,\n scale_offset=-2.3,\n opacity_offset=-2.0,\n num_cams=3, # to ablate\n max_scale=0.5,\n disable_pos_embed=False,\n use_sky_token=True,\n use_affine_token=True,\n num_motion_tokens=32,\n tau=0.5,\n projected_motion_dim=32,\n # ViT parameters\n patch_size=16,\n embed_dim=768,\n depth=12,\n num_heads=12,\n grad_checkpointing=True,\n use_latest_gsplat=False,\n sigmoid_rgb=False, # a legacy oversight: the sigmoid was accidentally omitted in the earlier implementation\n **kwargs,\n ):\n super(STORM, self).__init__(\n img_size=img_size,\n patch_size=patch_size,\n in_chans=in_chans,\n embed_dim=embed_dim,\n depth=depth,\n num_heads=num_heads,\n grad_checkpointing=grad_checkpointing,\n )\n # basic attributes\n self.disable_pos_embed = disable_pos_embed\n self.gs_dim = gs_dim\n self.out_channels = gs_dim + 9\n self.num_cams = num_cams\n self.grad_checkpointing = grad_checkpointing\n self.use_latest_gsplat = use_latest_gsplat\n\n # ------- STORM v.s. Latent-STORM -------\n self.decoder_type = decoder_type\n self.decoder_upsample_ratio = decoder_upsample_ratio = self.patch_size\n\n # ------- motion predictor -------\n self.num_motion_tokens = num_motion_tokens\n self.tau = tau\n num_velocity_channels = 3\n\n # ------- embedders -------\n self.plucker_embedder = PluckerEmbedder(img_size=img_size)\n self.time_embedder = TimestepEmbedder(embed_dim)\n\n # ------- auxiliary tokens -------\n self.use_sky_token = use_sky_token\n self.use_affine_token = use_affine_token\n\n if self.use_sky_token:\n self.sky_token = nn.Parameter(torch.randn(1, 1, embed_dim) * 0.02)\n self.sky_head = ModulatedLinearLayer(\n 3,\n hidden_channels=512,\n condition_channels=embed_dim,\n out_channels=self.gs_dim,\n )\n\n if self.use_affine_token:\n self.affine_token = nn.Parameter(torch.randn(1, self.num_cams, embed_dim) * 0.02)\n self.affine_linear = nn.Linear(embed_dim, self.gs_dim * (self.gs_dim + 1))\n\n # ------- gs predictor and mask decoder -------\n if decoder_type == \"dummy\":\n self.gs_pred = nn.Linear(embed_dim, decoder_upsample_ratio**2 * self.out_channels)\n self.decoder = DummyDecoder()\n self.unpatch_size = decoder_upsample_ratio\n\n if self.decoder_upsample_ratio == 8:\n # used for upscaling the low-resolution image features to the pixel-resolution\n # very handcrafted and never tuned\n self.output_upscaling = nn.Sequential(\n nn.ConvTranspose2d(embed_dim, 512, kernel_size=2, stride=2),\n LayerNorm2d(512),\n nn.GELU(),\n nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2),\n LayerNorm2d(256),\n nn.GELU(),\n nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2),\n LayerNorm2d(128),\n nn.GELU(),\n )\n elif self.decoder_upsample_ratio == 16:\n # used for upscaling the low-resolution image features to the pixel-resolution\n # very handcrafted and never tuned\n self.output_upscaling = nn.Sequential(\n nn.ConvTranspose2d(embed_dim, 512, kernel_size=2, stride=2),\n LayerNorm2d(512),\n nn.GELU(),\n nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2),\n LayerNorm2d(256),\n nn.GELU(),\n nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2),\n LayerNorm2d(128),\n nn.GELU(),\n nn.ConvTranspose2d(128, 128, kernel_size=2, stride=2),\n LayerNorm2d(128),\n nn.GELU(),\n )\n\n elif decoder_type == \"conv\":\n self.gs_pred = nn.Linear(embed_dim, self.out_channels)\n # latent-STORM decoder\n self.decoder = ConvDecoder(\n latent_dim=self.gs_dim,\n out_channels=4, # 3 for RGB, 1 for depth\n num_res_blocks=3,\n channels=[512, 256, 256, 128], # 8 times upsample\n grad_checkpointing=grad_checkpointing,\n )\n self.unpatch_size = 1\n # upscaling the low-resolution image features to the pixel-resolution\n # the \"pixel\" resolution here is essentially the feature map resolution\n # which is 1/patch_size of the image resolution\n self.output_upscaling = nn.Sequential(\n nn.Conv2d(embed_dim, 512, kernel_size=1),\n LayerNorm2d(512),\n nn.GELU(),\n nn.Conv2d(512, 256, kernel_size=1),\n LayerNorm2d(256),\n nn.GELU(),\n nn.Conv2d(256, 128, kernel_size=1),\n LayerNorm2d(128),\n nn.GELU(),\n )\n # ------- activation functions for gs parameters -------\n self.max_scale = nn.Parameter(torch.tensor([float(max_scale)]), requires_grad=False)\n self.scale_act_fn = lambda x: torch.minimum(torch.exp(x + scale_offset), self.max_scale)\n self.opacity_act_fn = lambda x: torch.sigmoid(x + opacity_offset)\n self.depth_act_fn = lambda x: near + torch.sigmoid(x) * (far - near)\n self.rgb_act_fn = lambda x: torch.sigmoid(x) * 2 - 1 if sigmoid_rgb else x\n self.near, self.far = near, far\n\n # ------- motion predictor -------\n self.motion_key_head = Mlp(128, 256, projected_motion_dim)\n if self.num_motion_tokens > 0:\n self.motion_tokens = nn.Parameter(torch.randn(1, num_motion_tokens, embed_dim) * 0.02)\n self.motion_query_heads = nn.ModuleList(\n [\n Mlp(embed_dim, embed_dim, projected_motion_dim)\n for _ in range(self.num_motion_tokens)\n ]\n )\n self.motion_basis_decoder = Mlp(embed_dim, 256, num_velocity_channels)\n else:\n self.motion_tokens = None\n self.motion_basis_decoder = Mlp(projected_motion_dim, 256, num_velocity_channels)\n\n self.init_weights()\n if disable_pos_embed: # remove the default pos_embed in vit\n del self.pos_embed\n self.pos_embed = None\n\n def _pos_embed(self, x: Tensor) -> Tensor:\n if not self.disable_pos_embed:\n return super()._pos_embed(x)\n return rearrange(x, \"b h w c -> b (h w) c\")\n\n def _time_embed(self, x: Tensor, time: Tensor, num_views=1) -> Tensor:\n if time.ndim == 3:\n b, t, v = time.shape\n time_embedding = (\n self.time_embedder(time.flatten()) # (bt, c)\n .view(b, t, v, -1) # (b, t, v, c)\n .view(-1, 1, self.embed_dim) # (btv, 1, c)\n .repeat(1, x.shape[1], 1) # (btv, n, c)\n )\n else:\n time_embedding = (\n self.time_embedder(time.flatten()) # (bt, c)\n .view(time.shape[0], time.shape[1], 1, -1) # (b, t, 1, c)\n .repeat(1, 1, num_views, 1) # (b, t, v, c)\n .view(-1, 1, self.embed_dim) # (btv, 1, c)\n .repeat(1, x.shape[1], 1) # (btv, n, c)\n )\n return x + time_embedding\n\n def forward_decoder(self, render_results):\n render_results[\"rgb_key\"] = \"rendered_image\"\n render_results[\"depth_key\"] = \"rendered_depth\"\n render_results[\"alpha_key\"] = \"rendered_alpha\"\n render_results[\"flow_key\"] = \"rendered_flow\"\n render_results[\"decoder_depth_key\"] = None\n render_results[\"decoder_alpha_key\"] = None\n render_results[\"decoder_flow_key\"] = None\n render_results = self.decoder(render_results)\n decoded_depth_key = render_results[\"decoder_depth_key\"]\n if decoded_depth_key is not None:\n decoded_depth = self.depth_act_fn(render_results[decoded_depth_key])\n render_results[decoded_depth_key] = decoded_depth\n return render_results\n\n def forward_features(self, x, plucker_embeds, time):\n b, t, v, c, h, w = x.size()\n x = rearrange(x, \"b t v c h w -> (b t v) c h w\")\n plucker_embeds = rearrange(plucker_embeds, \"b t v h w c-> (b t v) c h w\")\n x = torch.cat([x, plucker_embeds], dim=1)\n x = self.patch_embed(x) # (b t v) h w c2\n x = self._pos_embed(x) # (b t v) (h w) c2\n x = self._time_embed(x, time, num_views=v)\n x = rearrange(x, \"(b t v) hw c -> b (t v hw) c\", t=t, v=v)\n if self.num_motion_tokens > 0:\n motion_tokens = repeat(self.motion_tokens, \"1 k d -> b k d\", b=x.shape[0])\n x = torch.cat([motion_tokens, x], dim=-2)\n if self.use_affine_token:\n affine_token = repeat(self.affine_token, \"1 k d -> b k d\", b=b)\n x = torch.cat([affine_token, x], dim=-2)\n if self.use_sky_token:\n sky_token = repeat(self.sky_token, \"1 1 d -> b 1 d\", b=x.shape[0])\n x = torch.cat([sky_token, x], dim=-2)\n x = self.transformer(x)\n x = self.norm(x)\n return x\n\n def forward_motion_predictor(self, x, motion_tokens=None, gs_params=None):\n b, t, v, h, w, _ = gs_params[\"means\"].shape\n img_embeds = self.unpatchify(\n rearrange(x, \"b (t v hw) c -> (b t v) hw c\", t=t, v=v),\n hw=(h // self.unpatch_size, w // self.unpatch_size),\n patch_size=1,\n )\n if self.grad_checkpointing:\n img_embeds = checkpoint(self.output_upscaling, img_embeds)\n else:\n img_embeds = self.output_upscaling(img_embeds)\n img_embeds = rearrange(img_embeds, \"(b t v) c h w -> b t v h w c\", t=t, v=v)\n img_keys = self.motion_key_head(img_embeds)\n\n if self.num_motion_tokens > 0:\n hyper_in_list = []\n for i in range(self.num_motion_tokens):\n hyper_in = self.motion_query_heads[i](motion_tokens[:, i])\n hyper_in_list.append(hyper_in)\n motion_token_queries = torch.stack(hyper_in_list, dim=1)\n motion_bases = self.motion_basis_decoder(motion_tokens)\n dot_product_similarity = torch.einsum(\n \"b k c, b t v h w c -> b t v h w k\",\n motion_token_queries,\n img_keys,\n )\n motion_weights = torch.softmax(dot_product_similarity / self.tau, dim=-1)\n forward_flow = torch.einsum(\n \"b t v h w k, b k c -> b t v h w c\", motion_weights, motion_bases\n )\n gs_params[\"motion_weights\"] = motion_weights\n gs_params[\"motion_bases\"] = motion_bases\n else:\n # if there's no motion token, directly predict the velocity from the upsampled image features\n forward_flow = self.motion_basis_decoder(img_keys)\n\n gs_params[\"forward_flow\"] = forward_flow\n return {k: v for k, v in gs_params.items() if v is not None}\n\n def forward_gs_predictor(self, x, origins, directions):\n b, t, v, h, w, _ = origins.shape\n x = rearrange(x, \"b (t v hw) c -> (b t v) hw c\", t=t, v=v)\n gs_params = self.gs_pred(x)\n gs_params = self.unpatchify(gs_params, hw=(h, w), patch_size=self.unpatch_size)\n gs_params = rearrange(gs_params, \"(b t v) c h w -> b t v h w c\", t=t, v=v)\n depth, scales, quats, opacitys, colors = gs_params.split([1, 3, 4, 1, self.gs_dim], dim=-1)\n scales = self.scale_act_fn(scales)\n opacitys = self.opacity_act_fn(opacitys)\n depths = self.depth_act_fn(depth)\n colors = self.rgb_act_fn(colors)\n means = origins + directions * depths\n return {\n \"means\": means,\n \"scales\": scales,\n \"quats\": quats,\n \"opacities\": opacitys.squeeze(-1),\n \"colors\": colors,\n \"depths\": depths.squeeze(-1),\n }\n\n def forward_renderer(self, gs_params, data_dict, render_motion_seg=True, radius_clip=0.0):\n b, t, v, h, w, _ = gs_params[\"means\"].shape\n tgt_h, tgt_w = data_dict[\"height\"], data_dict[\"width\"]\n tgt_t, tgt_v = data_dict[\"target_camtoworlds\"].shape[1:3]\n means = rearrange(gs_params[\"means\"], \"b t v h w c -> b (t v h w) c\")\n scales = rearrange(gs_params[\"scales\"], \"b t v h w c -> b (t v h w) c\")\n quats = rearrange(gs_params[\"quats\"], \"b t v h w c -> b (t v h w) c\")\n opacities = rearrange(gs_params[\"opacities\"], \"b t v h w -> b (t v h w)\")\n colors = rearrange(gs_params[\"colors\"], \"b t v h w c -> b (t v h w) c\")\n forward_v = rearrange(gs_params[\"forward_flow\"], \"b t v h w c -> b (t v h w) c\")\n\n means_batched = means.repeat_interleave(tgt_t, dim=0)\n scales_batched = scales.repeat_interleave(tgt_t, dim=0)\n quats_batched = quats.repeat_interleave(tgt_t, dim=0)\n opacities_batched = opacities.repeat_interleave(tgt_t, dim=0)\n color_batched = colors.repeat_interleave(tgt_t, dim=0)\n forward_v_batched = forward_v.repeat_interleave(tgt_t, dim=0)\n\n ctx_time = data_dict[\"context_time\"] * data_dict[\"timespan\"]\n tgt_time = data_dict[\"target_time\"] * data_dict[\"timespan\"]\n if tgt_time.ndim == 3:\n tdiff_forward = tgt_time.unsqueeze(2) - ctx_time.unsqueeze(1)\n tdiff_forward = tdiff_forward.view(b * tgt_t, t * v, 1)\n tdiff_forward_batched = tdiff_forward.repeat_interleave(h * w, dim=1)\n else:\n tdiff_forward = tgt_time.unsqueeze(-1) - ctx_time.unsqueeze(-2)\n tdiff_forward = tdiff_forward.view(b * tgt_t, t, 1)\n tdiff_forward_batched = tdiff_forward.repeat_interleave(v * h * w, dim=1)\n forward_translation = forward_v_batched * tdiff_forward_batched\n means_batched = means_batched + forward_translation\n\n if not self.training: # mask out some noisy flow\n forward_v[forward_v.norm(dim=-1) < 1.0] = 0.0\n forward_v_batched = forward_v.repeat_interleave(tgt_t, dim=0)\n\n if not self.training and self.num_motion_tokens > 0 and render_motion_seg:\n # render the motion segmentation map\n motion_weights = rearrange(gs_params[\"motion_weights\"], \"b t v h w k -> b (t v h w) k\")\n weights_batched = motion_weights.repeat_interleave(tgt_t, dim=0)\n colors_batched = torch.cat([color_batched, forward_v_batched, weights_batched], dim=-1)\n else:\n colors_batched = torch.cat([color_batched, forward_v_batched], dim=-1)\n\n camtoworlds_batched = data_dict[\"target_camtoworlds\"].view(b * tgt_t, -1, 4, 4)\n viewmats_batched = torch.linalg.inv(camtoworlds_batched.float())\n Ks_batched = data_dict[\"target_intrinsics\"].view(b * tgt_t, -1, 3, 3)\n\n motion_seg = None\n if self.use_latest_gsplat:\n means_batched = means_batched.float()\n quats_batched = quats_batched.float()\n scales_batched = scales_batched.float()\n opacities_batched = opacities_batched.float()\n colors_batched = colors_batched.float()\n viewmats_batched = viewmats_batched.float()\n Ks_batched = Ks_batched.float()\n\n if not self.training:\n rendered_colors, rendered_alphas, rendered_flow, motion_seg = [], [], [], []\n rendered_depths = []\n with torch.autocast(\"cuda\", enabled=False):\n for bid in range(means_batched.size(0)):\n renderings, alpha, _ = rasterization(\n means=means_batched[bid],\n quats=quats_batched[bid],\n scales=scales_batched[bid],\n opacities=opacities_batched[bid],\n colors=colors_batched[bid],\n viewmats=viewmats_batched[bid],\n Ks=Ks_batched[bid],\n width=data_dict[\"width\"],\n height=data_dict[\"height\"],\n render_mode=\"RGB+ED\",\n near_plane=self.near,\n far_plane=self.far,\n packed=False,\n radius_clip=radius_clip,\n )\n color, forward_flow, weights, depth = renderings.split(\n [self.gs_dim, 3, self.num_motion_tokens, 1], dim=-1\n )\n rendered_colors.append(color)\n rendered_alphas.append(alpha)\n rendered_flow.append(forward_flow)\n motion_seg.append(weights)\n rendered_depths.append(depth)\n color = torch.stack(rendered_colors, dim=0)\n rendered_alpha = torch.stack(rendered_alphas, dim=0)\n forward_flow = torch.stack(rendered_flow, dim=0)\n depth = torch.stack(rendered_depths, dim=0)\n motion_seg = torch.stack(motion_seg, dim=0)\n if motion_seg.numel() > 0:\n motion_seg = motion_seg.reshape(b, tgt_t, v, h, w, -1).argmax(dim=-1)\n else:\n motion_seg = None\n else:\n rendered_colors, rendered_alphas, rendered_flow, rendered_depths = [], [], [], []\n with torch.autocast(\"cuda\", enabled=False):\n for bid in range(means_batched.size(0)):\n renderings, alpha, _ = rasterization(\n means=means_batched[bid],\n quats=quats_batched[bid],\n scales=scales_batched[bid],\n opacities=opacities_batched[bid],\n colors=colors_batched[bid],\n viewmats=viewmats_batched[bid],\n Ks=Ks_batched[bid],\n width=data_dict[\"width\"],\n height=data_dict[\"height\"],\n render_mode=\"RGB+ED\",\n near_plane=self.near,\n far_plane=self.far,\n packed=False,\n radius_clip=radius_clip,\n )\n color, forward_flow, depth = renderings.split([self.gs_dim, 3, 1], dim=-1)\n rendered_colors.append(color)\n rendered_alphas.append(alpha)\n rendered_flow.append(forward_flow)\n rendered_depths.append(depth)\n color = torch.stack(rendered_colors, dim=0)\n rendered_alpha = torch.stack(rendered_alphas, dim=0)\n forward_flow = torch.stack(rendered_flow, dim=0)\n depth = torch.stack(rendered_depths, dim=0)\n\n else:\n if not self.training:\n with torch.autocast(\"cuda\", enabled=False):\n rendered_color, rendered_alpha, _ = rasterization(\n means=means_batched.float(),\n quats=quats_batched.float(),\n scales=scales_batched.float(),\n opacities=opacities_batched.float(),\n colors=(\n colors_batched[..., : -self.num_motion_tokens].float()\n if self.num_motion_tokens > 0 and render_motion_seg\n else colors_batched.float()\n ),\n viewmats=viewmats_batched,\n Ks=Ks_batched,\n width=tgt_w,\n height=tgt_h,\n render_mode=\"RGB+ED\",\n near_plane=self.near,\n far_plane=self.far,\n packed=False,\n radius_clip=radius_clip,\n )\n color, forward_flow, depth = rendered_color.split([self.gs_dim, 3, 1], dim=-1)\n if self.num_motion_tokens > 0 and render_motion_seg:\n chunksize = 32\n assignment_map = []\n rendered_colors = colors_batched[..., -self.num_motion_tokens :]\n for i in range(0, self.num_motion_tokens, chunksize):\n weights, _, _ = rasterization(\n means=means_batched.float(),\n quats=quats_batched.float(),\n scales=scales_batched.float(),\n opacities=opacities_batched.float(),\n colors=rendered_colors[..., i : i + chunksize],\n viewmats=viewmats_batched,\n Ks=Ks_batched,\n width=tgt_w,\n height=tgt_h,\n render_mode=\"RGB+ED\",\n near_plane=self.near,\n far_plane=self.far,\n packed=False,\n radius_clip=radius_clip,\n )\n weights = weights.split([weights.size(-1) - 1, 1], dim=-1)[0]\n assignment_map.append(weights)\n motion_seg = torch.cat(assignment_map, dim=-1)\n motion_seg = motion_seg.reshape(b, tgt_t, tgt_v, tgt_h, tgt_w, -1).argmax(\n dim=-1\n )\n else:\n with torch.autocast(\"cuda\", enabled=False):\n rendered_color, rendered_alpha, _ = rasterization(\n means=means_batched.float(),\n quats=quats_batched.float(),\n scales=scales_batched.float(),\n opacities=opacities_batched.float(),\n colors=colors_batched.float(),\n viewmats=viewmats_batched,\n Ks=Ks_batched,\n width=tgt_w,\n height=tgt_h,\n render_mode=\"RGB+ED\",\n near_plane=self.near,\n far_plane=self.far,\n packed=False,\n radius_clip=radius_clip,\n )\n color, forward_flow, depth = rendered_color.split([self.gs_dim, 3, 1], dim=-1)\n output_dict = {\n \"rendered_image\": color.view(b, tgt_t, tgt_v, tgt_h, tgt_w, -1),\n \"rendered_depth\": depth.view(b, tgt_t, tgt_v, tgt_h, tgt_w),\n \"rendered_alpha\": rendered_alpha.view(b, tgt_t, tgt_v, tgt_h, tgt_w),\n \"rendered_flow\": forward_flow.view(b, tgt_t, tgt_v, tgt_h, tgt_w, -1),\n \"means_batched\": means_batched,\n }\n if motion_seg is not None:\n output_dict[\"rendered_motion_seg\"] = motion_seg.squeeze(-1)\n return output_dict\n\n def get_ray_dict(self, data_dict):\n ray_dict = self.plucker_embedder(\n data_dict[\"context_intrinsics\"],\n data_dict[\"context_camtoworlds\"],\n image_size=data_dict[\"context_image\"].shape[-2:],\n )\n if self.decoder_type != \"dummy\":\n feat_ray_dict = self.plucker_embedder(\n data_dict[\"context_intrinsics\"],\n data_dict[\"context_camtoworlds\"],\n image_size=data_dict[\"context_image\"].shape[-2:],\n patch_size=self.patch_size,\n )\n ray_dict[\"origins\"] = feat_ray_dict[\"origins\"]\n ray_dict[\"dirs\"] = feat_ray_dict[\"dirs\"]\n\n tgt_intrinsics = data_dict[\"target_intrinsics\"]\n tgt_intrinsics[..., 0, 0] = tgt_intrinsics[..., 0, 0] / self.patch_size\n tgt_intrinsics[..., 1, 1] = tgt_intrinsics[..., 1, 1] / self.patch_size\n tgt_intrinsics[..., 0, 2] = tgt_intrinsics[..., 0, 2] / self.patch_size\n tgt_intrinsics[..., 1, 2] = tgt_intrinsics[..., 1, 2] / self.patch_size\n data_dict[\"target_intrinsics\"] = tgt_intrinsics\n data_dict[\"width\"] //= self.patch_size\n data_dict[\"height\"] //= self.patch_size\n return data_dict, ray_dict\n\n def forward(self, data_dict):\n x = data_dict[\"context_image\"]\n b, t, v, c, h, w = x.size()\n data_dict, ray_dict = self.get_ray_dict(data_dict)\n x = self.forward_features(x, ray_dict[\"plucker\"], data_dict[\"context_time\"])\n\n sky_token, affine_tokens, motion_tokens = None, None, None\n if self.use_sky_token:\n sky_token = x[:, :1]\n x = x[:, 1:]\n\n if self.use_affine_token:\n affine_tokens = x[:, : self.num_cams]\n x = x[:, self.num_cams :]\n\n if self.num_motion_tokens > 0:\n motion_tokens = x[:, : self.num_motion_tokens]\n x = x[:, self.num_motion_tokens :]\n\n gs_params = self.forward_gs_predictor(x, ray_dict[\"origins\"], ray_dict[\"dirs\"])\n gs_params = self.forward_motion_predictor(x, motion_tokens, gs_params)\n # sometimes the number of views is too large, so we split the rendering into chunks\n step = 20\n if data_dict[\"target_camtoworlds\"].shape[1] <= step:\n render_results = self.forward_renderer(gs_params, data_dict)\n else:\n chunk_data_dict = data_dict.copy()\n for chunk_start in range(0, data_dict[\"target_camtoworlds\"].shape[1], step):\n chunk_end = min(chunk_start + step, data_dict[\"target_camtoworlds\"].shape[1])\n chunk_data_dict[\"target_camtoworlds\"] = data_dict[\"target_camtoworlds\"][\n :, chunk_start:chunk_end\n ]\n chunk_data_dict[\"target_intrinsics\"] = data_dict[\"target_intrinsics\"][\n :, chunk_start:chunk_end\n ]\n chunk_data_dict[\"target_time\"] = data_dict[\"target_time\"][:, chunk_start:chunk_end]\n chunk_render_results = self.forward_renderer(gs_params, chunk_data_dict)\n if chunk_start == 0:\n render_results = chunk_render_results\n else:\n for k, v in chunk_render_results.items():\n render_results[k] = torch.cat([render_results[k], v], dim=1)\n images, opacities = render_results[\"rendered_image\"], render_results[\"rendered_alpha\"]\n if self.use_sky_token:\n target_ray_dict = self.plucker_embedder(\n data_dict[\"target_intrinsics\"],\n data_dict[\"target_camtoworlds\"],\n image_size=(data_dict[\"height\"], data_dict[\"width\"]),\n )\n if data_dict[\"target_camtoworlds\"].shape[1] <= step:\n sky = self.sky_head(target_ray_dict[\"dirs\"], sky_token)\n images = images + (1 - opacities[..., None]) * sky\n else:\n for chunk_start in range(0, data_dict[\"target_camtoworlds\"].shape[1], step):\n dirs = target_ray_dict[\"dirs\"][:, chunk_start : chunk_start + step]\n chunk_sky = self.sky_head(dirs, sky_token)\n images[:, chunk_start : chunk_start + step] += (\n 1 - opacities[:, chunk_start : chunk_start + step][..., None]\n ) * chunk_sky\n gs_params[\"sky_token\"] = sky_token\n\n if self.use_affine_token:\n affine = self.affine_linear(affine_tokens) # b v (gs_dim * (gs_dim + 1))\n affine = rearrange(affine, \"b v (p q) -> b v p q\", p=self.gs_dim)\n images = torch.einsum(\"b t v h w p, b v p q -> b t v h w p\", images, affine)\n gs_params[\"affine\"] = affine\n render_results[\"rendered_image\"] = images\n render_results = self.forward_decoder(render_results)\n return {\n \"ray_dict\": ray_dict,\n \"gs_params\": gs_params,\n \"render_results\": render_results,\n }\n\n def from_gs_params_to_output(self, gs_params, target_dict, num_cams=1):\n render_results = self.forward_renderer(\n gs_params, target_dict, render_motion_seg=False, radius_clip=4.0\n )\n rendered_images = render_results[\"rendered_image\"]\n if self.use_sky_token:\n sky_token = gs_params[\"sky_token\"]\n target_ray_dict = self.plucker_embedder(\n target_dict[\"target_intrinsics\"],\n target_dict[\"target_camtoworlds\"],\n image_size=(target_dict[\"height\"], target_dict[\"width\"]),\n )\n sky = self.sky_head(target_ray_dict[\"dirs\"], sky_token)\n rendered_opacities = render_results[\"rendered_alpha\"]\n rendered_images = rendered_images + (1 - rendered_opacities[..., None]) * sky\n\n if self.use_affine_token:\n if num_cams == 1:\n affine = gs_params[\"affine\"].mean(dim=1)\n rendered_images = torch.einsum(\n \"b t v h w p, b p q -> b t v h w p\", rendered_images, affine\n )\n else:\n affine = gs_params[\"affine\"]\n rendered_images = torch.einsum(\n \"b t v h w p, b v p q -> b t v h w p\", rendered_images, affine\n )\n render_results[\"rendered_image\"] = rendered_images\n render_results = self.forward_decoder(render_results)\n return {\"render_results\": render_results}\n\n def get_gs_params(self, data_dict):\n x = data_dict[\"context_image\"]\n data_dict, ray_dict = self.get_ray_dict(data_dict)\n x = self.forward_features(x, ray_dict[\"plucker\"], data_dict[\"context_time\"])\n\n sky_token, affine_tokens, motion_tokens = None, None, None\n if self.use_sky_token:\n sky_token = x[:, :1]\n x = x[:, 1:]\n\n if self.use_affine_token:\n affine_tokens = x[:, : self.num_cams]\n x = x[:, self.num_cams :]\n\n if self.num_motion_tokens > 0:\n motion_tokens = x[:, : self.num_motion_tokens]\n x = x[:, self.num_motion_tokens :]\n\n gs_params = self.forward_gs_predictor(x, ray_dict[\"origins\"], ray_dict[\"dirs\"])\n gs_params = self.forward_motion_predictor(x, motion_tokens, gs_params)\n if self.use_sky_token:\n gs_params[\"sky_token\"] = sky_token\n\n if self.use_affine_token:\n affine = self.affine_linear(affine_tokens) # b v (gs_dim * (gs_dim + 1))\n affine = rearrange(affine, \"b v (p q) -> b v p q\", p=self.gs_dim)\n gs_params[\"affine\"] = affine\n return gs_params\n\n\ndef STORM_B_8(**kwargs):\n return STORM(patch_size=8, embed_dim=768, depth=12, num_heads=12, **kwargs)\n\n\ndef STORM_L_8(**kwargs):\n return STORM(patch_size=8, embed_dim=1024, depth=24, num_heads=16, **kwargs)\n\n\ndef STORM_B_16(**kwargs):\n return STORM(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)\n\n\ndef STORM_L_16(**kwargs):\n return STORM(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)\n\n\ndef STORM_XL_8(**kwargs):\n return STORM(patch_size=8, embed_dim=1152, depth=28, num_heads=16, **kwargs)\n\n\ndef STORM_H_8(**kwargs):\n return STORM(patch_size=8, embed_dim=1280, depth=32, num_heads=16, **kwargs)\n\n\ndef STORM_H_16(**kwargs):\n return STORM(patch_size=16, embed_dim=1280, depth=32, num_heads=16, **kwargs)\n\n\nSTORM_models = {\n \"STORM-B/8\": STORM_B_8,\n \"STORM-L/8\": STORM_L_8,\n \"STORM-XL/8\": STORM_XL_8,\n \"STORM-H/8\": STORM_H_8,\n \"STORM-B/16\": STORM_B_16,\n \"STORM-L/16\": STORM_L_16,\n \"STORM-H/16\": STORM_H_16,\n}\n" }, { "path": "storm/models/vit.py", "content": "# --------------------------------------------------------\n# References:\n# timm: https://github.com/rwightman/pytorch-image-models/tree/master/timm\n# --------------------------------------------------------\n\nfrom functools import partial\nfrom typing import Tuple, Union\n\nimport torch\nimport torch.nn as nn\nfrom einops import rearrange\n\nfrom .embedders import PatchEmbed\nfrom .layers import Transformer, get_2d_sincos_pos_embed, resample_abs_pos_embed\n\n\nclass VisionTransformer(nn.Module):\n \"\"\"Vision Transformer\"\"\"\n\n def __init__(\n self,\n img_size: Union[int, Tuple[int, int]] = 224,\n patch_size: Union[int, Tuple[int, int]] = 16,\n in_chans: int = 3,\n embed_dim: int = 768,\n depth: int = 12,\n num_heads: int = 12,\n qk_norm: bool = False,\n pos_embed_requires_grad: bool = True,\n norm_layer: nn.Module = partial(nn.LayerNorm, eps=1e-6),\n grad_checkpointing: bool = False,\n ) -> None:\n super().__init__()\n self.patch_size = patch_size\n self.embed_dim = embed_dim\n self.depth = depth\n\n self.patch_embed = PatchEmbed(\n img_size=img_size,\n patch_size=patch_size,\n in_chans=in_chans,\n embed_dim=embed_dim,\n output_fmt=\"NHWC\",\n )\n self.num_patches = self.patch_embed.num_patches\n self.img_size = self.patch_embed.img_size\n\n self.pos_embed = nn.Parameter(\n torch.randn(1, self.num_patches, embed_dim) * 0.02,\n requires_grad=pos_embed_requires_grad,\n )\n self.pos_embed_requires_grad = pos_embed_requires_grad\n self.transformer = Transformer(\n embed_dim=embed_dim,\n depth=depth,\n num_heads=num_heads,\n qk_norm=qk_norm,\n norm_layer=norm_layer,\n grad_checkpointing=grad_checkpointing,\n )\n self.norm = norm_layer(embed_dim)\n self.init_weights()\n\n def init_weights(self):\n def _basic_init(module):\n if isinstance(module, nn.Linear):\n nn.init.trunc_normal_(module.weight, std=0.02)\n if module.bias is not None:\n nn.init.zeros_(module.bias)\n\n self.apply(_basic_init)\n if not self.pos_embed_requires_grad:\n pos_embed = get_2d_sincos_pos_embed(\n self.pos_embed.shape[-1], self.patch_embed.grid_size\n )\n self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))\n\n def _pos_embed(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Compared to timm's implementation, this handles non-square images.\n \"\"\"\n B, H, W, C = x.shape\n pos_embed = resample_abs_pos_embed(\n posemb=self.pos_embed,\n new_size=(H, W),\n old_size=self.patch_embed.grid_size,\n n_prefix_tokens=0,\n )\n x = x.view(B, -1, C) + pos_embed\n return x\n\n def unpatchify(self, x, hw=None, channel_first=True, patch_size=None) -> torch.Tensor:\n hw = hw or self.img_size\n imgs = rearrange(\n x,\n \"b (h w) (p1 p2 c) -> b c (h p1) (w p2)\",\n p1=self.patch_size if patch_size is None else patch_size,\n p2=self.patch_size if patch_size is None else patch_size,\n h=hw[0] // (self.patch_size if patch_size is None else patch_size),\n w=hw[1] // (self.patch_size if patch_size is None else patch_size),\n )\n if not channel_first:\n imgs = rearrange(imgs, \"b c h w -> b h w c\")\n return imgs\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n x = self.patch_embed(x)\n x = self._pos_embed(x)\n x = self.transformer(x)\n x = self.norm(x)\n return x\n\n\ndef ViT_S_16(**kwargs):\n return VisionTransformer(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)\n\n\ndef ViT_B_16(**kwargs):\n return VisionTransformer(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)\n\n\ndef ViT_L_16(**kwargs):\n return VisionTransformer(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)\n\n\ndef ViT_H_14(**kwargs):\n return VisionTransformer(patch_size=14, embed_dim=1280, depth=32, num_heads=16, **kwargs)\n\n\nViT_models = {\n \"ViT-B/16\": ViT_B_16,\n \"ViT-L/16\": ViT_L_16,\n \"ViT-H/14\": ViT_H_14,\n}\n" }, { "path": "storm/utils/__init__.py", "content": "" }, { "path": "storm/utils/distributed.py", "content": "import logging\nimport os\nimport random\nimport re\nimport socket\nimport sys\nfrom typing import Dict, List\n\nimport torch\nimport torch.distributed as dist\n\n_LOCAL_RANK = -1\n_LOCAL_WORLD_SIZE = -1\n\n_TORCH_DISTRIBUTED_ENV_VARS = (\n \"MASTER_ADDR\",\n \"MASTER_PORT\",\n \"RANK\",\n \"WORLD_SIZE\",\n \"LOCAL_RANK\",\n \"LOCAL_WORLD_SIZE\",\n)\n\n\nlogger = logging.getLogger(\"STORM\")\n\n\ndef is_enabled() -> bool:\n \"\"\"Check if distributed mode is enabled.\"\"\"\n return dist.is_available() and dist.is_initialized()\n\n\ndef get_global_rank() -> int:\n \"\"\"Get the global rank of the current process.\"\"\"\n return dist.get_rank() if is_enabled() else 0\n\n\ndef get_world_size() -> int:\n \"\"\"Get the world size (number of processes).\"\"\"\n return dist.get_world_size() if is_enabled() else 1\n\n\ndef is_main_process() -> bool:\n \"\"\"Check if the current process is the main process.\"\"\"\n return get_global_rank() == 0\n\n\ndef _collect_env_vars() -> Dict[str, str]:\n \"\"\"Collect PyTorch distributed environment variables.\"\"\"\n env_vars = {\n env_var: os.environ[env_var]\n for env_var in _TORCH_DISTRIBUTED_ENV_VARS\n if env_var in os.environ\n }\n if \"WORLD_SIZE\" in env_vars and \"LOCAL_WORLD_SIZE\" not in env_vars:\n env_vars[\"LOCAL_WORLD_SIZE\"] = env_vars[\"WORLD_SIZE\"]\n return env_vars\n\n\ndef _is_slurm_job_process() -> bool:\n \"\"\"Check if the process is running as part of a Slurm job.\"\"\"\n return \"SLURM_JOB_ID\" in os.environ and not os.isatty(sys.stdout.fileno())\n\n\ndef _parse_slurm_node_list(s: str) -> List[str]:\n \"\"\"Parse a Slurm node list into a list of hostnames.\"\"\"\n nodes = []\n # Extract \"hostname\", \"hostname[1-2,3,4-5],\" substrings\n pattern = re.compile(r\"(([^\\[]+)(?:\\[([^\\]]+)\\])?),?\")\n for match in pattern.finditer(s):\n prefix, suffixes = s[match.start(2) : match.end(2)], s[match.start(3) : match.end(3)]\n for suffix in suffixes.split(\",\"):\n span = suffix.split(\"-\")\n if len(span) == 1:\n nodes.append(prefix + suffix)\n else:\n width = len(span[0])\n start, end = int(span[0]), int(span[1]) + 1\n nodes.extend([prefix + f\"{i:0{width}}\" for i in range(start, end)])\n return nodes\n\n\ndef _check_env_variable(key: str, new_value: str):\n \"\"\"Ensure that environment variables are consistent.\"\"\"\n if key in os.environ and os.environ[key] != new_value:\n raise RuntimeError(f\"Environment variable conflict: {key} is already set\")\n\n\ndef _restrict_print_to_main_process() -> None:\n \"\"\"\n This function disables printing when not in the main process\n \"\"\"\n import builtins as __builtin__\n\n builtin_print = __builtin__.print\n\n def print(*args, **kwargs):\n force = kwargs.pop(\"force\", False)\n if is_main_process() or force:\n builtin_print(*args, **kwargs)\n\n __builtin__.print = print\n\n\ndef _get_master_port(seed: int = 0) -> int:\n \"\"\"Get a master port, either from the environment or randomly.\"\"\"\n MIN_MASTER_PORT, MAX_MASTER_PORT = 20_000, 60_000\n\n master_port_str = os.environ.get(\"MASTER_PORT\")\n if master_port_str is None:\n rng = random.Random(seed)\n return rng.randint(MIN_MASTER_PORT, MAX_MASTER_PORT)\n\n return int(master_port_str)\n\n\ndef _get_available_port() -> int:\n \"\"\"Find an available port on the current machine.\"\"\"\n with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n # A \"\" host address means INADDR_ANY i.e. binding to all interfaces.\n # Note this is not compatible with IPv6.\n s.bind((\"\", 0))\n return s.getsockname()[1]\n\n\nclass _TorchDistributedEnvironment:\n \"\"\"Manages PyTorch distributed environment variables.\"\"\"\n\n def __init__(self):\n self.master_addr = \"127.0.0.1\"\n self.master_port = 0\n self.rank = -1\n self.world_size = -1\n self.local_rank = -1\n self.local_world_size = -1\n\n if _is_slurm_job_process():\n return self._set_from_slurm_env()\n\n env_vars = _collect_env_vars()\n if not env_vars:\n # Environment is not set\n pass\n elif len(env_vars) == len(_TORCH_DISTRIBUTED_ENV_VARS):\n # Environment is fully set\n return self._set_from_preset_env()\n else:\n # Environment is partially set\n collected_env_vars = \", \".join(env_vars.keys())\n raise RuntimeError(\n f\"Partially set environment: {collected_env_vars}.\"\n f\"Unset environment variables: {[env_var for env_var in _TORCH_DISTRIBUTED_ENV_VARS if env_var not in env_vars]}\"\n )\n\n if torch.cuda.device_count() > 0:\n return self._set_from_local()\n\n raise RuntimeError(\"Can't initialize PyTorch distributed environment\")\n\n def _set_from_slurm_env(self):\n \"\"\"Slurm job created with sbatch, submitit, etc...\"\"\"\n logger.info(\"Initializing from Slurm environment\")\n job_id = int(os.environ[\"SLURM_JOB_ID\"])\n node_count = int(os.environ[\"SLURM_JOB_NUM_NODES\"])\n nodes = _parse_slurm_node_list(os.environ[\"SLURM_JOB_NODELIST\"])\n assert len(nodes) == node_count, f\"SLURM_JOB_NODELIST mismatch: {nodes} vs {node_count}\"\n\n self.master_addr = nodes[0]\n self.master_port = _get_master_port(seed=job_id)\n self.rank = int(os.environ[\"SLURM_PROCID\"])\n self.world_size = int(os.environ[\"SLURM_NTASKS\"])\n logger.info(\n f\"Master address: {self.master_addr}, Master port: {self.master_port}, Rank: {self.rank}, World size: {self.world_size}\"\n )\n assert self.rank < self.world_size\n self.local_rank = int(os.environ[\"SLURM_LOCALID\"])\n self.local_world_size = self.world_size // node_count\n assert self.local_rank < self.local_world_size\n\n def _set_from_preset_env(self):\n logger.info(\"Initialization from preset environment\")\n self.master_addr = os.environ[\"MASTER_ADDR\"]\n self.master_port = os.environ[\"MASTER_PORT\"]\n self.rank = int(os.environ[\"RANK\"])\n self.world_size = int(os.environ[\"WORLD_SIZE\"])\n assert self.rank < self.world_size\n self.local_rank = int(os.environ[\"LOCAL_RANK\"])\n self.local_world_size = (\n int(os.environ[\"LOCAL_WORLD_SIZE\"])\n if \"LOCAL_WORLD_SIZE\" in os.environ\n else self.world_size\n )\n assert self.local_rank < self.local_world_size\n\n def _set_from_local(self):\n \"\"\"Single node and GPU job (i.e. local script run)\"\"\"\n logger.info(\"Initializing from local environment\")\n self.master_addr = \"127.0.0.1\"\n self.master_port = _get_available_port()\n self.rank = 0\n self.world_size = 1\n self.local_rank = 0\n self.local_world_size = 1\n\n def export(self, *, overwrite: bool) -> \"_TorchDistributedEnvironment\":\n \"\"\"Export the environment variables for distributed initialization.\"\"\"\n env_vars = {\n \"MASTER_ADDR\": self.master_addr,\n \"MASTER_PORT\": str(self.master_port),\n \"RANK\": str(self.rank),\n \"WORLD_SIZE\": str(self.world_size),\n \"LOCAL_RANK\": str(self.local_rank),\n \"LOCAL_WORLD_SIZE\": str(self.local_world_size),\n }\n if not overwrite:\n for k, v in env_vars.items():\n _check_env_variable(k, v)\n\n os.environ.update(env_vars)\n return self\n\n\ndef enable(\n *,\n set_cuda_current_device: bool = True,\n overwrite: bool = False,\n allow_nccl_timeout: bool = False,\n):\n \"\"\"Enable distributed mode\n\n Args:\n set_cuda_current_device: If True, call torch.cuda.set_device() to set the\n current PyTorch CUDA device to the one matching the local rank.\n overwrite: If True, overwrites already set variables. Else fails.\n \"\"\"\n\n global _LOCAL_RANK, _LOCAL_WORLD_SIZE\n if _LOCAL_RANK >= 0 or _LOCAL_WORLD_SIZE >= 0:\n raise RuntimeError(\"Distributed mode already enabled\")\n torch_env = _TorchDistributedEnvironment()\n torch_env.export(overwrite=overwrite)\n\n if set_cuda_current_device:\n torch.cuda.set_device(torch_env.local_rank)\n\n if allow_nccl_timeout:\n # This allows to use torch distributed timeout in a NCCL backend\n key, value = \"NCCL_ASYNC_ERROR_HANDLING\", \"1\"\n if not overwrite:\n _check_env_variable(key, value)\n os.environ[key] = value\n\n dist.init_process_group(backend=\"nccl\")\n dist.barrier()\n\n # Finalize setup\n _LOCAL_RANK = torch_env.local_rank\n _LOCAL_WORLD_SIZE = torch_env.local_world_size\n _restrict_print_to_main_process()\n" }, { "path": "storm/utils/logging.py", "content": "import datetime\nimport functools\nimport json\nimport logging\nimport os\nimport sys\nimport time\nfrom collections import defaultdict, deque\nfrom typing import List, Optional\n\nimport torch\nimport torch.distributed as dist\nimport wandb\n\nfrom .distributed import get_global_rank, is_enabled, is_main_process\n\nlogger = logging.getLogger(\"STORM\")\n\n\nclass SmoothedValue:\n \"\"\"Tracks a series of values and computes smoothed statistics (e.g., median, average).\"\"\"\n\n def __init__(self, window_size=20, fmt=None):\n self.deque = deque(maxlen=window_size)\n self.total = 0.0\n self.count = 0\n self.fmt = fmt or \"{median:.4f} ({global_avg:.4f})\"\n\n def update(self, value, num=1):\n \"\"\"Updates the tracked values with a new value.\"\"\"\n self.deque.append(value)\n self.total += value * num\n self.count += num\n\n def synchronize_between_processes(self):\n \"\"\"Synchronizes the metric values across distributed processes.\"\"\"\n if not is_enabled():\n return\n t = torch.tensor([self.count, self.total], dtype=torch.float64, device=\"cuda\")\n dist.barrier()\n dist.all_reduce(t)\n self.count, self.total = int(t[0].item()), t[1].item()\n\n @property\n def median(self):\n \"\"\"Computes the median of the tracked values.\"\"\"\n return torch.tensor(list(self.deque)).median().item()\n\n @property\n def avg(self):\n \"\"\"Computes the average of the tracked values.\"\"\"\n return torch.tensor(list(self.deque), dtype=torch.float32).mean().item()\n\n @property\n def global_avg(self):\n \"\"\"Computes the global average across all processes.\"\"\"\n return self.total / self.count\n\n @property\n def max(self):\n \"\"\"Returns the maximum tracked value.\"\"\"\n return max(self.deque)\n\n @property\n def value(self):\n \"\"\"Returns the most recent value.\"\"\"\n return self.deque[-1]\n\n def __str__(self):\n \"\"\"Formats the smoothed statistics as a string.\"\"\"\n return self.fmt.format(\n median=self.median,\n avg=self.avg,\n global_avg=self.global_avg,\n max=self.max,\n value=self.value,\n )\n\n\nclass MetricLogger:\n def __init__(self, delimiter=\"\\t\", output_file=None):\n self.meters = defaultdict(SmoothedValue)\n self.delimiter = delimiter\n self.output_file = output_file\n\n def update(self, **kwargs):\n for k, v in kwargs.items():\n if v is None:\n continue\n if isinstance(v, torch.Tensor):\n v = v.item()\n assert isinstance(v, (float, int))\n self.meters[k].update(v)\n\n def __getattr__(self, attr):\n if attr in self.meters:\n return self.meters[attr]\n if attr in self.__dict__:\n return self.__dict__[attr]\n raise AttributeError(\"'{}' object has no attribute '{}'\".format(type(self).__name__, attr))\n\n def __str__(self):\n loss_str = []\n for name, meter in self.meters.items():\n loss_str.append(\"{}: {}\".format(name, str(meter)))\n return self.delimiter.join(loss_str)\n\n def synchronize_between_processes(self):\n for meter in self.meters.values():\n meter.synchronize_between_processes()\n\n def add_meter(self, name, meter):\n self.meters[name] = meter\n\n def dump_in_output_file(self, iteration, iter_time, data_time):\n if self.output_file is None or not is_main_process():\n return\n dict_to_dump = dict(\n iteration=iteration,\n iter_time=iter_time,\n data_time=data_time,\n )\n dict_to_dump.update({k: v.median for k, v in self.meters.items()})\n with open(self.output_file, \"a\") as f:\n f.write(json.dumps(dict_to_dump) + \"\\n\")\n\n def log_every(self, iterable, print_freq, header=None, n_iterations=None, start_iteration=0):\n i = start_iteration\n if not header:\n header = \"\"\n start_time = time.time()\n end = time.time()\n iter_time, data_time = SmoothedValue(fmt=\"{avg:.4f}\"), SmoothedValue(fmt=\"{avg:.4f}\")\n\n n_iterations = n_iterations or len(iterable)\n\n space_fmt = \":\" + str(len(str(n_iterations))) + \"d\"\n\n log_list = [\n header,\n \"[{0\" + space_fmt + \"}/{1}]\",\n \"eta: {eta}\",\n \"elapsed: {elapsed_time_str}\",\n \"{meters}\",\n \"time: {time}\",\n \"data: {data}\",\n ]\n if torch.cuda.is_available():\n log_list += [\"max mem: {memory:.0f}\"]\n\n log_msg = self.delimiter.join(log_list)\n MB = 1024.0 * 1024.0\n for obj in iterable:\n data_time.update(time.time() - end)\n yield obj\n iter_time.update(time.time() - end)\n if i % print_freq == 0 or i == n_iterations - 1:\n self.dump_in_output_file(\n iteration=i, iter_time=iter_time.avg, data_time=data_time.avg\n )\n eta_seconds = iter_time.global_avg * (n_iterations - i)\n eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))\n elapsed_time = time.time() - start_time\n elapsed_time_str = str(datetime.timedelta(seconds=int(elapsed_time)))\n\n if torch.cuda.is_available():\n logger.info(\n log_msg.format(\n i,\n n_iterations,\n eta=eta_string,\n elapsed_time_str=elapsed_time_str,\n meters=str(self),\n time=str(iter_time),\n data=str(data_time),\n memory=torch.cuda.max_memory_allocated() / MB,\n )\n )\n else:\n logger.info(\n log_msg.format(\n i,\n n_iterations,\n eta=eta_string,\n meters=str(self),\n time=str(iter_time),\n data=str(data_time),\n )\n )\n i += 1\n end = time.time()\n if i >= n_iterations:\n break\n total_time = time.time() - start_time\n total_time_str = str(datetime.timedelta(seconds=int(total_time)))\n logger.info(\n \"{} Total time: {} ({:.6f} s / it)\".format(\n header, total_time_str, total_time / n_iterations\n )\n )\n\n\nclass WandbLogger:\n def __init__(self, args, resume=\"must\", id=None):\n if id is None:\n resume = \"allow\" if resume else \"never\"\n wandb.init(\n config=args,\n entity=args.entity,\n project=args.project,\n name=args.exp_name,\n dir=args.log_dir,\n resume=resume,\n id=id,\n )\n self.run_id = wandb.run.id\n self.step = 0\n wandb.run.save()\n\n @staticmethod\n def wandb_safe_log(*args, **kwargs):\n \"\"\"Safely logs metrics to Wandb, handling errors.\"\"\"\n try:\n wandb.log(*args, **kwargs)\n except (wandb.CommError, BrokenPipeError):\n logger.error(\"Wandb logging failed, skipping...\")\n\n def set_step(self, step=None):\n \"\"\"Sets the logging step.\"\"\"\n self.step = step if step is not None else self.step + 1\n\n def update(self, metrics):\n \"\"\"Updates metrics in Weights & Biases.\"\"\"\n log_dict = {\n k: (v.item() if isinstance(v, torch.Tensor) else v)\n for k, v in metrics.items()\n if v is not None\n }\n self.wandb_safe_log(log_dict, step=self.step)\n\n def flush(self):\n pass\n\n def finish(self):\n try:\n wandb.finish()\n except (wandb.CommError, BrokenPipeError):\n logger.error(\"Wandb failed to finish\")\n\n\n@functools.lru_cache()\ndef _configure_logger(\n name: Optional[str] = None,\n *,\n level: int = logging.DEBUG,\n output: Optional[str] = None,\n time_string: Optional[str] = None,\n rank0_log: bool = True,\n) -> logging.Logger:\n \"\"\"\n Configure a logger with optional file and console outputs.\n\n Args:\n name: Name of the logger to configure. Defaults to the root logger.\n level: Logging level (e.g., DEBUG, INFO). Default is DEBUG.\n output: Path to save logs. If None, logs are not saved.\n - If it ends with \".txt\" or \".log\", treated as a file name.\n - Otherwise, logs are saved to `output/logs/log.txt`.\n time_string: Timestamp string to append to log filenames.\n\n Returns:\n A configured logger instance.\n \"\"\"\n logger = logging.getLogger(name)\n logger.setLevel(level)\n logger.propagate = False\n\n # Loosely match Google glog format:\n # [IWEF]yyyymmdd hh:mm:ss.uuuuuu threadid file:line] msg\n # but use a shorter timestamp and include the logger name:\n # [IWEF]yyyymmdd hh:mm:ss logger threadid file:line] msg\n fmt_prefix = \"%(levelname).1s%(asctime)s %(name)s %(filename)s:%(lineno)s] \"\n fmt_message = \"%(message)s\"\n fmt = fmt_prefix + fmt_message\n datefmt = \"%Y%m%d %H:%M:%S\"\n formatter = logging.Formatter(fmt=fmt, datefmt=datefmt)\n\n # stdout logging for main worker only\n if is_main_process():\n handler = logging.StreamHandler(stream=sys.stdout)\n handler.setLevel(logging.DEBUG)\n handler.setFormatter(formatter)\n logger.addHandler(handler)\n\n # File logging\n # Only create a file handler if:\n # - There is an output path, AND\n # - (We are rank 0 or rank0_log=False)\n if output and (is_main_process() or not rank0_log):\n if os.path.splitext(output)[-1] in (\".txt\", \".log\"):\n filename = output\n else:\n if time_string is None:\n filename = os.path.join(output, \"logs\", \"log.txt\")\n else:\n filename = os.path.join(output, \"logs\", f\"log_{time_string}.txt\")\n\n # If it's not rank 0 but rank0_log=False, append the rank ID\n if not is_main_process() and not rank0_log:\n global_rank = get_global_rank()\n filename = f\"{filename}.rank{global_rank}\"\n\n os.makedirs(os.path.dirname(filename), exist_ok=True)\n\n handler = logging.StreamHandler(open(filename, \"a\"))\n handler.setLevel(logging.DEBUG)\n handler.setFormatter(formatter)\n logger.addHandler(handler)\n\n return logger\n\n\ndef setup_logging(\n output: Optional[str] = None,\n *,\n name: Optional[str] = None,\n level: int = logging.DEBUG,\n capture_warnings: bool = True,\n time_string: Optional[str] = None,\n) -> None:\n \"\"\"\n Setup logging with optional console and file handlers.\n\n Args:\n output: Path to save log files. If None, logs are not saved.\n - If it ends with \".txt\" or \".log\", treated as a file name.\n - Otherwise, logs are saved to `output/logs/log.txt`.\n name: Name of the logger to configure. Defaults to the root logger.\n level: Logging level (e.g., DEBUG, INFO). Default is DEBUG.\n capture_warnings: Whether Python warnings should be captured as logs.\n time_string: Timestamp string to append to log filenames.\n \"\"\"\n logging.captureWarnings(capture_warnings)\n _configure_logger(name, level=level, output=output, time_string=time_string)\n" }, { "path": "storm/utils/losses.py", "content": "import torch\nimport torch.nn.functional as F\nfrom einops import rearrange\n\nfrom storm.dataset.constants import MEAN, STD\n\n\ndef compute_depth_loss(pred_depth, gt_depth, max_depth=None):\n pred_depth = pred_depth.squeeze()\n gt_depth = gt_depth.squeeze()\n if pred_depth.shape != gt_depth.shape:\n # resize pred_depth to match gt depth size\n try:\n b, v, h, w = pred_depth.shape\n gt_h, gt_w = gt_depth.shape[-2:]\n pred_depth = F.interpolate(\n rearrange(pred_depth, \"b v h w -> (b v) 1 h w\"),\n size=(gt_h, gt_w),\n mode=\"bilinear\",\n align_corners=False,\n )\n pred_depth = rearrange(pred_depth, \"(b v) 1 h w -> b v h w\", b=b, v=v)\n except:\n b, t, v, h, w = pred_depth.shape\n gt_h, gt_w = gt_depth.shape[-2:]\n pred_depth = F.interpolate(\n rearrange(pred_depth, \"b t v h w -> (b t v) 1 h w\"),\n size=(gt_h, gt_w),\n mode=\"bilinear\",\n align_corners=False,\n )\n pred_depth = rearrange(pred_depth, \"(b t v) 1 h w -> b t v h w\", b=b, t=t, v=v)\n valid_mask = gt_depth > 0.01\n if max_depth is None:\n max_depth = gt_depth.max()\n pred_depth = pred_depth[valid_mask] / max_depth\n gt_depth = gt_depth[valid_mask] / max_depth\n return F.l1_loss(pred_depth, gt_depth)\n\n\ndef compute_sky_depth_loss(pred_depth, gt_sky_mask, sky_depth: float = 1e3, flow=None):\n pred_depth = pred_depth.squeeze()\n gt_sky_mask = gt_sky_mask.squeeze()\n gt_h, gt_w = gt_sky_mask.shape[-2:]\n if pred_depth.shape != gt_sky_mask.shape:\n # resize pred_depth to match gt depth size\n b, t, v, h, w = pred_depth.shape\n pred_depth = F.interpolate(\n rearrange(pred_depth, \"b t v h w -> (b t v) 1 h w\"),\n size=(gt_h, gt_w),\n mode=\"bilinear\",\n align_corners=False,\n )\n pred_depth = rearrange(pred_depth, \"(b t v) 1 h w -> b t v h w\", b=b, t=t, v=v)\n if flow is not None and (flow.shape[-3] != gt_h or flow.shape[-2] != gt_w):\n flow = F.interpolate(\n rearrange(flow, \"b t v h w c -> (b t v) c h w\"),\n size=(gt_h, gt_w),\n mode=\"bilinear\",\n align_corners=False,\n )\n flow = rearrange(flow, \"(b t v) c h w -> b t v h w c\", b=b, t=t, v=v)\n # penalize flow in sky region\n sky_flow = flow[gt_sky_mask > 0]\n sky_flow_reg_loss = F.mse_loss(sky_flow, torch.zeros_like(sky_flow))\n else:\n sky_flow_reg_loss = torch.tensor(0.0).to(pred_depth.device)\n\n sky_region = gt_sky_mask > 0\n pred_depth = pred_depth[sky_region]\n return (\n F.mse_loss(pred_depth / sky_depth, torch.ones_like(pred_depth)) * 0.01,\n sky_flow_reg_loss,\n )\n\n\ndef compute_loss(output_dict, target_dict, args=None, lpips_loss=None):\n gs_params, pred_dict = output_dict[\"gs_params\"], output_dict[\"render_results\"]\n device = pred_dict[pred_dict[\"rgb_key\"]].device\n mean, std = torch.tensor(MEAN).to(device), torch.tensor(STD).to(device)\n pred_rgb = pred_dict[pred_dict[\"rgb_key\"]] * std + mean\n target_rgb = rearrange(target_dict[\"target_image\"], \"b t v c h w -> b t v h w c\") * std + mean\n\n if lpips_loss is not None:\n loss_dict = lpips_loss(pred_rgb, target_rgb)\n else:\n rgb_loss = F.mse_loss(pred_rgb, target_rgb)\n loss_dict = {\"rgb_loss\": rgb_loss}\n\n if args.enable_depth_loss and \"target_depth\" in target_dict:\n pred_depth, target_depth = pred_dict[pred_dict[\"depth_key\"]], target_dict[\"target_depth\"]\n depth_loss = compute_depth_loss(pred_depth, target_depth)\n loss_dict[\"depth_loss\"] = depth_loss\n\n if pred_dict[\"decoder_depth_key\"] is not None:\n pred_decoder_depth = pred_dict[pred_dict[\"decoder_depth_key\"]]\n decoded_depth_loss = compute_depth_loss(pred_decoder_depth, target_depth)\n loss_dict[\"decoded_depth_loss\"] = decoded_depth_loss\n if (\n args.enable_sky_depth_loss or args.enable_sky_opacity_loss\n ) and \"target_sky_masks\" in target_dict:\n sky_decoded_depth_loss, _ = compute_sky_depth_loss(\n pred_decoder_depth,\n target_dict[\"target_sky_masks\"],\n sky_depth=args.sky_depth,\n )\n loss_dict[\"sky_decodede_depth_loss\"] = sky_decoded_depth_loss\n\n if args.enable_flow_reg_loss and pred_dict[\"flow_key\"] is not None:\n pred_flow = gs_params[\"forward_flow\"]\n zero_flow = torch.zeros_like(gs_params[\"forward_flow\"]).to(device)\n forward_flow_reg = F.mse_loss(pred_flow, zero_flow, reduction=\"none\")\n loss_dict[\"flow_reg_loss\"] = args.flow_reg_coeff * forward_flow_reg.mean()\n\n if args.enable_sky_depth_loss and \"target_sky_masks\" in target_dict:\n # real gaussian depth\n sky_depth_loss, sky_flow_reg_loss = compute_sky_depth_loss(\n pred_dict[pred_dict[\"depth_key\"]],\n target_dict[\"target_sky_masks\"],\n sky_depth=args.sky_depth,\n flow=(pred_dict[pred_dict[\"flow_key\"]] if pred_dict[\"flow_key\"] is not None else None),\n )\n loss_dict[\"sky_depth_loss\"] = sky_depth_loss\n loss_dict[\"sky_flow_reg_loss\"] = sky_flow_reg_loss\n loss_dict[\"opacity_loss\"] = 0.01 * F.mse_loss(\n pred_dict[pred_dict[\"alpha_key\"]],\n torch.ones_like(pred_dict[pred_dict[\"alpha_key\"]]),\n )\n if pred_dict[\"decoder_depth_key\"] is not None:\n (sky_decoded_depth_loss, sky_decoded_flow_reg_loss,) = compute_sky_depth_loss(\n pred_dict[pred_dict[\"decoder_depth_key\"]],\n target_dict[\"target_sky_masks\"],\n sky_depth=args.sky_depth,\n flow=(\n pred_dict[pred_dict[\"decoder_flow_key\"]]\n if pred_dict[\"decoder_flow_key\"] is not None\n else None\n ),\n )\n loss_dict[\"sky_decodede_depth_loss\"] = sky_decoded_depth_loss\n loss_dict[\"sky_decoded_flow_reg_loss\"] = sky_decoded_flow_reg_loss\n\n elif args.enable_sky_opacity_loss and \"target_sky_masks\" in target_dict:\n opacity = pred_dict[pred_dict[\"alpha_key\"]].squeeze(-1)\n b, t, v, h, w = opacity.shape\n gt_h, gt_w = target_dict[\"target_sky_masks\"].shape[-2:]\n if h != gt_h or w != gt_w:\n opacity = F.interpolate(\n rearrange(opacity, \"b t v h w -> (b t v) 1 h w\"),\n size=(gt_h, gt_w),\n mode=\"bilinear\",\n align_corners=False,\n )\n opacity = rearrange(opacity, \"(b t v) 1 h w -> b t v h w\", b=b, t=t, v=v)\n sky_opacity_loss = F.l1_loss(opacity, 1 - target_dict[\"target_sky_masks\"])\n loss_dict[\"sky_opacity_loss\"] = sky_opacity_loss * args.sky_opacity_loss_coeff\n\n return loss_dict\n\n\ndef compute_scene_flow_metrics(pred, labels):\n \"\"\"\n Computes the scene flow metrics between the predicted and target scene flow values.\n # modified from https://github.com/Lilac-Lee/Neural_Scene_Flow_Prior/blob/0e4f403c73cb3fcd5503294a7c461926a4cdd1ad/utils.py#L12\n\n Args:\n pred (Tensor): predicted scene flow values\n labels (Tensor): target scene flow values\n Returns:\n dict: scene flow metrics\n \"\"\"\n l2_norm = torch.sqrt(torch.sum((pred - labels) ** 2, -1)).cpu()\n # Absolute distance error.\n labels_norm = torch.sqrt(torch.sum(labels * labels, -1)).cpu()\n relative_err = l2_norm / (labels_norm + 1e-20)\n\n EPE3D = torch.mean(l2_norm).item() # Mean absolute distance error\n\n # NOTE: Acc_5\n error_lt_5 = torch.BoolTensor((l2_norm < 0.05))\n relative_err_lt_5 = torch.BoolTensor((relative_err < 0.05))\n acc3d_strict = torch.mean((error_lt_5 | relative_err_lt_5).float()).item()\n\n # NOTE: Acc_10\n error_lt_10 = torch.BoolTensor((l2_norm < 0.1))\n relative_err_lt_10 = torch.BoolTensor((relative_err < 0.1))\n acc3d_relax = torch.mean((error_lt_10 | relative_err_lt_10).float()).item()\n\n # NOTE: outliers\n l2_norm_gt_3 = torch.BoolTensor(l2_norm > 0.3)\n relative_err_gt_10 = torch.BoolTensor(relative_err > 0.1)\n outlier = torch.mean((l2_norm_gt_3 | relative_err_gt_10).float()).item()\n\n # NOTE: angle error\n unit_label = labels / (labels.norm(dim=-1, keepdim=True) + 1e-7)\n unit_pred = pred / (pred.norm(dim=-1, keepdim=True) + 1e-7)\n\n # it doesn't make sense to compute angle error on zero vectors\n # we use a threshold of 0.1 to avoid noisy gt flow\n non_zero_flow_mask = labels_norm > 0.1\n # Apply the mask to filter out zero vectors\n unit_label = unit_label[non_zero_flow_mask]\n unit_pred = unit_pred[non_zero_flow_mask]\n # Initialize angle_error\n angle_error = 0.0\n # Check if there are any valid vectors to compute the angle error\n if unit_label.numel() > 0:\n eps = 1e-7\n # Compute the dot product and clamp its values to avoid numerical issues with acos\n dot_product = (unit_label * unit_pred).sum(dim=-1).clamp(min=-1 + eps, max=1 - eps)\n\n # Optionally, handle any remaining NaNs in the dot product\n dot_product = torch.nan_to_num(dot_product, nan=0.0)\n\n # Compute the angle error in radians and take the mean\n angle_error = torch.acos(dot_product).mean().item()\n\n torch.cuda.empty_cache()\n return {\n \"EPE3D\": EPE3D,\n \"acc3d_strict\": acc3d_strict,\n \"acc3d_relax\": acc3d_relax,\n \"outlier\": outlier,\n \"angle_error\": angle_error,\n }\n" }, { "path": "storm/utils/lpips.py", "content": "\"\"\"Stripped version of https://github.com/richzhang/PerceptualSimilarity/tree/master/models\"\"\"\n\nimport hashlib\nimport os\nfrom collections import namedtuple\n\nimport requests\nimport torch\nimport torch.nn as nn\nfrom torchvision import models\nfrom tqdm import tqdm\n\nURL_MAP = {\"vgg_lpips\": \"https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1\"}\n\nCKPT_MAP = {\"vgg_lpips\": \"vgg.pth\"}\n\nMD5_MAP = {\"vgg_lpips\": \"d507d7349b931f0638a25a48a722f98a\"}\n\n\ndef download(url, local_path, chunk_size=1024):\n os.makedirs(os.path.split(local_path)[0], exist_ok=True)\n with requests.get(url, stream=True) as r:\n total_size = int(r.headers.get(\"content-length\", 0))\n with tqdm(total=total_size, unit=\"B\", unit_scale=True) as pbar:\n with open(local_path, \"wb\") as f:\n for data in r.iter_content(chunk_size=chunk_size):\n if data:\n f.write(data)\n pbar.update(chunk_size)\n\n\ndef md5_hash(path):\n with open(path, \"rb\") as f:\n content = f.read()\n return hashlib.md5(content).hexdigest()\n\n\ndef get_ckpt_path(name, root, check=False):\n assert name in URL_MAP\n path = os.path.join(root, CKPT_MAP[name])\n if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]):\n print(\"Downloading {} model from {} to {}\".format(name, URL_MAP[name], path))\n download(URL_MAP[name], path)\n md5 = md5_hash(path)\n assert md5 == MD5_MAP[name], md5\n return path\n\n\nclass LPIPS(nn.Module):\n # Learned perceptual metric\n def __init__(self, use_dropout=True):\n super().__init__()\n self.scaling_layer = ScalingLayer()\n self.chns = [64, 128, 256, 512, 512] # vg16 features\n self.net = vgg16(pretrained=True, requires_grad=False)\n self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)\n self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)\n self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)\n self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)\n self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)\n self.load_from_pretrained()\n for param in self.parameters():\n param.requires_grad = False\n\n def load_from_pretrained(self, name=\"vgg_lpips\"):\n ckpt = get_ckpt_path(name, \"ckpts/lpips\", check=True)\n self.load_state_dict(torch.load(ckpt, map_location=torch.device(\"cpu\")), strict=False)\n print(\"loaded pretrained LPIPS loss from {}\".format(ckpt))\n\n @classmethod\n def from_pretrained(cls, name=\"vgg_lpips\"):\n if name != \"vgg_lpips\":\n raise NotImplementedError\n model = cls()\n ckpt = get_ckpt_path(name)\n model.load_state_dict(torch.load(ckpt, map_location=torch.device(\"cpu\")), strict=False)\n return model\n\n def forward(self, input, target):\n in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target))\n outs0, outs1 = self.net(in0_input), self.net(in1_input)\n feats0, feats1, diffs = {}, {}, {}\n lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]\n for kk in range(len(self.chns)):\n feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk])\n diffs[kk] = (feats0[kk] - feats1[kk]) ** 2\n\n res = [\n spatial_average(lins[kk].model(diffs[kk]), keepdim=True) for kk in range(len(self.chns))\n ]\n val = res[0]\n for l in range(1, len(self.chns)):\n val += res[l]\n return val\n\n\nclass ScalingLayer(nn.Module):\n def __init__(self):\n super(ScalingLayer, self).__init__()\n self.register_buffer(\"shift\", torch.Tensor([-0.030, -0.088, -0.188])[None, :, None, None])\n self.register_buffer(\"scale\", torch.Tensor([0.458, 0.448, 0.450])[None, :, None, None])\n\n def forward(self, inp):\n return (inp - self.shift) / self.scale\n\n\nclass NetLinLayer(nn.Module):\n \"\"\"A single linear layer which does a 1x1 conv\"\"\"\n\n def __init__(self, chn_in, chn_out=1, use_dropout=False):\n super(NetLinLayer, self).__init__()\n layers = (\n [\n nn.Dropout(),\n ]\n if (use_dropout)\n else []\n )\n layers += [\n nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False),\n ]\n self.model = nn.Sequential(*layers)\n\n\nclass vgg16(torch.nn.Module):\n def __init__(self, requires_grad=False, pretrained=True):\n super(vgg16, self).__init__()\n vgg_pretrained_features = models.vgg16(pretrained=pretrained).features\n self.slice1 = torch.nn.Sequential()\n self.slice2 = torch.nn.Sequential()\n self.slice3 = torch.nn.Sequential()\n self.slice4 = torch.nn.Sequential()\n self.slice5 = torch.nn.Sequential()\n self.N_slices = 5\n for x in range(4):\n self.slice1.add_module(str(x), vgg_pretrained_features[x])\n for x in range(4, 9):\n self.slice2.add_module(str(x), vgg_pretrained_features[x])\n for x in range(9, 16):\n self.slice3.add_module(str(x), vgg_pretrained_features[x])\n for x in range(16, 23):\n self.slice4.add_module(str(x), vgg_pretrained_features[x])\n for x in range(23, 30):\n self.slice5.add_module(str(x), vgg_pretrained_features[x])\n if not requires_grad:\n for param in self.parameters():\n param.requires_grad = False\n\n def forward(self, X):\n h = self.slice1(X)\n h_relu1_2 = h\n h = self.slice2(h)\n h_relu2_2 = h\n h = self.slice3(h)\n h_relu3_3 = h\n h = self.slice4(h)\n h_relu4_3 = h\n h = self.slice5(h)\n h_relu5_3 = h\n vgg_outputs = namedtuple(\n \"VggOutputs\", [\"relu1_2\", \"relu2_2\", \"relu3_3\", \"relu4_3\", \"relu5_3\"]\n )\n out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)\n return out\n\n\ndef normalize_tensor(x, eps=1e-8):\n norm_factor = torch.sqrt(torch.sum(x**2, dim=1, keepdim=True))\n return x / (norm_factor + eps)\n\n\ndef spatial_average(x, keepdim=True):\n return x.mean([2, 3], keepdim=keepdim)\n" }, { "path": "storm/utils/lpips_loss.py", "content": "import torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange\n\nfrom .lpips import LPIPS\n\n\nclass RGBLpipsLoss(nn.Module):\n \"\"\"\n Loss module that combines RGB reconstruction loss (MSE) and optional perceptual loss (LPIPS).\n\n Args:\n perceptual_weight (float): Weight for the perceptual loss.\n use_perceptual_loss (bool): Flag to determine whether perceptual loss is used.\n enable_perceptual_loss (bool): Initial state of perceptual loss usage.\n \"\"\"\n\n def __init__(\n self,\n perceptual_weight=0.5,\n use_perceptual_loss=True,\n enable_perceptual_loss=True,\n ):\n super().__init__()\n\n # Initialize the perceptual loss (LPIPS) if enabled\n if enable_perceptual_loss:\n self.perceptual_loss = LPIPS().eval()\n for param in self.perceptual_loss.parameters():\n param.requires_grad = False\n\n self.perceptual_weight = perceptual_weight\n self.use_perceptual_loss = use_perceptual_loss\n self.enable_perceptual_loss = enable_perceptual_loss\n\n def set_perceptual_loss(self, enable=True):\n \"\"\"\n Enable or disable the perceptual loss.\n\n Args:\n enable (bool): Whether to enable perceptual loss.\n \"\"\"\n self.enable_perceptual_loss = enable and self.use_perceptual_loss\n\n def forward(self, rgb, targets):\n \"\"\"\n Compute the RGB reconstruction loss and (optionally) perceptual loss.\n\n Args:\n rgb (Tensor): Predicted RGB values with shape (..., H, W, C).\n targets (Tensor): Ground truth RGB values with shape (..., H, W, C).\n\n Returns:\n dict: Dictionary containing 'rgb_loss' and optionally 'perceptual_loss'.\n \"\"\"\n # Rearrange input tensors to the format (batch, channels, height, width)\n rgb = rearrange(rgb, \"... h w c -> (...) c h w\")\n targets = rearrange(targets, \"... h w c -> (...) c h w\")\n rgb_loss = F.mse_loss(rgb, targets)\n loss_dict = {\"rgb_loss\": rgb_loss}\n if self.enable_perceptual_loss:\n perceptual_loss = self.perceptual_weight * self.perceptual_loss(rgb, targets)\n loss_dict[\"perceptual_loss\"] = perceptual_loss.mean()\n\n return loss_dict\n" }, { "path": "storm/utils/misc.py", "content": "import collections.abc\nimport datetime\nimport logging\nimport math\nimport os\nimport random\nfrom collections import OrderedDict\nfrom glob import glob\nfrom itertools import repeat\n\nimport numpy as np\nimport torch\nfrom torch import inf\n\nlogger = logging.getLogger(\"STORM\")\n\n\ndef fix_random_seeds(seed=31):\n torch.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n np.random.seed(seed)\n random.seed(seed)\n\n\ndef _ntuple(n):\n \"\"\"\n Creates a parser that converts an input to a tuple of length n.\n\n Args:\n n (int): Length of the tuple.\n\n Returns:\n Callable: A function that parses the input into a tuple of length n.\n \"\"\"\n\n def parse(x):\n if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):\n return tuple(x)\n return tuple(repeat(x, n))\n\n return parse\n\n\nto_2tuple = _ntuple(2)\n\n\ndef cleanup_checkpoints(ckpt_dir, keep_num=1):\n \"\"\"\n Clean up old checkpoints, keeping only the latest 'keep_num' checkpoints.\n\n Args:\n ckpt_dir (str): Directory containing the checkpoints.\n keep_num (int): Number of recent checkpoints to keep.\n \"\"\"\n ckpts = glob(f\"{ckpt_dir}/*.pth\")\n ckpts = [ckpt for ckpt in ckpts if \"latest\" not in ckpt and \"best\" not in ckpt]\n ckpts = sorted(ckpts, key=lambda x: int(x.split(\"_\")[-1].split(\".\")[0]))\n\n # Remove older checkpoints\n for ckpt in ckpts[:-keep_num]:\n os.remove(ckpt)\n logger.info(f\"Removed checkpoint: {ckpt}\")\n\n # Create or update latest symlink\n if ckpts:\n latest_symlink = f\"{ckpt_dir}/latest.pth\"\n try:\n os.remove(latest_symlink)\n except FileNotFoundError:\n pass\n os.symlink(os.path.abspath(ckpts[-1]), latest_symlink)\n logger.info(f\"Created symlink: {latest_symlink} -> {ckpts[-1]}\")\n\n\ndef load_model(args, model_without_ddp, optimizer=None, loss_scaler=None):\n \"\"\"\n Load model, optimizer, and loss scaler states from a checkpoint.\n\n Args:\n args: Arguments containing checkpoint paths and loading configurations.\n model_without_ddp (torch.nn.Module): Model to load the state into.\n optimizer (torch.optim.Optimizer, optional): Optimizer for loading states.\n loss_scaler (torch.cuda.amp.GradScaler, optional): Loss scaler for AMP.\n\n Returns:\n int: Visualization slice ID if available.\n \"\"\"\n vis_slice_id, checkpoint_loaded = 0, False\n if args.resume_from or args.auto_resume:\n if not args.resume_from:\n # Checkpoint not provided, auto-resume from the latest checkpoint\n checkpoints = [ckpt for ckpt in glob(f\"{args.ckpt_dir}/*.pth\") if \"latest\" not in ckpt]\n checkpoints = sorted(checkpoints, key=os.path.getmtime)\n if len(checkpoints) > 0:\n # Resume from the latest checkpoint\n args.resume_from = checkpoints[-1]\n\n if args.resume_from and os.path.exists(args.resume_from):\n logger.info(f\"[Model-resume] Resuming from: {args.resume_from}\")\n checkpoint = torch.load(args.resume_from, map_location=\"cpu\", weights_only=False)\n msg = model_without_ddp.load_state_dict(checkpoint[\"model\"], strict=True)\n logger.info(f\"[Model-resume] Loaded model: {msg}\")\n checkpoint_loaded = True\n if \"optimizer\" in checkpoint and \"latest_step\" in checkpoint and optimizer is not None:\n msg = optimizer.load_state_dict(checkpoint[\"optimizer\"])\n logger.info(f\"[Model-resume] Loaded optimizer: {msg}\")\n args.start_iteration = checkpoint[\"latest_step\"] + 1\n if \"loss_scaler\" in checkpoint and loss_scaler is not None:\n msg = loss_scaler.load_state_dict(checkpoint[\"loss_scaler\"])\n logger.info(f\"[Model-resume] Loaded loss_scaler: {msg}\")\n if \"vis_slice_id\" in checkpoint:\n vis_slice_id = checkpoint[\"vis_slice_id\"] + 1\n if \"latest_step\" in checkpoint:\n args.prev_num_iterations = checkpoint[\"latest_step\"]\n args.start_iteration = checkpoint[\"latest_step\"] + 1\n\n if \"total_elapsed_time\" in checkpoint:\n args.total_elapsed_time = float(checkpoint[\"total_elapsed_time\"])\n elapsed_time_str = str(datetime.timedelta(seconds=int(args.total_elapsed_time)))\n logger.info(f\"Loaded elapsed_time: {elapsed_time_str}\")\n del checkpoint\n\n if not checkpoint_loaded and args.load_from and os.path.exists(args.load_from):\n # args.resume_from has the highest priority. If it's not found, try args.load_from\n # this is useful for loading a model without optimizer and scheduler states\n # or for loading a pre-trained model for initialization, fine-tuning, or evaluation.\n logger.info(f\"Loading checkpoint from: {args.load_from}\")\n checkpoint = torch.load(args.load_from)\n if \"model\" in checkpoint:\n checkpoint = checkpoint[\"model\"]\n try:\n msg = model_without_ddp.load_state_dict(checkpoint, strict=False)\n checkpoint_loaded = True\n logger.info(f\"[Model-init] Loaded model: {msg}\")\n except Exception as e:\n logger.error(e)\n logger.info(f\"[Model-init] Loading model from {args.load_from} failed. Error: {e}\")\n model_state_dict = model_without_ddp.state_dict()\n # Create a new OrderedDict that will only contain matching parameter shapes\n filtered_dict = OrderedDict()\n for k, v in checkpoint.items():\n if k in model_state_dict:\n if v.shape == model_state_dict[k].shape:\n filtered_dict[k] = v\n else:\n logger.info(\n f\"Skipping parameter due to shape mismatch: {k} \"\n f\"({v.shape} vs {model_state_dict[k].shape})\"\n )\n else:\n logger.info(f\"Skipping unexpected key: {k}\")\n\n # Load the filtered state dict into the model (strict=False to allow missing keys)\n msg = model_without_ddp.load_state_dict(filtered_dict, strict=False)\n logger.info(f\"Load status: {msg}\")\n del checkpoint\n\n if not checkpoint_loaded:\n logger.info(f\"Training from scratch. No checkpoint found.\")\n return vis_slice_id\n\n\ndef adjust_learning_rate(optimizer, iteration, args):\n \"\"\"\n Adjust the learning rate using a cosine decay schedule with warmup.\n\n Args:\n optimizer (torch.optim.Optimizer): Optimizer to update learning rate.\n iteration (int): Current training iteration.\n args: Arguments defining the learning rate schedule.\n\n Returns:\n float: Updated learning rate.\n \"\"\"\n if iteration < args.warmup_iters:\n lr = args.lr * iteration / args.warmup_iters\n else:\n if args.lr_sched == \"constant\":\n lr = args.lr\n elif args.lr_sched == \"cosine\":\n lr = args.min_lr + (args.lr - args.min_lr) * 0.5 * (\n 1.0\n + math.cos(\n math.pi\n * (iteration - args.warmup_iters)\n / (args.num_iterations - args.warmup_iters)\n )\n )\n else:\n raise ValueError(f\"Unknown lr_sched: {args.lr_sched}\")\n\n for param_group in optimizer.param_groups:\n param_group[\"lr\"] = lr * param_group.get(\"lr_scale\", 1.0)\n\n return lr\n\n\ndef unwrap_model(model):\n return model.module if hasattr(model, \"module\") else model\n\n\ndef get_grad_norm_(parameters, norm_type=2.0):\n \"\"\"\n Compute gradient norm for a set of parameters.\n\n Args:\n parameters (Iterable): Parameters to compute gradients for.\n norm_type (float): Norm type for gradient computation.\n\n Returns:\n torch.Tensor: Gradient norm.\n \"\"\"\n parameters = [p for p in parameters if p.grad is not None]\n norm_type = float(norm_type)\n if len(parameters) == 0:\n return torch.tensor(0.0)\n device = parameters[0].grad.device\n if norm_type == inf:\n total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)\n else:\n total_norm = torch.norm(\n torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]),\n norm_type,\n )\n return total_norm\n\n\nclass NativeScalerWithGradNormCount:\n \"\"\"\n A wrapper for torch.cuda.amp.GradScaler with gradient norm tracking.\n\n Args:\n enabled (bool): Whether to enable automatic mixed precision.\n \"\"\"\n\n state_dict_key = \"amp_scaler\"\n\n def __init__(self, enabled=True):\n self._scaler = torch.cuda.amp.GradScaler(enabled=enabled)\n\n def __call__(\n self,\n loss,\n optimizer,\n parameters,\n clip_grad=None,\n create_graph=False,\n update_grad=True,\n ):\n self._scaler.scale(loss).backward(create_graph=create_graph)\n norm = None\n if update_grad:\n self._scaler.unscale_(optimizer)\n if clip_grad is not None and clip_grad > 0.0:\n norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)\n else:\n norm = get_grad_norm_(parameters)\n self._scaler.step(optimizer)\n self._scaler.update()\n return norm\n\n def state_dict(self):\n \"\"\"Save state dictionary for the scaler.\"\"\"\n return self._scaler.state_dict()\n\n def load_state_dict(self, state_dict):\n \"\"\"Load state dictionary for the scaler.\"\"\"\n self._scaler.load_state_dict(state_dict)\n" }, { "path": "storm/visualization/annotation.py", "content": "\"\"\"\nFrom https://github.com/dcharatan/pixelsplat/blob/main/src/visualization/annotation.py\n\"\"\"\n\nfrom pathlib import Path\nfrom string import ascii_letters, digits, punctuation\n\nimport numpy as np\nimport torch\nfrom einops import rearrange\nfrom jaxtyping import Float\nfrom PIL import Image, ImageDraw, ImageFont\nfrom torch import Tensor\n\nfrom .layout import vcat\n\nEXPECTED_CHARACTERS = digits + punctuation + ascii_letters\n\n\ndef draw_label(\n text: str,\n font: Path,\n font_size: int,\n device: torch.device = torch.device(\"cpu\"),\n) -> Float[Tensor, \"3 height width\"]:\n \"\"\"Draw a black label on a white background with no border.\"\"\"\n try:\n font = ImageFont.truetype(str(font), font_size)\n except OSError:\n font = ImageFont.load_default()\n left, _, right, _ = font.getbbox(text)\n width = right - left\n _, top, _, bottom = font.getbbox(EXPECTED_CHARACTERS)\n height = bottom - top\n image = Image.new(\"RGB\", (width, height), color=\"white\")\n draw = ImageDraw.Draw(image)\n draw.text((0, 0), text, font=font, fill=\"black\")\n image = torch.tensor(np.array(image) / 255, dtype=torch.float32, device=device)\n return rearrange(image, \"h w c -> c h w\")\n\n\ndef add_label(\n image: Float[Tensor, \"3 width height\"],\n label: str,\n font: Path = Path(\"assets/Inter-Regular.otf\"),\n font_size: int = 24,\n align: str = \"center\",\n) -> Float[Tensor, \"3 width_with_label height_with_label\"]:\n return vcat(\n draw_label(label, font, font_size, image.device),\n image,\n align=align,\n gap=4,\n )\n" }, { "path": "storm/visualization/layout.py", "content": "\"\"\"\nFrom https://github.com/dcharatan/pixelsplat/blob/main/src/visualization/layout.py\nThis file contains useful layout utilities for images. They are:\n\n- add_border: Add a border to an image.\n- cat/hcat/vcat: Join images by arranging them in a line. If the images have different\n sizes, they are aligned as specified (start, end, center). Allows you to specify a gap\n between images.\n\nImages are assumed to be float32 tensors with shape (channel, height, width).\n\"\"\"\n\nfrom typing import Any, Generator, Iterable, Literal, Optional, Union\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom einops import rearrange, repeat\nfrom jaxtyping import Float, UInt8\nfrom torch import Tensor\n\nAlignment = Literal[\"start\", \"center\", \"end\"]\nAxis = Literal[\"horizontal\", \"vertical\"]\nColor = Union[\n int,\n float,\n Iterable[int],\n Iterable[float],\n Float[Tensor, \"#channel\"],\n Float[Tensor, \"\"],\n]\n\n\ndef _sanitize_color(color: Color) -> Float[Tensor, \"#channel\"]:\n # Convert tensor to list (or individual item).\n if isinstance(color, torch.Tensor):\n color = color.tolist()\n\n # Turn iterators and individual items into lists.\n if isinstance(color, Iterable):\n color = list(color)\n else:\n color = [color]\n\n return torch.tensor(color, dtype=torch.float32)\n\n\ndef _intersperse(iterable: Iterable, delimiter: Any) -> Generator[Any, None, None]:\n it = iter(iterable)\n yield next(it)\n for item in it:\n yield delimiter\n yield item\n\n\ndef _get_main_dim(main_axis: Axis) -> int:\n return {\n \"horizontal\": 2,\n \"vertical\": 1,\n }[main_axis]\n\n\ndef _get_cross_dim(main_axis: Axis) -> int:\n return {\n \"horizontal\": 1,\n \"vertical\": 2,\n }[main_axis]\n\n\ndef _compute_offset(base: int, overlay: int, align: Alignment) -> slice:\n assert base >= overlay\n offset = {\n \"start\": 0,\n \"center\": (base - overlay) // 2,\n \"end\": base - overlay,\n }[align]\n return slice(offset, offset + overlay)\n\n\ndef overlay(\n base: Float[Tensor, \"channel base_height base_width\"],\n overlay: Float[Tensor, \"channel overlay_height overlay_width\"],\n main_axis: Axis,\n main_axis_alignment: Alignment,\n cross_axis_alignment: Alignment,\n) -> Float[Tensor, \"channel base_height base_width\"]:\n # The overlay must be smaller than the base.\n _, base_height, base_width = base.shape\n _, overlay_height, overlay_width = overlay.shape\n assert base_height >= overlay_height and base_width >= overlay_width\n\n # Compute spacing on the main dimension.\n main_dim = _get_main_dim(main_axis)\n main_slice = _compute_offset(base.shape[main_dim], overlay.shape[main_dim], main_axis_alignment)\n\n # Compute spacing on the cross dimension.\n cross_dim = _get_cross_dim(main_axis)\n cross_slice = _compute_offset(\n base.shape[cross_dim], overlay.shape[cross_dim], cross_axis_alignment\n )\n\n # Combine the slices and paste the overlay onto the base accordingly.\n selector = [..., None, None]\n selector[main_dim] = main_slice\n selector[cross_dim] = cross_slice\n result = base.clone()\n result[selector] = overlay\n return result\n\n\ndef cat(\n main_axis: Axis,\n *images: Iterable[Float[Tensor, \"channel _ _\"]],\n align: Alignment = \"center\",\n gap: int = 8,\n gap_color: Color = 1,\n) -> Float[Tensor, \"channel height width\"]:\n \"\"\"Arrange images in a line. The interface resembles a CSS div with flexbox.\"\"\"\n device = images[0].device\n gap_color = _sanitize_color(gap_color).to(device)\n\n # Find the maximum image side length in the cross axis dimension.\n cross_dim = _get_cross_dim(main_axis)\n cross_axis_length = max(image.shape[cross_dim] for image in images)\n\n # Pad the images.\n padded_images = []\n for image in images:\n # Create an empty image with the correct size.\n padded_shape = list(image.shape)\n padded_shape[cross_dim] = cross_axis_length\n base = torch.ones(padded_shape, dtype=torch.float32, device=device)\n base = base * gap_color[:, None, None]\n padded_images.append(overlay(base, image, main_axis, \"start\", align))\n\n # Intersperse separators if necessary.\n if gap > 0:\n # Generate a separator.\n c, _, _ = images[0].shape\n separator_size = [gap, gap]\n separator_size[cross_dim - 1] = cross_axis_length\n separator = torch.ones((c, *separator_size), dtype=torch.float32, device=device)\n separator = separator * gap_color[:, None, None]\n\n # Intersperse the separator between the images.\n padded_images = list(_intersperse(padded_images, separator))\n\n return torch.cat(padded_images, dim=_get_main_dim(main_axis))\n\n\ndef hcat(\n *images: Iterable[Float[Tensor, \"channel _ _\"]],\n align: Literal[\"start\", \"center\", \"end\", \"top\", \"bottom\"] = \"start\",\n gap: int = 8,\n gap_color: Color = 1,\n):\n \"\"\"Shorthand for a horizontal linear concatenation.\"\"\"\n return cat(\n \"horizontal\",\n *images,\n align={\n \"start\": \"start\",\n \"center\": \"center\",\n \"end\": \"end\",\n \"top\": \"start\",\n \"bottom\": \"end\",\n }[align],\n gap=gap,\n gap_color=gap_color,\n )\n\n\ndef vcat(\n *images: Iterable[Float[Tensor, \"channel _ _\"]],\n align: Literal[\"start\", \"center\", \"end\", \"left\", \"right\"] = \"start\",\n gap: int = 8,\n gap_color: Color = 1,\n):\n \"\"\"Shorthand for a horizontal linear concatenation.\"\"\"\n return cat(\n \"vertical\",\n *images,\n align={\n \"start\": \"start\",\n \"center\": \"center\",\n \"end\": \"end\",\n \"left\": \"start\",\n \"right\": \"end\",\n }[align],\n gap=gap,\n gap_color=gap_color,\n )\n\n\ndef add_border(\n image: Float[Tensor, \"channel height width\"],\n border: int = 8,\n color: Color = 1,\n) -> Float[Tensor, \"channel new_height new_width\"]:\n color = _sanitize_color(color).to(image)\n c, h, w = image.shape\n result = torch.empty(\n (c, h + 2 * border, w + 2 * border), dtype=torch.float32, device=image.device\n )\n result[:] = color[:, None, None]\n result[:, border : h + border, border : w + border] = image\n return result\n\n\ndef resize(\n image: Float[Tensor, \"channel height width\"],\n shape: Optional[tuple[int, int]] = None,\n width: Optional[int] = None,\n height: Optional[int] = None,\n) -> Float[Tensor, \"channel new_height new_width\"]:\n assert (shape is not None) + (width is not None) + (height is not None) == 1\n _, h, w = image.shape\n\n if width is not None:\n shape = (int(h * width / w), width)\n elif height is not None:\n shape = (height, int(w * height / h))\n\n return F.interpolate(\n image[None],\n shape,\n mode=\"bilinear\",\n align_corners=False,\n antialias=\"bilinear\",\n )[0]\n\n\ndef prep_image(image: torch.Tensor) -> UInt8[np.ndarray, \"height width channel\"]:\n # Handle batched images.\n if image.ndim == 4:\n image = rearrange(image, \"b c h w -> c h (b w)\")\n\n # Handle single-channel images.\n if image.ndim == 2:\n image = rearrange(image, \"h w -> () h w\")\n\n # Ensure that there are 3 or 4 channels.\n channel, _, _ = image.shape\n if channel == 1:\n image = repeat(image, \"() h w -> c h w\", c=3)\n assert image.shape[0] in (3, 4)\n\n image = (image.detach().clip(min=0, max=1) * 255).type(torch.uint8)\n return rearrange(image, \"c h w -> h w c\").cpu().numpy()\n" }, { "path": "storm/visualization/video_maker.py", "content": "import logging\nimport time\n\nimport imageio\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom einops import rearrange\nfrom matplotlib import cm\n\nfrom storm.dataset.constants import MEAN, STD\nfrom storm.dataset.data_utils import (\n prepare_inputs_and_targets,\n prepare_inputs_and_targets_novel_view,\n to_batch_tensor,\n)\n\nfrom .annotation import add_label\nfrom .layout import add_border, hcat, prep_image, vcat\nfrom .visualization_tools import depth_visualizer, scene_flow_to_rgb\n\nlogger = logging.getLogger(\"STORM\")\n\n\ndef get_pca_map(x):\n # x: (...., c) channel last\n x_shape = x.shape\n x = x.view(-1, x.shape[-1])\n x = x @ torch.pca_lowrank(x, q=3, niter=20)[2]\n x = (x - x.min(dim=0)[0]) / (x.max(dim=0)[0] - x.min(dim=0)[0])\n return x.view(*x_shape[:-1], 3)\n\n\n@torch.no_grad()\ndef make_video(\n dataset,\n model,\n device,\n output_filename,\n scene_id=None,\n skip_plot_gt_depth_and_flow: bool = False,\n data_dict=None,\n input_dict=None,\n target_dict=None,\n pred_dict=None,\n):\n\n if data_dict is None:\n if scene_id is None:\n scene_id = np.random.randint(0, len(dataset))\n data_dict = dataset.__getitem__(scene_id, np.random.randint(10, 100), return_all=True)\n data_dict = to_batch_tensor(data_dict)\n input_dict, target_dict = prepare_inputs_and_targets(data_dict, device)\n model = model.eval()\n\n if pred_dict is None:\n with torch.autocast(device_type=device.type, dtype=torch.bfloat16):\n start_time = time.perf_counter()\n gs_params = model.get_gs_params(input_dict)\n end_time = time.perf_counter()\n logger.info(f\"Time taken to get gs_params: {end_time - start_time} seconds\")\n start_time = time.perf_counter()\n pred_dict = model.from_gs_params_to_output(gs_params, input_dict)\n end_time = time.perf_counter()\n logger.info(f\"Time taken to get rendered results: {end_time - start_time} seconds\")\n # pred_dict = model(input_dict)\n B, context_t, context_v, _, H, W = input_dict[\"context_image\"].shape\n _, target_t, target_v, _, H_tgt, W_tgt = target_dict[\"target_image\"].shape\n\n device = input_dict[\"context_image\"].device\n mean = torch.tensor([[MEAN]], device=device)\n std = torch.tensor([[STD]], device=device)\n\n def denormalize(x, already_channel_last=False):\n if not already_channel_last:\n x = rearrange(x, \"t v c h w -> t v h w c\")\n x = (x * std + mean).clamp(0.0, 1.0)\n return rearrange(x, \"t v h w c -> t v c h w\")\n\n # t, v, c, h, w\n context_images = input_dict[\"context_image\"][0]\n context_images = denormalize(context_images)\n\n if context_v <= 3:\n n_ctx_per_row = 2\n else:\n n_ctx_per_row = 1\n # concate context images horizontally\n context_frames = []\n for t in range(context_t):\n current_frame_idx = int(input_dict[\"context_frame_idx\"][0][t].item())\n row = add_label(\n hcat(*[context_images[t][v_id] for v_id in range(context_v)]),\n f\"Context RGB (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n context_frames.append(row)\n num_rows = max(1, len(context_frames) // n_ctx_per_row)\n context_frames = vcat(\n *[\n hcat(\n *context_frames[row * n_ctx_per_row : (row + 1) * n_ctx_per_row],\n gap=24,\n )\n for row in range(num_rows)\n ]\n )\n\n target_images = target_dict[\"target_image\"][0]\n target_images = denormalize(target_images)\n render_results = pred_dict[\"render_results\"]\n\n pred_images = render_results[render_results[\"rgb_key\"]][0]\n pred_images = denormalize(pred_images, already_channel_last=True)\n if \"rendered_motion_seg\" in render_results:\n # Get the max index (clusters) from the rendered results\n max_idx = render_results[\"rendered_motion_seg\"][0]\n\n # Identify unique clusters\n unique_clusters = torch.unique(max_idx)\n try:\n velocities = pred_dict[\"gs_params\"][\"motion_bases\"][0][unique_clusters]\n except:\n velocities = pred_dict[\"gs_params\"][\"motion_bases\"][0].mean(dim=0)[unique_clusters]\n velocity_norm = torch.norm(velocities, dim=-1)\n # Sort the unique clusters according to velocity norm (lowest first)\n sorted_indices = torch.argsort(velocity_norm)\n sorted_clusters = unique_clusters[sorted_indices]\n\n # Number of unique clusters\n num_unique_clusters = len(unique_clusters)\n\n # Create a new colormap based on the unique clusters\n cmap = cm.get_cmap(\"rainbow\", num_unique_clusters)\n\n # Map sorted unique clusters to new colors\n cluster_to_color_map = torch.tensor([cmap(i) for i in range(num_unique_clusters)])[\n :, :3\n ].to(max_idx.device)\n\n # Create a mapping from original clusters to the reassigned clusters\n cluster_mapping = torch.zeros_like(max_idx)\n\n # Map each pixel in max_idx to the new cluster index based on sorted clusters\n for new_cluster_idx, original_cluster in enumerate(sorted_clusters):\n cluster_mapping[max_idx == original_cluster] = new_cluster_idx\n\n # Assign the new colors to the cluster image\n cluster_image = cluster_to_color_map[cluster_mapping]\n if cluster_image.shape[-3] != H_tgt or cluster_image.shape[-2] != W_tgt:\n cluster_image = F.interpolate(\n rearrange(cluster_image, \"t v h w c -> (t v) c h w\"),\n size=(H_tgt, W_tgt),\n mode=\"nearest\",\n )\n cluster_image = rearrange(\n cluster_image, \"(t v) c h w -> t v h w c\", t=target_t, v=target_v\n )\n else:\n cluster_image = None\n\n video_frames = []\n for t in range(target_t):\n frame_list = []\n current_frame_idx = int(target_dict[\"target_frame_idx\"][0][t].item())\n pred_rgb = add_label(\n hcat(*[pred_images[t][v_id] for v_id in range(target_v)]),\n f\"Predicted RGB (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(pred_rgb)\n\n gt_rgb = add_label(\n hcat(*[target_images[t][v_id] for v_id in range(target_v)]),\n f\"Target GT RGB (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(gt_rgb)\n\n if render_results[\"decoder_depth_key\"] is not None:\n # this is a decoder depth map\n depth_image = render_results[render_results[\"decoder_depth_key\"]][0][t]\n alpha_image = None\n depth_image = depth_image.detach().cpu().numpy()\n\n depth_image = depth_visualizer(depth_image, alpha_image)\n depth_image = torch.from_numpy(depth_image)\n depth_image = rearrange(depth_image, \"v h w c -> v c h w\")\n pred_depth = add_label(\n hcat(*depth_image),\n f\"Predicted Decoder Depth (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(pred_depth)\n\n if render_results[\"depth_key\"] is not None:\n # actual gs depth map\n depth_image = render_results[render_results[\"depth_key\"]][0][t]\n alpha_image = render_results[render_results[\"alpha_key\"]][0][t]\n if depth_image.shape[-2] != H_tgt or depth_image.shape[-1] != W_tgt:\n depth_image = F.interpolate(\n depth_image.unsqueeze(-3),\n size=(H_tgt, W_tgt),\n mode=\"bilinear\",\n align_corners=False,\n ).squeeze(-3)\n alpha_image = F.interpolate(\n alpha_image.unsqueeze(-3),\n size=(H_tgt, W_tgt),\n mode=\"bilinear\",\n align_corners=False,\n ).squeeze(-3)\n depth_image = depth_image.detach().cpu().numpy()\n alpha_image = alpha_image.detach().cpu().numpy()\n depth_image = depth_visualizer(depth_image, alpha_image)\n depth_image = torch.from_numpy(depth_image)\n depth_image = rearrange(depth_image, \"v h w c -> v c h w\")\n pred_depth = add_label(\n hcat(*depth_image),\n f\"Predicted Depth (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(pred_depth)\n\n if \"target_depth\" in target_dict.keys():\n gt_depth = target_dict[\"target_depth\"][0][t]\n gt_depth = gt_depth.detach().cpu().numpy()\n gt_depth = depth_visualizer(gt_depth, gt_depth > 0)\n gt_depth = torch.from_numpy(gt_depth)\n gt_depth = rearrange(gt_depth, \"v h w c -> v c h w\")\n gt_depth = add_label(\n hcat(*gt_depth),\n f\"Target GT Depth (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(gt_depth)\n else:\n if not skip_plot_gt_depth_and_flow:\n gt_depth = torch.full((target_v, 3, H_tgt, W_tgt), 0.5)\n gt_depth = add_label(\n hcat(*gt_depth),\n f\"Target GT Depth (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(gt_depth)\n\n if render_results[\"flow_key\"] is not None:\n flow_image = render_results[render_results[\"flow_key\"]][0][t]\n flow_image = scene_flow_to_rgb(flow_image, flow_max_radius=15)\n flow_image = rearrange(flow_image, \"v h w c -> v c h w\")\n if flow_image.shape[-2] != H_tgt or flow_image.shape[-1] != W_tgt:\n flow_image = F.interpolate(\n flow_image,\n size=(H_tgt, W_tgt),\n mode=\"bilinear\",\n align_corners=False,\n )\n flow_image = add_label(\n hcat(*flow_image),\n f\"Predicted Flow (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(flow_image)\n\n if \"target_flow\" in target_dict.keys():\n gt_flow = target_dict[\"target_flow\"][0][t]\n gt_flow = scene_flow_to_rgb(gt_flow, flow_max_radius=15)\n gt_flow = rearrange(gt_flow, \"v h w c -> v c h w\")\n gt_flow = add_label(\n hcat(*gt_flow),\n f\"Target GT Flow (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(gt_flow)\n if render_results[\"depth_key\"] is not None:\n alpha_image = torch.from_numpy(alpha_image).unsqueeze(1)\n alpha_image = alpha_image.repeat(1, 3, 1, 1)\n alpha_image = add_label(\n hcat(*alpha_image),\n f\"Predicted Opacity (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(alpha_image)\n if \"target_sky_masks\" in target_dict.keys():\n sky_mask = target_dict[\"target_sky_masks\"][0][t].unsqueeze(1)\n sky_mask = sky_mask.repeat(1, 3, 1, 1)\n sky_mask = add_label(\n hcat(*sky_mask),\n f\"GT Sky Mask (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(sky_mask)\n if cluster_image is not None:\n cluster_image_t = cluster_image[t]\n cluster_image_t = rearrange(cluster_image_t, \"v h w c -> v c h w\")\n cluster_image_t = add_label(\n hcat(*cluster_image_t),\n f\"Motion Segmentation (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(cluster_image_t)\n\n num_rows = len(frame_list) // n_ctx_per_row\n frame = vcat(\n context_frames,\n vcat(\n *[\n hcat(\n *frame_list[row * n_ctx_per_row : (row + 1) * n_ctx_per_row],\n gap=24,\n )\n for row in range(num_rows)\n ]\n ),\n )\n # if there's a residual, we add it to the end\n if len(frame_list) % n_ctx_per_row != 0:\n frame = vcat(\n frame,\n hcat(\n *frame_list[num_rows * n_ctx_per_row :],\n gap=24,\n ),\n )\n\n frame = add_border(\n add_label(\n frame,\n f\"Scene{input_dict['scene_id']:03d}-{input_dict['scene_name'][:15]}\",\n font_size=24,\n align=\"center\",\n )\n )\n video_frames.append(prep_image(frame))\n video_frame_reversed = video_frames[::-1][1:-1]\n video_frames.extend(video_frame_reversed)\n imageio.mimsave(output_filename, video_frames, fps=data_dict[\"fps\"])\n\n\n@torch.no_grad()\ndef make_video_vis(\n dataset,\n model,\n device,\n output_filename,\n scene_id=None,\n data_dict=None,\n args=None,\n input_dict=None,\n target_dict=None,\n pred_dict=None,\n time_step=10,\n):\n data_dict = dataset.__getitem__(scene_id, time_step, return_all=True)\n data_dict = to_batch_tensor(data_dict)\n input_dict, target_dict = prepare_inputs_and_targets(\n # input_dict, target_dict = prepare_inputs_and_targets_novel_view(\n data_dict,\n device,\n )\n model = model.eval()\n pred_dict = model(input_dict)\n B, context_t, context_v, _, H, W = input_dict[\"context_image\"].shape\n _, target_t, target_v, _, H_tgt, W_tgt = target_dict[\"target_image\"].shape\n\n device = input_dict[\"context_image\"].device\n mean = torch.tensor([[MEAN]], device=device)\n std = torch.tensor([[STD]], device=device)\n\n def denormalize(x, already_channel_last=False):\n if not already_channel_last:\n x = rearrange(x, \"t v c h w -> t v h w c\")\n x = (x * std + mean).clamp(0.0, 1.0)\n return rearrange(x, \"t v h w c -> t v c h w\")\n\n # t, v, c, h, w\n context_images = input_dict[\"context_image\"][0]\n context_images = denormalize(context_images)\n\n if context_v <= 3:\n n_ctx_per_row = 2\n else:\n n_ctx_per_row = 1\n # concate context images horizontally\n context_frames = []\n for t in range(context_t):\n current_frame_idx = int(input_dict[\"context_frame_idx\"][0][t].item())\n row = add_label(\n hcat(*[context_images[t][v_id] for v_id in range(context_v)]),\n f\"Context RGB (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n context_frames.append(row)\n num_rows = max(1, len(context_frames) // n_ctx_per_row)\n context_frames = vcat(\n *[\n hcat(\n *context_frames[row * n_ctx_per_row : (row + 1) * n_ctx_per_row],\n gap=24,\n )\n for row in range(num_rows)\n ]\n )\n\n target_images = target_dict[\"target_image\"][0]\n target_images = denormalize(target_images)\n render_results = pred_dict[\"render_results\"]\n\n pred_images = render_results[render_results[\"rgb_key\"]][0]\n pred_images = denormalize(pred_images, already_channel_last=True)\n if \"rendered_motion_seg\" in render_results:\n # Get the max index (clusters) from the rendered results\n max_idx = render_results[\"rendered_motion_seg\"][0]\n\n # Identify unique clusters\n unique_clusters = torch.unique(max_idx)\n velocities = pred_dict[\"gs_params\"][\"motion_bases\"][0][unique_clusters]\n velocity_norm = torch.norm(velocities, dim=-1)\n # Sort the unique clusters according to velocity norm (lowest first)\n sorted_indices = torch.argsort(velocity_norm)\n sorted_clusters = unique_clusters[sorted_indices]\n\n # Number of unique clusters\n num_unique_clusters = len(sorted_clusters)\n\n # Create a new colormap based on the sorted unique clusters\n cmap = cm.get_cmap(\"rainbow\", num_unique_clusters)\n\n # Map sorted unique clusters to new colors\n cluster_to_color_map = torch.tensor([cmap(i) for i in range(num_unique_clusters)])[\n :, :3\n ].to(max_idx.device)\n\n # Create a mapping from original clusters to the reassigned clusters\n cluster_mapping = torch.zeros_like(max_idx)\n\n # Map each pixel in max_idx to the new cluster index based on sorted clusters\n for new_cluster_idx, original_cluster in enumerate(sorted_clusters):\n cluster_mapping[max_idx == original_cluster] = new_cluster_idx\n\n # Assign the new colors to the cluster image\n cluster_image = cluster_to_color_map[cluster_mapping]\n if cluster_image.shape[-3] != H_tgt or cluster_image.shape[-2] != W_tgt:\n cluster_image = F.interpolate(\n rearrange(cluster_image, \"t v h w c -> (t v) c h w\"),\n size=(H_tgt, W_tgt),\n mode=\"nearest\",\n )\n cluster_image = rearrange(\n cluster_image, \"(t v) c h w -> t v h w c\", t=target_t, v=target_v\n )\n else:\n cluster_image = None\n\n video_frames = []\n for t in range(target_t):\n frame_list = []\n current_frame_idx = int(target_dict[\"target_frame_idx\"][0][t].item())\n pred_rgb = add_label(\n hcat(*[pred_images[t][v_id] for v_id in range(target_v)]),\n f\"Predicted RGB (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(pred_rgb)\n\n gt_rgb = add_label(\n hcat(*[target_images[t][v_id] for v_id in range(target_v)]),\n f\"Target GT RGB (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(gt_rgb)\n\n if render_results[\"decoder_depth_key\"] is not None:\n # this is a decoder depth map\n depth_image = render_results[render_results[\"decoder_depth_key\"]][0][t]\n alpha_image = None\n depth_image = depth_image.detach().cpu().numpy()\n\n depth_image = depth_visualizer(depth_image, alpha_image)\n depth_image = torch.from_numpy(depth_image)\n depth_image = rearrange(depth_image, \"v h w c -> v c h w\")\n pred_depth = add_label(\n hcat(*depth_image),\n f\"Predicted Decoder Depth (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(pred_depth)\n\n if render_results[\"depth_key\"] is not None:\n # actual gs depth map\n depth_image = render_results[render_results[\"depth_key\"]][0][t]\n alpha_image = render_results[render_results[\"alpha_key\"]][0][t]\n if depth_image.shape[-2] != H_tgt or depth_image.shape[-1] != W_tgt:\n depth_image = F.interpolate(\n depth_image.unsqueeze(-3),\n size=(H_tgt, W_tgt),\n mode=\"bilinear\",\n align_corners=False,\n ).squeeze(-3)\n alpha_image = F.interpolate(\n alpha_image.unsqueeze(-3),\n size=(H_tgt, W_tgt),\n mode=\"bilinear\",\n align_corners=False,\n ).squeeze(-3)\n depth_image = depth_image.detach().cpu().numpy()\n alpha_image = alpha_image.detach().cpu().numpy()\n depth_image = depth_visualizer(depth_image, alpha_image)\n depth_image = torch.from_numpy(depth_image)\n depth_image = rearrange(depth_image, \"v h w c -> v c h w\")\n pred_depth = add_label(\n hcat(*depth_image),\n f\"Predicted Depth (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(pred_depth)\n\n if render_results[\"depth_key\"] is not None:\n alpha_image = torch.from_numpy(alpha_image).unsqueeze(1)\n alpha_image = alpha_image.repeat(1, 3, 1, 1)\n alpha_image = add_label(\n hcat(*alpha_image),\n f\"Predicted Opacity (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(alpha_image)\n\n if render_results[\"flow_key\"] is not None:\n flow_image = render_results[render_results[\"flow_key\"]][0][t]\n flow_image = scene_flow_to_rgb(flow_image, flow_max_radius=15)\n flow_image = rearrange(flow_image, \"v h w c -> v c h w\")\n if flow_image.shape[-2] != H_tgt or flow_image.shape[-1] != W_tgt:\n flow_image = F.interpolate(\n flow_image,\n size=(H_tgt, W_tgt),\n mode=\"bilinear\",\n align_corners=False,\n )\n flow_image = add_label(\n hcat(*flow_image),\n f\"Predicted Flow (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(flow_image)\n\n if \"target_flow\" in target_dict.keys():\n gt_flow = target_dict[\"target_flow\"][0][t]\n gt_flow = scene_flow_to_rgb(gt_flow, flow_max_radius=15)\n gt_flow = rearrange(gt_flow, \"v h w c -> v c h w\")\n gt_flow = add_label(\n hcat(*gt_flow),\n f\"GT Flow (t={current_frame_idx}) (Not used as supervision)\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(gt_flow)\n\n if cluster_image is not None:\n cluster_image_t = cluster_image[t]\n cluster_image_t = rearrange(cluster_image_t, \"v h w c -> v c h w\")\n cluster_image_t = add_label(\n hcat(*cluster_image_t),\n f\"Motion Segmentation (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(cluster_image_t)\n\n num_rows = len(frame_list) // n_ctx_per_row\n frame = vcat(\n context_frames,\n vcat(\n *[\n hcat(\n *frame_list[row * n_ctx_per_row : (row + 1) * n_ctx_per_row],\n gap=24,\n )\n for row in range(num_rows)\n ]\n ),\n )\n # if there's a residual, we add it to the end\n if len(frame_list) % n_ctx_per_row != 0:\n frame = vcat(\n frame,\n hcat(\n *frame_list[num_rows * n_ctx_per_row :],\n gap=24,\n ),\n )\n\n frame = add_border(\n add_label(\n frame,\n f\"Scene{input_dict['scene_id']:03d}-{input_dict['scene_name'][:15]}\",\n font_size=24,\n align=\"center\",\n )\n )\n video_frames.append(prep_image(frame))\n video_frame_reversed = video_frames[::-1][1:-1]\n video_frames.extend(video_frame_reversed)\n imageio.mimsave(output_filename, video_frames, fps=data_dict[\"fps\"])\n\n\n@torch.no_grad()\ndef make_video_av2(\n dataset,\n model,\n device,\n output_filename,\n scene_id=None,\n skip_plot_gt_depth_and_flow: bool = False,\n):\n if scene_id is None:\n scene_id = np.random.randint(0, len(dataset))\n data_dict = dataset.__getitem__(scene_id, 10, return_all=True)\n data_dict = to_batch_tensor(data_dict)\n input_dict, target_dict = prepare_inputs_and_targets(\n data_dict,\n device,\n )\n\n with torch.no_grad():\n pred_dict = model(input_dict)\n B, context_t, context_v, _, H, W = input_dict[\"context_image\"].shape\n _, target_t, target_v, _, H_tgt, W_tgt = target_dict[\"target_image\"].shape\n\n device = input_dict[\"context_image\"].device\n mean = torch.tensor([[MEAN]], device=device)\n std = torch.tensor([[STD]], device=device)\n\n def denormalize(x, already_channel_last=False):\n if not already_channel_last:\n x = rearrange(x, \"t v c h w -> t v h w c\")\n x = (x * std + mean).clamp(0.0, 1.0)\n return rearrange(x, \"t v h w c -> t v c h w\")\n\n # t, v, c, h, w\n context_images = input_dict[\"context_image\"][0]\n context_images = denormalize(context_images)\n\n def resize(input, size, mode=\"bilinear\"):\n if len(input.shape) == 3:\n input = input.unsqueeze(0)\n\n elif len(input.shape) == 2:\n input = input.unsqueeze(0).unsqueeze(0)\n output = F.interpolate(input, size=size, mode=mode, align_corners=False)\n return output.squeeze()\n\n reduct_mat = None\n if context_v <= 3:\n n_ctx_per_row = 2\n else:\n n_ctx_per_row = 1\n # concate context images horizontally\n context_frames = []\n for t in range(context_t):\n current_frame_idx = int(input_dict[\"context_frame_idx\"][0][t].item())\n row = add_label(\n hcat(\n *[\n (\n context_images[t][v_id]\n if v_id != context_v // 2\n else resize(context_images[t][v_id], (W, H))\n )\n for v_id in range(context_v)\n ],\n align=\"bottom\",\n ),\n f\"Context RGB (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n context_frames.append(row)\n num_rows = max(1, len(context_frames) // n_ctx_per_row)\n context_frames = vcat(\n *[\n hcat(\n *context_frames[row * n_ctx_per_row : (row + 1) * n_ctx_per_row],\n gap=24,\n )\n for row in range(num_rows)\n ]\n )\n\n target_images = target_dict[\"target_image\"][0]\n target_images = denormalize(target_images)\n render_results = pred_dict[\"render_results\"]\n\n pred_images = render_results[render_results[\"rgb_key\"]][0]\n pred_images = denormalize(pred_images, already_channel_last=True)\n video_frames = []\n for t in range(target_t):\n frame_list = []\n current_frame_idx = int(target_dict[\"target_frame_idx\"][0][t].item())\n pred_rgb = add_label(\n hcat(\n *[\n (\n pred_images[t][v_id]\n if v_id != target_v // 2\n else resize(pred_images[t][v_id], (W, H))\n )\n for v_id in range(target_v)\n ],\n align=\"bottom\",\n ),\n # hcat(*[pred_images[t][v_id] for v_id in range(target_v)]),\n f\"Predicted RGB (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(pred_rgb)\n\n gt_rgb = add_label(\n hcat(\n *[\n (\n target_images[t][v_id]\n if v_id != target_v // 2\n else resize(target_images[t][v_id], (W, H))\n )\n for v_id in range(target_v)\n ],\n align=\"bottom\",\n ),\n # hcat(*[target_images[t][v_id] for v_id in range(target_v)]),\n f\"Target GT RGB (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(gt_rgb)\n\n if render_results[\"decoder_depth_key\"] is not None:\n # this is a decoder depth map\n depth_image = render_results[render_results[\"decoder_depth_key\"]][0][t]\n alpha_image = None\n depth_image = depth_image.detach().cpu().numpy()\n\n depth_image = depth_visualizer(depth_image, alpha_image)\n depth_image = torch.from_numpy(depth_image)\n depth_image = rearrange(depth_image, \"v h w c -> v c h w\")\n pred_depth = add_label(\n hcat(\n *[\n (\n depth_image[v_id]\n if v_id != target_v // 2\n else resize(depth_image[v_id], (W, H))\n )\n for v_id in range(target_v)\n ],\n align=\"bottom\",\n ),\n # hcat(*depth_image),\n f\"Predicted Decoder Depth (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(pred_depth)\n\n if render_results[\"depth_key\"] is not None:\n # actual gs depth map\n depth_image = render_results[render_results[\"depth_key\"]][0][t]\n alpha_image = render_results[render_results[\"alpha_key\"]][0][t]\n if depth_image.shape[-2] != H_tgt or depth_image.shape[-1] != W_tgt:\n depth_image = F.interpolate(\n depth_image.unsqueeze(-3),\n size=(H_tgt, W_tgt),\n mode=\"bilinear\",\n align_corners=False,\n ).squeeze(-3)\n alpha_image = F.interpolate(\n alpha_image.unsqueeze(-3),\n size=(H_tgt, W_tgt),\n mode=\"bilinear\",\n align_corners=False,\n ).squeeze(-3)\n depth_image = depth_image.detach().cpu().numpy()\n alpha_image = alpha_image.detach().cpu().numpy()\n depth_image = depth_visualizer(depth_image, alpha_image)\n depth_image = torch.from_numpy(depth_image)\n depth_image = rearrange(depth_image, \"v h w c -> v c h w\")\n pred_depth = add_label(\n hcat(\n *[\n (\n depth_image[v_id]\n if v_id != target_v // 2\n else resize(depth_image[v_id], (W, H))\n )\n for v_id in range(target_v)\n ],\n align=\"bottom\",\n ),\n # hcat(*depth_image),\n f\"Predicted Depth (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(pred_depth)\n\n if \"target_depth\" in target_dict.keys():\n gt_depth = target_dict[\"target_depth\"][0][t]\n gt_depth = gt_depth.detach().cpu().numpy()\n gt_depth = depth_visualizer(gt_depth, gt_depth > 0)\n gt_depth = torch.from_numpy(gt_depth)\n gt_depth = rearrange(gt_depth, \"v h w c -> v c h w\")\n gt_depth = add_label(\n hcat(\n *[\n (\n gt_depth[v_id]\n if v_id != target_v // 2\n else resize(gt_depth[v_id], (W, H))\n )\n for v_id in range(target_v)\n ],\n align=\"bottom\",\n ),\n # hcat(*gt_depth),\n f\"Target GT Depth (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(gt_depth)\n else:\n if not skip_plot_gt_depth_and_flow:\n gt_depth = torch.full((target_v, 3, H_tgt, W_tgt), 0.5)\n gt_depth = add_label(\n hcat(\n *[\n (\n gt_depth[v_id]\n if v_id != target_v // 2\n else resize(gt_depth[v_id], (W, H))\n )\n for v_id in range(target_v)\n ],\n align=\"bottom\",\n ),\n # hcat(*gt_depth),\n f\"Target GT Depth (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(gt_depth)\n\n if render_results[\"flow_key\"] is not None:\n flow_image = render_results[render_results[\"flow_key\"]][0][t]\n flow_image = scene_flow_to_rgb(flow_image, flow_max_radius=15)\n flow_image = rearrange(flow_image, \"v h w c -> v c h w\")\n if flow_image.shape[-2] != H_tgt or flow_image.shape[-1] != W_tgt:\n flow_image = F.interpolate(\n flow_image,\n size=(H_tgt, W_tgt),\n mode=\"bilinear\",\n align_corners=False,\n )\n flow_image = add_label(\n hcat(\n *[\n (\n flow_image[v_id]\n if v_id != target_v // 2\n else resize(flow_image[v_id], (W, H))\n )\n for v_id in range(target_v)\n ],\n align=\"bottom\",\n ),\n # hcat(*flow_image),\n f\"Predicted Flow (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(flow_image)\n\n if \"target_flow\" in target_dict.keys():\n gt_flow = target_dict[\"target_flow\"][0][t]\n gt_flow = scene_flow_to_rgb(gt_flow, flow_max_radius=15)\n gt_flow = rearrange(gt_flow, \"v h w c -> v c h w\")\n gt_flow = add_label(\n hcat(\n *[\n (\n gt_flow[v_id]\n if v_id != target_v // 2\n else resize(gt_flow[v_id], (W, H))\n )\n for v_id in range(target_v)\n ],\n align=\"bottom\",\n ),\n # hcat(*gt_flow),\n f\"Target GT Flow (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(gt_flow)\n if render_results[\"depth_key\"] is not None:\n alpha_image = torch.from_numpy(alpha_image).unsqueeze(1)\n alpha_image = alpha_image.repeat(1, 3, 1, 1)\n alpha_image = add_label(\n hcat(\n *[\n (\n alpha_image[v_id]\n if v_id != target_v // 2\n else resize(alpha_image[v_id], (W, H))\n )\n for v_id in range(target_v)\n ],\n align=\"bottom\",\n ),\n # hcat(*alpha_image),\n f\"Predicted Opacity (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(alpha_image)\n if \"target_sky_masks\" in target_dict.keys():\n sky_mask = target_dict[\"target_sky_masks\"][0][t].unsqueeze(1)\n sky_mask = sky_mask.repeat(1, 3, 1, 1)\n sky_mask = add_label(\n hcat(\n *[\n (\n sky_mask[v_id]\n if v_id != target_v // 2\n else resize(sky_mask[v_id], (W, H))\n )\n for v_id in range(target_v)\n ],\n align=\"bottom\",\n ),\n # hcat(*sky_mask),\n f\"GT Sky&/Road Mask (t={current_frame_idx})\",\n font_size=24,\n align=\"center\",\n )\n frame_list.append(sky_mask)\n num_rows = len(frame_list) // n_ctx_per_row\n frame = vcat(\n context_frames,\n vcat(\n *[\n hcat(\n *frame_list[row * n_ctx_per_row : (row + 1) * n_ctx_per_row],\n gap=24,\n )\n for row in range(num_rows)\n ]\n ),\n )\n # if there's a residual, we add it to the end\n if len(frame_list) % n_ctx_per_row != 0:\n frame = vcat(\n frame,\n hcat(\n *frame_list[num_rows * n_ctx_per_row :],\n gap=24,\n ),\n )\n\n frame = add_border(\n add_label(\n frame,\n f\"Scene{input_dict['scene_id']:03d}-{input_dict['scene_name'][:15]}\",\n font_size=24,\n align=\"center\",\n )\n )\n video_frames.append(prep_image(frame))\n video_frame_reversed = video_frames[::-1][1:-1]\n video_frames.extend(video_frame_reversed)\n imageio.mimsave(output_filename, video_frames, fps=data_dict[\"fps\"])\n return output_filename\n" }, { "path": "storm/visualization/visualization_tools.py", "content": "import logging\nimport os\nfrom collections import namedtuple\nfrom itertools import accumulate\nfrom typing import Optional, Union\n\nimport matplotlib.cm as cm\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom torch import Tensor\n\nDEFAULT_TRANSITIONS = (15, 6, 4, 11, 13, 6)\n\nlogger = logging.getLogger(\"STORM\")\nturbo_cmap = cm.get_cmap(\"turbo\")\n\ndepth_visualizer = lambda frame, opacity: visualize_depth(\n frame,\n opacity,\n lo=4.0,\n hi=120,\n depth_curve_fn=lambda x: -np.log(x + 1e-6),\n)\n\nflow_visualizer = (\n lambda frame: scene_flow_to_rgb(\n frame,\n background=\"bright\",\n flow_max_radius=1.0,\n )\n .cpu()\n .numpy()\n)\n\n\ndef to8b(x):\n if isinstance(x, torch.Tensor):\n x = x.detach().cpu().numpy()\n return (255 * np.clip(x, 0, 1)).astype(np.uint8)\n\n\ndef sinebow(h):\n \"\"\"A cyclic and uniform colormap, see http://basecase.org/env/on-rainbows.\"\"\"\n f = lambda x: np.sin(np.pi * x) ** 2\n return np.stack([f(3 / 6 - h), f(5 / 6 - h), f(7 / 6 - h)], -1)\n\n\ndef matte(vis, acc, dark=0.8, light=1.0, width=8):\n \"\"\"Set non-accumulated pixels to a Photoshop-esque checker pattern.\"\"\"\n bg_mask = np.logical_xor(\n (np.arange(acc.shape[0]) % (2 * width) // width)[:, None],\n (np.arange(acc.shape[1]) % (2 * width) // width)[None, :],\n )\n bg = np.where(bg_mask, light, dark)\n return vis * acc[:, :, None] + (bg * (1 - acc))[:, :, None]\n\n\ndef weighted_percentile(x, w, ps, assume_sorted=False):\n \"\"\"Compute the weighted percentile(s) of a single vector.\"\"\"\n x = x.reshape([-1])\n w = w.reshape([-1])\n if not assume_sorted:\n sortidx = np.argsort(x)\n x, w = x[sortidx], w[sortidx]\n acc_w = np.cumsum(w)\n return np.interp(np.array(ps) * (acc_w[-1] / 100), acc_w, x)\n\n\ndef visualize_cmap(\n value,\n weight,\n colormap,\n lo=None,\n hi=None,\n percentile=99.0,\n curve_fn=lambda x: x,\n modulus=None,\n matte_background=True,\n):\n \"\"\"Visualize a 1D image and a 1D weighting according to some colormap.\n from mipnerf\n\n Args:\n value: A 1D image.\n weight: A weight map, in [0, 1].\n colormap: A colormap function.\n lo: The lower bound to use when rendering, if None then use a percentile.\n hi: The upper bound to use when rendering, if None then use a percentile.\n percentile: What percentile of the value map to crop to when automatically\n generating `lo` and `hi`. Depends on `weight` as well as `value'.\n curve_fn: A curve function that gets applied to `value`, `lo`, and `hi`\n before the rest of visualization. Good choices: x, 1/(x+eps), log(x+eps).\n modulus: If not None, mod the normalized value by `modulus`. Use (0, 1]. If\n `modulus` is not None, `lo`, `hi` and `percentile` will have no effect.\n matte_background: If True, matte the image over a checkerboard.\n\n Returns:\n A colormap rendering.\n \"\"\"\n # Identify the values that bound the middle of `value' according to `weight`.\n if lo is None or hi is None:\n lo_auto, hi_auto = weighted_percentile(\n value, weight, [50 - percentile / 2, 50 + percentile / 2]\n )\n # If `lo` or `hi` are None, use the automatically-computed bounds above.\n eps = np.finfo(np.float32).eps\n lo = lo or (lo_auto - eps)\n hi = hi or (hi_auto + eps)\n\n # Curve all values.\n value, lo, hi = [curve_fn(x) for x in [value, lo, hi]]\n\n # Wrap the values around if requested.\n if modulus:\n value = np.mod(value, modulus) / modulus\n else:\n # Otherwise, just scale to [0, 1].\n value = np.nan_to_num(np.clip((value - np.minimum(lo, hi)) / np.abs(hi - lo), 0, 1))\n if weight is not None:\n value *= weight\n else:\n weight = np.ones_like(value)\n if colormap:\n colorized = colormap(value)[..., :3]\n else:\n assert len(value.shape) == 3 and value.shape[-1] == 3\n colorized = value\n\n return matte(colorized, weight) if matte_background else colorized\n\n\ndef visualize_depth(x, acc=None, lo=None, hi=None, depth_curve_fn=lambda x: -np.log(x + 1e-6)):\n \"\"\"Visualizes depth maps.\"\"\"\n return visualize_cmap(\n x,\n acc,\n cm.get_cmap(\"turbo\"),\n curve_fn=depth_curve_fn,\n lo=lo,\n hi=hi,\n matte_background=False,\n )\n\n\ndef _make_colorwheel(\n transitions: tuple = DEFAULT_TRANSITIONS, backend=\"torch\"\n) -> Union[np.ndarray, torch.Tensor]:\n \"\"\"Creates a colorwheel (borrowed/modified from flowpy).\n A colorwheel defines the transitions between the six primary hues:\n Red(255, 0, 0), Yellow(255, 255, 0), Green(0, 255, 0), Cyan(0, 255, 255), Blue(0, 0, 255) and Magenta(255, 0, 255).\n Args:\n transitions: Contains the length of the six transitions, based on human color perception.\n Returns:\n colorwheel: The RGB values of the transitions in the color space.\n Notes:\n For more information, see:\n https://web.archive.org/web/20051107102013/http://members.shaw.ca/quadibloc/other/colint.htm\n http://vision.middlebury.edu/flow/flowEval-iccv07.pdf\n \"\"\"\n colorwheel_length = sum(transitions)\n # The red hue is repeated to make the colorwheel cyclic\n base_hues = map(\n np.array,\n (\n [255, 0, 0],\n [255, 255, 0],\n [0, 255, 0],\n [0, 255, 255],\n [0, 0, 255],\n [255, 0, 255],\n [255, 0, 0],\n ),\n )\n colorwheel = np.zeros((colorwheel_length, 3), dtype=\"uint8\")\n hue_from = next(base_hues)\n start_index = 0\n for hue_to, end_index in zip(base_hues, accumulate(transitions)):\n transition_length = end_index - start_index\n colorwheel[start_index:end_index] = np.linspace(\n hue_from, hue_to, transition_length, endpoint=False\n )\n hue_from = hue_to\n start_index = end_index\n if backend == \"torch\":\n return torch.FloatTensor(colorwheel)\n else:\n return colorwheel\n\n\nWHEEL = _make_colorwheel()\nN_COLS = len(WHEEL)\nWHEEL = torch.vstack((WHEEL, WHEEL[0])) # Make the wheel cyclic for interpolation\n\n\ndef scene_flow_to_rgb(\n flow: torch.Tensor,\n flow_max_radius: Optional[float] = None,\n background: Optional[str] = \"bright\",\n) -> Union[torch.Tensor, np.ndarray]:\n \"\"\"Creates a RGB representation of an optical flow (borrowed/modified from flowpy).\n Adapted from https://github.com/Lilac-Lee/Neural_Scene_Flow_Prior/blob/main/visualize.py\n Args:\n flow: scene flow.\n flow[..., 0] should be the x-displacement\n flow[..., 1] should be the y-displacement\n flow[..., 2] should be the z-displacement\n flow_max_radius: Set the radius that gives the maximum color intensity, useful for comparing different flows.\n Default: The normalization is based on the input flow maximum radius.\n background: States if zero-valued flow should look 'bright' or 'dark'.\n Returns: An array of RGB colors.\n \"\"\"\n valid_backgrounds = (\"bright\", \"dark\")\n if background not in valid_backgrounds:\n raise ValueError(\n f\"background should be one the following: {valid_backgrounds}, not {background}.\"\n )\n if isinstance(flow, np.ndarray):\n backend = \"np\"\n op = np\n else:\n backend = \"torch\"\n op = torch\n\n # For scene flow, it's reasonable to assume displacements in x and y directions only for visualization pursposes.\n complex_flow = flow[..., 0] + 1j * flow[..., 1]\n radius, angle = op.abs(complex_flow), op.angle(complex_flow)\n if flow_max_radius is None:\n # flow_max_radius = torch.max(radius)\n flow_max_radius = op.quantile(radius, 0.99)\n if flow_max_radius > 0:\n radius /= flow_max_radius\n # Map the angles from (-pi, pi] to [0, 2pi) to [0, ncols - 1)\n wheel = _make_colorwheel(backend=backend)\n n_cols = len(wheel)\n wheel = op.vstack((wheel, wheel[0]))\n angle[angle < 0] += 2 * np.pi\n angle = angle * ((n_cols - 1) / (2 * np.pi))\n\n # Interpolate the hues\n angle_fractional, angle_floor, angle_ceil = (\n op.fmod(angle, 1),\n op.trunc(angle),\n op.ceil(angle),\n )\n angle_fractional = angle_fractional[..., None]\n if backend == \"torch\":\n _wheel = wheel.to(angle_floor.device)\n float_hue = (\n _wheel[angle_floor.long()] * (1 - angle_fractional)\n + _wheel[angle_ceil.long()] * angle_fractional\n )\n else:\n float_hue = (\n wheel[angle_floor.astype(op.int64)] * (1 - angle_fractional)\n + wheel[angle_ceil.astype(op.int64)] * angle_fractional\n )\n ColorizationArgs = namedtuple(\n \"ColorizationArgs\",\n [\"move_hue_valid_radius\", \"move_hue_oversized_radius\", \"invalid_color\"],\n )\n\n def move_hue_on_V_axis(hues, factors):\n return hues * factors[..., None]\n\n def move_hue_on_S_axis(hues, factors):\n return 255.0 - factors[..., None] * (255.0 - hues)\n\n if background == \"dark\":\n parameters = ColorizationArgs(\n move_hue_on_V_axis, move_hue_on_S_axis, op.array([255.0, 255.0, 255.0])\n )\n else:\n parameters = ColorizationArgs(move_hue_on_S_axis, move_hue_on_V_axis, op.zeros(3))\n colors = parameters.move_hue_valid_radius(float_hue, radius)\n oversized_radius_mask = radius > 1\n colors[oversized_radius_mask] = parameters.move_hue_oversized_radius(\n float_hue[oversized_radius_mask], 1 / radius[oversized_radius_mask]\n )\n colors = colors / 255.0\n return colors\n\n\ndef get_robust_pca(features: Tensor, m: float = 2, remove_first_component=False):\n # features: (N, C)\n # m: a hyperparam controlling how many std dev outside for outliers\n assert len(features.shape) == 2, \"features should be (N, C)\"\n reduction_mat = torch.pca_lowrank(features, q=3, niter=20)[2]\n colors = features @ reduction_mat\n if remove_first_component:\n colors_min = colors.min(dim=0).values\n colors_max = colors.max(dim=0).values\n tmp_colors = (colors - colors_min) / (colors_max - colors_min)\n fg_mask = tmp_colors[..., 0] < 0.2\n reduction_mat = torch.pca_lowrank(features[fg_mask], q=3, niter=20)[2]\n colors = features @ reduction_mat\n else:\n fg_mask = torch.ones_like(colors[:, 0]).bool()\n d = torch.abs(colors[fg_mask] - torch.median(colors[fg_mask], dim=0).values)\n mdev = torch.median(d, dim=0).values\n s = d / mdev\n try:\n rins = colors[fg_mask][s[:, 0] < m, 0]\n gins = colors[fg_mask][s[:, 1] < m, 1]\n bins = colors[fg_mask][s[:, 2] < m, 2]\n rgb_min = torch.tensor([rins.min(), gins.min(), bins.min()])\n rgb_max = torch.tensor([rins.max(), gins.max(), bins.max()])\n except:\n rins = colors\n gins = colors\n bins = colors\n rgb_min = torch.tensor([rins.min(), gins.min(), bins.min()])\n rgb_max = torch.tensor([rins.max(), gins.max(), bins.max()])\n\n return reduction_mat, rgb_min.to(reduction_mat), rgb_max.to(reduction_mat)\n\n\ndef get_pca_map(\n feature_map: torch.Tensor,\n img_size,\n interpolation=\"nearest\",\n return_pca_stats=False,\n pca_stats=None,\n):\n \"\"\"\n feature_map: (1, h, w, C) is the feature map of a single image.\n \"\"\"\n if len(feature_map.shape) != 4:\n # make it (1, h, w, C)\n feature_map = feature_map[None]\n batch_size = feature_map.shape[0]\n if pca_stats is None:\n pca_stats = []\n for i in range(batch_size):\n reduct_mat, color_min, color_max = get_robust_pca(\n feature_map[i].reshape(-1, feature_map.shape[-1])\n )\n pca_stats.append((reduct_mat, color_min, color_max))\n pca_colors = []\n for i in range(batch_size):\n reduct_mat, color_min, color_max = pca_stats[i]\n pca_color = feature_map[i] @ reduct_mat\n pca_color = (pca_color - color_min) / (color_max - color_min)\n pca_color = pca_color.clamp(0, 1)\n pca_color = F.interpolate(\n pca_color.permute(2, 0, 1)[None],\n size=img_size,\n mode=interpolation,\n )\n pca_color = pca_color.permute(0, 2, 3, 1)\n pca_colors.append(pca_color)\n pca_color = torch.cat(pca_colors, dim=0)\n if return_pca_stats:\n return pca_color.cpu(), pca_stats\n return pca_color.cpu()\n\n\ndef get_scale_map(\n scalar_map: torch.Tensor,\n img_size,\n interpolation=\"nearest\",\n):\n \"\"\"\n scalar_map: (1, h, w, C) is the feature map of a single image.\n \"\"\"\n if len(scalar_map.shape) != 1:\n scalar_map = scalar_map[None]\n scalar_map = (scalar_map - scalar_map.min(dim=-1).values) / (\n scalar_map.max(dim=-1).values - scalar_map.min(dim=-1).values + 1e-6\n )\n scalar_map = F.interpolate(\n scalar_map.permute(0, 3, 1, 2),\n size=img_size,\n mode=interpolation,\n ).permute(0, 2, 3, 1)\n # cmap = plt.get_cmap(\"viridis\")\n # scalar_map = cmap(scalar_map)[..., :3]\n # make it 3 channels\n scalar_map = torch.cat([scalar_map] * 3, dim=-1)\n return scalar_map.cpu()\n\n\ndef get_similarity_map(features: Tensor, img_size=(224, 224)):\n \"\"\"\n compute the similarity map of the central patch to the rest of the image\n \"\"\"\n assert len(features.shape) == 4, \"features should be (1, C, H, W)\"\n H, W, C = features.shape[1:]\n center_patch_feature = features[0, H // 2, W // 2, :]\n center_patch_feature_normalized = center_patch_feature / center_patch_feature.norm()\n center_patch_feature_normalized = center_patch_feature_normalized.unsqueeze(1)\n # Reshape and normalize the entire feature tensor\n features_flat = features.view(-1, C)\n features_normalized = features_flat / features_flat.norm(dim=1, keepdim=True)\n\n similarity_map_flat = features_normalized @ center_patch_feature_normalized\n # Reshape the flat similarity map back to the spatial dimensions (H, W)\n similarity_map = similarity_map_flat.view(H, W)\n\n # Normalize the similarity map to be in the range [0, 1] for visualization\n similarity_map = (similarity_map - similarity_map.min()) / (\n similarity_map.max() - similarity_map.min()\n )\n # we don't want the center patch to be the most similar\n similarity_map[H // 2, W // 2] = -1.0\n similarity_map = (\n F.interpolate(\n similarity_map.unsqueeze(0).unsqueeze(0),\n size=img_size,\n mode=\"bilinear\",\n )\n .squeeze(0)\n .squeeze(0)\n )\n\n similarity_map_np = similarity_map.cpu().numpy()\n negative_mask = similarity_map_np < 0\n\n colormap = plt.get_cmap(\"turbo\")\n\n # Apply the colormap directly to the normalized similarity map and multiply by 255 to get RGB values\n similarity_map_rgb = colormap(similarity_map_np)[..., :3]\n similarity_map_rgb[negative_mask] = [1.0, 0.0, 0.0]\n return similarity_map_rgb.cpu()\n" }, { "path": "third_party/depth_anything_v2/dinov2.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n#\n# This source code is licensed under the Apache License, Version 2.0\n# found in the LICENSE file in the root directory of this source tree.\n\n# References:\n# https://github.com/facebookresearch/dino/blob/main/vision_transformer.py\n# https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py\n\nimport logging\nimport math\nfrom functools import partial\nfrom typing import Callable, Sequence, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.utils.checkpoint\nfrom torch.nn.init import trunc_normal_\n\nfrom .dinov2_layers import MemEffAttention, Mlp\nfrom .dinov2_layers import NestedTensorBlock as Block\nfrom .dinov2_layers import PatchEmbed, SwiGLUFFNFused\n\nlogger = logging.getLogger(\"dinov2\")\n\n\ndef named_apply(\n fn: Callable, module: nn.Module, name=\"\", depth_first=True, include_root=False\n) -> nn.Module:\n if not depth_first and include_root:\n fn(module=module, name=name)\n for child_name, child_module in module.named_children():\n child_name = \".\".join((name, child_name)) if name else child_name\n named_apply(\n fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True\n )\n if depth_first and include_root:\n fn(module=module, name=name)\n return module\n\n\nclass BlockChunk(nn.ModuleList):\n def forward(self, x):\n for b in self:\n x = b(x)\n return x\n\n\nclass DinoVisionTransformer(nn.Module):\n def __init__(\n self,\n img_size=224,\n patch_size=16,\n in_chans=3,\n embed_dim=768,\n depth=12,\n num_heads=12,\n mlp_ratio=4.0,\n qkv_bias=True,\n ffn_bias=True,\n proj_bias=True,\n drop_path_rate=0.0,\n drop_path_uniform=False,\n init_values=None, # for layerscale: None or 0 => no layerscale\n embed_layer=PatchEmbed,\n act_layer=nn.GELU,\n block_fn=Block,\n ffn_layer=\"mlp\",\n block_chunks=1,\n num_register_tokens=0,\n interpolate_antialias=False,\n interpolate_offset=0.1,\n ):\n \"\"\"\n Args:\n img_size (int, tuple): input image size\n patch_size (int, tuple): patch size\n in_chans (int): number of input channels\n embed_dim (int): embedding dimension\n depth (int): depth of transformer\n num_heads (int): number of attention heads\n mlp_ratio (int): ratio of mlp hidden dim to embedding dim\n qkv_bias (bool): enable bias for qkv if True\n proj_bias (bool): enable bias for proj in attn if True\n ffn_bias (bool): enable bias for ffn if True\n drop_path_rate (float): stochastic depth rate\n drop_path_uniform (bool): apply uniform drop rate across blocks\n weight_init (str): weight init scheme\n init_values (float): layer-scale init values\n embed_layer (nn.Module): patch embedding layer\n act_layer (nn.Module): MLP activation layer\n block_fn (nn.Module): transformer block class\n ffn_layer (str): \"mlp\", \"swiglu\", \"swiglufused\" or \"identity\"\n block_chunks: (int) split block sequence into block_chunks units for FSDP wrap\n num_register_tokens: (int) number of extra cls tokens (so-called \"registers\")\n interpolate_antialias: (str) flag to apply anti-aliasing when interpolating positional embeddings\n interpolate_offset: (float) work-around offset to apply when interpolating positional embeddings\n \"\"\"\n super().__init__()\n norm_layer = partial(nn.LayerNorm, eps=1e-6)\n\n self.num_features = self.embed_dim = (\n embed_dim # num_features for consistency with other models\n )\n self.num_tokens = 1\n self.n_blocks = depth\n self.num_heads = num_heads\n self.patch_size = patch_size\n self.num_register_tokens = num_register_tokens\n self.interpolate_antialias = interpolate_antialias\n self.interpolate_offset = interpolate_offset\n\n self.patch_embed = embed_layer(\n img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim\n )\n num_patches = self.patch_embed.num_patches\n\n self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))\n self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))\n assert num_register_tokens >= 0\n self.register_tokens = (\n nn.Parameter(torch.zeros(1, num_register_tokens, embed_dim))\n if num_register_tokens\n else None\n )\n\n if drop_path_uniform is True:\n dpr = [drop_path_rate] * depth\n else:\n dpr = [\n x.item() for x in torch.linspace(0, drop_path_rate, depth)\n ] # stochastic depth decay rule\n\n if ffn_layer == \"mlp\":\n logger.info(\"using MLP layer as FFN\")\n ffn_layer = Mlp\n elif ffn_layer == \"swiglufused\" or ffn_layer == \"swiglu\":\n logger.info(\"using SwiGLU layer as FFN\")\n ffn_layer = SwiGLUFFNFused\n elif ffn_layer == \"identity\":\n logger.info(\"using Identity layer as FFN\")\n\n def f(*args, **kwargs):\n return nn.Identity()\n\n ffn_layer = f\n else:\n raise NotImplementedError\n\n blocks_list = [\n block_fn(\n dim=embed_dim,\n num_heads=num_heads,\n mlp_ratio=mlp_ratio,\n qkv_bias=qkv_bias,\n proj_bias=proj_bias,\n ffn_bias=ffn_bias,\n drop_path=dpr[i],\n norm_layer=norm_layer,\n act_layer=act_layer,\n ffn_layer=ffn_layer,\n init_values=init_values,\n )\n for i in range(depth)\n ]\n if block_chunks > 0:\n self.chunked_blocks = True\n chunked_blocks = []\n chunksize = depth // block_chunks\n for i in range(0, depth, chunksize):\n # this is to keep the block index consistent if we chunk the block list\n chunked_blocks.append([nn.Identity()] * i + blocks_list[i : i + chunksize])\n self.blocks = nn.ModuleList([BlockChunk(p) for p in chunked_blocks])\n else:\n self.chunked_blocks = False\n self.blocks = nn.ModuleList(blocks_list)\n\n self.norm = norm_layer(embed_dim)\n self.head = nn.Identity()\n\n self.mask_token = nn.Parameter(torch.zeros(1, embed_dim))\n\n self.init_weights()\n\n def init_weights(self):\n trunc_normal_(self.pos_embed, std=0.02)\n nn.init.normal_(self.cls_token, std=1e-6)\n if self.register_tokens is not None:\n nn.init.normal_(self.register_tokens, std=1e-6)\n named_apply(init_weights_vit_timm, self)\n\n def interpolate_pos_encoding(self, x, w, h):\n previous_dtype = x.dtype\n npatch = x.shape[1] - 1\n N = self.pos_embed.shape[1] - 1\n if npatch == N and w == h:\n return self.pos_embed\n pos_embed = self.pos_embed.float()\n class_pos_embed = pos_embed[:, 0]\n patch_pos_embed = pos_embed[:, 1:]\n dim = x.shape[-1]\n w0 = w // self.patch_size\n h0 = h // self.patch_size\n # we add a small number to avoid floating point error in the interpolation\n # see discussion at https://github.com/facebookresearch/dino/issues/8\n # DINOv2 with register modify the interpolate_offset from 0.1 to 0.0\n w0, h0 = w0 + self.interpolate_offset, h0 + self.interpolate_offset\n # w0, h0 = w0 + 0.1, h0 + 0.1\n\n sqrt_N = math.sqrt(N)\n sx, sy = float(w0) / sqrt_N, float(h0) / sqrt_N\n patch_pos_embed = nn.functional.interpolate(\n patch_pos_embed.reshape(1, int(sqrt_N), int(sqrt_N), dim).permute(0, 3, 1, 2),\n scale_factor=(sx, sy),\n # (int(w0), int(h0)), # to solve the upsampling shape issue\n mode=\"bicubic\",\n antialias=self.interpolate_antialias,\n )\n\n assert int(w0) == patch_pos_embed.shape[-2]\n assert int(h0) == patch_pos_embed.shape[-1]\n patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)\n return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1).to(previous_dtype)\n\n def prepare_tokens_with_masks(self, x, masks=None):\n B, nc, w, h = x.shape\n x = self.patch_embed(x)\n if masks is not None:\n x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x)\n\n x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)\n x = x + self.interpolate_pos_encoding(x, w, h)\n\n if self.register_tokens is not None:\n x = torch.cat(\n (\n x[:, :1],\n self.register_tokens.expand(x.shape[0], -1, -1),\n x[:, 1:],\n ),\n dim=1,\n )\n\n return x\n\n def forward_features_list(self, x_list, masks_list):\n x = [self.prepare_tokens_with_masks(x, masks) for x, masks in zip(x_list, masks_list)]\n for blk in self.blocks:\n x = blk(x)\n\n all_x = x\n output = []\n for x, masks in zip(all_x, masks_list):\n x_norm = self.norm(x)\n output.append(\n {\n \"x_norm_clstoken\": x_norm[:, 0],\n \"x_norm_regtokens\": x_norm[:, 1 : self.num_register_tokens + 1],\n \"x_norm_patchtokens\": x_norm[:, self.num_register_tokens + 1 :],\n \"x_prenorm\": x,\n \"masks\": masks,\n }\n )\n return output\n\n def forward_features(self, x, masks=None):\n if isinstance(x, list):\n return self.forward_features_list(x, masks)\n\n x = self.prepare_tokens_with_masks(x, masks)\n\n for blk in self.blocks:\n x = blk(x)\n\n x_norm = self.norm(x)\n return {\n \"x_norm_clstoken\": x_norm[:, 0],\n \"x_norm_regtokens\": x_norm[:, 1 : self.num_register_tokens + 1],\n \"x_norm_patchtokens\": x_norm[:, self.num_register_tokens + 1 :],\n \"x_prenorm\": x,\n \"masks\": masks,\n }\n\n def _get_intermediate_layers_not_chunked(self, x, n=1):\n x = self.prepare_tokens_with_masks(x)\n # If n is an int, take the n last blocks. If it's a list, take them\n output, total_block_len = [], len(self.blocks)\n blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n\n for i, blk in enumerate(self.blocks):\n x = blk(x)\n if i in blocks_to_take:\n output.append(x)\n assert len(output) == len(\n blocks_to_take\n ), f\"only {len(output)} / {len(blocks_to_take)} blocks found\"\n return output\n\n def _get_intermediate_layers_chunked(self, x, n=1):\n x = self.prepare_tokens_with_masks(x)\n output, i, total_block_len = [], 0, len(self.blocks[-1])\n # If n is an int, take the n last blocks. If it's a list, take them\n blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n\n for block_chunk in self.blocks:\n for blk in block_chunk[i:]: # Passing the nn.Identity()\n x = blk(x)\n if i in blocks_to_take:\n output.append(x)\n i += 1\n assert len(output) == len(\n blocks_to_take\n ), f\"only {len(output)} / {len(blocks_to_take)} blocks found\"\n return output\n\n def get_intermediate_layers(\n self,\n x: torch.Tensor,\n n: Union[int, Sequence] = 1, # Layers or n last layers to take\n reshape: bool = False,\n return_class_token: bool = False,\n norm=True,\n ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]:\n if self.chunked_blocks:\n outputs = self._get_intermediate_layers_chunked(x, n)\n else:\n outputs = self._get_intermediate_layers_not_chunked(x, n)\n if norm:\n outputs = [self.norm(out) for out in outputs]\n class_tokens = [out[:, 0] for out in outputs]\n outputs = [out[:, 1 + self.num_register_tokens :] for out in outputs]\n if reshape:\n B, _, w, h = x.shape\n outputs = [\n out.reshape(B, w // self.patch_size, h // self.patch_size, -1)\n .permute(0, 3, 1, 2)\n .contiguous()\n for out in outputs\n ]\n if return_class_token:\n return tuple(zip(outputs, class_tokens))\n return tuple(outputs)\n\n def forward(self, *args, is_training=False, **kwargs):\n ret = self.forward_features(*args, **kwargs)\n if is_training:\n return ret\n else:\n return self.head(ret[\"x_norm_clstoken\"])\n\n\ndef init_weights_vit_timm(module: nn.Module, name: str = \"\"):\n \"\"\"ViT weight initialization, original timm impl (for reproducibility)\"\"\"\n if isinstance(module, nn.Linear):\n trunc_normal_(module.weight, std=0.02)\n if module.bias is not None:\n nn.init.zeros_(module.bias)\n\n\ndef vit_small(patch_size=16, num_register_tokens=0, **kwargs):\n model = DinoVisionTransformer(\n patch_size=patch_size,\n embed_dim=384,\n depth=12,\n num_heads=6,\n mlp_ratio=4,\n block_fn=partial(Block, attn_class=MemEffAttention),\n num_register_tokens=num_register_tokens,\n **kwargs,\n )\n return model\n\n\ndef vit_base(patch_size=16, num_register_tokens=0, **kwargs):\n model = DinoVisionTransformer(\n patch_size=patch_size,\n embed_dim=768,\n depth=12,\n num_heads=12,\n mlp_ratio=4,\n block_fn=partial(Block, attn_class=MemEffAttention),\n num_register_tokens=num_register_tokens,\n **kwargs,\n )\n return model\n\n\ndef vit_large(patch_size=16, num_register_tokens=0, **kwargs):\n model = DinoVisionTransformer(\n patch_size=patch_size,\n embed_dim=1024,\n depth=24,\n num_heads=16,\n mlp_ratio=4,\n block_fn=partial(Block, attn_class=MemEffAttention),\n num_register_tokens=num_register_tokens,\n **kwargs,\n )\n return model\n\n\ndef vit_giant2(patch_size=16, num_register_tokens=0, **kwargs):\n \"\"\"\n Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64\n \"\"\"\n model = DinoVisionTransformer(\n patch_size=patch_size,\n embed_dim=1536,\n depth=40,\n num_heads=24,\n mlp_ratio=4,\n block_fn=partial(Block, attn_class=MemEffAttention),\n num_register_tokens=num_register_tokens,\n **kwargs,\n )\n return model\n\n\ndef DINOv2(model_name):\n model_zoo = {\"vits\": vit_small, \"vitb\": vit_base, \"vitl\": vit_large, \"vitg\": vit_giant2}\n\n return model_zoo[model_name](\n img_size=518,\n patch_size=14,\n init_values=1.0,\n ffn_layer=\"mlp\" if model_name != \"vitg\" else \"swiglufused\",\n block_chunks=0,\n num_register_tokens=0,\n interpolate_antialias=False,\n interpolate_offset=0.1,\n )\n" }, { "path": "third_party/depth_anything_v2/dinov2_layers/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom .attention import MemEffAttention\nfrom .block import NestedTensorBlock\nfrom .mlp import Mlp\nfrom .patch_embed import PatchEmbed\nfrom .swiglu_ffn import SwiGLUFFN, SwiGLUFFNFused\n" }, { "path": "third_party/depth_anything_v2/dinov2_layers/attention.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n# References:\n# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py\n# https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py\n\nimport logging\n\nfrom torch import Tensor, nn\n\nlogger = logging.getLogger(\"dinov2\")\n\n\ntry:\n from xformers.ops import fmha, memory_efficient_attention, unbind\n\n XFORMERS_AVAILABLE = True\nexcept ImportError:\n logger.warning(\"xFormers not available\")\n XFORMERS_AVAILABLE = False\n\n\nclass Attention(nn.Module):\n def __init__(\n self,\n dim: int,\n num_heads: int = 8,\n qkv_bias: bool = False,\n proj_bias: bool = True,\n attn_drop: float = 0.0,\n proj_drop: float = 0.0,\n ) -> None:\n super().__init__()\n self.num_heads = num_heads\n head_dim = dim // num_heads\n self.scale = head_dim**-0.5\n\n self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n self.attn_drop = nn.Dropout(attn_drop)\n self.proj = nn.Linear(dim, dim, bias=proj_bias)\n self.proj_drop = nn.Dropout(proj_drop)\n\n def forward(self, x: Tensor) -> Tensor:\n B, N, C = x.shape\n qkv = (\n self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)\n )\n\n q, k, v = qkv.unbind(dim=0)\n x = nn.functional.scaled_dot_product_attention(\n q,\n k,\n v,\n dropout_p=self.attn_drop.p if self.training else 0.0,\n )\n\n x = x.transpose(1, 2).reshape(B, N, C)\n x = self.proj(x)\n x = self.proj_drop(x)\n return x\n\n\nclass MemEffAttention(Attention):\n def forward(self, x: Tensor, attn_bias=None) -> Tensor:\n if not XFORMERS_AVAILABLE:\n assert attn_bias is None, \"xFormers is required for nested tensors usage\"\n return super().forward(x)\n\n B, N, C = x.shape\n qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)\n\n q, k, v = unbind(qkv, 2)\n\n x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)\n x = x.reshape([B, N, C])\n\n x = self.proj(x)\n x = self.proj_drop(x)\n return x\n" }, { "path": "third_party/depth_anything_v2/dinov2_layers/block.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n# References:\n# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py\n# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py\n\nimport logging\nfrom typing import Any, Callable, Dict, List, Tuple\n\nimport torch\nfrom torch import Tensor, nn\n\nfrom .attention import Attention, MemEffAttention\nfrom .drop_path import DropPath\nfrom .layer_scale import LayerScale\nfrom .mlp import Mlp\n\nlogger = logging.getLogger(\"dinov2\")\n\n\ntry:\n from xformers.ops import fmha, index_select_cat, scaled_index_add\n\n XFORMERS_AVAILABLE = True\nexcept ImportError:\n logger.warning(\"xFormers not available\")\n XFORMERS_AVAILABLE = False\n\n\nclass Block(nn.Module):\n def __init__(\n self,\n dim: int,\n num_heads: int,\n mlp_ratio: float = 4.0,\n qkv_bias: bool = False,\n proj_bias: bool = True,\n ffn_bias: bool = True,\n drop: float = 0.0,\n attn_drop: float = 0.0,\n init_values=None,\n drop_path: float = 0.0,\n act_layer: Callable[..., nn.Module] = nn.GELU,\n norm_layer: Callable[..., nn.Module] = nn.LayerNorm,\n attn_class: Callable[..., nn.Module] = Attention,\n ffn_layer: Callable[..., nn.Module] = Mlp,\n ) -> None:\n super().__init__()\n # print(f\"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}\")\n self.norm1 = norm_layer(dim)\n self.attn = attn_class(\n dim,\n num_heads=num_heads,\n qkv_bias=qkv_bias,\n proj_bias=proj_bias,\n attn_drop=attn_drop,\n proj_drop=drop,\n )\n self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()\n self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n\n self.norm2 = norm_layer(dim)\n mlp_hidden_dim = int(dim * mlp_ratio)\n self.mlp = ffn_layer(\n in_features=dim,\n hidden_features=mlp_hidden_dim,\n act_layer=act_layer,\n drop=drop,\n bias=ffn_bias,\n )\n self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()\n self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n\n self.sample_drop_ratio = drop_path\n\n def forward(self, x: Tensor) -> Tensor:\n def attn_residual_func(x: Tensor) -> Tensor:\n return self.ls1(self.attn(self.norm1(x)))\n\n def ffn_residual_func(x: Tensor) -> Tensor:\n return self.ls2(self.mlp(self.norm2(x)))\n\n if self.training and self.sample_drop_ratio > 0.1:\n # the overhead is compensated only for a drop path rate larger than 0.1\n x = drop_add_residual_stochastic_depth(\n x,\n residual_func=attn_residual_func,\n sample_drop_ratio=self.sample_drop_ratio,\n )\n x = drop_add_residual_stochastic_depth(\n x,\n residual_func=ffn_residual_func,\n sample_drop_ratio=self.sample_drop_ratio,\n )\n elif self.training and self.sample_drop_ratio > 0.0:\n x = x + self.drop_path1(attn_residual_func(x))\n x = x + self.drop_path1(ffn_residual_func(x)) # FIXME: drop_path2\n else:\n x = x + attn_residual_func(x)\n x = x + ffn_residual_func(x)\n return x\n\n\ndef drop_add_residual_stochastic_depth(\n x: Tensor,\n residual_func: Callable[[Tensor], Tensor],\n sample_drop_ratio: float = 0.0,\n) -> Tensor:\n # 1) extract subset using permutation\n b, n, d = x.shape\n sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)\n brange = (torch.randperm(b, device=x.device))[:sample_subset_size]\n x_subset = x[brange]\n\n # 2) apply residual_func to get residual\n residual = residual_func(x_subset)\n\n x_flat = x.flatten(1)\n residual = residual.flatten(1)\n\n residual_scale_factor = b / sample_subset_size\n\n # 3) add the residual\n x_plus_residual = torch.index_add(\n x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor\n )\n return x_plus_residual.view_as(x)\n\n\ndef get_branges_scales(x, sample_drop_ratio=0.0):\n b, n, d = x.shape\n sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)\n brange = (torch.randperm(b, device=x.device))[:sample_subset_size]\n residual_scale_factor = b / sample_subset_size\n return brange, residual_scale_factor\n\n\ndef add_residual(x, brange, residual, residual_scale_factor, scaling_vector=None):\n if scaling_vector is None:\n x_flat = x.flatten(1)\n residual = residual.flatten(1)\n x_plus_residual = torch.index_add(\n x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor\n )\n else:\n x_plus_residual = scaled_index_add(\n x,\n brange,\n residual.to(dtype=x.dtype),\n scaling=scaling_vector,\n alpha=residual_scale_factor,\n )\n return x_plus_residual\n\n\nattn_bias_cache: Dict[Tuple, Any] = {}\n\n\ndef get_attn_bias_and_cat(x_list, branges=None):\n \"\"\"\n this will perform the index select, cat the tensors, and provide the attn_bias from cache\n \"\"\"\n batch_sizes = (\n [b.shape[0] for b in branges] if branges is not None else [x.shape[0] for x in x_list]\n )\n all_shapes = tuple((b, x.shape[1]) for b, x in zip(batch_sizes, x_list))\n if all_shapes not in attn_bias_cache.keys():\n seqlens = []\n for b, x in zip(batch_sizes, x_list):\n for _ in range(b):\n seqlens.append(x.shape[1])\n attn_bias = fmha.BlockDiagonalMask.from_seqlens(seqlens)\n attn_bias._batch_sizes = batch_sizes\n attn_bias_cache[all_shapes] = attn_bias\n\n if branges is not None:\n cat_tensors = index_select_cat([x.flatten(1) for x in x_list], branges).view(\n 1, -1, x_list[0].shape[-1]\n )\n else:\n tensors_bs1 = tuple(x.reshape([1, -1, *x.shape[2:]]) for x in x_list)\n cat_tensors = torch.cat(tensors_bs1, dim=1)\n\n return attn_bias_cache[all_shapes], cat_tensors\n\n\ndef drop_add_residual_stochastic_depth_list(\n x_list: List[Tensor],\n residual_func: Callable[[Tensor, Any], Tensor],\n sample_drop_ratio: float = 0.0,\n scaling_vector=None,\n) -> Tensor:\n # 1) generate random set of indices for dropping samples in the batch\n branges_scales = [get_branges_scales(x, sample_drop_ratio=sample_drop_ratio) for x in x_list]\n branges = [s[0] for s in branges_scales]\n residual_scale_factors = [s[1] for s in branges_scales]\n\n # 2) get attention bias and index+concat the tensors\n attn_bias, x_cat = get_attn_bias_and_cat(x_list, branges)\n\n # 3) apply residual_func to get residual, and split the result\n residual_list = attn_bias.split(residual_func(x_cat, attn_bias=attn_bias)) # type: ignore\n\n outputs = []\n for x, brange, residual, residual_scale_factor in zip(\n x_list, branges, residual_list, residual_scale_factors\n ):\n outputs.append(\n add_residual(x, brange, residual, residual_scale_factor, scaling_vector).view_as(x)\n )\n return outputs\n\n\nclass NestedTensorBlock(Block):\n def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]:\n \"\"\"\n x_list contains a list of tensors to nest together and run\n \"\"\"\n assert isinstance(self.attn, MemEffAttention)\n\n if self.training and self.sample_drop_ratio > 0.0:\n\n def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:\n return self.attn(self.norm1(x), attn_bias=attn_bias)\n\n def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:\n return self.mlp(self.norm2(x))\n\n x_list = drop_add_residual_stochastic_depth_list(\n x_list,\n residual_func=attn_residual_func,\n sample_drop_ratio=self.sample_drop_ratio,\n scaling_vector=self.ls1.gamma if isinstance(self.ls1, LayerScale) else None,\n )\n x_list = drop_add_residual_stochastic_depth_list(\n x_list,\n residual_func=ffn_residual_func,\n sample_drop_ratio=self.sample_drop_ratio,\n scaling_vector=self.ls2.gamma if isinstance(self.ls1, LayerScale) else None,\n )\n return x_list\n else:\n\n def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:\n return self.ls1(self.attn(self.norm1(x), attn_bias=attn_bias))\n\n def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:\n return self.ls2(self.mlp(self.norm2(x)))\n\n attn_bias, x = get_attn_bias_and_cat(x_list)\n x = x + attn_residual_func(x, attn_bias=attn_bias)\n x = x + ffn_residual_func(x)\n return attn_bias.split(x)\n\n def forward(self, x_or_x_list):\n if isinstance(x_or_x_list, Tensor):\n return super().forward(x_or_x_list)\n elif isinstance(x_or_x_list, list):\n assert XFORMERS_AVAILABLE, \"Please install xFormers for nested tensors usage\"\n return self.forward_nested(x_or_x_list)\n else:\n raise AssertionError\n" }, { "path": "third_party/depth_anything_v2/dinov2_layers/drop_path.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n# References:\n# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py\n# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/drop.py\n\n\nfrom torch import nn\n\n\ndef drop_path(x, drop_prob: float = 0.0, training: bool = False):\n if drop_prob == 0.0 or not training:\n return x\n keep_prob = 1 - drop_prob\n shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets\n random_tensor = x.new_empty(shape).bernoulli_(keep_prob)\n if keep_prob > 0.0:\n random_tensor.div_(keep_prob)\n output = x * random_tensor\n return output\n\n\nclass DropPath(nn.Module):\n \"\"\"Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).\"\"\"\n\n def __init__(self, drop_prob=None):\n super(DropPath, self).__init__()\n self.drop_prob = drop_prob\n\n def forward(self, x):\n return drop_path(x, self.drop_prob, self.training)\n" }, { "path": "third_party/depth_anything_v2/dinov2_layers/layer_scale.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n# Modified from: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L103-L110\n\nfrom typing import Union\n\nimport torch\nfrom torch import Tensor, nn\n\n\nclass LayerScale(nn.Module):\n def __init__(\n self,\n dim: int,\n init_values: Union[float, Tensor] = 1e-5,\n inplace: bool = False,\n ) -> None:\n super().__init__()\n self.inplace = inplace\n self.gamma = nn.Parameter(init_values * torch.ones(dim))\n\n def forward(self, x: Tensor) -> Tensor:\n return x.mul_(self.gamma) if self.inplace else x * self.gamma\n" }, { "path": "third_party/depth_anything_v2/dinov2_layers/mlp.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n# References:\n# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py\n# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/mlp.py\n\n\nfrom typing import Callable, Optional\n\nfrom torch import Tensor, nn\n\n\nclass Mlp(nn.Module):\n def __init__(\n self,\n in_features: int,\n hidden_features: Optional[int] = None,\n out_features: Optional[int] = None,\n act_layer: Callable[..., nn.Module] = nn.GELU,\n drop: float = 0.0,\n bias: bool = True,\n ) -> None:\n super().__init__()\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n self.fc1 = nn.Linear(in_features, hidden_features, bias=bias)\n self.act = act_layer()\n self.fc2 = nn.Linear(hidden_features, out_features, bias=bias)\n self.drop = nn.Dropout(drop)\n\n def forward(self, x: Tensor) -> Tensor:\n x = self.fc1(x)\n x = self.act(x)\n x = self.drop(x)\n x = self.fc2(x)\n x = self.drop(x)\n return x\n" }, { "path": "third_party/depth_anything_v2/dinov2_layers/patch_embed.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n# References:\n# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py\n# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py\n\nfrom typing import Callable, Optional, Tuple, Union\n\nimport torch.nn as nn\nfrom torch import Tensor\n\n\ndef make_2tuple(x):\n if isinstance(x, tuple):\n assert len(x) == 2\n return x\n\n assert isinstance(x, int)\n return (x, x)\n\n\nclass PatchEmbed(nn.Module):\n \"\"\"\n 2D image to patch embedding: (B,C,H,W) -> (B,N,D)\n\n Args:\n img_size: Image size.\n patch_size: Patch token size.\n in_chans: Number of input image channels.\n embed_dim: Number of linear projection output channels.\n norm_layer: Normalization layer.\n \"\"\"\n\n def __init__(\n self,\n img_size: Union[int, Tuple[int, int]] = 224,\n patch_size: Union[int, Tuple[int, int]] = 16,\n in_chans: int = 3,\n embed_dim: int = 768,\n norm_layer: Optional[Callable] = None,\n flatten_embedding: bool = True,\n ) -> None:\n super().__init__()\n\n image_HW = make_2tuple(img_size)\n patch_HW = make_2tuple(patch_size)\n patch_grid_size = (\n image_HW[0] // patch_HW[0],\n image_HW[1] // patch_HW[1],\n )\n\n self.img_size = image_HW\n self.patch_size = patch_HW\n self.patches_resolution = patch_grid_size\n self.num_patches = patch_grid_size[0] * patch_grid_size[1]\n\n self.in_chans = in_chans\n self.embed_dim = embed_dim\n\n self.flatten_embedding = flatten_embedding\n\n self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW)\n self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()\n\n def forward(self, x: Tensor) -> Tensor:\n _, _, H, W = x.shape\n patch_H, patch_W = self.patch_size\n\n assert (\n H % patch_H == 0\n ), f\"Input image height {H} is not a multiple of patch height {patch_H}\"\n assert (\n W % patch_W == 0\n ), f\"Input image width {W} is not a multiple of patch width: {patch_W}\"\n\n x = self.proj(x) # B C H W\n H, W = x.size(2), x.size(3)\n x = x.flatten(2).transpose(1, 2) # B HW C\n x = self.norm(x)\n if not self.flatten_embedding:\n x = x.reshape(-1, H, W, self.embed_dim) # B H W C\n return x\n\n def flops(self) -> float:\n Ho, Wo = self.patches_resolution\n flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])\n if self.norm is not None:\n flops += Ho * Wo * self.embed_dim\n return flops\n" }, { "path": "third_party/depth_anything_v2/dinov2_layers/swiglu_ffn.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom typing import Callable, Optional\n\nimport torch.nn.functional as F\nfrom torch import Tensor, nn\n\n\nclass SwiGLUFFN(nn.Module):\n def __init__(\n self,\n in_features: int,\n hidden_features: Optional[int] = None,\n out_features: Optional[int] = None,\n act_layer: Callable[..., nn.Module] = None,\n drop: float = 0.0,\n bias: bool = True,\n ) -> None:\n super().__init__()\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias)\n self.w3 = nn.Linear(hidden_features, out_features, bias=bias)\n\n def forward(self, x: Tensor) -> Tensor:\n x12 = self.w12(x)\n x1, x2 = x12.chunk(2, dim=-1)\n hidden = F.silu(x1) * x2\n return self.w3(hidden)\n\n\ntry:\n from xformers.ops import SwiGLU\n\n XFORMERS_AVAILABLE = True\nexcept ImportError:\n SwiGLU = SwiGLUFFN\n XFORMERS_AVAILABLE = False\n\n\nclass SwiGLUFFNFused(SwiGLU):\n def __init__(\n self,\n in_features: int,\n hidden_features: Optional[int] = None,\n out_features: Optional[int] = None,\n act_layer: Callable[..., nn.Module] = None,\n drop: float = 0.0,\n bias: bool = True,\n ) -> None:\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8\n super().__init__(\n in_features=in_features,\n hidden_features=hidden_features,\n out_features=out_features,\n bias=bias,\n )\n" }, { "path": "third_party/depth_anything_v2/dpt.py", "content": "import cv2\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision.transforms import Compose\n\nfrom .dinov2 import DINOv2\nfrom .util.blocks import FeatureFusionBlock, _make_scratch\nfrom .util.transform import NormalizeImage, PrepareForNet, Resize\n\n\ndef _make_fusion_block(features, use_bn, size=None):\n return FeatureFusionBlock(\n features,\n nn.ReLU(False),\n deconv=False,\n bn=use_bn,\n expand=False,\n align_corners=True,\n size=size,\n )\n\n\nclass ConvBlock(nn.Module):\n def __init__(self, in_feature, out_feature):\n super().__init__()\n\n self.conv_block = nn.Sequential(\n nn.Conv2d(in_feature, out_feature, kernel_size=3, stride=1, padding=1),\n nn.BatchNorm2d(out_feature),\n nn.ReLU(True),\n )\n\n def forward(self, x):\n return self.conv_block(x)\n\n\nclass DPTHead(nn.Module):\n def __init__(\n self,\n in_channels,\n features=256,\n use_bn=False,\n out_channels=[256, 512, 1024, 1024],\n use_clstoken=False,\n ):\n super(DPTHead, self).__init__()\n\n self.use_clstoken = use_clstoken\n\n self.projects = nn.ModuleList(\n [\n nn.Conv2d(\n in_channels=in_channels,\n out_channels=out_channel,\n kernel_size=1,\n stride=1,\n padding=0,\n )\n for out_channel in out_channels\n ]\n )\n\n self.resize_layers = nn.ModuleList(\n [\n nn.ConvTranspose2d(\n in_channels=out_channels[0],\n out_channels=out_channels[0],\n kernel_size=4,\n stride=4,\n padding=0,\n ),\n nn.ConvTranspose2d(\n in_channels=out_channels[1],\n out_channels=out_channels[1],\n kernel_size=2,\n stride=2,\n padding=0,\n ),\n nn.Identity(),\n nn.Conv2d(\n in_channels=out_channels[3],\n out_channels=out_channels[3],\n kernel_size=3,\n stride=2,\n padding=1,\n ),\n ]\n )\n\n if use_clstoken:\n self.readout_projects = nn.ModuleList()\n for _ in range(len(self.projects)):\n self.readout_projects.append(\n nn.Sequential(nn.Linear(2 * in_channels, in_channels), nn.GELU())\n )\n\n self.scratch = _make_scratch(\n out_channels,\n features,\n groups=1,\n expand=False,\n )\n\n self.scratch.stem_transpose = None\n\n self.scratch.refinenet1 = _make_fusion_block(features, use_bn)\n self.scratch.refinenet2 = _make_fusion_block(features, use_bn)\n self.scratch.refinenet3 = _make_fusion_block(features, use_bn)\n self.scratch.refinenet4 = _make_fusion_block(features, use_bn)\n\n head_features_1 = features\n head_features_2 = 32\n\n self.scratch.output_conv1 = nn.Conv2d(\n head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1\n )\n self.scratch.output_conv2 = nn.Sequential(\n nn.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1),\n nn.ReLU(True),\n nn.Conv2d(head_features_2, 1, kernel_size=1, stride=1, padding=0),\n nn.ReLU(True),\n nn.Identity(),\n )\n\n def forward(self, out_features, patch_h, patch_w):\n out = []\n for i, x in enumerate(out_features):\n if self.use_clstoken:\n x, cls_token = x[0], x[1]\n readout = cls_token.unsqueeze(1).expand_as(x)\n x = self.readout_projects[i](torch.cat((x, readout), -1))\n else:\n x = x[0]\n\n x = x.permute(0, 2, 1).reshape((x.shape[0], x.shape[-1], patch_h, patch_w))\n\n x = self.projects[i](x)\n x = self.resize_layers[i](x)\n\n out.append(x)\n\n layer_1, layer_2, layer_3, layer_4 = out\n\n layer_1_rn = self.scratch.layer1_rn(layer_1)\n layer_2_rn = self.scratch.layer2_rn(layer_2)\n layer_3_rn = self.scratch.layer3_rn(layer_3)\n layer_4_rn = self.scratch.layer4_rn(layer_4)\n\n path_4 = self.scratch.refinenet4(layer_4_rn, size=layer_3_rn.shape[2:])\n path_3 = self.scratch.refinenet3(path_4, layer_3_rn, size=layer_2_rn.shape[2:])\n path_2 = self.scratch.refinenet2(path_3, layer_2_rn, size=layer_1_rn.shape[2:])\n path_1 = self.scratch.refinenet1(path_2, layer_1_rn)\n\n out = self.scratch.output_conv1(path_1)\n out = F.interpolate(\n out, (int(patch_h * 14), int(patch_w * 14)), mode=\"bilinear\", align_corners=True\n )\n out = self.scratch.output_conv2(out)\n\n return out\n\n\nclass DepthAnythingV2(nn.Module):\n def __init__(\n self,\n encoder=\"vitl\",\n features=256,\n out_channels=[256, 512, 1024, 1024],\n use_bn=False,\n use_clstoken=False,\n ):\n super(DepthAnythingV2, self).__init__()\n\n self.intermediate_layer_idx = {\n \"vits\": [2, 5, 8, 11],\n \"vitb\": [2, 5, 8, 11],\n \"vitl\": [4, 11, 17, 23],\n \"vitg\": [9, 19, 29, 39],\n }[encoder]\n\n self.encoder = encoder\n self.pretrained = DINOv2(model_name=encoder)\n\n self.depth_head = DPTHead(\n self.pretrained.embed_dim,\n features,\n use_bn,\n out_channels=out_channels,\n use_clstoken=use_clstoken,\n )\n\n @torch.no_grad()\n def forward(self, x):\n patch_h, patch_w = x.shape[-2] // 14, x.shape[-1] // 14\n features = self.pretrained.get_intermediate_layers(\n x, self.intermediate_layer_idx, return_class_token=True\n )\n depth = self.depth_head(features, patch_h, patch_w)\n depth = F.relu(depth)\n return depth.squeeze(1)\n" }, { "path": "third_party/depth_anything_v2/util/blocks.py", "content": "import torch.nn as nn\n\n\ndef _make_scratch(in_shape, out_shape, groups=1, expand=False):\n scratch = nn.Module()\n\n out_shape1 = out_shape\n out_shape2 = out_shape\n out_shape3 = out_shape\n if len(in_shape) >= 4:\n out_shape4 = out_shape\n\n if expand:\n out_shape1 = out_shape\n out_shape2 = out_shape * 2\n out_shape3 = out_shape * 4\n if len(in_shape) >= 4:\n out_shape4 = out_shape * 8\n\n scratch.layer1_rn = nn.Conv2d(\n in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups\n )\n scratch.layer2_rn = nn.Conv2d(\n in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups\n )\n scratch.layer3_rn = nn.Conv2d(\n in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups\n )\n if len(in_shape) >= 4:\n scratch.layer4_rn = nn.Conv2d(\n in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups\n )\n\n return scratch\n\n\nclass ResidualConvUnit(nn.Module):\n \"\"\"Residual convolution module.\"\"\"\n\n def __init__(self, features, activation, bn):\n \"\"\"Init.\n\n Args:\n features (int): number of features\n \"\"\"\n super().__init__()\n\n self.bn = bn\n\n self.groups = 1\n\n self.conv1 = nn.Conv2d(\n features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups\n )\n\n self.conv2 = nn.Conv2d(\n features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups\n )\n\n if self.bn == True:\n self.bn1 = nn.BatchNorm2d(features)\n self.bn2 = nn.BatchNorm2d(features)\n\n self.activation = activation\n\n self.skip_add = nn.quantized.FloatFunctional()\n\n def forward(self, x):\n \"\"\"Forward pass.\n\n Args:\n x (tensor): input\n\n Returns:\n tensor: output\n \"\"\"\n\n out = self.activation(x)\n out = self.conv1(out)\n if self.bn == True:\n out = self.bn1(out)\n\n out = self.activation(out)\n out = self.conv2(out)\n if self.bn == True:\n out = self.bn2(out)\n\n if self.groups > 1:\n out = self.conv_merge(out)\n\n return self.skip_add.add(out, x)\n\n\nclass FeatureFusionBlock(nn.Module):\n \"\"\"Feature fusion block.\"\"\"\n\n def __init__(\n self,\n features,\n activation,\n deconv=False,\n bn=False,\n expand=False,\n align_corners=True,\n size=None,\n ):\n \"\"\"Init.\n\n Args:\n features (int): number of features\n \"\"\"\n super(FeatureFusionBlock, self).__init__()\n\n self.deconv = deconv\n self.align_corners = align_corners\n\n self.groups = 1\n\n self.expand = expand\n out_features = features\n if self.expand == True:\n out_features = features // 2\n\n self.out_conv = nn.Conv2d(\n features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1\n )\n\n self.resConfUnit1 = ResidualConvUnit(features, activation, bn)\n self.resConfUnit2 = ResidualConvUnit(features, activation, bn)\n\n self.skip_add = nn.quantized.FloatFunctional()\n\n self.size = size\n\n def forward(self, *xs, size=None):\n \"\"\"Forward pass.\n\n Returns:\n tensor: output\n \"\"\"\n output = xs[0]\n\n if len(xs) == 2:\n res = self.resConfUnit1(xs[1])\n output = self.skip_add.add(output, res)\n\n output = self.resConfUnit2(output)\n\n if (size is None) and (self.size is None):\n modifier = {\"scale_factor\": 2}\n elif size is None:\n modifier = {\"size\": self.size}\n else:\n modifier = {\"size\": size}\n\n output = nn.functional.interpolate(\n output, **modifier, mode=\"bilinear\", align_corners=self.align_corners\n )\n\n output = self.out_conv(output)\n\n return output\n" }, { "path": "third_party/depth_anything_v2/util/transform.py", "content": "import cv2\nimport numpy as np\n\n\nclass Resize(object):\n \"\"\"Resize sample to given size (width, height).\"\"\"\n\n def __init__(\n self,\n width,\n height,\n resize_target=True,\n keep_aspect_ratio=False,\n ensure_multiple_of=1,\n resize_method=\"lower_bound\",\n image_interpolation_method=cv2.INTER_AREA,\n ):\n \"\"\"Init.\n\n Args:\n width (int): desired output width\n height (int): desired output height\n resize_target (bool, optional):\n True: Resize the full sample (image, mask, target).\n False: Resize image only.\n Defaults to True.\n keep_aspect_ratio (bool, optional):\n True: Keep the aspect ratio of the input sample.\n Output sample might not have the given width and height, and\n resize behaviour depends on the parameter 'resize_method'.\n Defaults to False.\n ensure_multiple_of (int, optional):\n Output width and height is constrained to be multiple of this parameter.\n Defaults to 1.\n resize_method (str, optional):\n \"lower_bound\": Output will be at least as large as the given size.\n \"upper_bound\": Output will be at max as large as the given size. (Output size might be smaller than given size.)\n \"minimal\": Scale as least as possible. (Output size might be smaller than given size.)\n Defaults to \"lower_bound\".\n \"\"\"\n self.__width = width\n self.__height = height\n\n self.__resize_target = resize_target\n self.__keep_aspect_ratio = keep_aspect_ratio\n self.__multiple_of = ensure_multiple_of\n self.__resize_method = resize_method\n self.__image_interpolation_method = image_interpolation_method\n\n def constrain_to_multiple_of(self, x, min_val=0, max_val=None):\n y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)\n\n if max_val is not None and y > max_val:\n y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)\n\n if y < min_val:\n y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)\n\n return y\n\n def get_size(self, width, height):\n # determine new height and width\n scale_height = self.__height / height\n scale_width = self.__width / width\n\n if self.__keep_aspect_ratio:\n if self.__resize_method == \"lower_bound\":\n # scale such that output size is lower bound\n if scale_width > scale_height:\n # fit width\n scale_height = scale_width\n else:\n # fit height\n scale_width = scale_height\n elif self.__resize_method == \"upper_bound\":\n # scale such that output size is upper bound\n if scale_width < scale_height:\n # fit width\n scale_height = scale_width\n else:\n # fit height\n scale_width = scale_height\n elif self.__resize_method == \"minimal\":\n # scale as least as possbile\n if abs(1 - scale_width) < abs(1 - scale_height):\n # fit width\n scale_height = scale_width\n else:\n # fit height\n scale_width = scale_height\n else:\n raise ValueError(f\"resize_method {self.__resize_method} not implemented\")\n\n if self.__resize_method == \"lower_bound\":\n new_height = self.constrain_to_multiple_of(scale_height * height, min_val=self.__height)\n new_width = self.constrain_to_multiple_of(scale_width * width, min_val=self.__width)\n elif self.__resize_method == \"upper_bound\":\n new_height = self.constrain_to_multiple_of(scale_height * height, max_val=self.__height)\n new_width = self.constrain_to_multiple_of(scale_width * width, max_val=self.__width)\n elif self.__resize_method == \"minimal\":\n new_height = self.constrain_to_multiple_of(scale_height * height)\n new_width = self.constrain_to_multiple_of(scale_width * width)\n else:\n raise ValueError(f\"resize_method {self.__resize_method} not implemented\")\n\n return (new_width, new_height)\n\n def __call__(self, sample):\n width, height = self.get_size(sample[\"image\"].shape[1], sample[\"image\"].shape[0])\n\n # resize sample\n sample[\"image\"] = cv2.resize(\n sample[\"image\"], (width, height), interpolation=self.__image_interpolation_method\n )\n\n if self.__resize_target:\n if \"depth\" in sample:\n sample[\"depth\"] = cv2.resize(\n sample[\"depth\"], (width, height), interpolation=cv2.INTER_NEAREST\n )\n\n if \"mask\" in sample:\n sample[\"mask\"] = cv2.resize(\n sample[\"mask\"].astype(np.float32),\n (width, height),\n interpolation=cv2.INTER_NEAREST,\n )\n\n return sample\n\n\nclass NormalizeImage(object):\n \"\"\"Normlize image by given mean and std.\"\"\"\n\n def __init__(self, mean, std):\n self.__mean = mean\n self.__std = std\n\n def __call__(self, sample):\n sample[\"image\"] = (sample[\"image\"] - self.__mean) / self.__std\n\n return sample\n\n\nclass PrepareForNet(object):\n \"\"\"Prepare sample for usage as network input.\"\"\"\n\n def __init__(self):\n pass\n\n def __call__(self, sample):\n image = np.transpose(sample[\"image\"], (2, 0, 1))\n sample[\"image\"] = np.ascontiguousarray(image).astype(np.float32)\n\n if \"depth\" in sample:\n depth = sample[\"depth\"].astype(np.float32)\n sample[\"depth\"] = np.ascontiguousarray(depth)\n\n if \"mask\" in sample:\n sample[\"mask\"] = sample[\"mask\"].astype(np.float32)\n sample[\"mask\"] = np.ascontiguousarray(sample[\"mask\"])\n\n return sample\n" } ]