[ { "path": "OE_eval.py", "content": "from collections import OrderedDict\nfrom options.train_options import TrainOptions\nfrom data import CreateDataLoader\nfrom models import create_model\nfrom PIL import Image\nimport time\nimport math\nfrom sklearn.metrics import balanced_accuracy_score, mean_squared_error\nfrom skimage.color import rgb2lab\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport os\nimport shutil\n\nimport logging\n\n\nopt = TrainOptions().parse()\n#train_data_loader = CreateDataLoader(opt)\n#train_dataset = train_data_loader.load_data()\n#train_dataset_size = len(train_data_loader)\n\nopt.phase = 'test/test_'\nopt.batch_size = 1\nopt.serial_batches = True\nopt.isTrain = False\ntest_data_loader = CreateDataLoader(opt)\ntest_dataset = test_data_loader.load_data()\ntest_dataset_size = len(test_data_loader)\n\nmodel = create_model(opt)\nmodel.setup(opt)\n\n\n# Set logger\nmsg = []\nlogger = logging.getLogger('%s' % opt.name)\nlogger.setLevel(logging.INFO)\nif not os.path.isdir(model.save_dir):\n msg.append('%s not exist, make it' % model.save_dir)\n os.mkdir(opt.dir)\nlog_file_path = os.path.join(model.save_dir, 'log.log')\nif os.path.isfile(log_file_path):\n target_path = log_file_path + '.%s' % time.strftime(\"%Y%m%d%H%M%S\")\n msg.append('Log file exists, backup to %s' % target_path)\n shutil.move(log_file_path, target_path)\nfh = logging.FileHandler(log_file_path)\nfh.setLevel(logging.INFO)\nch = logging.StreamHandler()\nch.setLevel(logging.INFO)\n\nformatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nfh.setFormatter(formatter)\nch.setFormatter(formatter)\nlogger.addHandler(fh)\nlogger.addHandler(ch)\n\n\ndef tensor2im(input_image, imtype=np.uint8):\n \"\"\"\"Converts a Tensor array into a numpy image array.\n Parameters:\n input_image (tensor) -- the input image tensor array\n imtype (type) -- the desired type of the converted numpy array\n \"\"\"\n if not isinstance(input_image, np.ndarray):\n if isinstance(input_image, torch.Tensor): # get the data from a variable\n image_tensor = input_image.data\n else:\n return input_image\n image_numpy = image_tensor[0].cpu().float().numpy() # convert it into a numpy array\n if image_numpy.shape[0] == 1: # grayscale to RGB\n image_numpy = np.tile(image_numpy, (3, 1, 1))\n # image_numpy = image_numpy.convert('L')\n image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 # post-processing: tranpose and scaling\n else: # if it is a numpy array, do nothing\n image_numpy = input_image\n return np.clip(image_numpy, 0, 255).astype(imtype)\n\n\ndef calc_RMSE(real_img, fake_img):\n # convert to LAB color space\n real_lab = rgb2lab(real_img)\n fake_lab = rgb2lab(fake_img)\n return real_lab - fake_lab\n\nmodel.eval()\nmodel.load_networks('latest')\neval_shadow_rmse = 0\neval_nonshadow_rmse = 0\neval_rmse = 0\neval_loss = 0\nfor i, data in enumerate(test_dataset):\n iter_start_time = time.time()\n # total_steps += opt.batch_size\n # epoch_iter += opt.batch_size\n model.set_input(data)\n model.forward()\n\n # evaluation refers to matlab code\n # diff = calc_RMSE(tensor2im(model.shadowfree_img), tensor2im(model.final))\n # mask = model.shadow_mask.data[0].cpu().float().numpy()[..., None][0, ...]\n #\n # if mask.sum() < 2:\n # continue\n # shadow_rmse = np.sqrt(1.0 * (np.power(diff, 2) * mask).sum(axis=(0, 1)) / mask.sum())\n # nonshadow_rmse = np.sqrt(1.0 * (np.power(diff, 2) * (1 - mask)).sum(axis=(0, 1)) / (1 - mask).sum())\n # whole_rmse = np.sqrt(np.power(diff, 2).mean(axis=(0, 1)))\n #\n # eval_shadow_rmse += shadow_rmse.sum()\n # eval_nonshadow_rmse += nonshadow_rmse.sum()\n # eval_rmse += whole_rmse.sum()\n\n model.netR.zero_grad()\n\n model.vis(0, i, opt.name, True)\n\nlogger.info('[Eval] [Epoch] %d | rmse : %.3f | shadow_rmse : %.3f | nonshadow_rmse : %.3f' % \n (0, eval_rmse / len(test_dataset),\n eval_shadow_rmse / len(test_dataset), eval_nonshadow_rmse / len(test_dataset)))\n\n" }, { "path": "OE_eval.sh", "content": "#!/bin/bash\n\nMYGIT=/mnt/nvme/zcq/git\nREPO_PATH=/home/fulan/shadow_removal/exposure-fusion-shadow-removal\n# DATA_PATH=${MYGIT}/shadow_removal/data/SRD\n# datasetmode=srd\n\nDATA_PATH=./ISTD+\ndatasetmode=expo_param\n\n\nbatchs=8\nn=5\nks=3\nrks=3\nversion='fixed5-1-boundary-loss'\n\nlr_policy=lambda\nlr_decay_iters=50\noptimizer=adam\nshadow_loss=5.0\n\ntv_loss=0.0\ngrad_loss=0.1\npgrad_loss=0.0\n\ngpus=0\n\n\nlr=0.0001\nloadSize=256\nfineSize=256\nL1=10\nmodel=Refine\ncheckpoint=${REPO_PATH}/log\ndataroot=${DATA_PATH}\nNAME=\"M${model}_${datasetmode}_b${batchs}_lr${lr}_L1${L1}_n${n}_ks${ks}_v${version}_${optimizer}_${lr_policy}_${shadow_loss}_rks${rks}_TV${tv_loss}G${grad_loss}GP${pgrad_loss}\"\nOTHER=\"--save_epoch_freq 100 --niter 50 --niter_decay 350\"\n\n\ntrainmask=${dataroot}'/train_NOTUSE'\nCMD=\"python -u ./OE_eval.py --loadSize ${loadSize} \\\n --randomSize\n --name ${NAME} \\\n --dataroot ${dataroot}\\\n --checkpoints_dir ${checkpoint} \\\n --fineSize $fineSize --model $model \\\n --batch_size $batchs \\\n --randomSize --keep_ratio --phase train_ --gpu_ids ${gpus} --lr ${lr} \\\n --lambda_L1 ${L1} --num_threads 16 \\\n --dataset_mode $datasetmode\\\n --mask_train $trainmask --optimizer ${optimizer} \\\n --n ${n} --ks ${ks} --lr_policy ${lr_policy} --lr_decay_iters ${lr_decay_iters} \\\n --shadow_loss ${shadow_loss} --tv_loss ${tv_loss} --grad_loss ${grad_loss} --pgrad_loss ${pgrad_loss} \\\n $OTHER\n\"\necho $CMD\neval $CMD # >> ${checkpoint}/${NAME}.log 2>&1 &\n\n" }, { "path": "OE_train.py", "content": "from collections import OrderedDict\nfrom options.train_options import TrainOptions\nfrom data import CreateDataLoader\nfrom models import create_model\nfrom PIL import Image\nimport time\nimport math\nfrom sklearn.metrics import balanced_accuracy_score, mean_squared_error\nfrom skimage.color import rgb2lab\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport os\nimport shutil\n\nimport logging\n\nopt = TrainOptions().parse()\n\nopt.phase = 'train/train_'\nopt.serial_batches = False\ntrain_data_loader = CreateDataLoader(opt)\ntrain_dataset = train_data_loader.load_data()\ntrain_dataset_size = len(train_data_loader)\n\nopt.phase = 'test/test_'\nopt.batch_size = 1\nopt.serial_batches = True\ntest_data_loader = CreateDataLoader(opt)\ntest_dataset = test_data_loader.load_data()\ntest_dataset_size = len(test_data_loader)\n\nmodel = create_model(opt)\nmodel.setup(opt)\nif opt.load_dir and opt.load_dir != 'None':\n print('load fusion net from:', opt.load_dir)\n model.load_networks('latest', opt.load_dir)\n\n# Set logger\nmsg = []\nlogger = logging.getLogger('%s' % opt.name)\nlogger.setLevel(logging.INFO)\nif not os.path.isdir(model.save_dir):\n msg.append('%s not exist, make it' % model.save_dir)\n os.mkdir(args.dir)\nlog_file_path = os.path.join(model.save_dir, 'log.log')\nif os.path.isfile(log_file_path):\n target_path = log_file_path + '.%s' % time.strftime(\"%Y%m%d%H%M%S\")\n msg.append('Log file exists, backup to %s' % target_path)\n shutil.move(log_file_path, target_path)\nfh = logging.FileHandler(log_file_path)\nfh.setLevel(logging.INFO)\nch = logging.StreamHandler()\nch.setLevel(logging.INFO)\n\nformatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nfh.setFormatter(formatter)\nch.setFormatter(formatter)\nlogger.addHandler(fh)\nlogger.addHandler(ch)\n\n\ndef tensor2im(input_image, imtype=np.uint8):\n \"\"\"\"Converts a Tensor array into a numpy image array.\n Parameters:\n input_image (tensor) -- the input image tensor array\n imtype (type) -- the desired type of the converted numpy array\n \"\"\"\n if not isinstance(input_image, np.ndarray):\n if isinstance(input_image, torch.Tensor): # get the data from a variable\n image_tensor = input_image.data\n else:\n return input_image\n image_numpy = image_tensor[0].cpu().float().numpy() # convert it into a numpy array\n if image_numpy.shape[0] == 1: # grayscale to RGB\n image_numpy = np.tile(image_numpy, (3, 1, 1))\n # image_numpy = image_numpy.convert('L')\n image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 # post-processing: tranpose and scaling\n else: # if it is a numpy array, do nothing\n image_numpy = input_image\n return np.clip(image_numpy, 0, 255).astype(imtype)\n\n\ndef calc_RMSE(real_img, fake_img):\n # convert to LAB color space\n real_lab = rgb2lab(real_img)\n fake_lab = rgb2lab(fake_img)\n return real_lab - fake_lab\n\n\nmse_criterion = nn.MSELoss()\ntotal_steps = 0\n\nfor epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):\n epoch_start_time = time.time()\n epoch_iter = 0\n model.epoch = epoch\n\n model.train()\n for i, data in enumerate(train_dataset):\n iter_start_time = time.time()\n total_steps += 1\n model.set_input(data)\n # model.zero_grad()\n model.optimize_parameters()\n\n if total_steps % 10 == 0:\n # Do log\n train_loss = model.loss.detach().item()\n train_mse = mse_criterion(model.final, model.shadowfree_img).detach().item()\n logger.info('[Train] [Epoch] %d [Steps] %d | loss : %.3f' % (epoch, total_steps, train_loss))\n\n if (epoch and epoch % 30 == 0) or (epoch == opt.niter + opt.niter_decay):\n model.eval()\n eval_shadow_rmse = 0\n eval_nonshadow_rmse = 0\n eval_rmse = 0\n eval_loss = 0\n for i, data in enumerate(test_dataset):\n iter_start_time = time.time()\n total_steps += opt.batch_size\n epoch_iter += opt.batch_size\n model.set_input(data)\n model.forward()\n\n eval_loss += model.loss.detach().item()\n diff = calc_RMSE(tensor2im(model.shadowfree_img), tensor2im(model.final))\n mask = model.shadow_mask.data[0].cpu().float().numpy()[..., None][0, ...]\n\n if mask.sum() < 2:\n continue\n shadow_rmse = np.sqrt(1.0 * (np.power(diff, 2) * mask).sum(axis=(0, 1)) / mask.sum())\n nonshadow_rmse = np.sqrt(1.0 * (np.power(diff, 2) * (1 - mask)).sum(axis=(0, 1)) / (1 - mask).sum())\n whole_rmse = np.sqrt(np.power(diff, 2).mean(axis=(0, 1)))\n\n # (256, 256, 3) (3,) (3,) (256, 256, 1) (256, 256, 3)\n # print(diff.shape, whole_rmse.shape, shadow_rmse.shape, mask.shape, (diff * mask).shape)\n\n eval_shadow_rmse += shadow_rmse.sum()\n eval_nonshadow_rmse += nonshadow_rmse.sum()\n eval_rmse += whole_rmse.sum()\n\n model.zero_grad()\n\n if i % 20 == 0:\n model.vis(epoch, i)\n\n logger.info('[Eval] [Epoch] %d | loss : %.3f | rmse : %.3f | shadow_rmse : %.3f | nonshadow_rmse : %.3f' %\n (epoch, eval_loss / len(test_dataset), eval_rmse / len(test_dataset),\n eval_shadow_rmse / len(test_dataset), eval_nonshadow_rmse / len(test_dataset)))\n\n if epoch and epoch % 50 == 0 or (epoch == opt.niter + opt.niter_decay):\n logger.info('saving the model at the end of epoch %d, iters %d' %\n (epoch, total_steps))\n model.save_networks('latest')\n model.save_networks(epoch)\n\n spt_time = time.time() - epoch_start_time\n lft_time = (opt.niter + opt.niter_decay - epoch) * spt_time\n logger.info('End of epoch %d / %d | Time Taken: %d sec | eta %.2f' %\n (epoch, opt.niter + opt.niter_decay, spt_time, lft_time / 3600.0))\n model.update_learning_rate()\n" }, { "path": "OE_train.sh", "content": "#!/bin/bash\n\n\nMYGIT=/mnt/nvme/zcq/git\nREPO_PATH=/home/fulan/shadow_removal\n# DATA_PATH=${MYGIT}/shadow_removal/data/SRD\n# datasetmode=srd\n\nDATA_PATH=/home/fulan/ShadowRemoval/data\ndatasetmode=expo_param\n\n\nbatchs=4\nn=5\nks=3\nrks=3\nversion='fixed5-1-loss'\n\nlr_policy=lambda\nlr_decay_iters=50\noptimizer=adam\nshadow_loss=10.0\n\ntv_loss=0\ngrad_loss=0.0\npgrad_loss=0.1\n\ngpus=0\n\n\nlr=0.0001\nloadSize=256\nfineSize=256\nL1=10\nmodel=Fusion\nload_dir=None\ncheckpoint=${REPO_PATH}/log\ndataroot=${DATA_PATH}\nNAME=\"M${model}_${datasetmode}_b${batchs}_lr${lr}_L1${L1}_n${n}_ks${ks}_v${version}_${optimizer}_${lr_policy}_${shadow_loss}_TV${tv_loss}G${grad_loss}PG${pgrad_loss}\"\nOTHER=\"--save_epoch_freq 100 --niter 50 --niter_decay 350\"\n\n\ntrainmask=${dataroot}'/train_NOTUSE'\nCMD=\"python -u ./OE_train.py --loadSize ${loadSize} \\\n --randomSize\n --name ${NAME} \\\n --dataroot ${dataroot}\\\n --checkpoints_dir ${checkpoint} \\\n --fineSize $fineSize --model $model \\\n --batch_size $batchs \\\n --randomSize --keep_ratio --phase train_ --gpu_ids ${gpus} --lr ${lr} \\\n --lambda_L1 ${L1} --num_threads 16 \\\n --dataset_mode $datasetmode\\\n --mask_train $trainmask --optimizer ${optimizer} \\\n --n ${n} --ks ${ks} --lr_policy ${lr_policy} --lr_decay_iters ${lr_decay_iters} \\\n --shadow_loss ${shadow_loss} --rks ${rks} --tv_loss ${tv_loss} --grad_loss ${grad_loss} --pgrad_loss ${pgrad_loss} \\\n --load_dir ${load_dir} \\\n $OTHER\n\"\necho $CMD\neval $CMD # >> ${checkpoint}/${NAME}.log 2>&1 &\n\n" }, { "path": "README.md", "content": "# Auto-exposure fusion for single-image shadow removal\nWe propose a new method for effective shadow removal by regarding it as an exposure fusion problem. Please refer to the paper for details: https://openaccess.thecvf.com/content/CVPR2021/papers/Fu_Auto-Exposure_Fusion_for_Single-Image_Shadow_Removal_CVPR_2021_paper.pdf.\n\n![Framework](./images/framework.png)\n\n## Dataset\n\n- ISTD [ISTD](https://github.com/DeepInsight-PCALab/ST-CGAN)\n- ISTD+ [ISTD+](https://github.com/cvlab-stonybrook/SID)\n- SRD\n\n1. For data folder path (ISTD), train_A: shadow images, train_B: shadow masks, train_C: shadow free images, organize them as following:\n\n```shell\n--ISTD+\n --train\n --train_A\n --1-1.png\n --train_B\n --1-1.png \n --train_C_fixed_official \n --1-1.png\n --train_params_fixed # generate later\n --1-1.png.txt\n --test\n --test_A\n --1-1.png\n --test_B\n --1-1.png\n --test_C\n --1-1.png\n --mask_threshold # generate later\n --1-1.png\n ```\n \n 2. Run the code `./data_processing/compute_params.ipynb` for exposure parameters generation. \n The result will be put in `./ISTD/train/train_params_fixed`.\n Here, names `train_C_fixed_official` and `train_params_fixed` are for ISTD+ dataset, which are consitent with `self.dir_C` and `self.dir_param` in `./data/expo_param_dataset.py` .\n 3. For testing masks, please run the code `./data_processing/test_mask_generation.py`. \n The result will be put in `./ISTD/mask_threshold`.\n\n\n## Pretrained models\n\nWe release our pretrained model (ISTD+, SRD) at [models](https://drive.google.com/drive/folders/1riTtYvHpffYu-nqSizqSF4fhbZ2txrp5?usp=sharing)\n\npretrained model (ISTD) at [models](https://drive.google.com/drive/folders/1qECA9EjUSLMtUpN5fFZMjltQPzjp2gL9?usp=sharing)\n\nModify the parameter `model` in file `OE_eval.sh` to `Refine` and set `ks=3, n=5, rks=3` to load the model.\n\n## Train\n\nModify the corresponding path in file `OE_train.sh` and run the following script\n\n```shell\nsh OE_train.sh\n```\n1. For the parameters:\n```shell\n DATA_PATH=./Datasets/ISTD or your datapath\n n=5, ks=3 for FusionNet,\n n=5, ks=3, rks=3 for RefineNet.\n model=Fusion for FusionNet training,\n model=Refine for RefineNet training.\n ```\n \n The trained models are saved in `${REPO_PATH}/log/${Name}`, `Name` are customized for parameters setting.\n\n## Test\n\nIn order to test the performance of a trained model, you need to make sure that the hyper parameters in file `OE_eval.sh` match the ones in `OE_train.sh` and run the following script:\n\n```shell\nsh OE_eval.sh\n```\n1. The pretrained models are located in `${REPO_PATH}/log/${Name}`.\n\n## Evaluation\nThe results reported in the paper are calculated by the `matlab` script used in other SOTA, please see [evaluation](https://drive.google.com/file/d/1SAMqLy3dSONPgeC5ZQskPoeq60FEx9Vk/view?usp=sharing) for details. Our evaluation code will print the metrics calculated by `python` code and save the shadow removed result images which will be used by the `matlab` script.\n\n## Results\n\n- Comparsion with SOTA, see paper for details.\n\n![Framework](./images/vis_compare.png)\n\n\n- Penumbra comparsion between ours and SP+M Net\n\n![Framework](./images/edge_comparsion.png)\n\n- Testing result\n\nThe testing results on dataset ISTD+, ISTD, SRD are:[results](https://drive.google.com/drive/folders/1ubLj5r_ZMzWew4h2bNX7pQL6D62mM-dl?usp=sharing)\n\n\n**More details are coming soon**\n\n## Bibtex\n\n```\n@inproceedings{fu2021auto,\n title={Auto-exposure Fusion for Single-image Shadow Removal}, \n author={Lan Fu and Changqing Zhou and Qing Guo and Felix Juefei-Xu and Hongkai Yu and Wei Feng and Yang Liu and Song Wang},\n year={2021},\n booktitle={accepted to CVPR}\n}\n```\n" }, { "path": "data/__init__.py", "content": "import importlib\nimport torch.utils.data\nfrom data.base_data_loader import BaseDataLoader\nfrom data.base_dataset import BaseDataset\n\n\ndef find_dataset_using_name(dataset_name):\n # Given the option --dataset_mode [datasetname],\n # the file \"data/datasetname_dataset.py\"\n # will be imported.\n dataset_filename = \"data.\" + dataset_name + \"_dataset\"\n datasetlib = importlib.import_module(dataset_filename)\n\n # In the file, the class called DatasetNameDataset() will\n # be instantiated. It has to be a subclass of BaseDataset,\n # and it is case-insensitive.\n dataset = None\n target_dataset_name = dataset_name.replace('_', '') + 'dataset'\n for name, cls in datasetlib.__dict__.items():\n if name.lower() == target_dataset_name.lower() \\\n and issubclass(cls, BaseDataset):\n dataset = cls\n\n if dataset is None:\n print(\"In %s.py, there should be a subclass of BaseDataset with class name that matches %s in lowercase.\" % (dataset_filename, target_dataset_name))\n exit(0)\n\n return dataset\n\n\ndef get_option_setter(dataset_name):\n dataset_class = find_dataset_using_name(dataset_name)\n return dataset_class.modify_commandline_options\n\n\ndef create_dataset(opt):\n dataset = find_dataset_using_name(opt.dataset_mode)\n instance = dataset()\n instance.initialize(opt)\n print(\"dataset [%s] was created\" % (instance.name()))\n return instance\n\n\ndef CreateDataLoader(opt):\n data_loader = CustomDatasetDataLoader()\n data_loader.initialize(opt)\n return data_loader\n\n\n# Wrapper class of Dataset class that performs\n# multi-threaded data loading\nclass CustomDatasetDataLoader(BaseDataLoader):\n def name(self):\n return 'CustomDatasetDataLoader'\n\n def initialize(self, opt):\n BaseDataLoader.initialize(self, opt)\n self.dataset = create_dataset(opt)\n self.dataloader = torch.utils.data.DataLoader(\n self.dataset,\n batch_size=opt.batch_size,\n shuffle=not opt.serial_batches,\n num_workers=int(opt.num_threads))\n\n def load_data(self):\n return self\n\n def __len__(self):\n return min(len(self.dataset), self.opt.max_dataset_size)\n\n def __iter__(self):\n for i, data in enumerate(self.dataloader):\n if i * self.opt.batch_size >= self.opt.max_dataset_size:\n break\n yield data\n" }, { "path": "data/base_data_loader.py", "content": "class BaseDataLoader():\n def __init__(self):\n pass\n\n def initialize(self, opt):\n self.opt = opt\n pass\n\n def load_data():\n return None\n" }, { "path": "data/base_dataset.py", "content": "import torch.utils.data as data\nfrom PIL import Image\nimport torchvision.transforms as transforms\n\n\nclass BaseDataset(data.Dataset):\n def __init__(self):\n super(BaseDataset, self).__init__()\n\n def name(self):\n return 'BaseDataset'\n\n @staticmethod\n def modify_commandline_options(parser, is_train):\n return parser\n\n def initialize(self, opt):\n pass\n\n def __len__(self):\n return 0\n\n\ndef get_transform(opt):\n transform_list = []\n if opt.resize_or_crop == 'resize_and_crop':\n osize = [opt.loadSize, opt.loadSize]\n transform_list.append(transforms.Resize(osize, Image.BICUBIC))\n transform_list.append(transforms.RandomCrop(opt.fineSize))\n elif opt.resize_or_crop == 'crop':\n transform_list.append(transforms.RandomCrop(opt.fineSize))\n elif opt.resize_or_crop == 'scale_width':\n transform_list.append(transforms.Lambda(\n lambda img: __scale_width(img, opt.fineSize)))\n elif opt.resize_or_crop == 'scale_width_and_crop':\n transform_list.append(transforms.Lambda(\n lambda img: __scale_width(img, opt.loadSize)))\n transform_list.append(transforms.RandomCrop(opt.fineSize))\n elif opt.resize_or_crop == 'none':\n transform_list.append(transforms.Lambda(\n lambda img: __adjust(img)))\n else:\n raise ValueError('--resize_or_crop %s is not a valid option.' % opt.resize_or_crop)\n\n if opt.isTrain and not opt.no_flip:\n transform_list.append(transforms.RandomHorizontalFlip())\n\n transform_list += [transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5),\n (0.5, 0.5, 0.5))]\n return transforms.Compose(transform_list)\n\n\n# just modify the width and height to be multiple of 4\ndef __adjust(img):\n ow, oh = img.size\n\n # the size needs to be a multiple of this number,\n # because going through generator network may change img size\n # and eventually cause size mismatch error\n mult = 4\n if ow % mult == 0 and oh % mult == 0:\n return img\n w = (ow - 1) // mult\n w = (w + 1) * mult\n h = (oh - 1) // mult\n h = (h + 1) * mult\n\n if ow != w or oh != h:\n __print_size_warning(ow, oh, w, h)\n\n return img.resize((w, h), Image.BICUBIC)\n\n\ndef __scale_width(img, target_width):\n ow, oh = img.size\n\n # the size needs to be a multiple of this number,\n # because going through generator network may change img size\n # and eventually cause size mismatch error\n mult = 4\n assert target_width % mult == 0, \"the target width needs to be multiple of %d.\" % mult\n if (ow == target_width and oh % mult == 0):\n return img\n w = target_width\n target_height = int(target_width * oh / ow)\n m = (target_height - 1) // mult\n h = (m + 1) * mult\n\n if target_height != h:\n __print_size_warning(target_width, target_height, w, h)\n\n return img.resize((w, h), Image.BICUBIC)\n\n\ndef __print_size_warning(ow, oh, w, h):\n if not hasattr(__print_size_warning, 'has_printed'):\n print(\"The image size needs to be a multiple of 4. \"\n \"The loaded image size was (%d, %d), so it was adjusted to \"\n \"(%d, %d). This adjustment will be done to all images \"\n \"whose sizes are not multiples of 4\" % (ow, oh, w, h))\n __print_size_warning.has_printed = True\n" }, { "path": "data/expo_param_dataset.py", "content": "import os.path\nimport torchvision.transforms as transforms\nfrom data.base_dataset import BaseDataset, get_transform\nfrom data.image_folder import make_dataset\nfrom PIL import Image, ImageChops\nfrom PIL import ImageFilter\nimport torch\nfrom pdb import set_trace as st\nimport random\nimport numpy as np\nimport time\nimport cv2\n\n\nclass ExpoParamDataset(BaseDataset):\n def initialize(self, opt):\n self.opt = opt\n self.root = opt.dataroot\n self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A')\n # if not opt.use_our_mask:\n if 'test' in opt.phase:\n self.dir_B = os.path.join(opt.dataroot, 'test', 'mask_threshold') # for test masks generated\n self.dir_C = os.path.join(opt.dataroot, opt.phase + 'C')\n else:\n self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B')\n self.dir_C = os.path.join(opt.dataroot, opt.phase + 'C_fixed_official')\n\n self.dir_param = os.path.join(opt.dataroot, opt.phase + 'params_fixed') # opt.param_path\n print(self.dir_A)\n self.A_paths, self.imname = make_dataset(self.dir_A)\n self.A_size = len(self.A_paths)\n self.B_size = self.A_size\n\n transform_list = [transforms.ToTensor(),\n transforms.Normalize(mean=opt.norm_mean,\n std=opt.norm_std)]\n\n self.transformA = transforms.Compose(transform_list)\n self.transformB = transforms.Compose([transforms.ToTensor()])\n\n if 'train' in opt.phase:\n self.is_train = True\n else:\n self.is_train = False\n\n def __getitem__(self, index):\n colet = {}\n A_path = self.A_paths[index % self.A_size]\n imname = self.imname[index % self.A_size]\n index_A = index % self.A_size\n\n B_path = os.path.join(self.dir_B, imname.replace('.jpg', '.png'))\n if not os.path.isfile(B_path):\n B_path = os.path.join(self.dir_B, imname)\n A_img = Image.open(A_path).convert('RGB')\n\n if self.is_train:\n sparam = open(os.path.join(self.dir_param, imname + '.txt'))\n line = sparam.read()\n sparam.close()\n shadow_param = np.asarray([float(i) for i in line.split(\" \") if i.strip()])\n shadow_param = shadow_param[0:6]\n\n ow = A_img.size[0]\n oh = A_img.size[1]\n w = np.float(A_img.size[0])\n h = np.float(A_img.size[1])\n if os.path.isfile(B_path):\n B_img = Image.open(B_path)\n else:\n print('MASK NOT FOUND : %s' % (B_path))\n B_img = Image.fromarray(np.zeros((int(w), int(h)), dtype=np.float), mode='L')\n\n B_img_np = np.asarray(B_img)\n kernel = np.ones((7, 7), np.uint8)\n B_img_np_dilate = cv2.dilate(B_img_np, kernel, iterations=1)\n B_img_np_erode = cv2.erode(B_img_np, kernel, iterations=1)\n B_img_dilate = Image.fromarray(B_img_np_dilate)\n B_img_erode = Image.fromarray(B_img_np_erode)\n colet['B_dilate'] = B_img_dilate\n colet['B_erode'] = B_img_erode\n\n colet['C'] = Image.open(os.path.join(self.dir_C, imname)).convert('RGB')\n\n loadSize = self.opt.loadSize\n if self.is_train and self.opt.randomSize:\n loadSize = np.random.randint(loadSize + 1, loadSize * 1.3, 1)[0]\n\n if self.opt.keep_ratio:\n if w > h:\n ratio = np.float(loadSize) / np.float(h)\n neww = np.int(w * ratio)\n newh = loadSize\n else:\n ratio = np.float(loadSize) / np.float(w)\n neww = loadSize\n newh = np.int(h * ratio)\n else:\n neww = loadSize\n newh = loadSize\n\n colet['A'] = A_img\n colet['B'] = B_img\n\n if self.is_train:\n t = [Image.FLIP_LEFT_RIGHT, Image.ROTATE_90]\n for i in range(0, 4):\n c = np.random.randint(0, 3, 1, dtype=np.int)[0]\n if c == 2: continue\n for i in ['A', 'B', 'C', 'B_dilate', 'B_erode']:\n if i in colet:\n colet[i] = colet[i].transpose(t[c])\n\n if self.is_train:\n degree = np.random.randint(-20, 20, 1)[0]\n for i in ['A', 'B', 'C', 'B_dilate', 'B_erode']:\n colet[i] = colet[i].rotate(degree)\n\n for k, im in colet.items():\n if self.is_train:\n colet[k] = im.resize((neww, newh), Image.NEAREST)\n else:\n colet[k] = im.resize((self.opt.fineSize, self.opt.fineSize), Image.NEAREST)\n\n w = colet['A'].size[0]\n h = colet['A'].size[1]\n\n for k, im in colet.items():\n colet[k] = self.transformB(im)\n\n for i in ['A', 'C', 'B', 'B_dilate', 'B_erode']:\n if i in colet:\n colet[i] = (colet[i] - 0.5) * 2\n\n if self.is_train: # and not self.opt.no_crop:\n w_offset = random.randint(0, max(0, w - self.opt.fineSize - 1))\n h_offset = random.randint(0, max(0, h - self.opt.fineSize - 1))\n for k, im in colet.items():\n colet[k] = im[:, h_offset:h_offset + self.opt.fineSize, w_offset:w_offset + self.opt.fineSize]\n\n if self.is_train and (not self.opt.no_flip) and random.random() < 0.5:\n idx = [i for i in range(colet['A'].size(2) - 1, -1, -1)]\n idx = torch.LongTensor(idx)\n for k, im in colet.items():\n colet[k] = im.index_select(2, idx)\n\n for k, im in colet.items():\n colet[k] = im.type(torch.FloatTensor)\n\n colet['imname'] = imname\n colet['w'] = ow\n colet['h'] = oh\n colet['A_paths'] = A_path\n colet['B_baths'] = B_path\n\n if self.is_train:\n # if the shadow area is too small, let's not change anything:\n if torch.sum(colet['B'] > 0) < 30:\n shadow_param = [0, 1, 0, 1, 0, 1]\n\n\n colet['param'] = torch.FloatTensor(np.array(shadow_param))\n return colet\n\n def __len__(self):\n return max(self.A_size, self.B_size)\n\n def name(self):\n return 'ExpoParamDataset'\n" }, { "path": "data/image_folder.py", "content": "###############################################################################\n# Code from\n# https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py\n# Modified the original code so that it also loads images from the current\n# directory as well as the subdirectories\n###############################################################################\n\nimport torch.utils.data as data\n\nfrom PIL import Image\nimport os\nimport os.path\n\nIMG_EXTENSIONS = [\n '.jpg', '.JPG', '.jpeg', '.JPEG',\n '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',\n]\n\n\ndef is_image_file(filename):\n return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)\n\ndef make_dataset(dir):\n images = []\n imname = []\n assert os.path.isdir(dir), '%s is not a valid directory' % dir\n\n for root, _, fnames in sorted(os.walk(dir)):\n for fname in fnames:\n if is_image_file(fname):\n path = os.path.join(root, fname)\n images.append(path)\n imname.append(fname)\n return images,imname\n\ndef default_loader(path):\n return Image.open(path).convert('RGB')\n\n\nclass ImageFolder(data.Dataset):\n\n def __init__(self, root, transform=None, return_paths=False,\n loader=default_loader):\n imgs = make_dataset(root)\n if len(imgs) == 0:\n raise(RuntimeError(\"Found 0 images in: \" + root + \"\\n\"\n \"Supported image extensions are: \" +\n \",\".join(IMG_EXTENSIONS)))\n\n self.root = root\n self.imgs = imgs\n self.transform = transform\n self.return_paths = return_paths\n self.loader = loader\n\n def __getitem__(self, index):\n path = self.imgs[index]\n img = self.loader(path)\n if self.transform is not None:\n img = self.transform(img)\n if self.return_paths:\n return img, path\n else:\n return img\n\n def __len__(self):\n return len(self.imgs)\n" }, { "path": "data/srd_dataset.py", "content": "import os.path\nimport torchvision.transforms as transforms\nfrom data.base_dataset import BaseDataset, get_transform\nfrom data.image_folder import make_dataset\nfrom PIL import Image, ImageChops\nfrom PIL import ImageFilter\nimport torch\nfrom pdb import set_trace as st\nimport random\nimport numpy as np\nimport time\nimport cv2\n\n\nclass SRDDataset(BaseDataset):\n def initialize(self, opt):\n self.opt = opt\n self.root = opt.dataroot\n\n if 'train' in opt.phase:\n phase = 'train'\n elif 'test' in opt.phase:\n phase = 'test'\n else:\n raise False\n\n self.dir_A = os.path.join(opt.dataroot, phase, 'shadow')\n self.dir_B = os.path.join(opt.dataroot, phase, 'mask')\n print(self.dir_B)\n self.dir_C = os.path.join(opt.dataroot, phase, 'shadow_free')\n self.dir_param = os.path.join(opt.dataroot, phase, 'mask_params')\n print(self.dir_A)\n self.A_paths, self.imname = make_dataset(self.dir_A)\n self.A_size = len(self.A_paths)\n self.B_size = self.A_size\n\n transform_list = [transforms.ToTensor(),\n transforms.Normalize(mean=opt.norm_mean,\n std=opt.norm_std)]\n\n self.transformA = transforms.Compose(transform_list)\n self.transformB = transforms.Compose([transforms.ToTensor()])\n\n if 'train' in opt.phase:\n self.is_train = True\n else:\n self.is_train = False\n\n def __getitem__(self, index):\n colet = {}\n A_path = self.A_paths[index % self.A_size]\n imname = self.imname[index % self.A_size]\n index_A = index % self.A_size\n\n B_path = os.path.join(self.dir_B, imname.replace('.jpg', '.png'))\n if not os.path.isfile(B_path):\n B_path = os.path.join(self.dir_B, imname)\n A_img = Image.open(A_path).convert('RGB')\n\n # if self.is_train:\n sparam = open(os.path.join(self.dir_param, imname + '.txt'))\n line = sparam.read()\n sparam.close()\n shadow_param = np.asarray([float(i) for i in line.split(\" \") if i.strip()])\n shadow_param = shadow_param[0:6]\n\n ow = A_img.size[0]\n oh = A_img.size[1]\n w = np.float(A_img.size[0])\n h = np.float(A_img.size[1])\n if os.path.isfile(B_path):\n B_img = Image.open(B_path)\n else:\n print('MASK NOT FOUND : %s' % (B_path))\n B_img = Image.fromarray(np.zeros((int(w), int(h)), dtype=np.float), mode='L')\n if self.is_train:\n colet['C'] = Image.open(os.path.join(self.dir_C, imname.replace('.jpg', '_no_shadow.jpg'))).convert('RGB')\n else:\n colet['C'] = Image.open(os.path.join(self.dir_C, imname.replace('.jpg', '_free.jpg'))).convert('RGB')\n\n B_img_np = np.asarray(B_img)\n kernel = np.ones((7, 7), np.uint8)\n B_img_np_dilate = cv2.dilate(B_img_np, kernel, iterations=1)\n B_img_np_erode = cv2.erode(B_img_np, kernel, iterations=1)\n B_img_dilate = Image.fromarray(B_img_np_dilate)\n B_img_erode = Image.fromarray(B_img_np_erode)\n colet['B_dilate'] = B_img_dilate\n colet['B_erode'] = B_img_erode\n\n loadSize = self.opt.loadSize\n if self.is_train and self.opt.randomSize:\n loadSize = np.random.randint(loadSize + 1, loadSize * 1.3, 1)[0]\n\n if self.opt.keep_ratio:\n if w > h:\n ratio = np.float(loadSize) / np.float(h)\n neww = np.int(w * ratio)\n newh = loadSize\n else:\n ratio = np.float(loadSize) / np.float(w)\n neww = loadSize\n newh = np.int(h * ratio)\n else:\n neww = loadSize\n newh = loadSize\n\n colet['A'] = A_img\n colet['B'] = B_img\n\n if self.is_train:\n t = [Image.FLIP_LEFT_RIGHT, Image.ROTATE_90]\n for i in range(0, 4):\n c = np.random.randint(0, 3, 1, dtype=np.int)[0]\n if c == 2: continue\n for i in ['A', 'B', 'C', 'B_dilate', 'B_erode']:\n if i in colet:\n colet[i] = colet[i].transpose(t[c])\n\n if self.is_train:\n degree = np.random.randint(-20, 20, 1)[0]\n for i in ['A', 'B', 'C', 'B_dilate', 'B_erode']:\n colet[i] = colet[i].rotate(degree)\n\n for k, im in colet.items():\n if self.is_train:\n colet[k] = im.resize((neww, newh), Image.NEAREST)\n else:\n colet[k] = im.resize((self.opt.fineSize, self.opt.fineSize), Image.NEAREST)\n\n w = colet['A'].size[0]\n h = colet['A'].size[1]\n\n for k, im in colet.items():\n colet[k] = self.transformB(im)\n\n for i in ['A', 'C', 'B', 'B_dilate', 'B_erode']:\n if i in colet:\n colet[i] = (colet[i] - 0.5) * 2\n\n if self.is_train: # and not self.opt.no_crop:\n w_offset = random.randint(0, max(0, w - self.opt.fineSize - 1))\n h_offset = random.randint(0, max(0, h - self.opt.fineSize - 1))\n for k, im in colet.items():\n colet[k] = im[:, h_offset:h_offset + self.opt.fineSize, w_offset:w_offset + self.opt.fineSize]\n\n if self.is_train and (not self.opt.no_flip) and random.random() < 0.5:\n idx = [i for i in range(colet['A'].size(2) - 1, -1, -1)]\n idx = torch.LongTensor(idx)\n for k, im in colet.items():\n colet[k] = im.index_select(2, idx)\n\n for k, im in colet.items():\n colet[k] = im.type(torch.FloatTensor)\n colet['imname'] = imname\n colet['w'] = ow\n colet['h'] = oh\n colet['A_paths'] = A_path\n colet['B_baths'] = B_path\n\n # if the shadow area is too small, let's not change anything:\n if torch.sum(colet['B'] > 0) < 30:\n shadow_param = [0, 1, 0, 1, 0, 1]\n\n colet['param'] = torch.FloatTensor(np.array(shadow_param))\n return colet\n\n def __len__(self):\n return max(self.A_size, self.B_size)\n\n def name(self):\n return 'SRDDataset'\n" }, { "path": "data_processing/compute_params.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1330 13-14.png\\n\",\n \"5.009201165757417 [ 2.4577046 -0.0107077] [1.96292375 0.03742387] [1.66641561 0.05142359]\\n\",\n \"12.557166108231598 [1. 0.47089183] [1.01141423 0.40761759] [1. 0.35819274]\\n\",\n \"7.532163787052489 [1.36470302 0.11617836] [1.35946438 0.07884616] [1.2162239 0.09544635]\\n\",\n \"19.61040195664874 [1. 0.46429342] [1. 0.41372481] [1. 0.35411328]\\n\",\n \"12.387102593381845 [1. 0.40474525] [1. 0.36618001] [1. 0.30457438]\\n\",\n \"6.241485039361433 [1.24169934 0.24815966] [1.20049734 0.21402725] [1.20519596 0.12513773]\\n\",\n \"5.785971619706131 [1.51092218 0.34851648] [1.21188995 0.34101258] [1.02058334 0.29614014]\\n\",\n \"17.118862751395312 [1.66188257 0.26537164] [1.35520425 0.28154068] [1.77398978 0.11631677]\\n\",\n \"4.218141292961971 [1.13025247 0.38224351] [1.10667088 0.33890229] [1. 0.31539165]\\n\",\n \"11.894605985113497 [1.3901423 0.18765527] [1.35044863 0.16179696] [1.15484409 0.16976616]\\n\",\n \"11.361911276969092 [1.36932553 0.20350377] [1.18084095 0.20083562] [1.14959475 0.12058902]\\n\",\n \"9.86021931618332 [1. 0.3752986] [1. 0.32407264] [1. 0.26820877]\\n\",\n \"10.947278660158846 [1.20420267 0.5 ] [1.02247322 0.5 ] [1. 0.43628735]\\n\",\n \"6.657621145978359 [1. 0.20718372] [1. 0.17591685] [1. 0.13735604]\\n\",\n \"8.03704706604437 [1.32010748 0.31497458] [1.24215437 0.27602836] [1.0973914 0.2536129]\\n\",\n \"5.482880754502581 [1.18206069 0.3588552 ] [1.1275162 0.2879413] [1.05704887 0.21233905]\\n\",\n \"5.718165073349755 [1.29934299 0.16261593] [1.25885329 0.13936101] [1.1877504 0.13852079]\\n\",\n \"7.016956595464088 [1.25482309 0.37105025] [1.08263569 0.36350617] [1. 0.33235748]\\n\",\n \"6.897566305772376 [1.10145958 0.23318679] [1.09468612 0.19643508] [1.13940239 0.10195221]\\n\",\n \"8.532390927615896 [1.00302409 0.30406921] [1.0087635 0.26458958] [1.04911879 0.1827265 ]\\n\",\n \"11.39396945909047 [1. 0.40152748] [1. 0.36691934] [1. 0.32694646]\\n\",\n \"11.583954785730356 [1. 0.46116362] [1. 0.42340029] [1. 0.36889922]\\n\",\n \"13.963786390637335 [1.1974282 0.24911136] [1.16107098 0.21162959] [1.13311731 0.16491704]\\n\",\n \"6.253649587591729 [1.00713065 0.25310759] [1.0224591 0.21871152] [1.02950319 0.17496982]\\n\",\n \"3.6175409524276714 [1. 0.21817873] [1. 0.20064313] [1. 0.17062906]\\n\",\n \"14.433281122388786 [1. 0.37965783] [1. 0.33575514] [1. 0.27812208]\\n\",\n \"5.608802129687688 [1.7371973 0.36643941] [1.85441399 0.28241099] [1.35207175 0.28317621]\\n\",\n \"9.114906090452688 [1.8711927 0.36635029] [1.61305913 0.34840897] [1.50288754 0.26483322]\\n\",\n \"13.400176175652874 [1. 0.16318474] [1. 0.14702124] [1.09372213 0.04657046]\\n\",\n \"9.759745110892707 [1. 0.32173525] [1. 0.30398725] [1. 0.27439679]\\n\",\n \"2.8514657932585674 [1.25887751 0.14700267] [1.15925258 0.15962704] [1.12362727 0.14751425]\\n\",\n \"9.679825820689372 [1. 0.44854566] [1. 0.40815696] [1. 0.34736059]\\n\",\n \"11.073786320887729 [1. 0.45058488] [1. 0.42068032] [1. 0.37156344]\\n\",\n \"10.289516652242412 [1. 0.38406976] [1. 0.31952146] [1.00054163 0.24363679]\\n\",\n \"3.023031724227071 [1.05675238 0.29276224] [1.04012443 0.25366816] [1. 0.21798194]\\n\",\n \"6.168235449953648 [1.27013745 0.23122278] [1.35899604 0.12755769] [1.26931699 0.10761264]\\n\",\n \"7.248479700487893 [1. 0.35115461] [1. 0.3240957] [1. 0.28089445]\\n\",\n \"11.554876325373565 [1. 0.41879061] [1. 0.37834907] [1. 0.32560303]\\n\",\n \"11.26561615969872 [1. 0.32843726] [1. 0.30285016] [1. 0.25962058]\\n\",\n \"4.729147299093163 [1.14625236 0.45816921] [1.05362448 0.41870127] [1. 0.35341439]\\n\",\n \"4.691457132264248 [1.60396452 0.28880753] [1.52081418 0.2326116 ] [1.12194682 0.22492234]\\n\",\n \"5.084197838597331 [1.36933905 0.41946407] [1.15912625 0.40275493] [1. 0.36110154]\\n\",\n \"5.17239733776305 [1. 0.41599939] [1. 0.3926829] [1. 0.3514369]\\n\",\n \"10.250459697575046 [1. 0.33271481] [1. 0.30857566] [1. 0.2642584]\\n\",\n \"6.023400035645858 [1.65635511 0.13666266] [1.59200143 0.11156766] [1.45946394 0.10254822]\\n\",\n \"6.96555858019867 [1. 0.34628316] [1. 0.32011929] [1. 0.27775025]\\n\",\n \"10.783664343047759 [1. 0.38274873] [1. 0.31831562] [1.01258584 0.23625687]\\n\",\n \"9.495411617394018 [1. 0.44816713] [1. 0.41069855] [1. 0.35110202]\\n\",\n \"14.09482520738358 [1. 0.45434453] [1. 0.42086814] [1. 0.36903418]\\n\",\n \"10.68465403611464 [1. 0.32654092] [1. 0.30548285] [1. 0.2727354]\\n\",\n \"5.302505334273771 [1.23252662 0.17808913] [1.1569384 0.17391497] [1.13249722 0.15741759]\\n\",\n \"9.652637898854056 [1.54465585 0.37706192] [1.40578235 0.35342029] [1.31892734 0.27949605]\\n\",\n \"13.382923736675977 [1. 0.15977668] [1. 0.14423596] [1.08302745 0.03914978]\\n\",\n \"5.628477691117605 [1. 0.30895195] [1.01787094 0.27018671] [1.06078142 0.20326901]\\n\",\n \"10.542397198705956 [1. 0.33818339] [1. 0.28637546] [1. 0.2347259]\\n\",\n \"12.394137628705234 [1. 0.40037421] [1. 0.34983589] [1. 0.28427398]\\n\",\n \"6.105189830657419 [1.33760495 0.48293045] [1.15418303 0.45610392] [1. 0.41235753]\\n\",\n \"11.044696194770463 [1. 0.41055094] [1. 0.37572367] [1. 0.33471414]\\n\",\n \"5.078613467699998 [1.07033551 0.22390123] [1.0774847 0.17448601] [1.10485065 0.09423847]\\n\",\n \"9.706465582443778 [1.25644739 0.20911097] [1.21251639 0.18062476] [1.21865354 0.09875737]\\n\",\n \"6.362019292228281 [1.35115594 0.15168153] [1.28841872 0.13882535] [1.18778273 0.14618269]\\n\",\n \"6.704423057174779 [1.24648772 0.36934891] [1.08134669 0.35938695] [1. 0.32914258]\\n\",\n \"7.251582796373196 [1.29143978 0.32358843] [1.15770532 0.29475572] [1.06014823 0.25794168]\\n\",\n \"5.6684857048323405 [1.28888514 0.34600941] [1.20387569 0.27758208] [1.13847092 0.19283189]\\n\",\n \"6.796616557592407 [1. 0.21171769] [1. 0.17741299] [1. 0.13329753]\\n\",\n \"12.05718679827463 [1.01485253 0.5 ] [1.14592859 0.41653497] [1.11974356 0.35647225]\\n\",\n \"8.769923956648649 [1. 0.38290973] [1. 0.32841014] [1. 0.27251493]\\n\",\n \"10.697150010065231 [1.27189658 0.2268371 ] [1.1559388 0.2066911] [1.11178238 0.13857463]\\n\",\n \"4.379469658369307 [1.16802842 0.36942668] [1.09347926 0.34116608] [1. 0.30731457]\\n\",\n \"13.482325835777495 [1.54434777 0.1587063 ] [1.52810059 0.12175819] [1.36822658 0.12335494]\\n\",\n \"5.894642644532999 [1.5203849 0.34331712] [1.24154046 0.3330313 ] [1.07096582 0.28564876]\\n\",\n \"16.8750240387132 [1.62030722 0.2800683 ] [1.38151402 0.28671642] [1.62948089 0.14264266]\\n\",\n \"8.124019704549188 [1.151602 0.28472924] [1.12190576 0.24737406] [1.10995035 0.16743737]\\n\",\n \"18.279280570987343 [1. 0.37643914] [1. 0.34208776] [1. 0.28539649]\\n\",\n \"19.66636441325457 [1. 0.46023776] [1. 0.4140824] [1. 0.35460682]\\n\",\n \"6.357859224374215 [1.37712007 0.10885838] [1.38667765 0.0638982 ] [1.2322195 0.0838116]\\n\",\n \"9.206116831806321 [1.78888139 0.04242404] [1.56327996 0.06241268] [1.3965774 0.06631569]\\n\",\n \"13.076188356544833 [1. 0.4695472] [1.03514895 0.39739679] [1. 0.35989896]\\n\",\n \"13.199088640671294 [1. 0.46120124] [1.03512727 0.38995599] [1. 0.34793226]\\n\",\n \"14.463177949442647 [1.61275421 0.12496326] [1.48326322 0.1062259 ] [1.24045911 0.12648806]\\n\",\n \"18.107028250869547 [1. 0.46686428] [1. 0.42096719] [1.05246637 0.35092622]\\n\",\n \"3.5529687682538538 [1.05842061 0.26812992] [1. 0.27930972] [1. 0.18323113]\\n\",\n \"14.566396520709839 [1. 0.39312816] [1. 0.35634256] [1. 0.29522218]\\n\",\n \"12.528555453691613 [1.34846392 0.19787606] [1.32235555 0.1730581 ] [1.11582986 0.18224353]\\n\",\n \"5.521435314014978 [1.15536791 0.38957656] [1.06943945 0.37053483] [1. 0.33839323]\\n\",\n \"16.30208706976876 [1.71219158 0.26245386] [1.42384526 0.27685767] [1.77350493 0.11706204]\\n\",\n \"6.493081806732496 [1.57368805 0.34476957] [1.29733911 0.32246715] [1.02607377 0.29064953]\\n\",\n \"6.131783701655587 [1.02382791 0.23680186] [1. 0.21640777] [1. 0.17907762]\\n\",\n \"7.506828808097932 [1.19557558 0.2218131 ] [1.16212076 0.20064528] [1.11232589 0.15949729]\\n\",\n \"11.161900229477755 [1.48012875 0.15105422] [1.25319232 0.16843267] [1.23539921 0.07926511]\\n\",\n \"8.12040700176336 [1.42825465 0.5 ] [1.36049121 0.47434245] [1.17884733 0.43862526]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"9.132013795788252 [1. 0.34806853] [1. 0.31731916] [1. 0.27023051]\\n\",\n \"9.792317679752305 [1. 0.36588639] [1. 0.314011] [1. 0.25793671]\\n\",\n \"5.940965701437201 [1. 0.20803771] [1. 0.17323618] [1. 0.13119351]\\n\",\n \"9.882733508141538 [1.30672327 0.19066968] [1.11155731 0.21128187] [1.1492221 0.10612051]\\n\",\n \"10.405425120944802 [1. 0.43826299] [1. 0.39470588] [1. 0.34912023]\\n\",\n \"7.060083238993572 [1.3451903 0.17801084] [1.27648049 0.16115865] [1.19106949 0.16273088]\\n\",\n \"7.1223147442726695 [1.11513652 0.36908961] [1.55617714 0.18893727] [1.23582137 0.15631021]\\n\",\n \"9.293580082372218 [1. 0.32047473] [1. 0.2833482] [1. 0.23739715]\\n\",\n \"8.005524059669257 [1.28987369 0.21332551] [1.25316586 0.18238389] [1.20759584 0.11579795]\\n\",\n \"7.76911158735872 [1.21623172 0.20625933] [1.18067534 0.18222335] [1.16642815 0.10963884]\\n\",\n \"5.1918998443205195 [1.66332463 0.41683789] [1.39129656 0.3901788 ] [1.11576509 0.35980625]\\n\",\n \"10.69094639243862 [1. 0.39350621] [1. 0.3582786] [1. 0.31643687]\\n\",\n \"13.225567054079825 [1. 0.15839072] [1. 0.14323687] [1.10139522 0.04279168]\\n\",\n \"12.342932240171312 [1. 0.3664039] [1. 0.32446021] [1. 0.27077646]\\n\",\n \"11.043933890756614 [1.59163681 0.38698595] [1.49561461 0.35451359] [1.41116016 0.28198947]\\n\",\n \"9.094995620547467 [1.15021046 0.47727358] [1.08977463 0.42953044] [1.00129401 0.38367014]\\n\",\n \"14.125721761128755 [1.19515688 0.27054272] [1.15095819 0.23091429] [1.09924844 0.19193861]\\n\",\n \"3.6679252912694214 [1. 0.20445415] [1. 0.18896712] [1. 0.16061438]\\n\",\n \"12.062789162656694 [1. 0.46486263] [1. 0.43506065] [1.02549722 0.38234206]\\n\",\n \"9.68575062818469 [1. 0.3033293] [1. 0.26744613] [1. 0.22396062]\\n\",\n \"9.809815320535373 [1. 0.38113812] [1. 0.31209012] [1. 0.22537621]\\n\",\n \"10.430086093962018 [1.72870698 0.28330771] [1.83324658 0.20519742] [1.60239585 0.18656398]\\n\",\n \"5.113332299433989 [1.46823856 0.30763214] [1.33919629 0.26388057] [1.08677162 0.23732179]\\n\",\n \"16.71484743065607 [1.1872556 0.1499857] [1. 0.20026275] [1.32602403 0.03816908]\\n\",\n \"3.9663087281319838 [1.5753108 0.1781449] [1.51843292 0.16357543] [1.31442388 0.16156391]\\n\",\n \"18.307070983478926 [1.18915999 0.15383811] [1. 0.20327953] [1.38977648 0.03533171]\\n\",\n \"7.253530355545665 [1.65216101 0.15262161] [1.60852392 0.1270196 ] [1.50196351 0.10690405]\\n\",\n \"9.151588863604836 [1.89008521 0.27414988] [1.9818452 0.19755024] [1.69001509 0.19157092]\\n\",\n \"5.079656461406473 [1.30158568 0.32988674] [1.19199073 0.29013097] [1.17694334 0.20035483]\\n\",\n \"10.08549668679645 [1. 0.38304066] [1.10779293 0.28774702] [1.03929042 0.21792944]\\n\",\n \"11.615106346615681 [1. 0.2876923] [1. 0.2568599] [1. 0.21963735]\\n\",\n \"14.363588013460268 [1. 0.4534733] [1. 0.41606513] [1. 0.35647859]\\n\",\n \"8.741799427958053 [1.02639393 0.5 ] [1. 0.45461541] [1. 0.3876333]\\n\",\n \"4.187578833825149 [1. 0.19033489] [1. 0.17401085] [1. 0.14676121]\\n\",\n \"13.876034870909518 [1.53702276 0.15078739] [1.34135633 0.15243525] [1.31202896 0.1029404 ]\\n\",\n \"10.569313277856985 [1.00796339 0.36697105] [1.00255843 0.32743355] [1.0288915 0.2625588]\\n\",\n \"5.246471838025861 [1.68078264 0.42014281] [1.32655751 0.40559491] [1.01216441 0.3884433 ]\\n\",\n \"8.273949420403152 [1.33623799 0.19335715] [1.29346782 0.16115181] [1.26786039 0.08569809]\\n\",\n \"5.059069389593845 [1.18342216 0.35402176] [1.18418688 0.27000158] [1.07426841 0.20375367]\\n\",\n \"7.76921206807748 [1.378397 0.28277659] [1.21494323 0.26918975] [1.10196424 0.23397128]\\n\",\n \"6.43607447230013 [1.56492665 0.29695574] [1.28404774 0.29864386] [1.06882438 0.29462784]\\n\",\n \"6.398037303945606 [1.45403557 0.14811863] [1.32537206 0.15033624] [1.20354689 0.15931622]\\n\",\n \"9.55648203451093 [1.94467366 0.08649169] [1.76493891 0.1005126 ] [1.69881545 0.01980189]\\n\",\n \"10.856624268671712 [1. 0.3673825] [1. 0.31482481] [1. 0.25988508]\\n\",\n \"8.982680052796217 [1. 0.34127813] [1. 0.31518449] [1. 0.27009608]\\n\",\n \"6.555164803237054 [1.2562734 0.20015526] [1.22347882 0.18298066] [1.14746297 0.15280522]\\n\",\n \"11.42983616950065 [1.34457361 0.19784701] [1.19761175 0.18340349] [1.12131573 0.12921931]\\n\",\n \"6.181647983118242 [1.02393465 0.23669126] [1. 0.2158752] [1. 0.17661177]\\n\",\n \"14.264335481464023 [1.36231952 0.3193632 ] [1.2304746 0.29892214] [1.36514555 0.1746504 ]\\n\",\n \"6.572005530190152 [1.39173968 0.3702036 ] [1.17530396 0.34109917] [1.00265985 0.2970286 ]\\n\",\n \"8.319346420255288 [1.52055688 0.1575683 ] [1.39921609 0.14575519] [1.16881791 0.15182689]\\n\",\n \"5.611568650755693 [1.1495796 0.37269716] [1.14226881 0.32804759] [1.06802974 0.29075461]\\n\",\n \"18.01291292936981 [1. 0.37746319] [1. 0.34310854] [1. 0.28435723]\\n\",\n \"3.4836986777638694 [1.52675188 0.14018194] [1. 0.28277446] [1. 0.18527186]\\n\",\n \"4.3317783224781605 [1.12365544 0.22748433] [1.17878037 0.16106564] [1.22965427 0.09299422]\\n\",\n \"13.336767515409255 [1. 0.46458256] [1.12057777 0.37783388] [1.00717876 0.35011677]\\n\",\n \"9.864917943383352 [1.6404268 0.06709127] [1.65048543 0.04040782] [1.34801337 0.0844676 ]\\n\",\n \"16.846284924524845 [1.13472825 0.4631835 ] [1.21363804 0.3932583 ] [1.17821932 0.32699423]\\n\",\n \"5.358754536184186 [1.23806703 0.17083908] [1.33126131 0.09281625] [1.31528204 0.05769854]\\n\",\n \"10.353051767274382 [1.51270939 0.10647843] [1.654621 0.05133744] [1.41061676 0.07857836]\\n\",\n \"4.648328470473069 [1.11023048 0.39100936] [1.04205856 0.36523198] [1. 0.32489038]\\n\",\n \"13.316994078985502 [1.31008275 0.19355857] [1.42521755 0.13546461] [1.19475776 0.15705117]\\n\",\n \"9.819794122576932 [1. 0.46014678] [1. 0.41603047] [1. 0.34684396]\\n\",\n \"16.242143535088573 [1. 0.37921593] [1. 0.34364416] [1. 0.282832]\\n\",\n \"9.194804363968492 [1. 0.13570096] [1. 0.10512691] [1. 0.08188649]\\n\",\n \"11.379231412644675 [1.18159074 0.45204766] [1.33128828 0.36451897] [1.19303512 0.32488503]\\n\",\n \"9.069394621391316 [1. 0.38514646] [1.08043136 0.30390721] [1. 0.27154333]\\n\",\n \"6.846509829714752 [1.32147852 0.20677532] [1.28256451 0.18644628] [1.20912227 0.1462376 ]\\n\",\n \"10.966962761678616 [1.20763463 0.24372572] [1.14493124 0.2091043 ] [1.09776285 0.1472699 ]\\n\",\n \"6.6067592297084135 [1.11052289 0.13083523] [1.04818177 0.1221258 ] [1.12051966 0.04052324]\\n\",\n \"7.872035907686601 [1.28375202 0.20551887] [1.24402502 0.18556539] [1.1672168 0.18399265]\\n\",\n \"4.329569989383152 [1. 0.19670338] [1. 0.15933851] [1. 0.05613744]\\n\",\n \"7.17302436851573 [1.27467707 0.27052945] [1.39327588 0.20646313] [1.22131733 0.21167575]\\n\",\n \"11.16483308047798 [1.47515678 0.26839173] [1.29870927 0.25360881] [1.24493635 0.19210514]\\n\",\n \"5.319961589415595 [1.4293636 0.31697304] [1.34503128 0.24424028] [1.01072838 0.22989745]\\n\",\n \"5.6766943497891305 [1. 0.19707673] [1. 0.16416763] [1. 0.11920103]\\n\",\n \"7.718060246146565 [1.9227159 0.45543079] [1.73850337 0.40345459] [1.38004744 0.35508254]\\n\",\n \"3.343854000457899 [1. 0.21764038] [1. 0.2100391] [1. 0.18451299]\\n\",\n \"10.516698750314097 [1. 0.40731168] [1. 0.36490032] [1. 0.32373622]\\n\",\n \"7.115375375306831 [1.15684331 0.25528336] [1.16037676 0.19798879] [1.12641917 0.12297683]\\n\",\n \"5.948536479182282 [1.10257828 0.21344633] [1.09389845 0.17504921] [1.08962649 0.11284049]\\n\",\n \"6.87330452421931 [1.26082107 0.23185223] [1.22121265 0.1967585 ] [1.23299833 0.10899838]\\n\",\n \"9.963364348319965 [2.10171282 0.32908377] [1.89253652 0.30683977] [1.71861653 0.23969449]\\n\",\n \"12.034387592660073 [1. 0.15319226] [1. 0.13411761] [1.06743429 0.0451563 ]\\n\",\n \"5.109847361744034 [1. 0.23961853] [1.00441933 0.21933302] [1. 0.18597711]\\n\",\n \"9.18755024465854 [1.62144093 0.39797098] [1.64255571 0.33403221] [1.31080648 0.32105157]\\n\",\n \"11.399652647647011 [1.04295124 0.47268458] [1.00648189 0.44236597] [1. 0.39338003]\\n\",\n \"17.278222742674775 [1.19422497 0.29510424] [1.06277835 0.29093328] [1.19985579 0.14806508]\\n\",\n \"5.493054634920303 [1. 0.12229506] [1. 0.10049863] [1.03689346 0.0570521 ]\\n\",\n \"4.65671368314793 [1.4971448 0.30378396] [1.28672537 0.27268689] [1.14284681 0.21108244]\\n\",\n \"10.60311527962159 [1.00474998 0.41151018] [1.123333 0.34908594] [1.07609795 0.30092226]\\n\",\n \"9.807744768289167 [1.49269343 0.36348981] [1.49881221 0.30399422] [1.4106148 0.24874052]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"8.229391799374683 [1.11326697 0.28303568] [1.22700416 0.18165858] [1.18957228 0.16675759]\\n\",\n \"5.817884163170584 [1.01720958 0.32880787] [1.02453528 0.30305116] [1.00257847 0.26936701]\\n\",\n \"11.327098166250956 [1.03155823 0.25565992] [1.05183265 0.21572989] [1.02914384 0.1852006 ]\\n\",\n \"7.149110333966241 [1.1455228 0.21281691] [1.13961522 0.20255734] [1.13505086 0.16736176]\\n\",\n \"10.3813820806001 [1. 0.38640003] [1. 0.32192303] [1.02079451 0.23596475]\\n\",\n \"10.112401221394446 [1. 0.37742396] [1.15628001 0.27022072] [1.06136077 0.19953287]\\n\",\n \"8.233764537328963 [1.13845694 0.24339034] [1.13403178 0.2055601 ] [1.06929967 0.18204631]\\n\",\n \"7.166512160354089 [1.13639967 0.20977336] [1.13292441 0.19817585] [1.13138368 0.16510997]\\n\",\n \"9.43711205936275 [1. 0.33920725] [1.19067778 0.18427091] [1.17262074 0.15875156]\\n\",\n \"5.7627867405678685 [1. 0.33192273] [1. 0.30700443] [1. 0.26494425]\\n\",\n \"5.799446886327617 [1.55970436 0.28742759] [1.28359163 0.27651333] [1.26502914 0.17683042]\\n\",\n \"10.664803770222768 [1. 0.41277196] [1. 0.37605694] [1. 0.32483377]\\n\",\n \"8.616222839391543 [1.74020995 0.29400656] [1.69706417 0.23602105] [1.48933511 0.20889967]\\n\",\n \"6.99952774258929 [1.01321609 0.11497411] [1.00596574 0.10385598] [1.07796247 0.04209424]\\n\",\n \"19.28470232275173 [1.32517208 0.25716803] [1.24178746 0.24519606] [1.40388588 0.10476253]\\n\",\n \"10.585146058065424 [1.10167698 0.46575265] [1.04452282 0.43795931] [1. 0.39002365]\\n\",\n \"3.9971776903870557 [1. 0.21280227] [1.00979397 0.1903038 ] [1. 0.16226249]\\n\",\n \"8.600372691630932 [1.20916714 0.45718259] [1.22017397 0.399884 ] [1.06370449 0.36711354]\\n\",\n \"10.50748004297346 [2.21778785 0.33338072] [1.95175535 0.3162305 ] [1.80184223 0.2410771 ]\\n\",\n \"11.479631222666933 [1. 0.16122589] [1. 0.14020892] [1.07727474 0.0526391 ]\\n\",\n \"6.009275495328019 [1.33026588 0.18172235] [1.27577272 0.15615949] [1.16958633 0.11121259]\\n\",\n \"7.4231429910093 [1.25461666 0.23244019] [1.20777819 0.20047267] [1.22432189 0.10797243]\\n\",\n \"10.002934277973846 [1. 0.3920379] [1. 0.35239683] [1. 0.31007609]\\n\",\n \"3.9568556633323704 [1. 0.19923706] [1. 0.19080906] [1. 0.16440974]\\n\",\n \"3.1996850236422163 [1.00566765 0.20323289] [1.02180513 0.16075047] [1.02813139 0.11744938]\\n\",\n \"7.670147666302381 [1.32762098 0.30623213] [1.24472104 0.27057504] [1.17025815 0.22730785]\\n\",\n \"6.042809587371163 [1.38519011 0.32888136] [1.26299585 0.2628564 ] [1.06580149 0.21291402]\\n\",\n \"7.757869166937808 [1.07987583 0.1371057 ] [1.03446946 0.12497328] [1.10348111 0.04723894]\\n\",\n \"4.43258421132082 [1.11721103 0.16522905] [1.12024512 0.12830612] [1. 0.06038772]\\n\",\n \"11.271611030082378 [1.3841545 0.17942604] [1.19712893 0.18344897] [1.19232273 0.09630849]\\n\",\n \"3.698315232045804 [1. 0.3029356] [1. 0.29169131] [1.02321683 0.20416274]\\n\",\n \"8.822285100363505 [1. 0.38251011] [1.14003079 0.28550306] [1. 0.26887895]\\n\",\n \"6.753251142712777 [1.3186301 0.19447349] [1.277587 0.17462457] [1.19531644 0.13935587]\\n\",\n \"11.206065921234933 [1.28159469 0.43717107] [1.55191477 0.32874611] [1.28062657 0.30535132]\\n\",\n \"16.33983170885009 [1. 0.37769077] [1. 0.34479162] [1. 0.28664752]\\n\",\n \"6.295083971343806 [1.87053536 0.32743493] [1.58078417 0.30719014] [1.21212806 0.28227725]\\n\",\n \"9.656178527992829 [1. 0.42953287] [1. 0.39333673] [1. 0.32904069]\\n\",\n \"16.617923937413938 [1.88175886 0.24733114] [1.57921308 0.24949027] [1.65726625 0.13864633]\\n\",\n \"5.390447352010208 [1.13769409 0.39439566] [1.05165044 0.36885706] [1. 0.32786999]\\n\",\n \"13.459337896493485 [1.37798936 0.18296964] [1.56263841 0.12154884] [1.33853149 0.12959174]\\n\",\n \"7.970838933707072 [1.79384372 0.05637351] [1.63839352 0.05992842] [1.45749686 0.05608501]\\n\",\n \"6.1610743515074775 [1.27527954 0.14611874] [1.34536226 0.08104233] [1.272146 0.07017626]\\n\",\n \"16.583950762246825 [1.20690134 0.45788159] [1.26357457 0.3906088 ] [1.24508291 0.32545267]\\n\",\n \"6.2817083525225215 [1.43248899 0.08235235] [1.46319379 0.03151225] [1.34056285 0.04330153]\\n\",\n \"15.46772954580591 [1.20037753 0.45752591] [1.28222324 0.38221786] [1.22166844 0.31956128]\\n\",\n \"12.387730736901757 [1.37983368 0.15882999] [1.63323002 0.07241884] [1.4654103 0.08531616]\\n\",\n \"14.137613449311157 [1.32896481 0.32018107] [1.21052352 0.29922719] [1.3130999 0.18296167]\\n\",\n \"6.64786717329171 [1.24780339 0.3833198 ] [1.10150238 0.35102949] [1.04630689 0.27798395]\\n\",\n \"4.24135722166321 [1.07243444 0.0987732 ] [1.08612152 0.06698599] [1.03942819 0.05278534]\\n\",\n \"16.15573306961432 [1. 0.40620807] [1. 0.38199278] [1. 0.34536175]\\n\",\n \"15.733509098532744 [1. 0.38269878] [1. 0.34514066] [1. 0.28323307]\\n\",\n \"7.190322947344272 [1.33338819 0.20398601] [1.29896954 0.18313929] [1.22096887 0.14425465]\\n\",\n \"8.848243226737866 [1. 0.36463984] [1. 0.30499512] [1. 0.2532191]\\n\",\n \"9.192714346804564 [1.49756005 0.38641918] [1.47873111 0.33758922] [1.36019747 0.2924594 ]\\n\",\n \"11.319870167575406 [1.1507284 0.25025712] [1.10234282 0.21313653] [1.07017249 0.14967353]\\n\",\n \"3.122518940096929 [1. 0.25684418] [1. 0.2145787] [1.01112151 0.1713307 ]\\n\",\n \"4.973297440166892 [1.75629566 0.26087785] [1.51903931 0.20265438] [1.30394731 0.12126478]\\n\",\n \"17.577524487473408 [1. 0.32556971] [1. 0.30692654] [1.32407937 0.14465335]\\n\",\n \"9.477049842891258 [1.31975522 0.31669314] [1.2151192 0.28211103] [1.13099365 0.23748448]\\n\",\n \"8.08814505673164 [1.07652963 0.34287907] [1. 0.32370187] [1. 0.28736337]\\n\",\n \"9.65164923753427 [1.19063162 0.25259696] [1.18098305 0.22452833] [1.1382278 0.21833746]\\n\",\n \"3.401865598938056 [1.03189485 0.19275967] [1.03970229 0.15411936] [1.05574905 0.10739656]\\n\",\n \"14.176112013280372 [1. 0.37650505] [1. 0.34396706] [1. 0.30638204]\\n\",\n \"6.688746200490611 [1.32125092 0.48171739] [1.19045539 0.45402005] [1.03386745 0.41444949]\\n\",\n \"4.1057804673811615 [1. 0.10640612] [1. 0.08556885] [1. 0.05988287]\\n\",\n \"6.67515108253583 [1.30299822 0.21644816] [1.2627708 0.17855494] [1.2811692 0.08289794]\\n\",\n \"5.618221600263949 [1.13202922 0.20517403] [1.11029442 0.1728153 ] [1.11432531 0.09781313]\\n\",\n \"9.683319846054019 [1.41664575 0.10072695] [1.3350207 0.0975795] [1.35620321 0.02201072]\\n\",\n \"9.440038887014085 [1.18255191 0.45836029] [1.22659939 0.39898854] [1.0523846 0.37111137]\\n\",\n \"3.101464387779503 [1.00310226 0.18617054] [1.01341032 0.16490255] [1. 0.14288429]\\n\",\n \"11.430687928780806 [1. 0.14612842] [1. 0.13461617] [1.08796245 0.05549187]\\n\",\n \"13.27307578626223 [2.03265487 0.34461573] [1.89730784 0.31029694] [1.85614307 0.22885712]\\n\",\n \"13.522674934918868 [1.24809083 0.26623166] [1.15286954 0.24685906] [1.04216024 0.20770993]\\n\",\n \"10.723073515894926 [1. 0.46249963] [1. 0.43080009] [1. 0.38489059]\\n\",\n \"9.906265694175751 [1.62675252 0.08227807] [1.49673376 0.07923181] [1.35811367 0.06423116]\\n\",\n \"8.396315197343291 [1. 0.43845084] [1.05420866 0.39183131] [1.05239784 0.3264069 ]\\n\",\n \"5.9690038195265505 [1. 0.32685424] [1. 0.30352812] [1. 0.26298348]\\n\",\n \"7.840747356295273 [1.3913322 0.19861392] [1.34101817 0.1668658 ] [1.26671578 0.15635534]\\n\",\n \"6.827495181836394 [2.01031885 0.28656483] [1.8092051 0.24054918] [1.50658006 0.22962397]\\n\",\n \"9.895341011398294 [1. 0.417134] [1. 0.37778122] [1. 0.3224807]\\n\",\n \"4.236715657828287 [1.72151572 0.27422812] [1.50583213 0.22894572] [1.13164725 0.2143216 ]\\n\",\n \"9.78882843005207 [1. 0.38983699] [1.06821947 0.29867519] [1.02002297 0.21792161]\\n\",\n \"11.323618689434669 [1. 0.28905041] [1. 0.25813764] [1. 0.22025564]\\n\",\n \"2.827980676500224 [1.08528143 0.27993667] [1.0424405 0.24880152] [1. 0.21143698]\\n\",\n \"7.388237834536167 [1.02595207 0.21509063] [1.00603128 0.18301106] [1.01643689 0.12862386]\\n\",\n \"11.837966231279758 [1. 0.37373634] [1. 0.30779492] [1. 0.2218484]\\n\",\n \"6.727837508030034 [1.79801516 0.32114686] [1.7475416 0.25240467] [1.46188216 0.25007253]\\n\",\n \"9.794021663238883 [1. 0.42662672] [1.07878589 0.365327 ] [1.0410405 0.3164553]\\n\",\n \"4.083904398142607 [1.79993235 0.25247301] [1.5791356 0.20800874] [1.12746493 0.20749548]\\n\",\n \"5.721784128953531 [1.12182882 0.28560624] [1.12424996 0.26081497] [1.02604428 0.24848895]\\n\",\n \"8.621892828153207 [1.5241035 0.17616859] [1.40255741 0.16787739] [1.27289634 0.16637972]\\n\",\n \"5.4219216877500385 [1.46268234 0.37838065] [1.43721686 0.33107937] [1.30880109 0.27054054]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"12.92374294802015 [1. 0.43825739] [1. 0.40630202] [1. 0.35982225]\\n\",\n \"12.12330184772465 [1. 0.31878988] [1. 0.29516628] [1. 0.26243926]\\n\",\n \"13.02981427411799 [1.22953443 0.27054121] [1.13130629 0.25406708] [1.02533223 0.21277484]\\n\",\n \"11.11366702434372 [1.0001778 0.15644102] [1. 0.1407532] [1.07866234 0.05775561]\\n\",\n \"13.643759331445702 [2.14823332 0.33184356] [2.03092151 0.29867382] [1.92904248 0.22367221]\\n\",\n \"7.744886110161616 [1.20591221 0.45744155] [1.24288846 0.39719454] [1.08658366 0.36137132]\\n\",\n \"10.809995204901764 [1.18865417 0.15685477] [1.08331153 0.15662978] [1.14261109 0.08033617]\\n\",\n \"3.7238308588415934 [1.00328037 0.20303921] [1.00661734 0.18357931] [1. 0.15610408]\\n\",\n \"7.936937679628445 [1.07265777 0.29389113] [1.06746621 0.25758217] [1.09819489 0.17303536]\\n\",\n \"4.283265055990391 [1. 0.1119575] [1. 0.08966731] [1. 0.06296653]\\n\",\n \"6.534887905244455 [1.41717386 0.15807677] [1.33269811 0.14386502] [1.15669023 0.11879725]\\n\",\n \"5.944232542753346 [1.3929997 0.46064437] [1.2126758 0.43505099] [1.03033525 0.39986111]\\n\",\n \"8.727786256388198 [1. 0.39480769] [1. 0.35426979] [1. 0.31290909]\\n\",\n \"3.3846767085980463 [1.00647622 0.20466953] [1.01157291 0.16593815] [1.05980113 0.1060288 ]\\n\",\n \"7.803723665864124 [1. 0.38890162] [1. 0.34317824] [1. 0.30201535]\\n\",\n \"9.193485071927222 [1.22529701 0.23804733] [1.19621973 0.22068826] [1.17930709 0.20824439]\\n\",\n \"16.949125286054716 [1. 0.34446065] [1. 0.31762179] [1.33182696 0.15283466]\\n\",\n \"3.8748364223382645 [1.0611979 0.22382414] [1. 0.2178958] [1.12213093 0.13030333]\\n\",\n \"11.04633248837437 [1.31633276 0.20016345] [1.17997035 0.18777593] [1.1162778 0.13053015]\\n\",\n \"9.276156641897078 [1.07789592 0.47502514] [1.11088407 0.41493342] [1.08391157 0.35643613]\\n\",\n \"10.158945558624081 [1. 0.36348312] [1. 0.31503743] [1. 0.2628768]\\n\",\n \"14.34898535212909 [1. 0.38492621] [1. 0.34894339] [1. 0.29041724]\\n\",\n \"4.396947412245824 [1.04215175 0.11467678] [1.07034018 0.07286891] [1.02961519 0.06183437]\\n\",\n \"11.136538148338126 [1.90098929 0.04814792] [1.5652092 0.06581793] [1.36260503 0.06558594]\\n\",\n \"14.846407868283405 [1. 0.40598203] [1. 0.38136962] [1. 0.34648215]\\n\",\n \"3.489638923048433 [1.42120371 0.33105368] [1.53614666 0.25299199] [1.33302736 0.21516879]\\n\",\n \"15.151601952965605 [1.75116773 0.26517765] [1.52284581 0.26063396] [1.71683974 0.12902499]\\n\",\n \"7.474191432710572 [1.46522048 0.37145897] [1.16752892 0.36515838] [1. 0.32322039]\\n\",\n \"13.635094007444565 [1. 0.47359954] [1.0029611 0.39964454] [1. 0.35599858]\\n\",\n \"11.971244137120992 [1.72424873 0.09580737] [1.62864594 0.07245187] [1.37353869 0.09478861]\\n\",\n \"14.744004694629867 [1.47259561 0.42305383] [1.46576631 0.35352895] [1.35692132 0.29742234]\\n\",\n \"7.7499555168184875 [1.42205944 0.13015136] [1.45947697 0.0760536 ] [1.39622789 0.05506497]\\n\",\n \"10.318015080425308 [1.22651317 0.19469698] [1.2040185 0.18711634] [1.16993615 0.16435758]\\n\",\n \"7.508101468781912 [1. 0.27320416] [1. 0.24114559] [1. 0.18801963]\\n\",\n \"7.353069512400742 [1.07189384 0.40878328] [1.06308897 0.37137663] [1.05266062 0.32375307]\\n\",\n \"11.307551184664831 [1. 0.28636899] [1. 0.26937179] [1. 0.24237139]\\n\",\n \"10.636613739433855 [1.21212062 0.11645089] [1.19701997 0.10758946] [1.20047315 0.08228245]\\n\",\n \"12.87306339200131 [1.22334068 0.26602585] [1.12109215 0.25073462] [1.19059815 0.1187465 ]\\n\",\n \"16.46473971972104 [1.42511277 0.37471813] [1.3797633 0.32869782] [1.33329276 0.2587746 ]\\n\",\n \"7.7680470356588955 [1. 0.35555614] [1. 0.33216718] [1. 0.28608048]\\n\",\n \"4.438435829783888 [1.1681177 0.11714117] [1.18395561 0.08094577] [1.14427211 0.05225899]\\n\",\n \"5.014186239267677 [1. 0.27324214] [1. 0.25595753] [1. 0.21792883]\\n\",\n \"5.93390200897512 [1.91139199 0.01259635] [1.48077759 0.10922466] [1. 0.29773391]\\n\",\n \"9.32348760898458 [1.89333089 0.39569778] [1.55067934 0.3967098 ] [1.33230293 0.37010351]\\n\",\n \"12.903284216394288 [1. 0.38405575] [1. 0.35803423] [1. 0.31370152]\\n\",\n \"11.737460110071973 [1. 0.32668573] [1. 0.30409448] [1. 0.26355711]\\n\",\n \"12.764559174176856 [1.28387258 0.35579261] [1.14542861 0.32634888] [1.00283855 0.28218503]\\n\",\n \"7.358532248887396 [1.07442551 0.26832179] [1.10202569 0.20488099] [1.05784295 0.1513299 ]\\n\",\n \"3.9191805140817237 [1.09084507 0.26922418] [1.02677945 0.25541651] [1. 0.22129397]\\n\",\n \"8.851650846913161 [1. 0.3687964] [1. 0.34034031] [1. 0.29227185]\\n\",\n \"6.189058073514696 [1.23263448 0.31059963] [1.23537597 0.25595354] [1.10422191 0.24668319]\\n\",\n \"8.846340005888473 [1. 0.28203295] [1. 0.24657353] [1. 0.19505829]\\n\",\n \"5.855337803107138 [1. 0.31724028] [1. 0.29185951] [1. 0.24919558]\\n\",\n \"14.37666345589674 [1. 0.35874386] [1. 0.3136187] [1. 0.24626372]\\n\",\n \"11.560589828261861 [1.14596985 0.5 ] [1. 0.48494053] [1. 0.41586715]\\n\",\n \"9.17277299686883 [1. 0.37383266] [1. 0.33817492] [1. 0.288125]\\n\",\n \"13.109723379400041 [1. 0.34576873] [1. 0.30178404] [1. 0.2354756]\\n\",\n \"13.457486269408257 [1.14818376 0.47345392] [1.21100018 0.42181602] [1.19799818 0.36756067]\\n\",\n \"7.780002402065833 [1.47235295 0.21392684] [1.34075937 0.18970313] [1.16090543 0.15055719]\\n\",\n \"4.515234402687749 [1.30800007 0.05901288] [1.27854246 0.06173632] [1.26136428 0.04963119]\\n\",\n \"7.7002553235279 [1.3709947 0.40154594] [1.16884158 0.39594325] [1. 0.38857235]\\n\",\n \"8.519950021502659 [2.56890745 0.38903405] [1.99093146 0.40651462] [1.48469118 0.40834175]\\n\",\n \"11.096057531064517 [1. 0.32453121] [1. 0.29025584] [1. 0.24166803]\\n\",\n \"11.793027738096868 [1. 0.40430373] [1. 0.35858496] [1. 0.28624796]\\n\",\n \"10.858903574152004 [1.19549563 0.27543131] [1.1129744 0.25376468] [1.05648035 0.202586 ]\\n\",\n \"6.709065599768365 [1.3076391 0.1361815] [1.30242625 0.08963543] [1.24594118 0.07549374]\\n\",\n \"7.9727635055249015 [1.21539292 0.18875852] [1.19715729 0.1650433 ] [1.16333386 0.10402095]\\n\",\n \"16.899611590422623 [1.06484678 0.3397656 ] [1. 0.31900987] [1.12891387 0.17694057]\\n\",\n \"6.636931715410257 [1.47576206 0.30595888] [1.44386168 0.26626175] [1.21528856 0.24989117]\\n\",\n \"12.461808410693672 [1. 0.40222813] [1. 0.36594668] [1. 0.30994769]\\n\",\n \"7.228626965483202 [1.08957594 0.30282639] [1.10278698 0.27700384] [1.07887094 0.24984452]\\n\",\n \"4.717126212878538 [1. 0.27271711] [1.45203883e+00 1.36755657e-03] [1.2656421 0.01236516]\\n\",\n \"8.337957903790556 [1.44915374 0.30760317] [1.30228489 0.29604403] [1.51546082 0.15115157]\\n\",\n \"4.310434864075449 [1.04860806 0.23784963] [ 1.42965787 -0.00999866] [1.24416184 0.00394949]\\n\",\n \"8.954482582718153 [1.87589561 0.23543362] [1.46069088 0.25948162] [1.82186006 0.09131086]\\n\",\n \"10.76756632971838 [1. 0.4056859] [1. 0.37036067] [1. 0.31855702]\\n\",\n \"10.164545587235924 [1.5658485 0.37006108] [1.28072399 0.38086746] [1.02952132 0.37038356]\\n\",\n \"6.516128297771173 [1.04107228 0.31526783] [1.07097401 0.28386317] [1.05898164 0.25225454]\\n\",\n \"19.348904782894852 [1.07353974 0.34588991] [1. 0.32892898] [1.12542758 0.19515951]\\n\",\n \"6.224941790567825 [1.3365501 0.17358007] [1.30171357 0.14523596] [1.22255449 0.09457928]\\n\",\n \"5.668600164185342 [1.31913522 0.1013403 ] [1.28318693 0.06483723] [1.1684233 0.05374388]\\n\",\n \"5.879670066249189 [1.28783993 0.17444356] [1.24822519 0.14295736] [1.26440323 0.05057444]\\n\",\n \"7.0295185686981245 [1.32749237 0.14445933] [1.34649887 0.08903797] [1.28815073 0.07531211]\\n\",\n \"13.432121950887783 [1.09368324 0.26136207] [1.06315197 0.23826839] [1.02415159 0.194419 ]\\n\",\n \"7.431158701186877 [1.52387026 0.45575523] [1.26768842 0.46138969] [1.00912014 0.45951464]\\n\",\n \"9.133075847433641 [1. 0.32763083] [1. 0.2952476] [1. 0.247423]\\n\",\n \"8.086363101595888 [1. 0.42089985] [1. 0.37160776] [1. 0.29277675]\\n\",\n \"3.5368746256625876 [1.3152582 0.05525126] [1.28577703 0.05892926] [1.26281295 0.04770792]\\n\",\n \"8.912787507700697 [1.34011579 0.41647153] [1.32052103 0.38440041] [1.11624175 0.38066937]\\n\",\n \"7.251552657740937 [1.44296037 0.21621846] [1.33010513 0.18317351] [1.15585674 0.14626113]\\n\",\n \"11.852161540030494 [1. 0.36114575] [1. 0.31125812] [1. 0.23774902]\\n\",\n \"13.012564640990725 [1. 0.4774848] [1.08504104 0.42621422] [1.08582728 0.38513799]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10.399040863387082 [1. 0.34256646] [1. 0.31658962] [1. 0.2745839]\\n\",\n \"15.365551767647597 [1. 0.3479788] [1.06557821 0.29742744] [1.05550735 0.2351434 ]\\n\",\n \"5.530280188449658 [1.32775307 0.23470935] [1.26303032 0.22132559] [1.14345919 0.20878732]\\n\",\n \"8.872598365341643 [1. 0.29197827] [1. 0.25541775] [1. 0.20189065]\\n\",\n \"4.363754424405304 [1.08907136 0.26384361] [1.04647512 0.24163538] [1. 0.21388896]\\n\",\n \"13.529900174983718 [1. 0.41353479] [1. 0.4040192] [1. 0.38084503]\\n\",\n \"6.579937201837601 [1.09192602 0.35805918] [1.11899994 0.30419861] [1. 0.29325943]\\n\",\n \"11.521328965457522 [1. 0.32320311] [1. 0.30077645] [1. 0.25973495]\\n\",\n \"9.514036722453081 [1. 0.14215642] [1. 0.12358407] [1.01925728 0.08379428]\\n\",\n \"13.70804099395271 [1. 0.39063537] [1. 0.36265178] [1. 0.31751861]\\n\",\n \"11.592487347281375 [1.77474634 0.41289645] [1.42922063 0.41914452] [1.25133301 0.3907739 ]\\n\",\n \"4.588247546140393 [1. 0.1979672] [1. 0.18163717] [1. 0.15291255]\\n\",\n \"7.827889851702488 [1. 0.36113917] [1. 0.33438351] [1. 0.29454496]\\n\",\n \"11.02283657104899 [1. 0.44227969] [2.70764757 0.14443202] [2.08253508 0.10108426]\\n\",\n \"4.570555226809893 [1.12471671 0.13227695] [1.15569754 0.0912686 ] [1.12649879 0.05929105]\\n\",\n \"9.720891340088501 [1.11818475 0.28343482] [1.09872754 0.2782892 ] [1.03840465 0.27418947]\\n\",\n \"6.2448992341603615 [1.18327301 0.11860671] [1.16407577 0.11256122] [1.14903747 0.09490972]\\n\",\n \"19.334696900916832 [1. 0.46099643] [1. 0.4122927] [1.14149199 0.31152665]\\n\",\n \"13.052606369671384 [1.20489344 0.2677766 ] [1.14234011 0.24176071] [1.2235349 0.10701297]\\n\",\n \"7.488423484356947 [1.24143361 0.37978145] [1.13644446 0.36971169] [1.10550541 0.33195308]\\n\",\n \"7.878020226024404 [1. 0.27620113] [1. 0.24541771] [1. 0.19496444]\\n\",\n \"9.843909234368232 [1.27755267 0.19437357] [1.25222751 0.1835578 ] [1.20303539 0.16027326]\\n\",\n \"10.41734972168427 [1.2781173 0.16988161] [1.25269049 0.16499974] [1.21396005 0.13883092]\\n\",\n \"8.57782745968769 [1. 0.18388329] [1.12097025 0.13244987] [1.06700681 0.13676794]\\n\",\n \"6.752323987887464 [1. 0.41286498] [1. 0.3741202] [1. 0.31717428]\\n\",\n \"6.894033251838395 [1.14965681 0.23590677] [1.13442137 0.23065988] [1.07788449 0.22178575]\\n\",\n \"4.533588806434659 [1. 0.42401566] [1.66369299 0.13481584] [1.38499166 0.11752162]\\n\",\n \"17.447802191463197 [1.33086438 0.41907321] [1.35122617 0.36268071] [1.43848487 0.26100588]\\n\",\n \"8.855210245864539 [1.44391281 0.38613494] [1.28211709 0.35994396] [1.04366756 0.32924053]\\n\",\n \"8.593938087065613 [1.66144589 0.03043435] [1.17368481 0.18860503] [1. 0.3112879]\\n\",\n \"4.516192672820185 [1. 0.17315768] [1. 0.16027499] [1. 0.13601338]\\n\",\n \"9.930369039926079 [1. 0.45657104] [1. 0.41818039] [1. 0.3525668]\\n\",\n \"11.750623067675095 [1. 0.38959995] [1. 0.36583342] [1. 0.324724]\\n\",\n \"8.801304702139626 [1.86076771 0.39463772] [1.4964728 0.39759935] [1.27639272 0.36993752]\\n\",\n \"10.42730869983231 [1. 0.13676754] [1. 0.1192226] [1.04220164 0.0416786 ]\\n\",\n \"7.955986863592911 [1. 0.4017892] [1. 0.37192542] [1. 0.32327928]\\n\",\n \"4.797502828089043 [1.50201568 0.25921432] [1.54890249 0.19463649] [1.37743284 0.18441868]\\n\",\n \"9.819222389038643 [1. 0.34934257] [1. 0.32134333] [1. 0.27584404]\\n\",\n \"8.26535325760776 [1.06513529 0.09520454] [1.03612411 0.08847213] [1.01449466 0.07178682]\\n\",\n \"4.141517173120085 [1.39447253 0.18245722] [1.26644478 0.18135569] [1.17966932 0.16226805]\\n\",\n \"6.781159710831368 [1.02872215 0.26257408] [1.05882205 0.19904078] [1.03839647 0.14275096]\\n\",\n \"11.396194060489748 [1.02887201 0.42703845] [1. 0.38434703] [1. 0.2990352]\\n\",\n \"6.514048582404852 [1. 0.31228228] [1. 0.28870107] [1. 0.24761954]\\n\",\n \"12.263896963901542 [1. 0.32230866] [1. 0.29873797] [1. 0.2590367]\\n\",\n \"5.660882528116615 [1. 0.30433113] [1. 0.28598432] [1. 0.24896655]\\n\",\n \"12.641431911900085 [1. 0.49750816] [1. 0.45185861] [1. 0.37262997]\\n\",\n \"6.785688916406413 [1. 0.30198109] [1. 0.28480017] [1. 0.25618031]\\n\",\n \"14.816778587103881 [1.1193813 0.46257033] [1.14025848 0.4076001 ] [1.15249545 0.36044309]\\n\",\n \"14.0008259359064 [1. 0.33486403] [1. 0.29191746] [1. 0.22265205]\\n\",\n \"5.486457612990889 [1.28396691 0.38449935] [1.15504614 0.34593028] [1. 0.29126258]\\n\",\n \"10.525596361418229 [1. 0.35591955] [1. 0.32451637] [1. 0.2782514]\\n\",\n \"5.797371127000429 [2.64080811 0.43203536] [2.08242611 0.44599074] [1.43901104 0.44696342]\\n\",\n \"8.29309385803627 [1.43765078 0.38701073] [1.30139209 0.37205705] [1.09309882 0.37037415]\\n\",\n \"5.4541989864053075 [1.2645468 0.06761498] [1.24424396 0.0679545 ] [1.21825074 0.0590681 ]\\n\",\n \"12.111974237275414 [1.12780225 0.27006465] [1.08212607 0.24573226] [1.03817795 0.1977042 ]\\n\",\n \"10.035389830912521 [1.33452756 0.11448563] [1.27556688 0.09970234] [1.25475358 0.05040068]\\n\",\n \"7.122328355171524 [1.00631411 0.20625468] [1.00821955 0.18043911] [1.00000001 0.13051789]\\n\",\n \"6.74398160124192 [1.33823143 0.14356404] [1.34987935 0.09204396] [1.26482916 0.08544395]\\n\",\n \"4.541000483994441 [1.51780067 0.14736122] [1.36745694 0.12999866] [1.21296419 0.1007455 ]\\n\",\n \"16.44394747942648 [1.161461 0.30433507] [1.02411477 0.30137427] [1.15681405 0.15734204]\\n\",\n \"12.30836024100948 [1. 0.3582024] [1. 0.33574834] [1. 0.23375577]\\n\",\n \"4.921298954195917 [1. 0.26835638] [1.43996426e+00 2.10709231e-04] [1.25909389 0.01114293]\\n\",\n \"6.800284197181853 [1. 0.33739754] [1. 0.31960835] [1. 0.28985652]\\n\",\n \"6.047774476938553 [1.35031977 0.30326913] [1.33688011 0.25626945] [1.17761302 0.22920831]\\n\",\n \"12.285616934681238 [1. 0.41184596] [1. 0.37458676] [1. 0.32193021]\\n\",\n \"7.4385668492911154 [1. 0.34272029] [1. 0.31921037] [1. 0.28272231]\\n\",\n \"13.748919755251686 [1. 0.38760815] [1. 0.3527708] [1. 0.29410875]\\n\",\n \"7.573395290740324 [1.54059085 0.28434904] [1.45211232 0.25511036] [1.23431808 0.23588014]\\n\",\n \"12.827942385184194 [1. 0.34133679] [1. 0.32517513] [1.08081162 0.2048545 ]\\n\",\n \"5.315138803930238 [1. 0.26630479] [1.46135346 0.00174964] [1.26930529 0.01257144]\\n\",\n \"18.445641513586367 [1. 0.32009183] [1. 0.28623857] [1.10184002 0.15859606]\\n\",\n \"6.9039921609184 [1. 0.27452515] [1. 0.23706619] [1. 0.1808617]\\n\",\n \"6.933856541495104 [1.26836311 0.17292964] [1.23943197 0.13529919] [1.18344933 0.1190079 ]\\n\",\n \"9.1754855532081 [1.32012519 0.10581155] [1.26743775 0.08459798] [1.21211291 0.05402826]\\n\",\n \"7.95999259855081 [1. 0.2280663] [1. 0.19946997] [1. 0.14746733]\\n\",\n \"12.90117192118057 [1.07258321 0.27879639] [1.03918096 0.25109079] [1.00187957 0.20184881]\\n\",\n \"8.498572882655235 [1. 0.48467719] [1.0507401 0.43429832] [1. 0.40156388]\\n\",\n \"4.968862936015541 [1.27935359 0.06278018] [1.25493564 0.06435664] [1.23059556 0.05567309]\\n\",\n \"7.837028390588623 [1. 0.40538159] [1. 0.35924818] [1. 0.28033263]\\n\",\n \"8.370309345977399 [1. 0.33104577] [1. 0.29727104] [1. 0.24940342]\\n\",\n \"5.786796816872193 [2.78718743 0.43468676] [2.15784544 0.4521613 ] [1.49092268 0.45109614]\\n\",\n \"14.888725983674073 [1. 0.47870681] [1. 0.42638215] [1. 0.38860656]\\n\",\n \"12.042469117903966 [1. 0.34151152] [1. 0.29692816] [1. 0.22580898]\\n\",\n \"11.371645567619755 [1. 0.49242957] [1. 0.44780231] [1. 0.37439217]\\n\",\n \"6.448794133669505 [1. 0.30517096] [1. 0.28617864] [1. 0.25176324]\\n\",\n \"5.524627378888152 [1. 0.30355241] [1. 0.28945991] [1. 0.25898903]\\n\",\n \"11.51569566853841 [1. 0.33297855] [1. 0.30788312] [1. 0.26779871]\\n\",\n \"5.773443165884961 [1. 0.32568689] [1. 0.30010188] [1. 0.25709086]\\n\",\n \"6.748136657813239 [1.01374175 0.25088664] [1.04548775 0.18812538] [1.03199114 0.13496343]\\n\",\n \"12.052698614924967 [1.39627125 0.35087073] [1.23224636 0.32969554] [1.08285179 0.28375729]\\n\",\n \"10.060533011831275 [1. 0.34598965] [1. 0.31133551] [1. 0.26383932]\\n\",\n \"9.199727336978794 [1. 0.33325842] [1. 0.30006165] [1. 0.25371227]\\n\",\n \"6.709969394594997 [1.10918757 0.06658346] [1.06499626 0.06553977] [1.02883924 0.05662035]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"9.6060888160924 [1. 0.12829732] [1. 0.10727965] [1. 0.07368046]\\n\",\n \"7.822266741030743 [2.0912904 0.37328705] [1.66836271 0.37461134] [1.36645524 0.35356312]\\n\",\n \"10.359787060458036 [1. 0.37363668] [1. 0.34732112] [1. 0.2977534]\\n\",\n \"11.787197668597528 [1. 0.37676134] [1. 0.35483447] [1. 0.31241321]\\n\",\n \"6.347286602421171 [1.31041515 0.05925655] [1.29216435 0.06166785] [1.26878466 0.05273866]\\n\",\n \"3.266710456261983 [1. 0.16265129] [1. 0.15089868] [1. 0.12700483]\\n\",\n \"9.17760253205858 [1.65796412 0.02662391] [1.15413931 0.19511157] [1. 0.31638017]\\n\",\n \"5.901836666555211 [1.50092084 0.37688789] [1.27476517 0.3569254 ] [1.12850671 0.30477134]\\n\",\n \"16.567119942126844 [1.28121459 0.4361741 ] [1.33713373 0.3694556 ] [1.48853805 0.25064159]\\n\",\n \"14.200468274379704 [1.46160727 0.21797348] [1.26487462 0.21894439] [1.29068252 0.09228189]\\n\",\n \"9.558914669941702 [1.17834556 0.11091971] [1.11168435 0.13118322] [1.10126591 0.10788446]\\n\",\n \"8.630999472645442 [1.10455925 0.25939936] [1.07791108 0.25818788] [1.01876008 0.25475522]\\n\",\n \"7.1896574173042405 [1. 0.4394725] [1.69151733 0.15936391] [1.40745523 0.14090692]\\n\",\n \"9.028997222445492 [1.35449893 0.11153744] [1.3897975 0.08635315] [1.23613002 0.11002226]\\n\",\n \"9.540562872301349 [1.3576914 0.17826378] [1.3231857 0.16968261] [1.27488166 0.14102864]\\n\",\n \"15.217502969341245 [1. 0.46800261] [1.03400325 0.39430012] [1. 0.36108749]\\n\",\n \"8.149472820781698 [1.46439189 0.15456699] [1.41640802 0.15058807] [1.35129618 0.12745409]\\n\",\n \"6.976944992161395 [1. 0.41820539] [1.63197955 0.20528542] [1.30790648 0.18357009]\\n\",\n \"8.062994200864372 [1.2590772 0.18548839] [1.23136575 0.18143837] [1.17911986 0.17282262]\\n\",\n \"13.844743802537836 [1.2015369 0.15183412] [1.19427918 0.13726769] [1.18246524 0.11350421]\\n\",\n \"14.400292721241726 [1.43332449 0.2235953 ] [1.29722739 0.20826826] [1.32205969 0.08825859]\\n\",\n \"9.004881522367741 [2.32464637 0.28278733] [1.89162317 0.28131392] [1.59227461 0.24001558]\\n\",\n \"6.107589824184695 [1.39960932 0.31553911] [1.37343198 0.27532731] [1.27943831 0.24714296]\\n\",\n \"13.48237112635666 [1. 0.36446671] [1. 0.33977545] [1. 0.29601266]\\n\",\n \"3.121268510095921 [1. 0.18391253] [1. 0.17488635] [1. 0.14762028]\\n\",\n \"11.978946731228046 [1.86766493 0.38186767] [1.51454431 0.39137673] [1.27668655 0.37908934]\\n\",\n \"6.865283541115352 [1. 0.15293961] [1. 0.12481174] [1. 0.1027406]\\n\",\n \"7.281891843928655 [1.69451487 0.01952414] [1.23764544 0.15298789] [1. 0.27897591]\\n\",\n \"5.298772915637459 [1. 0.24827961] [1. 0.23037025] [1. 0.1959408]\\n\",\n \"3.8077487270183905 [1.16329354 0.24901332] [1.0779487 0.2385194] [1.00363016 0.21778446]\\n\",\n \"10.497606690401419 [1. 0.34818521] [1. 0.31596487] [1. 0.27036681]\\n\",\n \"7.158392552942906 [1.01385627 0.38510843] [1.04847678 0.33300206] [1. 0.3031797]\\n\",\n \"10.846809611522731 [1. 0.38793241] [1. 0.34907699] [1. 0.28209224]\\n\",\n \"7.490903894678285 [1. 0.26030496] [1.08122001 0.17188334] [1.05893602 0.11828488]\\n\",\n \"9.852946364225987 [1.42228082 0.19200603] [1.36081795 0.18114805] [1.32457667 0.13506045]\\n\",\n \"9.813044943909494 [1. 0.13507287] [1. 0.1211452] [1.06985331 0.03902373]\\n\",\n \"13.34587581319962 [1. 0.30546792] [1. 0.28546389] [1. 0.24660574]\\n\",\n \"7.418143411786704 [2.30965378 0.27421756] [2.36527214 0.22601405] [1.83067958 0.20473945]\\n\",\n \"6.487563025349676 [1. 0.27149387] [1. 0.25961742] [1. 0.23347115]\\n\",\n \"6.222757756016874 [1. 0.3155778] [1. 0.2964766] [1. 0.26161223]\\n\",\n \"11.059078872787278 [1.79205417 0.45790815] [1.50336918 0.43939687] [1.15875599 0.42807139]\\n\",\n \"12.627232736967574 [2.43846299 0.48689282] [1.8662622 0.49410938] [1.4464353 0.47503725]\\n\",\n \"8.497852968895268 [1. 0.33173961] [1. 0.29790762] [1. 0.24960098]\\n\",\n \"4.773945892232486 [1.38559259 0.37218966] [1.14653168 0.3521637 ] [1.05064959 0.28242611]\\n\",\n \"4.9798440733126625 [1.2818747 0.06413411] [1.26715682 0.06259901] [1.25116844 0.05028596]\\n\",\n \"7.215982714193697 [1.22008475 0.43899845] [1.1838212 0.40659116] [1.03220971 0.39996869]\\n\",\n \"12.222665510347309 [1. 0.35190148] [1. 0.30240314] [1. 0.23161851]\\n\",\n \"14.546352314783011 [1.01211239 0.47213658] [1.02836638 0.42257262] [1.04885107 0.38179193]\\n\",\n \"6.232905485930497 [1.14838793 0.21144955] [1.14887286 0.174099 ] [1.14116221 0.10557713]\\n\",\n \"6.2462598538040845 [1.28307071 0.11209189] [1.19406696 0.09839082] [1.20154404 0.03788191]\\n\",\n \"12.049837214204034 [1.17801886 0.26021165] [1.10615307 0.23640304] [1.04174162 0.19228388]\\n\",\n \"4.35580424954309 [1. 0.26060752] [ 1.43414028 -0.00861912] [1.24491131 0.00611205]\\n\",\n \"8.471650763073383 [1.56754364 0.2797233 ] [1.33000344 0.27946502] [1.59653944 0.12456281]\\n\",\n \"7.49044012651744 [1.32171485 0.39542063] [1.26162582 0.34219468] [1.22760822 0.2795295 ]\\n\",\n \"10.786327365502226 [1. 0.42439941] [1. 0.38285526] [1. 0.3303409]\\n\",\n \"10.373536489404659 [1. 0.25571275] [1.02855063 0.19742803] [1.05409558 0.14161136]\\n\",\n \"21.022477749453145 [1.11983787 0.33305407] [1.02719116 0.32128849] [1.19594603 0.17525151]\\n\",\n \"5.7531032804037325 [1.11015019 0.0588051 ] [1.06545661 0.05506 ] [1.02919721 0.04600535]\\n\",\n \"18.915284201266825 [1.12302054 0.32809132] [1.01549009 0.32142077] [1.17509954 0.17326784]\\n\",\n \"10.120576070558265 [1. 0.43104948] [1. 0.38759719] [1. 0.33643654]\\n\",\n \"10.366369407611174 [1.2204956 0.43281109] [1.17844132 0.37696907] [1.15221851 0.31668571]\\n\",\n \"8.920615707023098 [1.215459 0.37204049] [1.13545431 0.34796987] [1. 0.32275485]\\n\",\n \"10.309453079836256 [1. 0.28581225] [1. 0.23793582] [1. 0.18713733]\\n\",\n \"9.98892516276331 [1. 0.35439137] [1. 0.33185869] [1. 0.29803357]\\n\",\n \"6.165809009113636 [1. 0.24603162] [ 1.43675457 -0.00240027] [1.24853703 0.00712537]\\n\",\n \"8.393433910101315 [1.42325199 0.30029363] [1.27237161 0.28883931] [1.47440567 0.14601451]\\n\",\n \"12.787309279590836 [1.1576845 0.25694987] [1.10474044 0.23022065] [1.05309267 0.18348645]\\n\",\n \"7.2769603440583035 [1. 0.23596967] [1.02008553 0.18715022] [1.05534694 0.10902808]\\n\",\n \"7.108041328152496 [1.35727395 0.10880889] [1.28569031 0.10061569] [1.30496199 0.02775571]\\n\",\n \"13.216900123439 [1. 0.34101936] [1. 0.29523019] [1. 0.2311334]\\n\",\n \"14.692874439645578 [1. 0.48535927] [1. 0.43252866] [1.03359864 0.38608543]\\n\",\n \"4.304520284885788 [1.31688245 0.05907244] [1.29540368 0.0611252 ] [1.27733039 0.04833066]\\n\",\n \"6.330490988906221 [1.04545909 0.46497502] [1.01102258 0.43032254] [1. 0.38169003]\\n\",\n \"9.685975035829152 [1. 0.33264391] [1. 0.2974268] [1. 0.24907809]\\n\",\n \"15.702773916235305 [1. 0.5] [1.02657099 0.44381973] [1.10001176 0.35810512]\\n\",\n \"7.853615334358044 [1. 0.28074219] [1. 0.26840914] [1. 0.24217976]\\n\",\n \"5.975634138326699 [1. 0.31659836] [1. 0.29810188] [1. 0.26348462]\\n\",\n \"9.916002163802123 [1.59868211 0.38111511] [1.52662322 0.33446563] [1.36835491 0.29793856]\\n\",\n \"12.409547158848543 [1. 0.31865139] [1. 0.29673575] [1. 0.25524303]\\n\",\n \"12.575131449327872 [1.54827206 0.1353389 ] [1.45397267 0.13495303] [1.412522 0.09243483]\\n\",\n \"10.047461271617449 [1. 0.13838947] [1. 0.12543906] [1.07980201 0.03699989]\\n\",\n \"11.168585162085147 [1.40165996 0.36333975] [1.22556733 0.34348119] [1.03948977 0.30648717]\\n\",\n \"6.3148142448845785 [1.05192976 0.21690696] [1.04212699 0.17886409] [1.00050678 0.14293561]\\n\",\n \"4.213174439921227 [1.03662484 0.28966567] [1.00215127 0.26474702] [1. 0.21927834]\\n\",\n \"9.350377743405199 [1. 0.35654214] [1. 0.32229306] [1. 0.27400208]\\n\",\n \"7.092296103190132 [1.03188979 0.36834402] [1.05524235 0.32111044] [1. 0.29487285]\\n\",\n \"5.155585443443663 [1. 0.26704342] [1. 0.24899008] [1. 0.21170052]\\n\",\n \"8.949140629837553 [1.05108868 0.39110188] [1. 0.36651523] [1. 0.30836115]\\n\",\n \"8.716553166271584 [1. 0.14921731] [1. 0.1269959] [1. 0.1051833]\\n\",\n \"10.086518745680003 [1.69681287 0.04678019] [1.12508498 0.24327165] [1. 0.33185283]\\n\",\n \"11.182964161467057 [1.63311972 0.42401394] [1.38102156 0.42183174] [1.22833146 0.39035724]\\n\",\n \"13.76180563173197 [1. 0.3762183] [1. 0.34865231] [1. 0.30293159]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4.590938221163645 [1.14637812 0.3602448 ] [1.10077618 0.32862303] [1. 0.30323663]\\n\",\n \"10.918743442377107 [1.12710391 0.15037918] [1.10657724 0.14239141] [1.10051106 0.11869385]\\n\",\n \"11.804579155662669 [1.33688069 0.43758953] [1.34865482 0.37730493] [1.3432065 0.28776081]\\n\",\n \"13.177277116508062 [1.52878098 0.20696249] [1.28077 0.21824131] [1.30689721 0.08714808]\\n\",\n \"6.787660550087845 [1. 0.39119141] [1. 0.3606803] [1. 0.29720819]\\n\",\n \"4.451091387013824 [1.19332638 0.17125793] [1.16410742 0.16994217] [1.11273918 0.16180904]\\n\",\n \"8.513095756903981 [1.04369299 0.27813237] [1.04389932 0.26202727] [1.03733742 0.22642191]\\n\",\n \"7.386611194268827 [1.93391501 0.3312007 ] [2.002023 0.25603203] [1.590588 0.23676108]\\n\",\n \"15.14940803546385 [1. 0.47213044] [1.11423475 0.38502384] [1. 0.36287811]\\n\",\n \"16.421871987502197 [1. 0.37642323] [1. 0.3443427] [1.02078065 0.2809473 ]\\n\",\n \"7.161802208489492 [1.14310057 0.22848877] [1.13348165 0.21734642] [1.11509131 0.18413408]\\n\",\n \"7.6595161103083855 [1. 0.14029402] [1. 0.09303844] [1. 0.08251386]\\n\",\n \"13.35693014887036 [1.48071015 0.19590308] [1.29772968 0.19501838] [1.30705279 0.08045421]\\n\",\n \"10.803408667325941 [1.98737793 0.33374324] [1.75384086 0.30452603] [1.53861443 0.25186221]\\n\",\n \"10.075008722465236 [1.08200726 0.13105404] [1. 0.16296988] [1. 0.13251569]\\n\",\n \"9.195155565321723 [1.03749115 0.27621976] [1.02056792 0.27036357] [1. 0.25340891]\\n\",\n \"7.43204157510038 [1. 0.43050409] [1.67734648 0.21263812] [1.35465377 0.19194443]\\n\",\n \"5.896029385029159 [1.42870304 0.27649214] [1.2812687 0.26793401] [1.18041262 0.24718245]\\n\",\n \"8.123436416015343 [1. 0.3134811] [1. 0.2695824] [1. 0.19264644]\\n\",\n \"5.716475809488119 [1.50767532 0.08146423] [1.38561093 0.08507805] [1.26967229 0.0767172 ]\\n\",\n \"9.033193837283932 [2.98305142 0.24181145] [2.39644536 0.25587678] [1.79498076 0.26201245]\\n\",\n \"12.164324067161884 [1. 0.3947932] [1. 0.36898214] [1. 0.32283695]\\n\",\n \"5.339787973130951 [1. 0.24729247] [1. 0.22994155] [1. 0.19348346]\\n\",\n \"12.678490499063159 [1.64299616 0.04190122] [1.0591637 0.24535916] [1. 0.34403424]\\n\",\n \"6.077708548856496 [1.14963091 0.17937724] [1.13040526 0.15018877] [1.08640538 0.11397255]\\n\",\n \"12.178252021323798 [1.28310002 0.38013975] [1.11254179 0.36015164] [1. 0.30814744]\\n\",\n \"7.309460080103556 [1.06520598 0.37547398] [1.11286913 0.30832585] [1.00431014 0.30221934]\\n\",\n \"9.220776691475416 [1. 0.34339892] [1. 0.31220978] [1. 0.27417617]\\n\",\n \"5.098050429640151 [1. 0.29674274] [1. 0.26830966] [1. 0.22684651]\\n\",\n \"9.847574352492146 [1. 0.13295115] [1. 0.11941661] [1.07107621 0.03736894]\\n\",\n \"9.378924425161026 [1.66026543 0.37723044] [1.60835751 0.32637675] [1.36573792 0.29443589]\\n\",\n \"13.182248170063556 [1. 0.31282152] [1. 0.29206134] [1. 0.24996682]\\n\",\n \"5.318000365818506 [1. 0.3143855] [1. 0.29229958] [1. 0.25367857]\\n\",\n \"6.6286177536373945 [1.54499351 0.40292311] [1.70761229 0.35502976] [1.5882886 0.32408944]\\n\",\n \"5.441334460519029 [1. 0.23373452] [1. 0.19965821] [1. 0.15861831]\\n\",\n \"6.733051962029401 [1.13882056 0.115319 ] [1.12429207 0.11785498] [1.10701285 0.11406595]\\n\",\n \"4.808337374478711 [1.47909169 0.35586786] [1.19549607 0.33952498] [1.05558185 0.28169271]\\n\",\n \"7.18045223990615 [1. 0.35109998] [1. 0.31738902] [1. 0.2704406]\\n\",\n \"9.139950160459536 [2.73194726 0.41418685] [2.11928726 0.42496597] [1.67686128 0.40544779]\\n\",\n \"16.14149869210153 [1. 0.47548209] [1.04629857 0.42856518] [1.06596913 0.37772377]\\n\",\n \"12.070641071298047 [1. 0.34527724] [1. 0.29705856] [1. 0.2276855]\\n\",\n \"7.434200544866427 [1.32181479 0.11508353] [1.2032701 0.10868013] [1.24194895 0.03617664]\\n\",\n \"6.870374797147275 [1. 0.25905316] [1. 0.22340423] [1. 0.15733055]\\n\",\n \"11.709246462998976 [1.16809094 0.26245428] [1.10184073 0.23797386] [1.05045989 0.19024058]\\n\",\n \"8.20325483957443 [1. 0.33986977] [1. 0.31646446] [1. 0.28104671]\\n\",\n \"9.033009859575218 [1.17359599 0.37475019] [1.04596266 0.35637363] [1. 0.30693566]\\n\",\n \"15.702183543583855 [1. 0.37089796] [1. 0.33939589] [1. 0.30634559]\\n\",\n \"15.540954207408367 [1. 0.40327596] [1. 0.37952947] [1. 0.33862027]\\n\",\n \"21.38126183828421 [1.28070914 0.29927744] [1.14955948 0.29638129] [1.39224783 0.13822175]\\n\",\n \"22.436770425127463 [1.22642345 0.31261728] [1.09821051 0.30894634] [1.29405853 0.15898035]\\n\",\n \"6.792444514498642 [1.44001498 0.4332393 ] [1.34888428 0.40130308] [1.26794739 0.34149732]\\n\",\n \"8.968912513117655 [1.42961773 0.3099394 ] [1.23306083 0.30323857] [1.49129172 0.14860136]\\n\",\n \"5.674805610382074 [1. 0.24244748] [ 1.44426089 -0.00670134] [1.25205041 0.00637623]\\n\",\n \"9.20696195437281 [1. 0.34680365] [1. 0.32356847] [1. 0.28885825]\\n\",\n \"12.01006668627144 [1.20058944 0.45253004] [1.23001275 0.3922238 ] [1.20167105 0.32995753]\\n\",\n \"7.268419832489814 [1.22416277 0.33895806] [1.17951589 0.30673209] [1. 0.29074284]\\n\",\n \"12.0522052503042 [1.11756825 0.26540019] [1.07952285 0.2386899 ] [1.03653212 0.19095129]\\n\",\n \"10.475280985058973 [1.36359868 0.11570846] [1.30433452 0.10568103] [1.28807227 0.05140718]\\n\",\n \"7.492840276860108 [1.02794703 0.22600696] [1.04697332 0.17881622] [1.09720094 0.09238529]\\n\",\n \"13.681650161671499 [1.11399402 0.47833617] [1.18537375 0.41733039] [1.122122 0.37450852]\\n\",\n \"13.424825944230744 [1. 0.34504583] [1. 0.29594668] [1. 0.22853987]\\n\",\n \"4.780767547342776 [1.46095773 0.35687878] [1.19687247 0.33216925] [1.07251754 0.26640343]\\n\",\n \"7.911775322280817 [2.63443407 0.43824381] [2.05924642 0.45010572] [1.66740395 0.42959416]\\n\",\n \"4.015854860532171 [1. 0.20793752] [1. 0.17329306] [1.03386235 0.11967121]\\n\",\n \"6.713520362470293 [1. 0.32240739] [1. 0.30235687] [1. 0.26612081]\\n\",\n \"12.074188326332035 [1. 0.30551639] [1. 0.28382] [1. 0.24327509]\\n\",\n \"9.454394719378218 [1.75232135 0.36525822] [1.73434966 0.30734527] [1.47637131 0.2781888 ]\\n\",\n \"10.293786594978206 [1. 0.13087376] [1. 0.11884709] [1.0620732 0.04798402]\\n\",\n \"7.5419608178326385 [1.05831586 0.37541851] [1.1376635 0.30449258] [1.01610063 0.29506392]\\n\",\n \"6.520678105114464 [1. 0.36343357] [1. 0.33105139] [1. 0.28624847]\\n\",\n \"3.978041257149275 [1.0435017 0.29055885] [1.00155223 0.26582389] [1. 0.22054885]\\n\",\n \"6.415524536488719 [1.14623474 0.17867283] [1.12664102 0.14660054] [1.07920918 0.11174827]\\n\",\n \"9.048678022567225 [1.1420504 0.40753175] [1. 0.39215311] [1. 0.31516691]\\n\",\n \"12.858382628127798 [1.55014662 0.04664071] [1. 0.2662912] [1. 0.34158427]\\n\",\n \"12.04659315276056 [1. 0.37716955] [1. 0.35372715] [1. 0.30968543]\\n\",\n \"5.816629031044932 [1.51156517 0.08032432] [1.38840341 0.08455815] [1.2701874 0.07628197]\\n\",\n \"5.4013041206763015 [1.4884411 0.25501198] [1.40327348 0.23347025] [1.29894998 0.21259459]\\n\",\n \"7.914935200068231 [1. 0.34859634] [1. 0.29710985] [1. 0.20886223]\\n\",\n \"8.947884472998847 [1.03145903 0.28212011] [1.01350009 0.27661156] [1. 0.25647537]\\n\",\n \"7.219420391296332 [1. 0.44153257] [1.65049284 0.22948439] [1.37788939 0.19071623]\\n\",\n \"15.518736994613539 [1.26404283 0.44324559] [1.34474669 0.37493212] [1.43185122 0.26851484]\\n\",\n \"12.788760154664589 [1.42655951 0.21897641] [1.26131064 0.21251736] [1.29509424 0.0944886 ]\\n\",\n \"6.177922115924289 [1. 0.13960248] [1. 0.12342618] [1. 0.10192676]\\n\",\n \"8.04152307007944 [1. 0.14275038] [1. 0.09092113] [1. 0.08070935]\\n\",\n \"7.215196988886108 [1. 0.24482581] [1. 0.20797928] [1. 0.15821295]\\n\",\n \"8.09476838905765 [1.10566399 0.26031656] [1.09516876 0.24780982] [1.07277844 0.21927915]\\n\",\n \"13.639815646703246 [1.38631652 0.34327928] [1.38682424 0.29590406] [1.2800876 0.23538911]\\n\",\n \"14.468988791238358 [1. 0.44749568] [1.00451627 0.37178933] [1. 0.33439722]\\n\",\n \"6.988720319213014 [1. 0.12226574] [1. 0.10294753] [1. 0.08100132]\\n\",\n \"15.773147620581462 [1.29654139 0.37541121] [1.28474104 0.31503998] [1.33065061 0.20304062]\\n\",\n \"10.751327276204009 [1.23926839 0.21041204] [1.22484533 0.1981505 ] [1.19218005 0.17204274]\\n\",\n \"5.684842553233122 [1.03737285 0.12962043] [1.01477472 0.12172435] [1.00400735 0.099963 ]\\n\",\n \"5.417121817818472 [1. 0.27240543] [1. 0.2325963] [1. 0.17525595]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"5.891694302225043 [1.25972777 0.16373711] [1.22746573 0.15912013] [1.17665212 0.14910726]\\n\",\n \"8.793160183816639 [1. 0.39273616] [1. 0.34320608] [1. 0.27509644]\\n\",\n \"5.716706882708992 [2.13114339 0.31695666] [1.91464865 0.2893754 ] [1.66717715 0.23251986]\\n\",\n \"9.396427221145542 [1. 0.44386907] [1. 0.43682454] [1. 0.41753878]\\n\",\n \"4.060973086245111 [1.12811625 0.07502233] [1.09422964 0.05856901] [1.06109923 0.04190814]\\n\",\n \"4.4374517881175155 [1.07257936 0.30326746] [1.10072261 0.22900328] [1.12278412 0.12179577]\\n\",\n \"11.133661078559285 [1. 0.46226972] [1. 0.41594019] [1. 0.34609965]\\n\",\n \"6.150874953906226 [1.33361544 0.11011473] [1.24339714 0.10663268] [1.13506818 0.09751161]\\n\",\n \"4.653826185082733 [1. 0.12597343] [1. 0.09853081] [1. 0.0768966]\\n\",\n \"7.2510745336822495 [1.53460837 0.02320472] [1.13416 0.1553736] [1. 0.25563807]\\n\",\n \"4.356431560576406 [1.29197913 0.06236888] [1.27391104 0.06281774] [1.25477735 0.05111601]\\n\",\n \"7.112568932618397 [1. 0.3945393] [1. 0.35591552] [1. 0.30137758]\\n\",\n \"18.164439475148683 [1.21523226 0.31266801] [1.180494 0.27199212] [1.05022813 0.23082529]\\n\",\n \"7.742751882633314 [1.15416499 0.06784116] [1.10306645 0.06395505] [1.06683635 0.04797953]\\n\",\n \"6.350897941419369 [1.0541251 0.2380309] [1.15428629 0.14187908] [1.11715909 0.09057371]\\n\",\n \"14.767729380228179 [1. 0.37644373] [1. 0.33488604] [1. 0.26979247]\\n\",\n \"6.385047435437507 [1.04806365 0.38712485] [1. 0.35686888] [1. 0.29697577]\\n\",\n \"9.692609050090336 [1.40392011 0.20440658] [1.34051411 0.19120975] [1.28410855 0.1465873 ]\\n\",\n \"7.024719751551154 [1. 0.32698809] [1. 0.30520781] [1. 0.267538]\\n\",\n \"13.663091185062918 [1. 0.45325591] [1. 0.3664679] [1.05514643 0.33619966]\\n\",\n \"11.107894150487391 [1.41292528 0.32187687] [1.170399 0.30418462] [1. 0.26528342]\\n\",\n \"14.766242092442566 [1. 0.47659992] [1.26344653 0.37950707] [1.20590299 0.32832604]\\n\",\n \"6.4131843635100925 [1.06094015 0.25942477] [1.08727164 0.23325947] [1.06346143 0.21081528]\\n\",\n \"5.080159014798777 [1. 0.18962443] [1. 0.1509963] [1. 0.05423987]\\n\",\n \"5.719033079084207 [2.02944065 0.33118679] [1.64265725 0.32581619] [1.3396576 0.30601925]\\n\",\n \"4.591219990471863 [1.31803798 0.07871335] [1.26773763 0.0718669 ] [1.3020643 0.02877215]\\n\",\n \"3.7862175374727203 [1. 0.23186105] [1. 0.19123061] [1. 0.14931234]\\n\",\n \"8.039122145306324 [1.56302739 0.31515415] [1.25831753 0.29635916] [1.36207974 0.15380288]\\n\",\n \"9.677334364040973 [1.18170124 0.31586464] [1.13851834 0.26817432] [1.04093963 0.21807475]\\n\",\n \"2.7370408421682444 [1.13616131 0.2318686 ] [1.40806525 0.0115724 ] [1.22428605 0.02977322]\\n\",\n \"9.29730192539997 [1.30774979 0.11942896] [1.16347956 0.14512899] [1.3173694 0.01187218]\\n\",\n \"15.526524691289387 [1. 0.3310932] [1. 0.2844831] [1. 0.23075083]\\n\",\n \"11.017672565326 [1.18686802 0.24602856] [1.14106091 0.21916931] [1.09163688 0.17232029]\\n\",\n \"10.99085372091498 [1. 0.23665997] [1. 0.21189778] [1. 0.17769479]\\n\",\n \"9.463678038950732 [1.4321039 0.23182994] [1.3824293 0.18283563] [1.17269764 0.15142691]\\n\",\n \"8.244724159079153 [1.29948866 0.45833219] [1.24525979 0.41506679] [1.17430125 0.35533612]\\n\",\n \"10.76970808108283 [1. 0.2926715] [1. 0.24471049] [1. 0.196557]\\n\",\n \"8.453974950451215 [1.2738775 0.41625414] [1.12890283 0.39542715] [1.02955501 0.35860726]\\n\",\n \"7.0707075590839095 [1. 0.25485953] [1. 0.22364087] [1. 0.1733184]\\n\",\n \"6.593210405970022 [1. 0.28848014] [1. 0.25040597] [1. 0.1918077]\\n\",\n \"9.602109747672733 [1. 0.28655783] [1. 0.2396139] [1. 0.18953113]\\n\",\n \"9.903267217800787 [1. 0.41750385] [1. 0.38717919] [1. 0.33202579]\\n\",\n \"8.456703731077353 [1.31691898 0.44820206] [1.2646766 0.40121542] [1.20800165 0.33430126]\\n\",\n \"11.442953078440361 [1.04063187 0.21002153] [1.05936275 0.18194225] [1.04522708 0.15555158]\\n\",\n \"12.677632319692128 [1.1498834 0.2550538] [1.0994028 0.22739616] [1.05755804 0.17862199]\\n\",\n \"11.431287810767149 [1.42107401 0.22256108] [1.36944357 0.17256377] [1.17493751 0.1400153 ]\\n\",\n \"15.47924038477749 [1. 0.33688982] [1. 0.29763816] [1. 0.25367715]\\n\",\n \"5.809853441619306 [1.5210456 0.11331387] [1.57340061 0.07305487] [1.37659686 0.05108007]\\n\",\n \"8.02832602785699 [1.38440827 0.11370136] [1.31684005 0.10414527] [1.32822502 0.03368428]\\n\",\n \"2.1627993504342338 [1.23271853 0.19819153] [ 1.50047342 -0.00571418] [1.2730289 0.01018472]\\n\",\n \"5.784735548158705 [1.23161683 0.09958643] [1.21975339 0.08252126] [1.36840433 0.01555117]\\n\",\n \"6.867159456154496 [1.09601151 0.39852261] [1.08863841 0.33842392] [1.19910545 0.20261333]\\n\",\n \"3.479654525635185 [1. 0.23182781] [1. 0.18850923] [1. 0.1469227]\\n\",\n \"4.640527830169732 [1.11161203 0.17408629] [1.1134837 0.13638527] [1.13256368 0.05422848]\\n\",\n \"5.743096677976866 [2.4811614 0.2889833] [2.00574636 0.29374786] [1.5641532 0.27851168]\\n\",\n \"12.946199421719438 [1. 0.28075043] [1. 0.26039756] [1. 0.22096662]\\n\",\n \"6.074956281392489 [1.02888253 0.26223522] [1.04211541 0.23956685] [1.01987354 0.21830608]\\n\",\n \"13.434984280530074 [1. 0.45607302] [1.0592372 0.38333056] [1. 0.34673542]\\n\",\n \"6.8208066924477215 [1. 0.31946752] [1. 0.29469548] [1. 0.25358076]\\n\",\n \"9.793612636024221 [1.31029557 0.23113843] [1.25568947 0.21249858] [1.19808227 0.16464676]\\n\",\n \"9.494193456839135 [1. 0.42120974] [1. 0.37293915] [1. 0.29732569]\\n\",\n \"6.13118477880261 [1.01103742 0.2534833 ] [1.13576454 0.14824446] [1.10924289 0.09856326]\\n\",\n \"15.819647246381082 [1. 0.38783356] [1. 0.33566908] [1. 0.26309627]\\n\",\n \"9.600605129090999 [1. 0.41556355] [1. 0.35365931] [1. 0.2601237]\\n\",\n \"6.3574879048298545 [1. 0.38990792] [1. 0.35223975] [1. 0.2970689]\\n\",\n \"6.90601017156322 [1.15069262 0.06453205] [1.10300312 0.05805457] [1.06194101 0.04461918]\\n\",\n \"4.661849733982536 [1.29651397 0.06215004] [1.272838 0.06425507] [1.25147502 0.05305137]\\n\",\n \"8.761250791481844 [1. 0.14595345] [1. 0.13013114] [1. 0.11264322]\\n\",\n \"7.9036448804751664 [1.53889132 0.02408134] [1.14229798 0.16372788] [1. 0.26051279]\\n\",\n \"12.162078747056947 [1. 0.39060393] [1. 0.35973595] [1. 0.31356092]\\n\",\n \"5.451856216340836 [1.36560124 0.1024369 ] [1.27343169 0.10506909] [1.18994043 0.09010483]\\n\",\n \"10.840442889289944 [1. 0.4680975] [1. 0.42356851] [1. 0.3540948]\\n\",\n \"4.892808805866077 [1. 0.31285284] [1.08786949 0.22196679] [1.16825773 0.09104704]\\n\",\n \"3.91410393618042 [1.13000409 0.07581248] [1.0835281 0.06373103] [1.05629125 0.0436979 ]\\n\",\n \"6.69791580075448 [1.54250332 0.39868517] [1.32257885 0.38329475] [1.09516635 0.35167432]\\n\",\n \"10.534278235064187 [1. 0.45227187] [1. 0.44406083] [1. 0.41957123]\\n\",\n \"4.808537337151308 [1.20321121 0.16529559] [1.17718295 0.16302051] [1.13213581 0.15200777]\\n\",\n \"3.8779116434356427 [1.26273839 0.11194173] [1.29326685 0.06953592] [1.24823897 0.03262819]\\n\",\n \"10.331828146365424 [1. 0.39101359] [1.13691177 0.29844184] [1.1598001 0.21833517]\\n\",\n \"4.356549666351259 [1.03360225 0.41918878] [1. 0.40059] [1. 0.35745555]\\n\",\n \"5.435808897380261 [1. 0.28500083] [1. 0.24256721] [1.00652154 0.18118521]\\n\",\n \"4.530972308690726 [1.05142997 0.07730137] [1.01134688 0.09776861] [1.06021387 0.03916837]\\n\",\n \"8.713184312626323 [1.38576613 0.18030878] [1.3542891 0.1728741] [1.30368522 0.14759379]\\n\",\n \"4.982778419286587 [1. 0.1108381] [1. 0.09112781] [1. 0.06686228]\\n\",\n \"13.732649395522882 [1.47967034 0.29660066] [1.42575629 0.25034483] [1.41245788 0.15706471]\\n\",\n \"11.952034661936985 [1.19330595 0.21750693] [1.18816777 0.20535072] [1.17868428 0.17350153]\\n\",\n \"12.868449341343661 [1.5981332 0.30274196] [1.53698373 0.25092222] [1.41320284 0.17702285]\\n\",\n \"5.9065513403184 [1. 0.25653251] [1. 0.22001491] [1. 0.16433825]\\n\",\n \"7.889022281908016 [1. 0.13622052] [1. 0.08505973] [1. 0.07799846]\\n\",\n \"17.62149507636407 [1. 0.5] [1. 0.4868302] [1. 0.45386779]\\n\",\n \"14.184563511527882 [1.67857751 0.3744754 ] [1.65420228 0.32485745] [1.60230479 0.25334728]\\n\",\n \"8.23373358791664 [1.46165354 0.08280924] [1.42603347 0.0504182 ] [1.26485091 0.07282777]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"18.124118913101793 [1.32922321 0.23887569] [1.10528409 0.2573875 ] [1.22403664 0.10308437]\\n\",\n \"3.9868874555079876 [1.05720778 0.40419764] [1. 0.38784377] [1. 0.34279549]\\n\",\n \"4.599539602865495 [1.14186506 0.14652774] [1.15570267 0.10849091] [1.15647165 0.0610628 ]\\n\",\n \"5.759627160656901 [1.70349963 0.23830525] [1.61395007 0.21029727] [1.48694521 0.19041081]\\n\",\n \"6.54632418115673 [1.1821057 0.27387597] [1.23419344 0.208279 ] [1.2300459 0.11255374]\\n\",\n \"4.006927159625802 [1.12399294 0.07605867] [1.08566762 0.05949197] [1.048149 0.04550453]\\n\",\n \"5.155593872109015 [1.3669911 0.10548395] [1.2831182 0.10501239] [1.19686576 0.092191 ]\\n\",\n \"12.297423091066193 [1. 0.38016573] [1. 0.35825509] [1. 0.31677196]\\n\",\n \"11.112905763201406 [1. 0.4562093] [1. 0.40945472] [1. 0.33857818]\\n\",\n \"4.780549932697073 [1.30110912 0.06118039] [1.27517121 0.06293304] [1.25995527 0.04937645]\\n\",\n \"12.850462262114648 [1. 0.4017174] [1. 0.37097501] [1. 0.32171805]\\n\",\n \"4.61288722765694 [1. 0.10093775] [1. 0.08630901] [1. 0.06161945]\\n\",\n \"6.096782451635316 [1.54175764 0.32680338] [1.36718423 0.30584412] [1.17793683 0.27107083]\\n\",\n \"5.131156121131035 [1.17682994 0.22998307] [1.05034279 0.23655693] [1. 0.21443303]\\n\",\n \"7.879880527969907 [1.14493467 0.07164689] [1.11483468 0.06058582] [1.07292721 0.04559548]\\n\",\n \"9.387342953990725 [1. 0.3696372] [1.09627522 0.27817551] [1.05165484 0.26282338]\\n\",\n \"4.918880753691131 [1.45908293 0.29313014] [1.3782307 0.27507742] [1.23199078 0.24633128]\\n\",\n \"14.158239224299484 [1. 0.36476705] [1. 0.32522647] [1. 0.26000801]\\n\",\n \"9.517298273968473 [1. 0.42602333] [1. 0.37527014] [1. 0.29698766]\\n\",\n \"8.231845283312623 [1.18861333 0.29332819] [1.18790864 0.24573307] [1.13856228 0.17827134]\\n\",\n \"9.53983709142983 [1.13799697 0.38524838] [1. 0.36275799] [1. 0.28309355]\\n\",\n \"6.666709857548999 [1. 0.31742595] [1. 0.29604461] [1. 0.25653994]\\n\",\n \"6.270934162557976 [1.07343917 0.25619321] [1.08890573 0.23226074] [1.06953595 0.20817377]\\n\",\n \"14.344944542601246 [1. 0.4773223] [1.03664224 0.42563881] [1.0325325 0.37282668]\\n\",\n \"13.232647434100098 [1. 0.28509013] [1. 0.26535221] [1. 0.22192399]\\n\",\n \"5.415170282276734 [1.03703071 0.20910594] [1.03781338 0.17703365] [1.00296583 0.15074967]\\n\",\n \"7.195833600123861 [1. 0.41679496] [1. 0.35882238] [1.01981068 0.26818582]\\n\",\n \"5.835186775207567 [2.44922973 0.29453994] [1.95348276 0.30152249] [1.48030465 0.29045715]\\n\",\n \"3.914966619336214 [1. 0.20027453] [1. 0.15885312] [1. 0.05890127]\\n\",\n \"13.213401229836093 [1. 0.34283422] [1. 0.29034656] [1. 0.22714111]\\n\",\n \"2.9317032881679026 [1.10880727 0.25087744] [1.41523591 0.01255218] [1.22426241 0.03113065]\\n\",\n \"8.624558853353015 [1.18565813 0.2978048 ] [1.13924446 0.25165697] [1.04619305 0.20361098]\\n\",\n \"14.12549007603379 [1. 0.33063251] [1. 0.28683951] [1. 0.2317352]\\n\",\n \"3.4455625833028116 [1.73370339 0.05743556] [1.81161211 0.01492903] [1.28283606 0.05056141]\\n\",\n \"9.25790379122618 [1.27904293 0.24981154] [1.24774003 0.19116945] [1.13032598 0.15074178]\\n\",\n \"10.835337161227628 [1. 0.24161495] [1. 0.21326125] [1. 0.17767127]\\n\",\n \"7.911179455599862 [1.16541014 0.44322421] [1.01999164 0.43198894] [1. 0.37928913]\\n\",\n \"10.342808882158653 [1.05037901 0.24864016] [1.07215549 0.19487928] [1.07843212 0.14649988]\\n\",\n \"8.056915768772097 [1.30619205 0.45765377] [1.24991115 0.415563 ] [1.2128384 0.34760788]\\n\",\n \"14.85465376848101 [1. 0.40002233] [1. 0.37138823] [1. 0.31310465]\\n\",\n \"19.511684256662633 [1.26585356 0.31415552] [1.11639465 0.30895147] [1.21387797 0.1743843 ]\\n\",\n \"19.905318196850637 [1.27049544 0.30873869] [1.12271798 0.30548697] [1.22200986 0.1718376 ]\\n\",\n \"7.046293011243918 [1. 0.3732745] [1. 0.33097561] [1. 0.25958042]\\n\",\n \"19.706152544196275 [1. 0.39955553] [1. 0.35989177] [1. 0.32481473]\\n\",\n \"8.271463177472285 [1.43793467 0.42249578] [1.32703756 0.39426178] [1.31412245 0.32237713]\\n\",\n \"7.218605713310288 [1.0415818 0.47150815] [1. 0.43114047] [1. 0.36648088]\\n\",\n \"12.62778912841859 [1. 0.27095295] [1.01846325 0.21648754] [1.03289859 0.16241479]\\n\",\n \"9.88270569624501 [1.2978252 0.24223272] [1.26559263 0.18620396] [1.11782403 0.15160372]\\n\",\n \"1.5636127976922047 [1. 0.28248478] [1. 0.22363141] [1. 0.09104517]\\n\",\n \"13.390352111801858 [1. 0.34062208] [1. 0.29697274] [1. 0.24358278]\\n\",\n \"8.261674494393619 [1.25347048 0.27279468] [1.19417282 0.22676013] [1.11356917 0.17571076]\\n\",\n \"13.234432716213522 [1. 0.34471039] [1. 0.29848327] [1. 0.23258223]\\n\",\n \"3.169614729182636 [1.10947422 0.26262313] [1.47078038 0.01148745] [1.26961127 0.02460483]\\n\",\n \"5.852995628206429 [2.48916533 0.28730657] [1.95308589 0.29949853] [1.47909348 0.28945001]\\n\",\n \"4.131430687753246 [1.34952521 0.12119938] [1.33574652 0.09478586] [1.10795334 0.06341004]\\n\",\n \"6.340055497747631 [1. 0.42067454] [1.00844278 0.35966451] [1.05388587 0.25557233]\\n\",\n \"5.506406061399939 [1.05264599 0.21723827] [1.03226715 0.1870196 ] [1.00498817 0.15501192]\\n\",\n \"4.89467564828471 [1.1540556 0.12693867] [1.15079474 0.1005251 ] [1.0664712 0.04490176]\\n\",\n \"8.698451497948541 [1. 0.36944759] [1. 0.33819784] [1. 0.29021945]\\n\",\n \"5.8829784341171125 [1.00572393 0.28395658] [1.02614286 0.25901765] [1.01452655 0.23212322]\\n\",\n \"12.977928439478653 [1. 0.28127933] [1. 0.26068327] [1. 0.2213442]\\n\",\n \"14.755100213631243 [1. 0.46201402] [1.19924146 0.37012335] [1.11153423 0.33087121]\\n\",\n \"10.064144269856458 [1.14832169 0.37738287] [1. 0.35893565] [1. 0.28248572]\\n\",\n \"7.6368844832548515 [1. 0.31132055] [1. 0.28578546] [1. 0.24287838]\\n\",\n \"6.687217298797576 [1.15724037 0.28676805] [1.17081934 0.23879038] [1.13494045 0.17344283]\\n\",\n \"9.58202188153881 [1. 0.42426239] [1. 0.37442876] [1. 0.29812282]\\n\",\n \"4.868615337808833 [1.15919897 0.2433856 ] [1.07631749 0.23833285] [1. 0.22388433]\\n\",\n \"11.655515957723736 [1. 0.37358036] [1. 0.33007272] [1. 0.26131269]\\n\",\n \"9.596588977151242 [1. 0.36608441] [1.08511938 0.27830716] [1.00992861 0.26914553]\\n\",\n \"6.034714182004036 [1.58973187 0.31740757] [1.43175713 0.29578856] [1.23020381 0.26008328]\\n\",\n \"12.564334293206327 [1. 0.39437194] [1. 0.36352051] [1. 0.31566445]\\n\",\n \"5.889563098969405 [1. 0.14375823] [1. 0.11739547] [1. 0.09522836]\\n\",\n \"4.570063428873963 [1.30444438 0.06058142] [1.27588618 0.06371982] [1.26074365 0.0505582 ]\\n\",\n \"12.14829320880722 [1. 0.38080579] [1. 0.35939192] [1. 0.31904503]\\n\",\n \"9.939093277977596 [1. 0.45869984] [1. 0.41444018] [1. 0.34632318]\\n\",\n \"5.345084527136086 [1.36068286 0.09987179] [1.28112887 0.09920155] [1.19092264 0.08733046]\\n\",\n \"3.834859218594147 [1.06751783 0.10027921] [1.07723675 0.0691018 ] [1.03444079 0.05330765]\\n\",\n \"5.570271796656015 [1. 0.37750799] [1. 0.34380663] [1. 0.28871752]\\n\",\n \"6.632225582647623 [1.22723518 0.25349527] [1.20337442 0.22529021] [1.15206338 0.16473358]\\n\",\n \"3.7245353268928985 [1.151156 0.38800718] [1.02595136 0.38470179] [1. 0.33839395]\\n\",\n \"3.9682573428217274 [1.18939777 0.12761237] [1.21977333 0.08557312] [1.19246896 0.04557403]\\n\",\n \"20.755626834886233 [1. 0.5] [1. 0.49153093] [1. 0.45250084]\\n\",\n \"4.607417086387852 [1.04548425 0.1261651 ] [1.03005303 0.11349889] [1.00131411 0.10021228]\\n\",\n \"16.453616663613925 [1.22269744 0.26910482] [1.03089546 0.28080068] [1.16097286 0.12425261]\\n\",\n \"7.748004782649326 [1.40331675 0.11321791] [1.39996438 0.07066028] [1.30206376 0.06501894]\\n\",\n \"6.385436131632363 [1.70249006 0.36445203] [1.52836194 0.33543283] [1.25467478 0.29441277]\\n\",\n \"15.397874222942347 [1.4608715 0.4252542] [1.44220057 0.37527856] [1.36308945 0.30897187]\\n\",\n \"6.043950811136515 [1. 0.11375008] [1. 0.08301911] [1. 0.06138666]\\n\",\n \"7.443403802940812 [1. 0.25151869] [1. 0.21670456] [1. 0.16237954]\\n\",\n \"13.17500086957239 [1.44958733 0.33520476] [1.39339071 0.29233331] [1.24992011 0.23805035]\\n\",\n \"10.79201123984015 [1.32974063 0.16457546] [1.30392758 0.16093623] [1.27719686 0.13503117]\\n\",\n \"5.358439952320776 [1. 0.28845737] [1. 0.24758458] [1. 0.18727148]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4.839713107103963 [1. 0.12382349] [1.00983414 0.06859447] [1.0173457 0.06087906]\\n\",\n \"13.783040869059848 [1. 0.43234353] [1. 0.40366689] [1. 0.36655522]\\n\",\n \"11.355133210366915 [1.17375473 0.23277929] [1.1698897 0.21643841] [1.14696259 0.18725392]\\n\",\n \"13.629910266670032 [1.52758876 0.29331789] [1.47490035 0.24233049] [1.43440128 0.15101406]\\n\",\n \"6.168596795428792 [1.0998412 0.29026187] [1.13416883 0.2196127 ] [1.05986944 0.14377691]\\n\",\n \"4.288050436543725 [1.19741602 0.04366837] [1.1038339 0.05014255] [1.0634177 0.03838106]\\n\",\n \"5.836821811911606 [1.44333632 0.39423676] [1.20655943 0.37783335] [1.07501377 0.33167891]\\n\",\n \"5.526374437104748 [1.44935734 0.09082052] [1.42332044 0.05413665] [1.35768648 0.03412326]\\n\",\n \"19.879032561148527 [1.00143127 0.5 ] [1. 0.49989319] [1. 0.46461947]\\n\",\n \"5.753800656087086 [1. 0.19888411] [1. 0.17527174] [1. 0.14177691]\\n\",\n \"7.538761930151618 [1. 0.36227097] [1. 0.33502637] [1. 0.29019684]\\n\",\n \"4.377269401419613 [1.27585858 0.05518306] [1.25798571 0.05445539] [1.22536991 0.04757483]\\n\",\n \"14.113951039917655 [1. 0.39644585] [1. 0.36892543] [1. 0.3224152]\\n\",\n \"6.050573340274629 [1.37935154 0.09971442] [1.27961532 0.09755718] [1.18644881 0.08388369]\\n\",\n \"10.24287426790233 [1. 0.48176737] [1. 0.43454273] [1. 0.36321288]\\n\",\n \"9.854976871397522 [1. 0.43564793] [1. 0.37695982] [1. 0.29500269]\\n\",\n \"9.209505123130837 [1.10139433 0.31352503] [1.13433612 0.27430368] [1.06656546 0.21958064]\\n\",\n \"5.41147311417093 [1.30676009 0.34040911] [1.13732792 0.32128211] [1. 0.28302368]\\n\",\n \"6.800057809730648 [1. 0.39462282] [1. 0.36103037] [1. 0.31067217]\\n\",\n \"15.101050513955645 [1. 0.3653043] [1. 0.3153946] [1.00096094 0.24186618]\\n\",\n \"7.873072404582189 [1.14277229 0.07795869] [1.10102994 0.07166576] [1.07135518 0.05263471]\\n\",\n \"14.457991370510385 [1. 0.40917791] [1. 0.38303446] [1. 0.34620609]\\n\",\n \"4.842251137901636 [1.00934833 0.31111778] [1. 0.29347213] [1. 0.25553139]\\n\",\n \"6.367801696513165 [1. 0.28679118] [1. 0.25467389] [1. 0.20656831]\\n\",\n \"6.483138962526544 [1.13785268 0.26727746] [1.14264027 0.24596525] [1.08479983 0.2304965 ]\\n\",\n \"5.579665831228779 [1.22849478 0.24625582] [1.16458639 0.2330797 ] [1.06542959 0.21789782]\\n\",\n \"10.466927565218658 [1. 0.37320549] [1. 0.32605051] [1. 0.25532877]\\n\",\n \"9.093745011148197 [1. 0.36086579] [1. 0.33108925] [1. 0.28392246]\\n\",\n \"15.3284043988048 [1. 0.36927274] [1. 0.34175819] [1.34924593 0.1669367 ]\\n\",\n \"3.0393841381302527 [1.25619506 0.18919224] [ 1.48827333e+00 -2.32351488e-04] [1.26456789 0.02115308]\\n\",\n \"9.138319578529781 [1.36479105 0.24590478] [1.28245287 0.20958615] [1.14847979 0.16865384]\\n\",\n \"6.488615015580878 [1.12768567 0.12468495] [1.14242763 0.09519091] [1.16081641 0.03404052]\\n\",\n \"3.689592393714326 [1. 0.23685664] [1. 0.19886097] [1. 0.15789694]\\n\",\n \"10.703611767435968 [1. 0.38580316] [1. 0.33148521] [1. 0.26327223]\\n\",\n \"11.057495178556948 [1. 0.40839311] [1. 0.36271256] [1. 0.28938717]\\n\",\n \"11.257050659453116 [2.81169895 0.39462628] [2.17284109 0.41266398] [1.67387216 0.40595294]\\n\",\n \"5.377969402147548 [2.42672982 0.33523331] [1.88879513 0.33183145] [1.38520387 0.33660904]\\n\",\n \"10.583254643386892 [1.25119472 0.24808459] [1.22892184 0.19036331] [1.12883598 0.14981773]\\n\",\n \"16.983683328439167 [1. 0.3419402] [1. 0.30393983] [1. 0.25605751]\\n\",\n \"5.050869203557615 [1.29198231 0.15229284] [1.20352486 0.1392287 ] [1.24414849 0.04265336]\\n\",\n \"7.548112735771338 [1. 0.26548097] [1. 0.22705054] [1. 0.16898619]\\n\",\n \"5.9141866300442905 [1.22341841 0.11054603] [1.13235639 0.10897532] [1.27037594 0.01602645]\\n\",\n \"6.435282354321136 [1.13161061 0.26900905] [1.11807883 0.25184886] [1.08303097 0.22892779]\\n\",\n \"13.609323509609263 [1. 0.32877443] [1. 0.30341652] [1. 0.20613864]\\n\",\n \"15.184223641531718 [1. 0.40143023] [1. 0.36939884] [1. 0.31510046]\\n\",\n \"13.913675186771753 [1. 0.3426041] [1. 0.29922944] [1. 0.2458466]\\n\",\n \"8.477446212219109 [1.61136307 0.37478658] [1.48424498 0.34918918] [1.59215381 0.25505025]\\n\",\n \"9.48077344085526 [1.79955938 0.33832709] [1.63219262 0.31941699] [1.67976814 0.24321916]\\n\",\n \"12.133686880611858 [1. 0.33516168] [1. 0.32318787] [1.02600621 0.20780843]\\n\",\n \"6.431276338217847 [1.14769126 0.26820245] [1.13731583 0.2497082 ] [1.10222323 0.2258782 ]\\n\",\n \"14.602968511288568 [1. 0.26648705] [1. 0.22446566] [1. 0.17597921]\\n\",\n \"15.924964263030803 [1. 0.35419677] [1. 0.30666105] [1. 0.24987524]\\n\",\n \"10.073897762654642 [1. 0.48581412] [1. 0.43717362] [1. 0.37610968]\\n\",\n \"12.504329670174597 [1. 0.41409509] [1. 0.38374635] [1. 0.33433319]\\n\",\n \"6.148500583702118 [1.23945051 0.09895355] [1.13094388 0.10288653] [1.18233789 0.02107214]\\n\",\n \"7.220512159715028 [1. 0.25923894] [1. 0.22030217] [1. 0.16259607]\\n\",\n \"5.519340769919593 [1.28009163 0.16776537] [1.2402446 0.13756742] [1.2321024 0.05012039]\\n\",\n \"13.64398389396302 [1. 0.36034972] [1. 0.31352092] [1. 0.26309365]\\n\",\n \"10.634962837733285 [1.35753494 0.22295601] [1.29589863 0.17297748] [1.18312851 0.13153996]\\n\",\n \"9.454209269509137 [1. 0.44034392] [1. 0.38051256] [1. 0.29502614]\\n\",\n \"20.443099022794467 [1. 0.5] [1. 0.5] [1. 0.47353101]\\n\",\n \"5.223012826020312 [1.16866365 0.1297681 ] [1.155908 0.10422172] [1.19055712 0.02997721]\\n\",\n \"12.776858241921095 [1. 0.36682438] [1. 0.31388614] [1. 0.24573757]\\n\",\n \"3.3725971074362833 [1. 0.23624836] [1. 0.19662779] [1. 0.15500436]\\n\",\n \"8.96699163099819 [1.4497885 0.2409892] [1.30910507 0.20188193] [1.16561168 0.16120304]\\n\",\n \"8.932694276036234 [1. 0.36633319] [1. 0.3359799] [1. 0.288562]\\n\",\n \"16.184842224718512 [1. 0.34924031] [1. 0.32232702] [1.39812381 0.14642405]\\n\",\n \"5.659516528473731 [1.0277043 0.31353489] [1. 0.30011375] [1. 0.26279099]\\n\",\n \"8.572522665084962 [1.29433658 0.34716409] [1.12911407 0.32252244] [1. 0.27356586]\\n\",\n \"5.353650096895277 [1. 0.24643639] [1. 0.21292349] [1. 0.15890878]\\n\",\n \"5.214421749586937 [1.03779245 0.30160609] [1.00041772 0.28857636] [1. 0.25020107]\\n\",\n \"6.325109529375439 [1.08280791 0.28250599] [1.10108639 0.2571972 ] [1.04739482 0.24093943]\\n\",\n \"6.8436534333722925 [1. 0.38925963] [1. 0.35722972] [1. 0.30763456]\\n\",\n \"6.723067652469975 [1.10803599 0.07018331] [1.07515174 0.06293138] [1.04363398 0.05082909]\\n\",\n \"11.709164432486157 [1. 0.43642546] [1. 0.40740024] [1. 0.36830802]\\n\",\n \"5.3570439833478485 [1.12087528 0.36083822] [1. 0.34111] [1. 0.28348617]\\n\",\n \"10.311399780442231 [1. 0.43636906] [1. 0.37749159] [1. 0.29517295]\\n\",\n \"9.472151547175129 [1.01455281 0.48212093] [1. 0.44170811] [1. 0.37051675]\\n\",\n \"5.8702431454884945 [1.32963186 0.11462892] [1.24828534 0.10864314] [1.13822955 0.09836079]\\n\",\n \"6.131922164901829 [1. 0.15196716] [1. 0.12767654] [1. 0.10427294]\\n\",\n \"12.488894439856965 [1. 0.37644509] [1. 0.35530003] [1. 0.31405518]\\n\",\n \"3.4997343302104658 [1.30354141 0.04477158] [1.27031851 0.04788207] [1.24386786 0.03928125]\\n\",\n \"4.710214631785459 [1.15355498 0.12136588] [1.17552416 0.08284649] [1.15588257 0.04804761]\\n\",\n \"6.3508030133773605 [1. 0.36685683] [1. 0.33957127] [1. 0.29943642]\\n\",\n \"5.643347599813622 [1.47045292 0.0866719 ] [1.44325934 0.0513974 ] [1.35141321 0.04022204]\\n\",\n \"5.577196759912198 [2.11268657 0.30958052] [1.92493724 0.27673218] [1.66359874 0.20946944]\\n\",\n \"10.15041960848512 [1. 0.45245338] [1. 0.4455671] [1. 0.42473923]\\n\",\n \"6.547922807246284 [1.12589041 0.08070766] [1.05853416 0.0737925 ] [1.0288873 0.06116788]\\n\",\n \"4.820737038419537 [1. 0.32397085] [1.08401702 0.23804361] [1.15143934 0.11177209]\\n\",\n \"12.566550715047965 [1.79690964 0.25083387] [1.69366263 0.20882101] [1.53867345 0.13593018]\\n\",\n \"13.701983978967691 [1. 0.42134975] [1. 0.39213028] [1. 0.35769849]\\n\",\n \"5.04770263943654 [1. 0.13258621] [1. 0.11076661] [1. 0.08275563]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"5.581341597426848 [1. 0.2900611] [1. 0.24783373] [1. 0.18492683]\\n\",\n \"5.311604781287308 [1.08782496 0.09834222] [1.06928917 0.0586435 ] [1.10309036 0.04706468]\\n\",\n \"11.947792958577935 [1.18489773 0.22567682] [1.17241542 0.21127795] [1.14331286 0.18418474]\\n\",\n \"7.245979577607476 [1. 0.27493374] [1. 0.23770554] [1. 0.18181668]\\n\",\n \"9.495213434127194 [1.44451906 0.16169426] [1.39969108 0.15862781] [1.33265799 0.13646104]\\n\",\n \"11.979729601337414 [1. 0.41507488] [1. 0.38521274] [1. 0.34710955]\\n\",\n \"5.2193883889384045 [1. 0.39587723] [1. 0.37685163] [1. 0.33322685]\\n\",\n \"5.744075709511627 [1.02852747 0.23553349] [1.02116077 0.21611541] [1.00459175 0.19130408]\\n\",\n \"5.23308406819318 [1.4429346 0.39336271] [1.20736696 0.37503816] [1.07921141 0.32431613]\\n\",\n \"4.722516315006456 [1.3978819 0.08579017] [1.37456425 0.04520417] [1.2666831 0.04766728]\\n\",\n \"13.911817208795442 [1. 0.39398167] [1. 0.36708687] [1. 0.32054315]\\n\",\n \"6.461381801065701 [1. 0.16969502] [1. 0.14792357] [1. 0.12572392]\\n\",\n \"5.960267461947247 [1.26982858 0.05557321] [1.24874686 0.05688101] [1.22589303 0.05044706]\\n\",\n \"10.25300108948741 [1. 0.46187059] [1. 0.41989989] [1. 0.35074268]\\n\",\n \"11.787179414869579 [1.76632527 0.41869485] [1.43269394 0.4198818 ] [1.24296689 0.3933356 ]\\n\",\n \"5.958413956790485 [1.30835065 0.11653857] [1.22474741 0.11378343] [1.16048522 0.09304549]\\n\",\n \"10.221917231289439 [1. 0.42500705] [1. 0.38027495] [1. 0.30968143]\\n\",\n \"10.578450102041375 [1.25961277 0.25261408] [1.22239005 0.2208682 ] [1.19269214 0.14752483]\\n\",\n \"11.968923765333345 [1. 0.42743135] [1. 0.40085837] [1. 0.36184916]\\n\",\n \"6.499917748925294 [1.09647939 0.06982063] [1.05527086 0.06521202] [1.02322735 0.05498115]\\n\",\n \"6.085882935694625 [1. 0.39960601] [1. 0.36280198] [1. 0.3084111]\\n\",\n \"5.173288811059643 [1.11839091 0.36623351] [1.00609892 0.35280735] [1. 0.29913639]\\n\",\n \"5.5329798066873535 [1. 0.28536787] [1. 0.26654426] [1. 0.23642846]\\n\",\n \"5.4451650886986 [1. 0.22766355] [1. 0.1988036] [1. 0.14749155]\\n\",\n \"14.958895497278894 [1. 0.36682107] [1. 0.338425] [1.43888911 0.14979942]\\n\",\n \"8.940743720672465 [1.20547298 0.35744166] [1.07560059 0.33012045] [1. 0.27251407]\\n\",\n \"7.436545361874667 [1. 0.3183971] [1. 0.29659595] [1. 0.2602954]\\n\",\n \"9.134181236204086 [1.20762625 0.28111081] [1.14802603 0.2339364 ] [1.05651465 0.18709927]\\n\",\n \"5.731991104797348 [1.41909933 0.44318859] [1.21217277 0.42579532] [1.06809325 0.39116092]\\n\",\n \"5.206514265179677 [1.27050551 0.06583924] [1.25125421 0.06569998] [1.23254293 0.05498154]\\n\",\n \"9.104566721644789 [1. 0.23076818] [1. 0.20671271] [1. 0.1702677]\\n\",\n \"9.503861406452184 [1. 0.3933715] [1. 0.33789332] [1. 0.2698494]\\n\",\n \"13.423185718655846 [1. 0.41264971] [1. 0.38841542] [1. 0.35286958]\\n\",\n \"5.273742240055194 [1.31152661 0.08900905] [1.30530183 0.06828644] [1.29780875 0.02460142]\\n\",\n \"9.38267066376904 [1.28749799 0.24714228] [1.2401462 0.20082994] [1.13681382 0.1539198 ]\\n\",\n \"5.554657782269932 [1.00974729 0.1073955 ] [1. 0.09572028] [1. 0.07008646]\\n\",\n \"5.3551988638892825 [1.03802083 0.23392611] [1.04737131 0.19578504] [1.08963842 0.11075911]\\n\",\n \"5.078271939224496 [1.31380106 0.15165493] [1.22164349 0.14006279] [1.25888297 0.04407429]\\n\",\n \"12.209527653607935 [1.12610795 0.24827783] [1.09141976 0.22121143] [1.05152291 0.17796183]\\n\",\n \"7.236205279556641 [1. 0.26082362] [1. 0.2221064] [1. 0.16596905]\\n\",\n \"6.329694670513371 [1.03281315 0.15687788] [1.05061858 0.13566962] [1.28249508 0.01469789]\\n\",\n \"3.4062499660108245 [1.05565573 0.29287467] [1.4782522 0.01282449] [1.27482247 0.0235219 ]\\n\",\n \"13.728436212527166 [1. 0.34172437] [1. 0.3249018] [1.0515369 0.20364606]\\n\",\n \"7.910498525300672 [1.31618339 0.39247978] [1.09608015 0.38332792] [1. 0.34583144]\\n\",\n \"12.309733990060018 [1.02936961 0.23070053] [1.05595266 0.17433231] [1.06823147 0.12203047]\\n\",\n \"11.682042964572211 [1. 0.34170111] [1. 0.32074289] [1.35200494 0.14586391]\\n\",\n \"9.349333543933142 [1.12337531 0.45501582] [1.02980351 0.42866449] [1. 0.37751805]\\n\",\n \"12.479948608243525 [1. 0.33288736] [1. 0.30745312] [1.15279318 0.17813779]\\n\",\n \"12.218947880890472 [1. 0.26605717] [1. 0.22768542] [1. 0.18603149]\\n\",\n \"3.2464472905915756 [1.11891244 0.25752831] [1.44933499 0.00799026] [1.25140396 0.02325195]\\n\",\n \"13.56354539998666 [1. 0.34602254] [1. 0.32759299] [1. 0.22130759]\\n\",\n \"7.444098522183007 [1.67172634 0.37452495] [1.53363559 0.34770609] [1.50353595 0.28038547]\\n\",\n \"6.278158304990354 [1. 0.17046653] [1. 0.15237823] [1.18718045 0.03143365]\\n\",\n \"6.849653895905821 [1. 0.26536084] [1. 0.2303866] [1. 0.17477424]\\n\",\n \"14.1965028140593 [1. 0.34648494] [1. 0.3021621] [1. 0.25534017]\\n\",\n \"6.92101570965483 [1. 0.24208607] [1. 0.2148202] [1. 0.1584444]\\n\",\n \"5.601546672059479 [1. 0.10607985] [1. 0.09256911] [1. 0.06920562]\\n\",\n \"4.801359607137906 [1.30545915 0.15938466] [1.25225536 0.13460016] [1.26147593 0.04548683]\\n\",\n \"11.62181801747974 [1.27719147 0.25835791] [1.26022512 0.20337497] [1.11133752 0.16349657]\\n\",\n \"9.689429637875344 [1.03763838 0.3886762 ] [1. 0.34108572] [1. 0.2735025]\\n\",\n \"5.014053782790492 [1.27711913 0.06474456] [1.26184626 0.06451575] [1.23906064 0.05258765]\\n\",\n \"13.132059954438168 [1. 0.43430573] [1. 0.40595153] [1. 0.36851516]\\n\",\n \"4.851125703567041 [1.84975898 0.3715327 ] [1.49529655 0.36010651] [1.22477509 0.33813967]\\n\",\n \"9.956250610421554 [1.32748806 0.26442972] [1.20954762 0.22638284] [1.05842013 0.18972914]\\n\",\n \"9.843111602971783 [1.21746058 0.36294867] [1.05752727 0.33936087] [1. 0.27209566]\\n\",\n \"5.625140801016169 [1.18136395 0.254585 ] [1.1235811 0.23946801] [1.04228104 0.22170254]\\n\",\n \"7.339319468599995 [1.02401503 0.31604764] [1.05925218 0.28613123] [1.03930775 0.26056254]\\n\",\n \"5.863058274090753 [1. 0.25325056] [1. 0.22913271] [1. 0.18304533]\\n\",\n \"5.563912449697366 [1.15424635 0.35983367] [1.00025694 0.34335127] [1. 0.28503896]\\n\",\n \"10.411823162742717 [1. 0.43464808] [1. 0.40459801] [1. 0.3645003]\\n\",\n \"6.024901040524667 [1.10853677 0.06224004] [1.06573987 0.05866889] [1.03424444 0.04801264]\\n\",\n \"5.881619218655779 [1. 0.37912158] [1. 0.33593401] [1. 0.27946323]\\n\",\n \"9.201288617140614 [1.13673983 0.28871202] [1.12352696 0.25245452] [1.07993825 0.19068865]\\n\",\n \"10.799481336264586 [1. 0.41547583] [1. 0.36751483] [1. 0.29410016]\\n\",\n \"10.975704498754395 [2.13255797 0.35624651] [1.75726357 0.3653143 ] [1.49769767 0.35076586]\\n\",\n \"11.108866793622619 [1. 0.46047068] [1. 0.4200483] [1. 0.35238113]\\n\",\n \"5.362009213568577 [1.28449891 0.05576823] [1.26617913 0.05598244] [1.23530509 0.05178904]\\n\",\n \"13.60001791724086 [1. 0.39290307] [1. 0.36491517] [1. 0.31803772]\\n\",\n \"5.824120077981131 [1. 0.1691489] [1. 0.14150499] [1. 0.11773266]\\n\",\n \"5.227446595838796 [1.41883214 0.08755289] [1.39051572 0.04165076] [1.25145427 0.0517093 ]\\n\",\n \"5.61139609746584 [1.4652467 0.38710518] [1.22706474 0.36978388] [1.09243372 0.31964533]\\n\",\n \"4.171756045879388 [1.29394775 0.35944299] [1.07465225 0.36804221] [1. 0.34250625]\\n\",\n \"4.395064708870857 [1.11969056 0.14840197] [1.13031667 0.1135897 ] [1.12875825 0.06807764]\\n\",\n \"7.48165796112809 [1.20592439 0.28317858] [1.20088031 0.2446455 ] [1.14410797 0.17525922]\\n\",\n \"7.209986956838854 [1.023589 0.12307983] [1.01336926 0.09886015] [1.00226365 0.07933181]\\n\",\n \"10.678851050677565 [1.23783537 0.20936876] [1.22534 0.19914427] [1.20163314 0.17139288]\\n\",\n \"12.372406636226975 [1. 0.4488869] [1. 0.42012377] [1. 0.38271775]\\n\",\n \"15.577807191071372 [1.02523902 0.45866847] [1.08312272 0.37623742] [1.20766226 0.23768777]\\n\",\n \"8.734471938153105 [1.36523752 0.17271522] [1.3134492 0.16957917] [1.25456613 0.14811045]\\n\",\n \"7.610336442791224 [1. 0.26843504] [1. 0.23356436] [1. 0.17790642]\\n\",\n \"8.863838171017678 [1.41825983 0.31832998] [1.26990717 0.29132318] [1.19563001 0.22590613]\\n\",\n \"7.745268162820891 [1.13292471 0.26612377] [1. 0.30604906] [1. 0.21973743]\\n\",\n \"4.589678527103571 [1. 0.181678] [1. 0.15089917] [1. 0.12282483]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4.093994707301205 [1.08729292 0.08806179] [1.06692303 0.06636428] [1.03433912 0.05085375]\\n\",\n \"4.803442691616833 [1. 0.49318999] [1. 0.45994491] [1. 0.41414422]\\n\",\n \"8.141877447013025 [1. 0.378827] [1. 0.35032184] [1. 0.31377014]\\n\",\n \"5.469339275345639 [1.05194881 0.49235425] [1. 0.4531681] [1. 0.36628546]\\n\",\n \"10.552513200233147 [1. 0.46403819] [1.02642136 0.4204548 ] [1.00651206 0.36114381]\\n\",\n \"7.912086669941295 [1.4773809 0.02540787] [1. 0.24006762] [1. 0.22141444]\\n\",\n \"5.1324676655653985 [1.04788493 0.39280235] [1. 0.36007542] [1. 0.29963288]\\n\",\n \"8.464687671158348 [1. 0.41448182] [1. 0.37280512] [1. 0.30366964]\\n\",\n \"8.831959464848165 [1.01165928 0.38713134] [1.19958591 0.27802433] [1. 0.27581693]\\n\",\n \"3.689023065396748 [1.23016991 0.19426718] [1. 0.2285476] [1. 0.19498156]\\n\",\n \"7.739213941895897 [1.14601354 0.24850415] [1.13882119 0.21963422] [1.0931114 0.1761189]\\n\",\n \"4.760667671639348 [1.56818228 0.09992981] [1.51742308 0.07806965] [1.19779275 0.06806795]\\n\",\n \"7.158135476697359 [1.08911821 0.13319368] [1.03077099 0.12626594] [1.10715564 0.0431315 ]\\n\",\n \"12.517284088482697 [1.37487033 0.32657322] [1.22994953 0.32196908] [1.76583206 0.12341415]\\n\",\n \"23.968827345650084 [1. 0.41066761] [1. 0.37635411] [1.11578731 0.2836719 ]\\n\",\n \"9.643650548801416 [1.3668108 0.17932402] [1.14939936 0.20033119] [1.16055901 0.09596568]\\n\",\n \"3.0839077781933777 [1. 0.22836315] [1. 0.21865558] [1. 0.192191]\\n\",\n \"9.251162412565186 [1. 0.35180185] [1.06255665 0.2844368 ] [1. 0.2615466]\\n\",\n \"3.6316623787021496 [1.02245801 0.09782654] [1.00703226 0.0823112 ] [1.00851461 0.05555117]\\n\",\n \"8.711853634715693 [1.2598993 0.2082685] [1.2496592 0.16908508] [1.18118223 0.11168647]\\n\",\n \"5.71651001075999 [1.36176414 0.352993 ] [1.15731905 0.34234659] [1.12412015 0.27307774]\\n\",\n \"8.29629418921876 [1.68012885 0.38619025] [1.61051734 0.33103658] [1.35520496 0.30506579]\\n\",\n \"11.504291611676422 [1. 0.39222487] [1. 0.35314538] [1. 0.29158934]\\n\",\n \"6.6256613573123095 [1. 0.42684849] [1. 0.38465708] [1. 0.33054167]\\n\",\n \"4.404122811378351 [1.12461283 0.18642639] [1.16004867 0.15665071] [1.13695563 0.126951 ]\\n\",\n \"4.1977340356457615 [1.14924923 0.1855828 ] [1.16115423 0.1522952 ] [1.12954357 0.11237641]\\n\",\n \"13.427749437316683 [1.21345884 0.29035845] [1.11641823 0.26892753] [1.01931096 0.224942 ]\\n\",\n \"6.221542448393431 [1.47309959 0.07790591] [1.38973338 0.07566895] [1.27297753 0.06992654]\\n\",\n \"5.129024047187179 [1.53184879 0.39148192] [1.26484378 0.37476782] [1. 0.34922252]\\n\",\n \"18.89243184198175 [1.26707603 0.31528262] [1.11724298 0.31020941] [1.17470508 0.18765881]\\n\",\n \"3.8403752208504867 [1. 0.10222138] [1. 0.08408468] [1.00121213 0.05810218]\\n\",\n \"6.680468368549407 [1.07202455 0.26639423] [1.00132482 0.2604441 ] [1. 0.22536632]\\n\",\n \"18.141218087495645 [1.10184668 0.17468229] [1. 0.19923966] [1.19758084 0.05404643]\\n\",\n \"9.657772216906958 [1.35405939 0.39086437] [1.31007881 0.33573011] [1.24772003 0.28072832]\\n\",\n \"11.316065831914312 [1. 0.3488616] [1. 0.30311498] [1. 0.22630642]\\n\",\n \"7.070632914829092 [1.18157271 0.1220394 ] [1.16697923 0.11599484] [1.12292463 0.11577101]\\n\",\n \"18.838200457610366 [1.32470966 0.16083723] [1.0631238 0.21220793] [1.34402963 0.04964674]\\n\",\n \"12.797296176162535 [1. 0.3537742] [1. 0.28730011] [1. 0.20137252]\\n\",\n \"11.898751500782648 [1.0697212 0.21654601] [1.06388615 0.18858074] [1.03748862 0.16535724]\\n\",\n \"6.9805811813918766 [1.09589326 0.22441316] [1.0947465 0.21055229] [1.08107875 0.1782727 ]\\n\",\n \"5.784120861661739 [1.12294249 0.17919044] [1.1134077 0.14471304] [1.08794961 0.10354823]\\n\",\n \"7.325677825701441 [1.10865634 0.18510464] [1.09070859 0.15150712] [1.06172407 0.1120904 ]\\n\",\n \"12.02613260534443 [1.06568608 0.21866432] [1.06608933 0.18844478] [1.03981067 0.16484466]\\n\",\n \"7.352216317043304 [1.15006387 0.21032094] [1.14996928 0.19553264] [1.1314296 0.16176135]\\n\",\n \"5.320183537847537 [1.18137537 0.17844575] [1.36114489 0.09025131] [1.29681948 0.09351459]\\n\",\n \"12.26260767095218 [1. 0.36513769] [1.12747938 0.26495171] [1.19251323 0.1530195 ]\\n\",\n \"18.352927282901998 [1.45504738 0.14163784] [1.13158679 0.21087611] [1.55840008 0.02237506]\\n\",\n \"8.285321285112705 [1.2798559 0.40871858] [1.28288136 0.33896019] [1.21928331 0.28481416]\\n\",\n \"18.779324428542555 [1.25938515 0.15522197] [1.02509404 0.21349648] [1.40607741 0.03955527]\\n\",\n \"4.799003114546447 [2.10367131 0.30985834] [1.83451725 0.2763058 ] [1.37548665 0.24560329]\\n\",\n \"6.225630771591334 [1.65794811 0.2520898 ] [1.28025577 0.2659821 ] [1.37440044 0.11968063]\\n\",\n \"4.700024647442787 [1.18357376 0.2345532 ] [1.06782747 0.23658913] [1. 0.22019833]\\n\",\n \"4.89408498110031 [1.02430687 0.10613572] [1. 0.0956662] [1.08135166 0.03583199]\\n\",\n \"17.788725847584235 [1. 0.37425519] [1. 0.31710407] [1. 0.2181534]\\n\",\n \"10.424158600402126 [1.08436329 0.46019149] [1.06997713 0.42480411] [1.04629749 0.36934639]\\n\",\n \"8.856097946871373 [1.45906091 0.09125215] [1.40401634 0.0859803 ] [1.31688577 0.07523226]\\n\",\n \"10.792286172348518 [1. 0.39910361] [1. 0.37032642] [1. 0.32078214]\\n\",\n \"13.584834426854492 [1.25335423 0.276269 ] [1.15108925 0.25865294] [1.02356115 0.21844811]\\n\",\n \"15.965033650655808 [1. 0.38252867] [1. 0.33613805] [1. 0.26891776]\\n\",\n \"7.952627301951928 [1.43681818 0.40661874] [1.36344022 0.35890101] [1.23925417 0.32109843]\\n\",\n \"3.9407820659340667 [1. 0.20590304] [1. 0.19076009] [1. 0.16279285]\\n\",\n \"4.912038362884037 [1.20585882 0.37346754] [1.11804821 0.33153227] [1.14481908 0.23723253]\\n\",\n \"3.1842083872154476 [1.0278033 0.0922055] [1.01598649 0.07905709] [1.00054893 0.0633468 ]\\n\",\n \"8.471255354662132 [1.02855462 0.26801312] [1.01591953 0.23587559] [1. 0.17335107]\\n\",\n \"9.32718279753456 [1. 0.34868665] [1.05331739 0.28080309] [1. 0.25746615]\\n\",\n \"2.2439102538703395 [1. 0.22132079] [1. 0.21282756] [1. 0.18606632]\\n\",\n \"9.114192843725885 [1.34652548 0.17587747] [1.15092456 0.19459356] [1.16823653 0.09260065]\\n\",\n \"22.017509804779 [1. 0.42813566] [1. 0.39476122] [1. 0.33426201]\\n\",\n \"13.275015059811253 [1.57486061 0.31525481] [1.38372599 0.30776579] [1.84399762 0.12205398]\\n\",\n \"4.02098845703685 [1.69317948 0.064384 ] [1.69732246 0.03030978] [1. 0.0742018]\\n\",\n \"6.6819724736065815 [1.11272013 0.12540132] [1.03700053 0.12594568] [1.11223167 0.0400628 ]\\n\",\n \"6.847572869994068 [1.19752718 0.23147558] [1.17284419 0.20639019] [1.06399262 0.1794046 ]\\n\",\n \"6.398408999185493 [1. 0.27380186] [1. 0.24343145] [1. 0.20607567]\\n\",\n \"10.624225941524706 [1. 0.39706335] [1.19270675 0.29190137] [1.19549722 0.21258143]\\n\",\n \"8.302253204235976 [1.11878213 0.35309838] [1. 0.33641828] [1. 0.2801558]\\n\",\n \"8.646551408567877 [1.50749827 0.02628092] [1. 0.24240765] [1. 0.22220762]\\n\",\n \"6.861357749325711 [1.05921528 0.37447684] [1. 0.3469386] [1. 0.28926633]\\n\",\n \"12.230604804267584 [1. 0.41304914] [1. 0.36614359] [1. 0.29259791]\\n\",\n \"8.570476761110646 [1.07287849 0.28416565] [1.03545035 0.25449404] [1.01177274 0.19022635]\\n\",\n \"6.402176973208937 [1.27870191 0.42758859] [1.14803193 0.405654 ] [1. 0.35484543]\\n\",\n \"4.139203158019425 [1.18878555 0.05133137] [1.08794641 0.05691346] [1.05518293 0.04320333]\\n\",\n \"4.540621081127601 [1.07535234 0.48389816] [1. 0.45985159] [1. 0.4135118]\\n\",\n \"13.724873628346414 [1. 0.38667998] [1. 0.34295486] [1. 0.28298764]\\n\",\n \"6.502438182397647 [1.2524666 0.18903876] [1. 0.23122934] [1. 0.16804861]\\n\",\n \"9.340013114209574 [1.24206983 0.34294705] [1.15404461 0.31099692] [1.13876964 0.2399859 ]\\n\",\n \"4.818664433238953 [1.42342594 0.09841502] [1.40093916 0.06054477] [1.32942883 0.04353598]\\n\",\n \"9.597347521634239 [1.27180802 0.34215124] [1.17644176 0.3101468 ] [1.15953992 0.23879405]\\n\",\n \"6.531684024239703 [1.17225648 0.45090261] [1.04895417 0.42663058] [1. 0.3545852]\\n\",\n \"7.902970382306915 [1. 0.3828321] [1. 0.3509311] [1. 0.31221578]\\n\",\n \"3.572856543109783 [1.52084744 0.40874345] [1.42490507 0.38298202] [1.17717186 0.36366166]\\n\",\n \"3.7748913600772416 [1.05285317 0.10340779] [1.06472882 0.06956567] [1.03162317 0.05278179]\\n\",\n \"5.892350057077822 [1.15257191 0.22656358] [1.11848242 0.19031226] [1.12692081 0.11331438]\\n\",\n \"7.656406946637619 [1. 0.38780176] [1. 0.33601521] [1. 0.26281013]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"6.0598566365207 [1.47693262 0.29409661] [1.28154119 0.2810116 ] [1.17061817 0.24014481]\\n\",\n \"6.779243867416825 [1.45616599 0.02661183] [1.1304123 0.16180863] [1. 0.22385116]\\n\",\n \"14.01559868222701 [1. 0.47228961] [1. 0.43049894] [1. 0.37735332]\\n\",\n \"8.385329599986996 [1.17041771 0.24841201] [1.14274271 0.22358384] [1.09631354 0.17970203]\\n\",\n \"16.0227451383004 [1. 0.34002811] [1. 0.32543527] [1.50375927 0.12814954]\\n\",\n \"6.535906025140597 [1.08511613 0.13623745] [1.0503657 0.12126746] [1.12643462 0.03720826]\\n\",\n \"5.605220318668054 [1.48784368 0.09234066] [1.40130347 0.08766213] [1.27551035 0.06062238]\\n\",\n \"9.740896512046973 [1.43057947 0.16835786] [1.18733111 0.19233386] [1.18582144 0.09265421]\\n\",\n \"24.62920484905838 [1.19588232 0.39341377] [1.30426752 0.33270039] [1.31818841 0.24892338]\\n\",\n \"9.87361167273439 [1. 0.35505983] [1.03153041 0.28011414] [1. 0.25790464]\\n\",\n \"10.785432130473906 [1.0100719 0.22856359] [1. 0.20751649] [1. 0.17314927]\\n\",\n \"2.848822870218504 [1. 0.23324801] [1. 0.22447959] [1. 0.19795285]\\n\",\n \"7.415554338010629 [1. 0.28088956] [1. 0.24938439] [1. 0.18019241]\\n\",\n \"3.172457896671065 [1. 0.10193601] [1. 0.07618671] [1. 0.05141725]\\n\",\n \"9.133480894985293 [1. 0.31089858] [1. 0.27625971] [1. 0.21400311]\\n\",\n \"5.104756158934721 [1.1664368 0.16518938] [1.12257252 0.15407822] [1.05594699 0.14106247]\\n\",\n \"10.641007089861258 [1.19738839 0.39723323] [1.07591154 0.37229626] [1. 0.31978612]\\n\",\n \"8.219070864290465 [1. 0.46583664] [1. 0.41833496] [1. 0.34931202]\\n\",\n \"13.763388211606413 [2.20533378 0.29447472] [2.02780724 0.26782876] [1.84372838 0.21020024]\\n\",\n \"4.755651120898854 [2.0131304 0.28377263] [1.74854002 0.25357947] [1.49874913 0.19254194]\\n\",\n \"10.599803234656594 [1. 0.1375559] [1. 0.12323069] [1.066659 0.05801778]\\n\",\n \"13.2840212012749 [1.25982194 0.2601624 ] [1.1527094 0.24049561] [1.04625481 0.20054501]\\n\",\n \"16.480292791150053 [1.38280319 0.30029424] [1.18113575 0.30240947] [1.22205371 0.17240414]\\n\",\n \"11.512793233721805 [1. 0.4172398] [1. 0.37968987] [1. 0.31791679]\\n\",\n \"6.296638729448851 [1.20201688 0.19721292] [1.17676438 0.17814056] [1.13224075 0.14145692]\\n\",\n \"4.84576752198711 [1. 0.11971112] [1. 0.09699513] [1.03263381 0.0528492 ]\\n\",\n \"7.508196011967394 [1. 0.28483445] [1. 0.2603375] [1. 0.22496615]\\n\",\n \"7.298552585311891 [1.31441801 0.15227896] [1.33130135 0.09860831] [1.24900776 0.08241866]\\n\",\n \"5.974410989334576 [1. 0.32068934] [1. 0.30168836] [1. 0.26689476]\\n\",\n \"4.5573463750190335 [1.8507893 0.33166136] [1.62845947 0.29869509] [1.31513428 0.25783196]\\n\",\n \"8.650592174777097 [1.03185706 0.22336286] [1.01987652 0.18605172] [1.0036008 0.14088206]\\n\",\n \"8.041229519353713 [1.1450843 0.21811508] [1.12986112 0.20591502] [1.10609819 0.17487872]\\n\",\n \"9.202195094288346 [1.19192202 0.21032271] [1.1642241 0.19891529] [1.1283512 0.16956792]\\n\",\n \"8.474940572227336 [1.02833622 0.2146775 ] [1.01500988 0.18189212] [1.01208312 0.13188736]\\n\",\n \"4.679237754906878 [1.94209567 0.31654485] [1.72724612 0.27689186] [1.35220909 0.23855317]\\n\",\n \"8.844457686474382 [1.32179573 0.17843325] [1.30613842 0.14585346] [1.22776403 0.13407708]\\n\",\n \"12.67436564252188 [1. 0.37325004] [1.09665554 0.28260383] [1.21694107 0.14876266]\\n\",\n \"19.001589176677932 [1.37484114 0.15854447] [1.10288975 0.21824572] [1.56019944 0.02856289]\\n\",\n \"6.277685898917732 [1. 0.32013614] [1. 0.30090605] [1. 0.26586097]\\n\",\n \"7.4658412461713715 [1.15103985 0.16408361] [1.2410312 0.11025922] [1.18201128 0.11624398]\\n\",\n \"9.467478293219816 [1. 0.2930279] [1. 0.2693233] [1. 0.23600485]\\n\",\n \"5.421451808832461 [1. 0.1161232] [1. 0.09394996] [1.04443591 0.04547173]\\n\",\n \"9.910064275264103 [1. 0.42451735] [1. 0.38860386] [1. 0.32880158]\\n\",\n \"6.639947118006169 [1.12744732 0.21638269] [1.15605827 0.17751135] [1.14616089 0.12837949]\\n\",\n \"14.319552015517354 [1.27684374 0.30564773] [1.17582248 0.29122776] [1.24391082 0.15795376]\\n\",\n \"13.057527436425602 [1.22696498 0.26925168] [1.1368579 0.25169427] [1.02884318 0.21011612]\\n\",\n \"20.160947220029147 [1. 0.44599927] [1.15394538 0.37883883] [1.42786786 0.25589613]\\n\",\n \"10.326202242694293 [1. 0.13608993] [1. 0.12089174] [1.05266966 0.06732383]\\n\",\n \"5.121533611485272 [1.19741657 0.14656392] [1.13724815 0.14571089] [1.0618522 0.13704809]\\n\",\n \"8.675159652180747 [1. 0.46285123] [1. 0.40846725] [1. 0.33408502]\\n\",\n \"13.511408671173704 [1.08802566 0.40736015] [1.05257108 0.37110429] [1. 0.31684522]\\n\",\n \"10.480884661942895 [1. 0.2838687] [1. 0.25676719] [1. 0.19769731]\\n\",\n \"3.0063233366563167 [1. 0.10283038] [1. 0.07852243] [1. 0.05381072]\\n\",\n \"4.8062820810801945 [1. 0.26798569] [1.01153599 0.22781085] [1.10480981 0.11761344]\\n\",\n \"2.6561197373573475 [1. 0.23372729] [1. 0.22384593] [1. 0.19910501]\\n\",\n \"9.31748771964591 [1. 0.36031071] [1.04516442 0.27846026] [1. 0.26007056]\\n\",\n \"7.0285335908427315 [1.09811464 0.13826218] [1.02092417 0.13659478] [1.11624688 0.04206582]\\n\",\n \"5.881240986614894 [1.54909476 0.11237151] [1.48535367 0.08971277] [1.26212689 0.07374479]\\n\",\n \"13.600209256516814 [1.42836667 0.28293441] [1.21331278 0.29111539] [1.80904706 0.09467976]\\n\",\n \"7.818621978978676 [1.03597704 0.24015199] [1. 0.22871728] [1. 0.19159323]\\n\",\n \"10.472367783813572 [1.02827877 0.30487784] [1.04517289 0.26376533] [1.06375552 0.19330964]\\n\",\n \"8.330206486619348 [1.48694105 0.02501354] [1.02458533 0.22123065] [1. 0.22381229]\\n\",\n \"14.011543965087405 [1. 0.46722971] [1.19874565 0.37258221] [1.15463464 0.32165357]\\n\",\n \"10.097498540708527 [1. 0.38901564] [1.0837705 0.30853196] [1.08180111 0.23599267]\\n\",\n \"7.660246741096868 [1.1920754 0.2060254] [1.14358882 0.17106914] [1.13787037 0.09124709]\\n\",\n \"10.13680153648991 [1. 0.37900476] [1. 0.34928027] [1. 0.31010357]\\n\",\n \"11.010190874450128 [1. 0.42048089] [1. 0.39229723] [1. 0.35350634]\\n\",\n \"3.529463722512494 [1.06223271 0.09723751] [1.07215913 0.06952176] [1.04118846 0.04876519]\\n\",\n \"4.223505986082864 [1.18576262 0.45114874] [1.08547425 0.42215501] [1. 0.36016624]\\n\",\n \"4.030116636362381 [1.35428828 0.12524804] [1.33834494 0.08534257] [1.27148119 0.06374946]\\n\",\n \"4.40204128999036 [1.38641626 0.14479881] [1. 0.22534081] [1. 0.14847461]\\n\",\n \"8.129061521692863 [1. 0.31855541] [1. 0.32389108] [1. 0.23611306]\\n\",\n \"9.078389556832173 [1. 0.39043993] [1. 0.34562551] [1. 0.28046481]\\n\",\n \"10.922586828862263 [1. 0.44638453] [1.01467007 0.40765968] [1.01647234 0.35099867]\\n\",\n \"7.446449166826484 [1.04170848 0.31158162] [1. 0.28492783] [1. 0.21417657]\\n\",\n \"14.643796412881152 [1. 0.38914462] [1. 0.3448852] [1. 0.27999371]\\n\",\n \"4.633343637535566 [1.13118718 0.07027916] [1.06857895 0.06254089] [1.0429486 0.04713405]\\n\",\n \"3.5184877298587742 [1.31444618 0.42958184] [1.19518759 0.40567312] [1.05849899 0.37707793]\\n\",\n \"7.104549282939431 [1.19273211 0.22548999] [1.15754557 0.2041024 ] [1.06418321 0.17410454]\\n\",\n \"5.184466709805151 [1. 0.24197917] [1. 0.21527012] [1. 0.17588522]\\n\",\n \"8.573719732087964 [1. 0.39229011] [1.24729093 0.27826192] [1.17925231 0.21276987]\\n\",\n \"6.8874927234004275 [1.52488306 0.02261898] [1.17100034 0.1418223 ] [1. 0.25747024]\\n\",\n \"5.394806335622542 [1.15188767 0.35389505] [1.0431721 0.33231426] [1. 0.28469106]\\n\",\n \"9.012881614996276 [1.32162831 0.19644876] [1.15275734 0.20352503] [1.16051101 0.10955896]\\n\",\n \"13.107302558559942 [1.44262958 0.34513576] [1.31695146 0.31758483] [1.73725417 0.14804349]\\n\",\n \"6.356884312158647 [1.43232794 0.33397626] [1.19806779 0.33130407] [1.05299997 0.31444563]\\n\",\n \"4.361883497071163 [1.49528507 0.12212512] [1.43113383 0.10107 ] [1.30541974 0.06793306]\\n\",\n \"5.908451486291657 [1.27620447 0.16820832] [1.21399752 0.15641256] [1.16399164 0.14727655]\\n\",\n \"6.348470636794943 [1.06501106 0.14323209] [1.02946656 0.12921386] [1.09469555 0.05045214]\\n\",\n \"4.08822226206924 [1. 0.11199233] [1. 0.09263176] [1. 0.06998212]\\n\",\n \"9.446789209925925 [1. 0.29673502] [1. 0.26381312] [1. 0.20178498]\\n\",\n \"9.125675380237348 [1. 0.29819512] [1. 0.25752205] [1. 0.17762492]\\n\",\n \"10.097362922703098 [1. 0.35659346] [1.0360234 0.27651893] [1. 0.25969843]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3.7685768629199634 [1. 0.25506584] [1. 0.24477779] [1. 0.22149523]\\n\",\n \"12.170617812549592 [1. 0.43683909] [1. 0.40373471] [1. 0.34853848]\\n\",\n \"12.144325023976737 [1.16927628 0.29559312] [1.09902816 0.270262 ] [1.02206552 0.22197826]\\n\",\n \"7.8854542794127775 [1.07043808 0.4451352 ] [1. 0.41019962] [1. 0.34149892]\\n\",\n \"5.747458873321492 [1.04981411 0.24164911] [1.07435791 0.20507094] [1.11552737 0.13974482]\\n\",\n \"4.859785336888636 [1.32823536 0.35237545] [1.17587717 0.31881741] [1.13394924 0.24219468]\\n\",\n \"6.1713753736067 [1.14537152 0.22041008] [1.12506424 0.20464841] [1.0743455 0.18970252]\\n\",\n \"6.389784160204872 [1.00705619 0.12245689] [1. 0.10373429] [1.05016515 0.05450275]\\n\",\n \"4.843863878027568 [1.54593086 0.06172297] [1.4261355 0.06443288] [1.28244067 0.06144386]\\n\",\n \"9.944508788523496 [1. 0.43965845] [1. 0.40195183] [1. 0.34397386]\\n\",\n \"7.146629277531506 [1.08914226 0.18917886] [1.0781795 0.15343313] [1.04779075 0.11422093]\\n\",\n \"6.251557015695687 [1.1220385 0.13225557] [1.07936011 0.14064204] [1.04056439 0.14059275]\\n\",\n \"20.189889828617833 [1.21265497 0.20684389] [1. 0.24336379] [1.22663225 0.08532478]\\n\",\n \"18.547647853136688 [1. 0.19281915] [1. 0.19805366] [1. 0.07785673]\\n\",\n \"4.971371152995008 [1.72292261 0.3663594 ] [1.63078777 0.32088663] [1.19328626 0.30544493]\\n\",\n \"21.805115878754577 [1. 0.24911644] [1. 0.240072] [1.07342328 0.09893232]\\n\",\n \"4.84573124042842 [1.52474305 0.3949025 ] [1.33786432 0.36768522] [1.05297759 0.34824244]\\n\",\n \"6.826724211021385 [1.21798918 0.11667315] [1.19300584 0.11399387] [1.156586 0.10732942]\\n\",\n \"20.435224852598804 [1.19998484 0.21229644] [1. 0.24489777] [1.21058254 0.08707851]\\n\",\n \"5.749119594419538 [1. 0.34264062] [1. 0.31544507] [1. 0.26280049]\\n\",\n \"6.275567095482205 [1.05266183 0.22415031] [1.15728881 0.12948833] [1.11230191 0.09487598]\\n\",\n \"6.247209539950038 [1.51867317 0.06643887] [1.43394424 0.06542257] [1.32097504 0.06019745]\\n\",\n \"9.432280196342646 [1. 0.44425828] [1. 0.40673676] [1. 0.34992079]\\n\",\n \"14.049938924227257 [1.80710662 0.15977388] [1.60599774 0.16410479] [1.6769296 0.04289388]\\n\",\n \"5.819631747382949 [1. 0.27539881] [1. 0.25182073] [1. 0.22024107]\\n\",\n \"4.587155863561414 [1.34754112 0.3552102 ] [1.19470642 0.32066332] [1.08583461 0.26067415]\\n\",\n \"8.347046385947243 [1. 0.46269721] [1. 0.40744864] [1. 0.33118333]\\n\",\n \"4.614603587169393 [1.02019149 0.26165914] [1.09545259 0.21167945] [1.28019071 0.10303905]\\n\",\n \"12.14178602981697 [1.14771746 0.29656745] [1.08535981 0.27125944] [1.00982694 0.22497728]\\n\",\n \"11.150434599737167 [1. 0.4601252] [1. 0.42149625] [1. 0.35909137]\\n\",\n \"9.06735902747434 [1. 0.36828413] [1.04873482 0.28796787] [1. 0.2681195]\\n\",\n \"3.4308747939276114 [1. 0.10330157] [1. 0.08168463] [1. 0.05886519]\\n\",\n \"7.876642007206043 [1.07691179 0.29187841] [1.06225738 0.25031252] [1. 0.18798912]\\n\",\n \"8.160522686210383 [1.21685794 0.38932491] [1.07683591 0.37653055] [1. 0.34801735]\\n\",\n \"6.04527020655973 [1.25256324 0.1766149 ] [1.21054966 0.15639779] [1.16583198 0.14706248]\\n\",\n \"12.389366504458941 [1.42720437 0.34713776] [1.33544325 0.30839149] [1.70465889 0.15133562]\\n\",\n \"8.717500487207278 [1.31798132 0.1827578 ] [1.14294955 0.19668818] [1.15604166 0.10476818]\\n\",\n \"7.832932836765893 [1.63711141 0.02248008] [1.19689107 0.16297951] [1. 0.27430909]\\n\",\n \"4.731645658724988 [1.26138902 0.32859469] [1.11767305 0.30616558] [1.05693017 0.25837288]\\n\",\n \"7.232895693505282 [1. 0.37743186] [1. 0.32883281] [1. 0.2575448]\\n\",\n \"6.646969739152888 [1.08673304 0.19923439] [1.04800513 0.18852304] [1.0031643 0.16843263]\\n\",\n \"7.003046278484913 [1.17703727 0.22536045] [1.14921878 0.20254484] [1.08429754 0.16369367]\\n\",\n \"4.04756324291338 [1.16496385 0.05830398] [1.06852961 0.06222365] [1.04884139 0.04064035]\\n\",\n \"4.45505703871402 [1.22844084 0.45789614] [1.04784352 0.44878295] [1. 0.40978731]\\n\",\n \"3.0134300763434836 [1.95908206 0.32926769] [1.74670636 0.30533238] [1.39009546 0.2623041 ]\\n\",\n \"13.348832382173757 [1. 0.39161291] [1. 0.35091] [1. 0.28604537]\\n\",\n \"10.304710295664696 [1. 0.45893432] [1. 0.41864383] [1. 0.35694521]\\n\",\n \"9.785908436998039 [1.08372382 0.32098789] [1.01837139 0.30181823] [1. 0.23469834]\\n\",\n \"8.976889411683826 [1. 0.3959776] [1. 0.35170545] [1.02194185 0.27629988]\\n\",\n \"6.860965238430664 [1.14332745 0.21776023] [1. 0.23488931] [1. 0.17477226]\\n\",\n \"15.572344511846968 [1.69924381 0.12356604] [2.00048359 0.02007715] [1.6883586 0.05150387]\\n\",\n \"8.262607543929457 [1.07895506 0.37505895] [1.02074705 0.33990409] [1.0603653 0.25874 ]\\n\",\n \"4.912597143120675 [1. 0.22822034] [1. 0.19841094] [1. 0.12697328]\\n\",\n \"6.363851780499002 [1.08254776 0.28384135] [1.02512182 0.25842776] [1.00823914 0.19289162]\\n\",\n \"12.30228201372977 [1. 0.43639514] [1. 0.40232694] [1. 0.34632636]\\n\",\n \"11.588900327416342 [1. 0.3693939] [1. 0.3382938] [1. 0.29726383]\\n\",\n \"7.533132769290531 [1.13759613 0.35266774] [1.18449953 0.29064698] [1.09630072 0.25145613]\\n\",\n \"11.289702293060968 [1.50749144 0.16226019] [1.45744929 0.13049412] [1.23527793 0.14069548]\\n\",\n \"4.661812373082756 [1.75910219 0.35953738] [1.53991883 0.33878098] [1.05060127 0.3295266 ]\\n\",\n \"5.4460323603950425 [1. 0.24312404] [1. 0.21168479] [1. 0.17354187]\\n\",\n \"8.706177881674797 [1.0799126 0.36515226] [1.01270513 0.32855653] [1. 0.27853921]\\n\",\n \"8.00077235114351 [1.13150565 0.24845463] [1.13561437 0.21600817] [1.11821358 0.15460734]\\n\",\n \"8.781319222545337 [1. 0.36041255] [1. 0.32616131] [1. 0.27616279]\\n\",\n \"5.785696866658025 [1.49238935 0.02230037] [1.16906831 0.13467273] [1. 0.24162001]\\n\",\n \"8.478908450888166 [1. 0.37765749] [1. 0.32574951] [1. 0.25176544]\\n\",\n \"5.8693524852863375 [1. 0.30947209] [1.00026833 0.27751733] [1.04401617 0.21315743]\\n\",\n \"9.488132419170112 [1.28201812 0.20183249] [1.13807666 0.20555704] [1.15478091 0.11431506]\\n\",\n \"6.265242354450486 [1.17191284 0.11989237] [1.09503003 0.11605688] [1.1446114 0.02895524]\\n\",\n \"5.1572482448606625 [1.51466321 0.11179498] [1.47477233 0.08628483] [1.22591373 0.0704102 ]\\n\",\n \"10.877991402534544 [1. 0.32529559] [1. 0.28170299] [1. 0.22808252]\\n\",\n \"12.571582373210557 [1.73963119 0.25463019] [1.62983973 0.24014169] [2.00208608 0.06696168]\\n\",\n \"6.39314313081427 [1.32507248 0.31924145] [1.6659829 0.15794568] [1.29657599 0.13307339]\\n\",\n \"6.969672823392187 [1.1135408 0.26977364] [1.14255001 0.20186158] [1.12263319 0.12106629]\\n\",\n \"2.8356203449588677 [1. 0.24473362] [1. 0.23107451] [1. 0.20297414]\\n\",\n \"10.417356223180978 [1. 0.35744425] [1. 0.28276206] [1. 0.26162718]\\n\",\n \"13.750913044333586 [1.25206536 0.27687467] [1.16182884 0.25240209] [1.05208969 0.20944306]\\n\",\n \"12.150945773550827 [1. 0.38371884] [1. 0.34285822] [1. 0.284928]\\n\",\n \"5.389336374411643 [1.16121849 0.17694264] [1.13249659 0.16548833] [1.07270058 0.14587459]\\n\",\n \"10.523329017051536 [1. 0.42764285] [1. 0.38407481] [1. 0.31660827]\\n\",\n \"8.269418511118195 [1. 0.46234438] [1. 0.41054863] [1. 0.33883393]\\n\",\n \"9.167433277613362 [1. 0.29017861] [1. 0.26614638] [1. 0.23131604]\\n\",\n \"5.5609186219361595 [1. 0.12462872] [1. 0.10465619] [1.01838654 0.06773848]\\n\",\n \"9.594943678759213 [1. 0.42423773] [1. 0.38552999] [1. 0.32468429]\\n\",\n \"5.3791976022018515 [1.54201572 0.06628701] [1.40167858 0.07165457] [1.2690287 0.06592292]\\n\",\n \"19.77399570300559 [1.07824599 0.38281923] [1.00398616 0.36607595] [1.1181896 0.23795898]\\n\",\n \"9.580546761468955 [1.37306584 0.17577431] [1.30179416 0.16933436] [1.21704513 0.14663145]\\n\",\n \"13.33828004159378 [1. 0.36330007] [1. 0.30110656] [1.07022844 0.1971349 ]\\n\",\n \"7.26545664572203 [1.00320889 0.27332249] [1.08149899 0.20519183] [1.13360372 0.12610142]\\n\",\n \"4.020766820685916 [1.84646009 0.34098613] [1.67902187 0.29709569] [1.24429486 0.27404391]\\n\",\n \"19.273838139796524 [1.23985283 0.15511182] [1.07159674 0.18082891] [1.38035117 0.04312197]\\n\",\n \"18.747154847936542 [1.2879973 0.17396842] [1.02469479 0.2312224 ] [1.38216191 0.04543679]\\n\",\n \"7.687501494777646 [1.18911408 0.13068791] [1.21358109 0.10652954] [1.1600769 0.11167813]\\n\",\n \"17.41626227161925 [1.24286567 0.17648605] [1. 0.23681838] [1.37625362 0.04062536]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4.608339372745333 [1.74113426 0.34798842] [1.52317166 0.3191022 ] [1.16724291 0.2942582 ]\\n\",\n \"18.875741071133284 [1.2543283 0.15648117] [1.06798541 0.18717109] [1.34530144 0.04991616]\\n\",\n \"11.148313150594289 [1. 0.37511565] [1.09509921 0.28287656] [1.14942929 0.17369543]\\n\",\n \"7.233035561460236 [1.08561326 0.2354706 ] [1.20422952 0.14769201] [1.1853493 0.09605807]\\n\",\n \"8.863229059881018 [1.41297349 0.19649819] [1.33371269 0.18662474] [1.22792143 0.15976778]\\n\",\n \"19.45745868435802 [1.49994807 0.26682565] [1.26711735 0.27545718] [1.34188963 0.14555973]\\n\",\n \"9.468170012731786 [1. 0.42522692] [1. 0.38323518] [1. 0.32367061]\\n\",\n \"5.868515168416221 [1.52897243 0.06848402] [1.38364141 0.07468658] [1.25120255 0.06850521]\\n\",\n \"6.000131214338976 [1. 0.12002132] [1. 0.0980278] [1.0169329 0.05973255]\\n\",\n \"7.438143572576681 [1. 0.28502836] [1. 0.26303692] [1. 0.22860402]\\n\",\n \"5.289794627964023 [1.1335567 0.21179734] [1.11170322 0.19507687] [1.07163701 0.16266489]\\n\",\n \"7.964680770390557 [1.14781153 0.42575943] [1. 0.40190126] [1. 0.33838822]\\n\",\n \"10.032075253179388 [1.19303526 0.34074064] [1.17790367 0.30367674] [1.13926281 0.24261738]\\n\",\n \"9.507332791209954 [1. 0.36297141] [1.12549776 0.27003628] [1.00850659 0.26411807]\\n\",\n \"2.819872748989664 [1. 0.23983558] [1. 0.2266287] [1. 0.1979991]\\n\",\n \"7.495457263473503 [1.23996667 0.23219605] [1.25828273 0.17540812] [1.2256135 0.09706519]\\n\",\n \"3.3035183593098116 [1.05401011 0.0786346 ] [1.0064354 0.0834124] [1.00066125 0.06275393]\\n\",\n \"10.982202783668944 [1.01541626 0.32391523] [1.04335354 0.26363175] [1.02417349 0.21390277]\\n\",\n \"5.444681012424603 [1.21112693 0.34450231] [1.49266707 0.19883686] [1.21760034 0.16245562]\\n\",\n \"5.71585520980478 [1.38514994 0.07153152] [1.26599542 0.07115983] [1.28125242 0.00203232]\\n\",\n \"4.78489739965205 [1.42650945 0.09035256] [1.35666958 0.08265567] [1.25431092 0.05747103]\\n\",\n \"9.338768434828841 [1.28952457 0.20208728] [1.12484015 0.21011617] [1.14263045 0.1109362 ]\\n\",\n \"6.073680103146996 [1.00675102 0.26630249] [1.02387754 0.23440817] [1.02017898 0.1963962 ]\\n\",\n \"8.881113087703643 [1. 0.39147761] [1.13747849 0.30262937] [1.13791602 0.21990965]\\n\",\n \"6.288096344683792 [1.22002553 0.34162078] [1.05898669 0.32589906] [1. 0.28077121]\\n\",\n \"6.370692375869698 [1.47119504 0.02273981] [1.14891519 0.14920475] [1. 0.2303363]\\n\",\n \"7.1979636175223405 [1.11389871 0.23466379] [1.10305907 0.20912161] [1.06224552 0.16659362]\\n\",\n \"5.849074255529509 [1. 0.24786564] [1. 0.22290166] [1. 0.18581435]\\n\",\n \"3.6782213024410426 [1.54906327 0.38499582] [1.38170487 0.35972071] [1.05013607 0.33461307]\\n\",\n \"4.76170856930791 [1.13572491 0.38745464] [1.03104303 0.37008839] [1. 0.32496164]\\n\",\n \"5.155595609262272 [1. 0.49398408] [1. 0.46194917] [1. 0.41697888]\\n\",\n \"6.642882304921422 [1.59828797 0.1202163 ] [1.4459217 0.11160453] [1.19351043 0.12236486]\\n\",\n \"4.814149145007596 [1.06481287 0.11952267] [1.08056103 0.08142804] [1.04100738 0.05845668]\\n\",\n \"5.019245705742924 [1.08739817 0.26074022] [1.06015233 0.22453726] [1.08866459 0.14105046]\\n\",\n \"9.16744872104858 [1.23419148 0.43999962] [1.20295263 0.398843 ] [1.098967 0.34742564]\\n\",\n \"4.786575682308314 [1. 0.28540823] [1. 0.27403455] [1. 0.18088439]\\n\",\n \"8.250491528487505 [1. 0.39301486] [1. 0.34821779] [1. 0.27898305]\\n\",\n \"8.866099271504389\\n\"\n ]\n }\n ],\n \"source\": [\n \"import cv2\\n\",\n \"import os\\n\",\n \"from os import listdir\\n\",\n \"from os.path import isfile, join\\n\",\n \"from PIL import Image as Image\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"from scipy.optimize import curve_fit\\n\",\n \"\\n\",\n \"def relit(x, a, b):\\n\",\n \" return (a * x.astype(np.float)/255 + b)*255\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"from matplotlib import pyplot as plt\\n\",\n \"def plshow(im,title='MINE'):\\n\",\n \" if len(im.shape)>2:\\n\",\n \" # plt.imshow(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))\\n\",\n \" plt.imshow(im)\\n\",\n \" else:\\n\",\n \" plt.imshow(im,cmap='gray')\\n\",\n \" plt.title(title)\\n\",\n \" plt.rcParams[\\\"figure.figsize\\\"] = (80,12)\\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \"sd_path = 'dataset/ISTD/train_A'\\n\",\n \"mask_path = 'dataset/ISTD/train_B'\\n\",\n \"sdfree_path = 'dataset/ISTD/train_C_fixed_ours'\\n\",\n \"\\n\",\n \"out = 'dataset/ISTD/train_params/'\\n\",\n \"if not os.path.exists(out):\\n\",\n \" os.makedirs(out)\\n\",\n \"\\n\",\n \"im_list = [f for f in listdir(sd_path) if isfile(join(sd_path, f)) and f.endswith('png')]\\n\",\n \"print(len(im_list),im_list[0])\\n\",\n \"kernel = np.ones((5,5),np.uint8)\\n\",\n \"\\n\",\n \"\\n\",\n \"def im_relit(Rpopt,Gpopt,Bpopt,dump):\\n\",\n \" #fc this shit\\n\",\n \" sdim = dump.copy()\\n\",\n \" sdim.setflags(write=1)\\n\",\n \" sdim = sdim.astype(np.float)\\n\",\n \" sdim[:,:,0] = (sdim[:,:,0]/255) * Rpopt[0] + Rpopt[1]\\n\",\n \" sdim[:,:,1] = (sdim[:,:,1]/255) * Gpopt[0] + Gpopt[1]\\n\",\n \" sdim[:,:,2] = (sdim[:,:,2]/255) * Bpopt[0] + Bpopt[1]\\n\",\n \" sdim = sdim*255\\n\",\n \" # print(np.amin(sdim),np.amax(sdim))\\n\",\n \" return sdim\\n\",\n \"\\n\",\n \"errors= []\\n\",\n \"for im in im_list:\\n\",\n \" sd = np.asarray(Image.open(join(sd_path,im)))\\n\",\n \" mean_sdim = np.mean(sd,axis=2)\\n\",\n \" \\n\",\n \" mask_ori = np.asarray(Image.open(join(mask_path,im)))\\n\",\n \" mask = cv2.erode(mask_ori ,kernel,iterations = 2)\\n\",\n \"\\n\",\n \" \\n\",\n \" sdfree = np.asarray(Image.open(join(sdfree_path,im)))\\n\",\n \" mean_sdfreeim = np.mean(sdfree,axis=2)\\n\",\n \" \\n\",\n \" #pixels for regression funtion\\n\",\n \" i, j = np.where(np.logical_and(np.logical_and(np.logical_and(mask>=1,mean_sdim>5),mean_sdfreeim<230),np.abs(mean_sdim-mean_sdfreeim)>10))\\n\",\n \"\\n\",\n \" source = sd*0\\n\",\n \" source[tuple([i,j])] = sd[tuple([i,j])] \\n\",\n \" target = sd*0\\n\",\n \" target[tuple([i,j])]= sdfree[tuple([i,j])]\\n\",\n \" \\n\",\n \" R_s = source[:,:,0][tuple([i,j])]\\n\",\n \" G_s = source[:,:,1][tuple([i,j])]\\n\",\n \" B_s = source[:,:,2][tuple([i,j])]\\n\",\n \" \\n\",\n \" R_t = target[:,:,0][tuple([i,j])]\\n\",\n \" G_t = target[:,:,1][tuple([i,j])]\\n\",\n \" B_t = target[:,:,2][tuple([i,j])]\\n\",\n \" \\n\",\n \" c_bounds = [[1,-0.1],[10,0.5]]\\n\",\n \"\\n\",\n \" \\n\",\n \" Rpopt, pcov = curve_fit(relit, R_s, R_t,bounds=c_bounds)\\n\",\n \" Gpopt, pcov = curve_fit(relit, G_s, G_t,bounds=c_bounds)\\n\",\n \" Bpopt, pcov = curve_fit(relit, B_s, B_t,bounds=c_bounds)\\n\",\n \" \\n\",\n \" \\n\",\n \" relitim = im_relit(Rpopt,Gpopt,Bpopt,sd)\\n\",\n \" \\n\",\n \" #final = sd.copy()\\n\",\n \" #final[tuple([i,j])] = relitim[tuple([i,j])]\\n\",\n \" #final[final>255] =255\\n\",\n \" #final[final<0] = 0\\n\",\n \"\\n\",\n \" #plshow(final)\\n\",\n \" error = np.mean(np.abs(relitim[tuple([i,j])].astype(np.float) - sdfree[tuple([i,j])]).astype(np.float))\\n\",\n \" print(error,Rpopt,Gpopt,Bpopt)\\n\",\n \" f = open(join(out,im+'.txt'),\\\"a\\\")\\n\",\n \" f.write(\\\"%f %f %f %f %f %f\\\"%(Rpopt[1],Rpopt[0],Gpopt[1],Gpopt[0],Bpopt[1],Bpopt[0]))\\n\",\n \" f.close()\\n\",\n \" \\n\",\n \" # print(error)\\n\",\n \" errors.append(error)\\n\",\n \" \\n\",\n \" \\n\",\n \"print(np.mean(errors))\\n\",\n \"#no bound - 8.55\\n\",\n \"#### y_bound ###error\\n\",\n \"# 0.5 8.86\\n\",\n \"# 0.1 15.692271753155671 \\n\",\n \"# 0.25 10.830443545867785\\n\",\n \"# 1 8.86\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "data_processing/test_mask_generation.py", "content": "import os\n\nimport cv2\nimport matplotlib.pyplot as plt\n\nimport PIL.Image as Image\nfrom skimage.filters import threshold_otsu\nfrom skimage import filters\nimport numpy as np\nfrom skimage.morphology import disk\n\n# get mask from shadow image and shadow free image\n\ntrain_A_path = '../data/ISTD+/test_A'\ntrain_C_path = '../data/ISTD+/test_C'\nroot_path = os.listdir(train_A_path)\n\nfor file in root_path:\n s_name = os.path.join(train_A_path, file)\n sf_name = os.path.join(train_C_path, file)\n\n s_img = Image.open(s_name).convert(\"L\")\n s_img = np.array(s_img).astype(np.float32)\n sf_img = Image.open(sf_name).convert(\"L\")\n sf_img = np.array(sf_img).astype(np.float32)\n diff = (np.asarray(sf_img, dtype='float32') - np.asarray(s_img, dtype='float32'))\n # diff[diff < 0] = 0\n L = threshold_otsu(diff)\n mask = np.float32(diff > L) * 255\n mask = filters.median(mask, disk(5))\n mask = Image.fromarray(mask).convert(\"L\")\n # plt.imshow(mask)\n # plt.show()\n mask.save('../data/ISTD+/test_mask/' + file)\n" }, { "path": "models/Fusion_model.py", "content": "import torch\nfrom collections import OrderedDict\nimport time\nimport numpy as np\nimport os\nimport torch.nn.functional as F\nimport torch.nn as nn\nfrom util.image_pool import ImagePool\nfrom .base_model import BaseModel\nfrom . import networks\nimport util.util as util\nfrom .distangle_model import DistangleModel\nfrom PIL import ImageOps,Image\nimport cv2\n\n\ndef tensor2im(input_image, imtype=np.uint8):\n \"\"\"\"Converts a Tensor array into a numpy image array.\n Parameters:\n input_image (tensor) -- the input image tensor array\n imtype (type) -- the desired type of the converted numpy array\n \"\"\"\n if not isinstance(input_image, np.ndarray):\n if isinstance(input_image, torch.Tensor): # get the data from a variable\n image_tensor = input_image.data\n else:\n return input_image\n image_numpy = image_tensor.cpu().float().numpy() # convert it into a numpy array\n if image_numpy.shape[0] == 1: # grayscale to RGB\n image_numpy = np.tile(image_numpy, (3, 1, 1))\n # image_numpy = image_numpy.convert('L')\n image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 # post-processing: tranpose and scaling\n else: # if it is a numpy array, do nothing\n image_numpy = input_image\n return np.clip(image_numpy, 0, 255).astype(imtype)\n\n\nclass L_TV(nn.Module):\n def __init__(self):\n super(L_TV, self).__init__()\n def forward(self, x):\n _, _, h, w = x.size()\n count_h = (h - 1) * w\n count_w = (w - 1) * h\n\n h_tv = torch.pow(x[:, :, 1:, :] - x[:, :, :h - 1, :], 2).sum()\n w_tv = torch.pow(x[:, :, :, 1:] - x[:, :, :, :w - 1], 2).sum()\n return (h_tv / count_h + w_tv / count_w) / 2.0\n\n\nclass GradientLoss(nn.Module):\n def __init__(self, loss_weight=1.0, reduction='mean'):\n super(GradientLoss, self).__init__()\n self.loss_weight = loss_weight\n self.reduction = reduction\n if self.reduction not in ['none', 'mean', 'sum']:\n raise ValueError(f'Unsupported reduction mode: {self.reduction}. '\n f'Supported ones are: {_reduction_modes}')\n\n def forward(self, pred, target):\n _, cin, _, _ = pred.shape\n _, cout, _, _ = target.shape\n assert cin == 3 and cout == 3\n kx = torch.Tensor([[1, 0, -1], [2, 0, -2],\n [1, 0, -1]]).view(1, 1, 3, 3).to(target)\n ky = torch.Tensor([[1, 2, 1], [0, 0, 0],\n [-1, -2, -1]]).view(1, 1, 3, 3).to(target)\n kx = kx.repeat((3, 1, 1, 1))\n ky = ky.repeat((3, 1, 1, 1))\n\n pred_grad_x = F.conv2d(pred, kx, padding=1, groups=3)\n pred_grad_y = F.conv2d(pred, ky, padding=1, groups=3)\n target_grad_x = F.conv2d(target, kx, padding=1, groups=3)\n target_grad_y = F.conv2d(target, ky, padding=1, groups=3)\n\n loss = (\n nn.L1Loss(reduction=self.reduction)(\n pred_grad_x, target_grad_x) +\n nn.L1Loss(reduction=self.reduction)(\n pred_grad_y, target_grad_y))\n return loss * self.loss_weight\n\n\nclass PoissonGradientLoss(nn.Module):\n def __init__(self, reduction='mean'):\n \"\"\"L_{grad} = \\frac{1}{2hw}\\sum_{m=1}^{H}\\sum_{n=1}{W}(\\partial f(I_{Blend}) - \n (\\partial f(I_{Source}) + \\partial f(I_{Target})))_{mn}^2\n\n See **Deep Image Blending** for detail.\n \"\"\"\n super(PoissonGradientLoss, self).__init__()\n self.reduction = reduction\n\n def forward(self, source, target, blend, mask):\n f = torch.Tensor([[0, -1, 0], [-1, 4, -1], [0, -1, 0]]).view(1, 1, 3, 3).to(target)\n f = f.repeat((3, 1, 1, 1))\n grad_s = F.conv2d(source, f, padding=1, groups=3) * mask\n grad_t = F.conv2d(target, f, padding=1, groups=3) * (1 - mask)\n grad_b = F.conv2d(blend, f, padding=1, groups=3)\n return nn.MSELoss(reduction=self.reduction)(grad_b, (grad_t + grad_s))\n\n\nclass FusionModel(DistangleModel):\n def name(self):\n return 'fusion net cvpr 21'\n\n @staticmethod\n def modify_commandline_options(parser, is_train=True):\n\n parser.set_defaults(pool_size=0, no_lsgan=True, norm='batch')\n parser.set_defaults(dataset_mode='expo_param')\n parser.add_argument('--wdataroot',default='None', help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')\n parser.add_argument('--use_our_mask', action='store_true')\n parser.add_argument('--mask_train',type=str,default=None)\n parser.add_argument('--mask_test',type=str,default=None)\n return parser\n\n def initialize(self, opt):\n BaseModel.initialize(self, opt)\n self.isTrain = opt.isTrain\n self.loss_names = ['G_param', 'alpha', 'rescontruction']\n self.visual_names = ['input_img', 'litgt', 'alpha_pred', 'out', 'final', 'outgt']\n self.model_names = ['G', 'M']\n opt.output_nc = 3 \n\n self.ks = ks = opt.ks\n self.n = n = opt.n\n self.shadow_loss = opt.shadow_loss\n self.tv_loss = opt.tv_loss\n self.grad_loss = opt.grad_loss\n self.pgrad_loss = opt.pgrad_loss\n\n self.netG = networks.define_G(4, 2 * 3, opt.ngf, 'RESNEXT', opt.norm,\n not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)\n self.netM = networks.define_G(1 + 3 + n * 3, ((1 + n) * 3) * 3 * ks * ks, opt.ngf, 'unet_256', opt.norm,\n not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)\n\n self.netG.to(self.device)\n self.netM.to(self.device)\n print(self.netG)\n print(self.netM)\n if self.isTrain:\n # define loss functions\n self.MSELoss = torch.nn.MSELoss()\n self.criterionL1 = torch.nn.L1Loss()\n self.bce = torch.nn.BCEWithLogitsLoss()\n # initialize optimizers\n self.optimizers = []\n\n if opt.optimizer == 'adam':\n self.optimizer_G = torch.optim.Adam(self.netG.parameters(),\n lr=opt.lr, betas=(opt.beta1, 0.999), weight_decay=1e-5)\n self.optimizer_M = torch.optim.Adam(self.netM.parameters(),\n lr=opt.lr, betas=(opt.beta1, 0.999), weight_decay=1e-5)\n elif opt.optimizer == 'sgd':\n self.optimizer_G = torch.optim.SGD(self.netG.parameters(), momentum=0.9,\n lr=opt.lr, weight_decay=1e-5)\n self.optimizer_M = torch.optim.SGD(self.netM.parameters(), momentum=0.9,\n lr=opt.lr, weight_decay=1e-5) \n else:\n assert False\n\n self.optimizers.append(self.optimizer_G)\n self.optimizers.append(self.optimizer_M)\n \n def set_input(self, input):\n self.input_img = input['A'].to(self.device)\n self.shadow_mask = input['B'].to(self.device)\n self.imname = input['imname']\n self.shadow_param = input['param'].to(self.device).type(torch.float)\n self.shadow_mask = (self.shadow_mask > 0.9).type(torch.float) # * 2 - 1\n self.nim = self.input_img.shape[1]\n self.shadowfree_img = input['C'].to(self.device)\n self.shadow_mask_3d = (self.shadow_mask > 0).type(torch.float).expand(self.input_img.shape)\n self.shadow_mask_dilate = input['B_dilate'].to(self.device)\n self.shadow_mask_erode = input['B_erode'].to(self.device)\n\n def forward(self):\n inputG = torch.cat([self.input_img, self.shadow_mask], 1)\n shadow_param_pred = self.netG(inputG)\n\n n = shadow_param_pred.shape[0]\n w = inputG.shape[2]\n h = inputG.shape[3]\n\n addgt = self.shadow_param[:, [0, 2, 4]]\n mulgt = self.shadow_param[:, [1, 3, 5]]\n addgt = addgt.view(n, 3, 1, 1).expand((n, 3, w, h))\n mulgt = mulgt.view(n, 3, 1, 1).expand((n, 3, w, h))\n\n base_shadow_param_pred = shadow_param_pred[:, :2 * 3] # shadow_param_pred.view((n, self.n * 3, 2, 1, 1))\n self.base_shadow_param_pred = base_shadow_param_pred\n\n shadow_image = self.input_img.clone() / 2 + 0.5\n base_shadow_output = shadow_image * base_shadow_param_pred[:, :3].view((n, 3, 1, 1)) + \\\n base_shadow_param_pred[:, 3:].view((n, 3, 1, 1))\n shadow_output_list = []\n for i in range(0, self.n - 1):\n if i % 2 == 0:\n scale = 1 + i * 0.01\n else:\n scale = 1 - i * 0.01\n shadow_output_list.append(base_shadow_output * scale)\n shadow_output = torch.cat([base_shadow_output] + shadow_output_list, dim=1)\n self.lit = torch.cat([base_shadow_output] + shadow_output_list, dim=-1) * 2 - 1\n\n shadow_output = shadow_output * 2 - 1\n self.shadow_output = shadow_output\n\n self.litgt = self.input_img.clone() / 2 + 0.5\n self.litgt = (self.litgt * mulgt + addgt) * 2 - 1 # [-1, 1]\n\n inputM = torch.cat([self.input_img, shadow_output, self.shadow_mask], 1)\n out = torch.cat([self.input_img, shadow_output], 1)\n out = out / 2 + 0.5\n out_matrix = F.unfold(out, stride=1, padding=self.ks // 2, kernel_size=self.ks) # N, C x \\mul_(kernel_size), L\n\n kernel = self.netM(inputM) # b, (3+1)*n * 3 * ks * ks, Tanh\n\n b, c, h, w = self.input_img.shape\n output = []\n for i in range(b):\n # feature = out[i, ...]\n feature = out_matrix[i, ...] # ((1 + n) * 3) * ks * ks, L\n weight = kernel[i, ...] # ((1 + n) * 3) * 3 * ks * ks, H, W\n feature = feature.unsqueeze(0) # 1, C, L\n weight = weight.view((3, (self.n + 1) * 3 * self.ks * self.ks, h * w))\n weight = F.softmax(weight, dim=1)\n iout = feature * weight # (3, C, L)\n iout = torch.sum(iout, dim=1, keepdim=False)\n iout = iout.view((1, 3, h, w))\n\n output.append(iout)\n self.final = torch.cat(output, dim=0) * 2 -1\n\n\n def backward(self):\n criterion = self.criterionL1\n lambda_ = self.opt.lambda_L1\n\n addgt = self.shadow_param[:, [0, 2, 4]] # [b, 3]\n mulgt = self.shadow_param[:, [1, 3, 5]] # [b, 3]\n\n loss_G_param_mul = self.MSELoss(self.base_shadow_param_pred[:, :3], mulgt) * lambda_\n loss_G_param_add = self.MSELoss(self.base_shadow_param_pred[:, 3:], addgt) * lambda_\n self.loss_G_param = (loss_G_param_add + loss_G_param_mul) / 2.0 * self.shadow_loss\n\n if self.tv_loss > 0:\n tv_loss = L_TV()(self.final - self.shadowfree_img) * lambda_ * self.tv_loss\n else:\n tv_loss = 0.0\n \n if self.grad_loss > 0:\n grad_loss = GradientLoss()(self.final, self.shadowfree_img) * lambda_ * self.grad_loss\n else:\n grad_loss = 0.0\n\n if self.pgrad_loss > 0:\n pgrad_loss = PoissonGradientLoss()(target=self.input_img, blend=self.final,\n source=self.shadowfree_img, mask=self.shadow_mask_dilate) \\\n * lambda_ * self.pgrad_loss\n else:\n pgrad_loss = 0.0\n\n self.loss_rescontruction = criterion(self.final, self.shadowfree_img) * lambda_\n self.loss = self.loss_rescontruction + self.loss_G_param + tv_loss + grad_loss + pgrad_loss\n self.loss.backward()\n\n def optimize_parameters(self):\n self.netM.zero_grad()\n self.netG.zero_grad()\n self.forward()\n self.optimizer_G.zero_grad()\n self.optimizer_M.zero_grad()\n self.backward()\n self.optimizer_G.step()\n self.optimizer_M.step()\n \n def zero_grad(self):\n self.netM.zero_grad()\n self.netG.zero_grad()\n self.optimizer_G.zero_grad()\n self.optimizer_M.zero_grad()\n\n def vis(self, e, s, path='', eval=False):\n if len(path) > 0:\n save_dir = os.path.join(self.save_dir, path)\n else:\n save_dir = self.save_dir\n if not os.path.isdir(save_dir):\n os.mkdir(save_dir)\n shadow = self.input_img\n output = self.final\n gt = self.shadowfree_img\n if eval:\n img = self.final[0, ...]\n filename = os.path.join(save_dir, self.imname[0])\n else:\n img = torch.cat([shadow, output, gt, self.litgt, self.lit], axis=-1)[0, ...]\n filename = os.path.join(save_dir, \"epoch_%d_step_%d.png\" % (e, s))\n img = tensor2im(img)\n\n img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n cv2.imwrite(filename, img)\n\n" }, { "path": "models/Refine_model.py", "content": "import torch\nfrom collections import OrderedDict\nimport time\nimport numpy as np\nimport os\nimport torch.nn.functional as F\nimport torch.nn as nn\nfrom util.image_pool import ImagePool\nfrom .base_model import BaseModel\nfrom . import networks\nimport util.util as util\nfrom .distangle_model import DistangleModel\nfrom PIL import ImageOps, Image\nimport cv2\n\n\ndef tensor2im(input_image, imtype=np.uint8):\n if not isinstance(input_image, np.ndarray):\n if isinstance(input_image, torch.Tensor): # get the data from a variable\n image_tensor = input_image.data\n else:\n return input_image\n image_numpy = image_tensor.cpu().float().numpy() # convert it into a numpy array\n if image_numpy.shape[0] == 1: # grayscale to RGB\n image_numpy = np.tile(image_numpy, (3, 1, 1))\n # image_numpy = image_numpy.convert('L')\n image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 # post-processing: tranpose and scaling\n else: # if it is a numpy array, do nothing\n image_numpy = input_image\n return np.clip(image_numpy, 0, 255).astype(imtype)\n\n\nclass L_TV(nn.Module):\n def __init__(self):\n super(L_TV, self).__init__()\n\n def forward(self, x):\n _, _, h, w = x.size()\n count_h = (h - 1) * w\n count_w = (w - 1) * h\n\n h_tv = torch.pow(x[:, :, 1:, :] - x[:, :, :h - 1, :], 2).sum()\n w_tv = torch.pow(x[:, :, :, 1:] - x[:, :, :, :w - 1], 2).sum()\n return (h_tv / count_h + w_tv / count_w) / 2.0\n\n\nclass GradientLoss(nn.Module):\n def __init__(self, loss_weight=1.0, reduction='mean'):\n super(GradientLoss, self).__init__()\n self.loss_weight = loss_weight\n self.reduction = reduction\n if self.reduction not in ['none', 'mean', 'sum']:\n raise ValueError(f'Unsupported reduction mode: {self.reduction}. '\n f'Supported ones are: {_reduction_modes}')\n\n def forward(self, pred, target):\n _, cin, _, _ = pred.shape\n _, cout, _, _ = target.shape\n assert cin == 3 and cout == 3\n kx = torch.Tensor([[1, 0, -1], [2, 0, -2],\n [1, 0, -1]]).view(1, 1, 3, 3).to(target)\n ky = torch.Tensor([[1, 2, 1], [0, 0, 0],\n [-1, -2, -1]]).view(1, 1, 3, 3).to(target)\n kx = kx.repeat((cout, 1, 1, 1))\n ky = ky.repeat((cout, 1, 1, 1))\n\n pred_grad_x = F.conv2d(pred, kx, padding=1, groups=3)\n pred_grad_y = F.conv2d(pred, ky, padding=1, groups=3)\n target_grad_x = F.conv2d(target, kx, padding=1, groups=3)\n target_grad_y = F.conv2d(target, ky, padding=1, groups=3)\n\n loss = (\n nn.L1Loss(reduction=self.reduction)\n (pred_grad_x, target_grad_x) +\n nn.L1Loss(reduction=self.reduction)\n (pred_grad_y, target_grad_y))\n return loss * self.loss_weight\n\n\nclass PoissonGradientLoss(nn.Module):\n def __init__(self, reduction='mean'):\n \"\"\"L_{grad} = \\frac{1}{2hw}\\sum_{m=1}^{H}\\sum_{n=1}{W}(\\partial f(I_{Blend}) - \n (\\partial f(I_{Source}) + \\partial f(I_{Target})))_{mn}^2\n See **Deep Image Blending** for detail.\n \"\"\"\n super(PoissonGradientLoss, self).__init__()\n self.reduction = reduction\n\n def forward(self, source, target, blend, mask):\n f = torch.Tensor([[0, -1, 0], [-1, 4, -1], [0, -1, 0]]).view(1, 1, 3, 3).to(target)\n f = f.repeat((3, 1, 1, 1))\n grad_s = F.conv2d(source, f, padding=1, groups=3) * mask\n grad_t = F.conv2d(target, f, padding=1, groups=3) * (1 - mask)\n grad_b = F.conv2d(blend, f, padding=1, groups=3)\n return nn.MSELoss(reduction=self.reduction)(grad_b, (grad_t + grad_s))\n\n\nclass RefineModel(DistangleModel):\n def name(self):\n return 'auto exposure cvpr21'\n\n @staticmethod\n def modify_commandline_options(parser, is_train=True):\n\n parser.set_defaults(pool_size=0, no_lsgan=True, norm='batch')\n parser.set_defaults(dataset_mode='expo_param')\n parser.add_argument('--wdataroot', default='None',\n help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')\n parser.add_argument('--use_our_mask', action='store_true')\n parser.add_argument('--mask_train', type=str, default=None)\n parser.add_argument('--mask_test', type=str, default=None)\n return parser\n\n def initialize(self, opt):\n BaseModel.initialize(self, opt)\n self.isTrain = opt.isTrain\n self.loss_names = ['G_param', 'alpha', 'rescontruction']\n self.visual_names = ['input_img', 'litgt', 'alpha_pred', 'out', 'final', 'outgt']\n self.model_names = ['G', 'M', 'R']\n # load/define networks\n opt.output_nc = 3\n\n self.ks = ks = opt.ks\n self.rks = opt.rks\n self.n = n = opt.n\n self.shadow_loss = opt.shadow_loss\n self.tv_loss = opt.tv_loss\n self.grad_loss = opt.grad_loss\n self.pgrad_loss = opt.pgrad_loss\n\n self.netG = networks.define_G(4, 2 * 3, opt.ngf, 'RESNEXT', opt.norm,\n not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)\n self.netM = networks.define_G(1 + 3 + n * 3, ((1 + n) * 3) * 3 * ks * ks, opt.ngf, 'unet_256', opt.norm,\n not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)\n self.netR = networks.define_G(3 + 3 + 2, 3 * 3 * self.rks * self.rks, opt.ngf, 'unet_256', opt.norm,\n not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)\n\n self.netG.to(self.device)\n self.netM.to(self.device)\n self.netR.to(self.device)\n self.netG.eval()\n self.netM.eval()\n self.netG.requires_grad = False\n self.netM.requires_grad = False\n if self.isTrain:\n # define loss functions\n self.MSELoss = torch.nn.MSELoss()\n self.criterionL1 = torch.nn.L1Loss()\n self.bce = torch.nn.BCEWithLogitsLoss()\n # initialize optimizers\n self.optimizers = []\n\n if opt.optimizer == 'adam':\n self.optimizer_R = torch.optim.Adam(self.netR.parameters(),\n lr=opt.lr, betas=(opt.beta1, 0.999), weight_decay=1e-5)\n elif opt.optimizer == 'sgd':\n self.optimizer_R = torch.optim.SGD(self.netR.parameters(), momentum=0.9,\n lr=opt.lr, weight_decay=1e-5)\n else:\n assert False\n\n self.optimizers.append(self.optimizer_R)\n\n def set_input(self, input):\n self.input_img = input['A'].to(self.device)\n self.shadow_mask = input['B'].to(self.device)\n self.imname = input['imname']\n if self.isTrain:\n self.shadow_param = input['param'].to(self.device).type(torch.float)\n self.shadow_mask = (self.shadow_mask > 0.9).type(torch.float) # * 2 - 1\n self.nim = self.input_img.shape[1]\n self.shadowfree_img = input['C'].to(self.device)\n self.shadow_mask_3d = (self.shadow_mask > 0).type(torch.float).expand(self.input_img.shape)\n self.shadow_mask_dilate = input['B_dilate'].to(self.device)\n self.shadow_mask_erode = input['B_erode'].to(self.device)\n\n def forward(self):\n inputG = torch.cat([self.input_img, self.shadow_mask], 1)\n shadow_param_pred = self.netG(inputG)\n\n n = shadow_param_pred.shape[0]\n w = inputG.shape[2]\n h = inputG.shape[3]\n\n base_shadow_param_pred = shadow_param_pred[:, :2 * 3] # shadow_param_pred.view((n, self.n * 3, 2, 1, 1))\n self.base_shadow_param_pred = base_shadow_param_pred\n\n shadow_image = self.input_img.clone() / 2 + 0.5\n base_shadow_output = shadow_image * base_shadow_param_pred[:, :3].view((n, 3, 1, 1)) + \\\n base_shadow_param_pred[:, 3:].view((n, 3, 1, 1))\n shadow_output_list = []\n for i in range(0, self.n - 1):\n if i % 2 == 0:\n scale = 1 + i * 0.01\n else:\n scale = 1 - i * 0.01\n shadow_output_list.append(base_shadow_output * scale)\n shadow_output = torch.cat([base_shadow_output] + shadow_output_list, dim=1)\n self.lit = torch.cat([base_shadow_output] + shadow_output_list, dim=-1) * 2 - 1\n\n shadow_output = shadow_output * 2 - 1\n self.shadow_output = shadow_output\n\n inputM = torch.cat([self.input_img, shadow_output, self.shadow_mask], 1)\n out = torch.cat([self.input_img, shadow_output], 1)\n out = out / 2 + 0.5\n out_matrix = F.unfold(out, stride=1, padding=self.ks // 2, kernel_size=self.ks) # N, C x \\mul_(kernel_size), L\n\n kernel = self.netM(inputM) # b, (3+1)*n * 3 * ks * ks, Tanh\n\n b, c, h, w = self.input_img.shape\n output = []\n for i in range(b):\n # feature = out[i, ...]\n feature = out_matrix[i, ...] # ((1 + n) * 3) * ks * ks, L\n weight = kernel[i, ...] # ((1 + n) * 3) * 3 * ks * ks, H, W\n feature = feature.unsqueeze(0) # 1, C, L\n weight = weight.view((3, (self.n + 1) * 3 * self.ks * self.ks, h * w))\n weight = F.softmax(weight, dim=1)\n iout = feature * weight # (3, C, L)\n iout = torch.sum(iout, dim=1, keepdim=False)\n iout = iout.view((1, 3, h, w))\n output.append(iout)\n self.final = torch.cat(output, dim=0) * 2 - 1\n\n final_output = self.final.detach()\n final_output.requires_grad = False\n rkernel = self.netR(torch.cat([self.input_img, final_output, self.shadow_mask,\n self.shadow_mask_dilate - self.shadow_mask_erode], 1))\n\n final_output = final_output / 2 + 0.5\n rout_matrix = F.unfold(final_output, stride=1, padding=self.rks // 2, kernel_size=self.rks)\n output = []\n for i in range(b):\n feature = rout_matrix[i, ...] # ((1 + n) * 3) * ks * ks, L\n weight = rkernel[i, ...] # ((1 + n) * 3) * 3 * ks * ks, H, W\n feature = feature.unsqueeze(0) # 1, C, L\n weight = weight.view((3, 3 * self.rks * self.rks, h * w))\n iout = feature * weight # (3, C, L)\n iout = torch.sum(iout, dim=1, keepdim=False)\n iout = iout.view((1, 3, h, w))\n\n output.append(iout)\n self.rfinal = torch.cat(output, dim=0) * 2 - 1\n\n def backward(self):\n # criterion = self.criterionL1\n lambda_ = self.opt.lambda_L1\n\n if self.tv_loss > 0:\n tv_loss = L_TV()(self.rfinal - self.shadowfree_img) * lambda_ * self.tv_loss\n else:\n tv_loss = 0.0\n\n if self.grad_loss > 0:\n grad_loss = GradientLoss()(self.rfinal, self.shadowfree_img) * lambda_ * self.grad_loss\n else:\n grad_loss = 0.0\n\n if self.pgrad_loss > 0:\n pgrad_loss = PoissonGradientLoss()(target=self.input_img, blend=self.rfinal,\n source=self.shadowfree_img, mask=self.shadow_mask_dilate) \\\n * lambda_ * self.pgrad_loss\n else:\n pgrad_loss = 0.0\n\n self.loss_rescontruction = nn.L1Loss()(self.rfinal, self.shadowfree_img) * lambda_\n self.loss = self.loss_rescontruction + tv_loss + grad_loss + pgrad_loss\n self.loss.backward()\n\n def optimize_parameters(self):\n self.netM.zero_grad()\n self.netG.zero_grad()\n self.zero_grad()\n self.forward()\n self.optimizer_R.zero_grad()\n self.backward()\n self.optimizer_R.step()\n\n def zero_grad(self):\n self.netR.zero_grad()\n self.optimizer_R.zero_grad()\n\n def vis(self, e, s, path='', eval=False):\n if len(path) > 0:\n save_dir = os.path.join(self.save_dir, path)\n else:\n save_dir = self.save_dir\n if not os.path.isdir(save_dir):\n os.mkdir(save_dir)\n shadow = self.input_img\n ooutput = self.final\n output = self.rfinal\n gt = self.shadowfree_img\n\n if not eval:\n img = torch.cat([shadow, ooutput, output, gt], dim=-1)[0, ...]\n filename = os.path.join(save_dir, self.imname[0])\n\n img = tensor2im(img)\n img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n cv2.imwrite(filename, img)\n\n else:\n filename = os.path.join(save_dir, self.imname[0])\n img = output[0, ...]\n img = tensor2im(img)\n img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n cv2.imwrite(filename, img)\n\n filename = os.path.join(save_dir, self.imname[0].replace('.png', '-o.png'))\n img = ooutput[0, ...]\n img = tensor2im(img)\n img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n cv2.imwrite(filename, img)\n" }, { "path": "models/__init__.py", "content": "import importlib\nfrom models.base_model import BaseModel\n\n\ndef find_model_using_name(model_name):\n # Given the option --model [modelname],\n # the file \"models/modelname_model.py\"\n # will be imported.\n model_filename = \"models.\" + model_name + \"_model\"\n modellib = importlib.import_module(model_filename)\n\n # In the file, the class called ModelNameModel() will\n # be instantiated. It has to be a subclass of BaseModel,\n # and it is case-insensitive.\n model = None\n target_model_name = model_name.replace('_', '') + 'model'\n for name, cls in modellib.__dict__.items():\n if name.lower() == target_model_name.lower() \\\n and issubclass(cls, BaseModel):\n model = cls\n\n if model is None:\n print(\"In %s.py, there should be a subclass of BaseModel with class name that matches %s in lowercase.\" % (model_filename, target_model_name))\n exit(0)\n\n return model\n\n\ndef get_option_setter(model_name):\n model_class = find_model_using_name(model_name)\n return model_class.modify_commandline_options\n\n\ndef create_model(opt):\n model = find_model_using_name(opt.model)\n instance = model()\n instance.initialize(opt)\n print(\"model [%s] was created\" % (instance.name()))\n return instance\n" }, { "path": "models/base_model.py", "content": "import os\nimport time\nimport torch\nfrom collections import OrderedDict\nfrom . import networks\nimport util.util as util\nimport numpy as np\nclass BaseModel():\n\n # modify parser to add command line options,\n # and also change the default values if needed\n @staticmethod\n def modify_commandline_options(parser, is_train):\n return parser\n \n\n def train(self):\n print('switching to training mode')\n for name in self.model_names:\n if isinstance(name, str):\n net = getattr(self, 'net' + name)\n net.train()\n # make models eval mode during test time\n def eval(self):\n print('switching to testing mode')\n for name in self.model_names:\n if isinstance(name, str):\n net = getattr(self, 'net' + name)\n net.eval()\n\n def name(self):\n return 'BaseModel'\n\n def initialize(self, opt):\n self.epoch=0\n self.opt = opt\n self.gpu_ids = opt.gpu_ids\n self.isTrain = opt.isTrain\n self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu')\n self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)\n if not os.path.isdir(self.save_dir):\n os.mkdir(self.save_dir)\n # if opt.resize_or_crop != 'scale_width':\n # torch.backends.cudnn.benchmark = True\n self.loss_names = []\n self.model_names = []\n self.visual_names = []\n self.image_paths = []\n\n #def set_input(self, input):\n # self.input = input\n\n\n def set_input(self, input):\n self.input_img = input['A'].to(self.device)\n self.shadow_mask = input['B'].to(self.device)\n self.shadow_mask = (self.shadow_mask>0.9).type(torch.float)*2-1\n #self.shadow_mask = (self.shadow_mask==1).type(torch.float)*2-1\n self.nim = self.input_img.shape[1]\n self.shadowfree_img = input['C'].to(self.device)\n self.shadow_mask_3d= (self.shadow_mask>0).type(torch.float).expand(self.input_img.shape) \n #self.shadow_mask_3d_over = (self.shadow_mask_over>0).type(torch.float).expand(self.input_img.shape)\n\n def get_prediction(self,input):\n self.input_img = input['A'].to(self.device)\n self.shadow_mask = input['B'].to(self.device)\n self.shadow_mask = (self.shadow_mask>0.9).type(torch.float)*2-1\n self.shadow_mask_3d= (self.shadow_mask>0).type(torch.float).expand(self.input_img.shape) \n inputG = torch.cat([self.input_img,self.shadow_mask],1)\n out = self.netG(inputG)\n return util.tensor2im(out)\n\n def forward(self):\n pass\n\n # load and print networks; create schedulers\n def setup(self, opt, parser=None):\n print(self.name)\n if self.isTrain:\n self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers]\n\n if not self.isTrain or opt.continue_train or opt.finetuning:\n print(\"LOADING %s\"%(self.name))\n self.load_networks(opt.epoch)\n self.print_networks(opt.verbose)\n\n\n # used in test time, wrapping `forward` in no_grad() so we don't save\n # intermediate steps for backprop\n def test(self):\n with torch.no_grad():\n self.forward()\n\n # get image paths\n def get_image_paths(self):\n return self.image_paths\n\n def optimize_parameters(self):\n pass\n\n # update learning rate (called once every epoch)\n def update_learning_rate(self,loss=None):\n for scheduler in self.schedulers:\n if not loss:\n scheduler.step()\n else:\n scheduler.step(loss)\n\n lr = self.optimizers[0].param_groups[0]['lr']\n print('learning rate = %.7f' % lr)\n\n # return visualization images. train.py will display these images, and save the images to a html\n def get_current_visuals(self):\n t= time.time()\n nim = self.shadow.shape[0]\n visual_ret = OrderedDict()\n all =[]\n for i in range(0,min(nim-1,5)):\n row=[]\n for name in self.visual_names:\n if isinstance(name, str):\n if hasattr(self,name):\n im = util.tensor2im(getattr(self, name).data[i:i+1,:,:,:])\n row.append(im)\n row=tuple(row)\n all.append(np.hstack(row))\n all = tuple(all)\n \n allim = np.vstack(all)\n return OrderedDict([(self.opt.name,allim)])\n \n # return traning losses/errors. train.py will print out these errors as debugging information\n def get_current_losses(self):\n errors_ret = OrderedDict()\n for name in self.loss_names:\n if isinstance(name, str):\n # float(...) works for both scalar tensor and float number\n if hasattr(self, 'loss_' + name):\n errors_ret[name] = float(getattr(self, 'loss_' + name))\n return errors_ret\n\n # save models to the disk\n def save_networks(self, epoch):\n for name in self.model_names:\n if isinstance(name, str):\n save_filename = '%s_net_%s.pth' % (epoch, name)\n save_path = os.path.join(self.save_dir, save_filename)\n net = getattr(self, 'net' + name)\n\n if len(self.gpu_ids) > 0 and torch.cuda.is_available():\n torch.save(net.module.cpu().state_dict(), save_path)\n net.cuda(self.gpu_ids[0])\n else:\n torch.save(net.cpu().state_dict(), save_path)\n\n def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0):\n key = keys[i]\n if i + 1 == len(keys): # at the end, pointing to a parameter/buffer\n if module.__class__.__name__.startswith('InstanceNorm') and \\\n (key == 'running_mean' or key == 'running_var'):\n if getattr(module, key) is None:\n state_dict.pop('.'.join(keys))\n if module.__class__.__name__.startswith('InstanceNorm') and \\\n (key == 'num_batches_tracked'):\n state_dict.pop('.'.join(keys))\n else:\n self.__patch_instance_norm_state_dict(state_dict, getattr(module,key), keys, i + 1)\n\n # load models from the disk\n def load_networks(self, epoch, save_dir=None):\n print(epoch)\n if save_dir is None:\n save_dir = self.save_dir\n \n for name in self.model_names:\n if isinstance(name, str):\n load_filename = '%s_net_%s.pth' % (epoch, name)\n load_path = os.path.join(save_dir, load_filename)\n if self.opt.finetuning:\n\n load_filename = '%s_net_%s.pth' % (self.opt.finetuning_epoch, name)\n load_path = os.path.join(self.opt.finetuning_dir, load_filename)\n\n net = getattr(self, 'net' + name)\n if isinstance(net, torch.nn.DataParallel):\n net = net.module\n print('loading the model from %s' % load_path)\n # if you are using PyTorch newer than 0.4 (e.g., built from\n # GitHub source), you can remove str() on self.device\n state_dict = torch.load(load_path, map_location=str(self.device))\n #\n if hasattr(state_dict, '_metadata'):\n del state_dict._metadata\n\n # patch InstanceNorm checkpoints prior to 0.4\n for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop\n self.__patch_instance_norm_state_dict(state_dict, net, key.split('.'))\n net.load_state_dict(state_dict)\n\n # print network information\n def print_networks(self, verbose):\n print('---------- Networks initialized -------------')\n for name in self.model_names:\n if isinstance(name, str):\n net = getattr(self, 'net' + name)\n num_params = 0\n for param in net.parameters():\n num_params += param.numel()\n print(net)\n print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6))\n print('-----------------------------------------------')\n\n # set requies_grad=Fasle to avoid computation\n def set_requires_grad(self, nets, requires_grad=False):\n if not isinstance(nets, list):\n nets = [nets]\n for net in nets:\n if net is not None:\n for param in net.parameters():\n param.requires_grad = requires_grad\n" }, { "path": "models/distangle_model.py", "content": "import torch\nfrom util.image_pool import ImagePool\nfrom .base_model import BaseModel\nfrom . import networks\nimport util.util as util\n\n\n\nclass DistangleModel(BaseModel):\n def name(self):\n return 'DistangleModel'\n\n @staticmethod\n def modify_commandline_options(parser, is_train=True):\n\n parser.set_defaults(pool_size=0, no_lsgan=True, norm='batch')\n parser.set_defaults(dataset_mode='expo_param')\n parser.set_defaults(netG='RESNEXT')\n if is_train:\n parser.add_argument('--lambda_L1', type=float, default=100.0, help='weight for L1 loss')\n\n return parser\n\n def initialize(self, opt):\n BaseModel.initialize(self, opt)\n self.isTrain = opt.isTrain\n self.loss_names = ['G']\n # specify the images you want to save/display. The program will call base_model.get_current_visuals\n self.visual_names = ['input_img', 'shadow_mask','out','outgt']\n # specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks\n if self.isTrain:\n self.model_names = ['G']\n else: # during test time, only load Gs\n self.model_names = ['G']\n # load/define networks\n opt.output_nc= 3 if opt.task=='sr' else 1 #3 for shadow removal, 1 for detection\n self.netG = networks.define_G(4, opt.output_nc, opt.ngf, 'RESNEXT', opt.norm,\n not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)\n self.netG.to(self.device)\n print(self.netG)\n if self.isTrain:\n # define loss functions\n self.criterionL1 = torch.nn.L1Loss()\n self.bce = torch.nn.BCEWithLogitsLoss()\n # initialize optimizers\n self.optimizers = []\n self.optimizer_G = torch.optim.Adam(self.netG.parameters(),\n lr=opt.lr, betas=(opt.beta1, 0.999),weight_decay=1e-5)\n self.optimizers.append(self.optimizer_G)\n\n def set_input(self, input):\n self.input_img = input['A'].to(self.device)\n self.shadow_mask = input['B'].to(self.device)\n self.shadow_param = input['param'].to(self.device).type(torch.float)\n self.shadow_mask = (self.shadow_mask>0.9).type(torch.float)*2-1\n self.nim = self.input_img.shape[1]\n self.shadowfree_img = input['C'].to(self.device)\n self.shadow_mask_3d= (self.shadow_mask>0).type(torch.float).expand(self.input_img.shape) \n\n def forward(self):\n inputG = torch.cat([self.input_img, self.shadow_mask], 1)\n self.Gout = self.netG(inputG)\n self.lit = self.input_img.clone() / 2 + 0.5\n n = self.Gout.shape[0]\n\n add = add.view(n, 3, 1, 1).expand((n, 3, 256, 256))\n mul = mul.view(n, 3, 1, 1).expand((n, 3, 256, 256))\n\n self.litgt = (self.input_img.clone() + 1) / 2\n self.lit = self.lit * mul + add\n self.litgt = self.litgt * mulgt + addgt\n self.out = (self.input_img / 2 + 0.5) * (1 - self.shadow_mask_3d) + self.lit * self.shadow_mask_3d\n self.out = self.out * 2 - 1\n self.outgt = (self.input_img / 2 + 0.5) * (1 - self.shadow_mask_3d) + self.litgt * self.shadow_mask_3d\n self.outgt = self.outgt * 2 - 1\n self.alpha = torch.mean(self.shadowfree_img / self.lit,dim=1,keepdim=True)\n\n\n def get_prediction(self,input):\n self.input_img = input['A'].to(self.device)\n self.shadow_mask = input['B'].to(self.device)\n inputG = torch.cat([self.input_img,self.shadow_mask],1)\n self.shadow_mask = (self.shadow_mask>0.9).type(torch.float)*2-1\n self.shadow_mask_3d= (self.shadow_mask>0).type(torch.float).expand(self.input_img.shape) \n self.Gout = self.netG(inputG)\n self.lit = self.input_img.clone()/2+0.5\n add = self.Gout[:,[0,2,4]]\n mul = self.Gout[:,[1,3,5]]\n n = self.Gout.shape[0]\n add = add.view(n,3,1,1).expand((n,3,256,256))\n mul = mul.view(n,3,1,1).expand((n,3,256,256))\n self.lit = self.lit*mul + add\n self.out = (self.input_img/2+0.5)*(1-self.shadow_mask_3d) + self.lit*self.shadow_mask_3d\n self.out = self.out*2-1\n return util.tensor2im(self.out,scale =0) \n\n def backward_G(self):\n criterion = self.criterionL1 if self.opt.task =='sr' else self.bce\n lambda_ = self.opt.lambda_L1 if self.opt.task =='sr' else 1\n self.loss_G = criterion(self.Gout, self.shadow_param) * lambda_\n self.loss_G.backward()\n\n def optimize_parameters(self):\n self.forward()\n self.optimizer_G.zero_grad()\n self.backward_G()\n self.optimizer_G.step()\n\n\nif __name__=='__main__':\n parser = argparse.ArgumentParser()\n" }, { "path": "models/loss_function.py", "content": "import torch\nfrom torch import nn\nimport torch.nn.functional as F\n\ndef smooth_loss(pred_map):\n def gradient(pred):\n D_dy = pred[:, :, 1:] - pred[:, :, :-1]\n D_dx = pred[:, :, :, 1:] - pred[:, :, :, :-1]\n return D_dx, D_dy\n\n loss = 0\n weight = 1.\n\n dx, dy = gradient(pred_map)\n dx2, dxdy = gradient(dx)\n dydx, dy2 = gradient(dy)\n loss += (dx2.abs().mean() + dxdy.abs().mean() + dydx.abs().mean() + dy2.abs().mean())*weight\n return loss\n\n" }, { "path": "models/networks.py", "content": "import torch\nimport torch.nn as nn\nfrom torch.nn import init\nimport functools\nfrom torch.optim import lr_scheduler\n###############################################################################\n# Helper Functions\n###############################################################################\n\n\ndef get_norm_layer(norm_type='instance'):\n if norm_type == 'batch':\n norm_layer = functools.partial(nn.BatchNorm2d, affine=True)\n elif norm_type == 'instance':\n norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)\n elif norm_type == 'none':\n norm_layer = None\n else:\n raise NotImplementedError('normalization layer [%s] is not found' % norm_type)\n return norm_layer\n\n\ndef get_scheduler(optimizer, opt):\n if opt.lr_policy == 'lambda':\n def lambda_rule(epoch):\n lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.niter) / float(opt.niter_decay + 1)\n return lr_l\n scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)\n elif opt.lr_policy == 'step':\n scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1)\n elif opt.lr_policy == 'shadow_step':\n scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[70000,90000,13200], gamma=0.3)\n elif opt.lr_policy == 'plateau':\n scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5)\n elif opt.lr_policy == 'cosine':\n scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.niter, eta_min=0)\n else:\n return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy)\n return scheduler\n\n\ndef init_weights(net, init_type='normal', gain=0.02):\n def init_func(m):\n classname = m.__class__.__name__\n if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):\n if init_type == 'normal':\n init.normal_(m.weight.data, 0.0, gain)\n elif init_type == 'xavier':\n init.xavier_normal_(m.weight.data, gain=gain)\n elif init_type == 'kaiming':\n init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')\n elif init_type == 'orthogonal':\n init.orthogonal_(m.weight.data, gain=gain)\n else:\n raise NotImplementedError('initialization method [%s] is not implemented' % init_type)\n if hasattr(m, 'bias') and m.bias is not None:\n init.constant_(m.bias.data, 0.0)\n elif classname.find('BatchNorm2d') != -1:\n init.normal_(m.weight.data, 1.0, gain)\n init.constant_(m.bias.data, 0.0)\n\n print('initialize network with %s' % init_type)\n net.apply(init_func)\n\n\ndef init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]):\n if len(gpu_ids) > 0:\n assert(torch.cuda.is_available())\n net.to(gpu_ids[0])\n net = torch.nn.DataParallel(net, gpu_ids)\n init_weights(net, init_type, gain=init_gain)\n return net\n\ndef define_vgg(num_input,num_classes,init_type='normal', init_gain=0.02, gpu_ids=[]):\n print(gpu_ids)\n from .vgg import create_vgg\n net = create_vgg(num_input,num_classes)\n net.to(gpu_ids[0])\n net = torch.nn.DataParallel(net,gpu_ids)\n init_weights(net,init_type,gain=init_gain)\n return net\n\n\n\ndef define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]):\n net = None\n norm_layer = get_norm_layer(norm_type=norm)\n\n if netG == 'resnet_9blocks':\n net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9)\n elif netG == 'resnet_6blocks':\n net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6)\n elif netG == 'unet_128':\n net = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout)\n elif netG == 'unet_256':\n net = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout)\n elif netG == 'unet_32':\n net = UnetGenerator(input_nc, output_nc, 5, ngf, norm_layer=norm_layer, use_dropout=use_dropout)\n elif netG == 'RESNEXT':\n from .resnet import resnext101_32x8d\n net = resnext101_32x8d(pretrained=False, num_classes=output_nc, num_inputchannels=input_nc)\n if len(gpu_ids)>0:\n assert(torch.cuda.is_available())\n net.to(gpu_ids[0])\n net = torch.nn.DataParallel(net,gpu_ids)\n return net\n elif netG == 'resnet50':\n from .resnet import resnet50\n net = resnet50(pretrained=False, num_classes=output_nc, num_inputchannels=input_nc) \n else:\n raise NotImplementedError('Generator model name [%s] is not recognized' % netG)\n return init_net(net, init_type, init_gain, gpu_ids)\n\n\ndef define_D(input_nc, ndf, netD,\n n_layers_D=3, norm='batch', use_sigmoid=False, init_type='normal', init_gain=0.02, gpu_ids=[]):\n net = None\n norm_layer = get_norm_layer(norm_type=norm)\n\n if netD == 'basic':\n net = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer, use_sigmoid=use_sigmoid)\n elif netD == 'n_layers':\n net = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer, use_sigmoid=use_sigmoid)\n elif netD == 'pixel':\n net = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer, use_sigmoid=use_sigmoid)\n elif netD == 'unet_32':\n net = UnetGenerator(input_nc, output_nc, 5, ngf, norm_layer=norm_layer, use_dropout=use_dropout)\n else:\n raise NotImplementedError('Discriminator model name [%s] is not recognized' % net)\n return init_net(net, init_type, init_gain, gpu_ids)\n\n\n##############################################################################\n# Classes\n##############################################################################\n\n\n# Defines the GAN loss which uses either LSGAN or the regular GAN.\n# When LSGAN is used, it is basically same as MSELoss,\n# but it abstracts away the need to create the target label tensor\n# that has the same size as the input\nclass GANLoss(nn.Module):\n def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0):\n super(GANLoss, self).__init__()\n self.register_buffer('real_label', torch.tensor(target_real_label))\n self.register_buffer('fake_label', torch.tensor(target_fake_label))\n if use_lsgan:\n self.loss = nn.MSELoss()\n else:\n self.loss = nn.BCELoss()\n\n def get_target_tensor(self, input, target_is_real):\n if target_is_real:\n target_tensor = self.real_label\n else:\n target_tensor = self.fake_label\n return target_tensor.expand_as(input)\n\n def __call__(self, input, target_is_real):\n target_tensor = self.get_target_tensor(input, target_is_real)\n return self.loss(input, target_tensor)\n\n\n# Defines the generator that consists of Resnet blocks between a few\n# downsampling/upsampling operations.\n# Code and idea originally from Justin Johnson's architecture.\n# https://github.com/jcjohnson/fast-neural-style/\nclass ResnetGenerator(nn.Module):\n def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'):\n assert(n_blocks >= 0)\n super(ResnetGenerator, self).__init__()\n self.input_nc = input_nc\n self.output_nc = output_nc\n self.ngf = ngf\n if type(norm_layer) == functools.partial:\n use_bias = norm_layer.func == nn.InstanceNorm2d\n else:\n use_bias = norm_layer == nn.InstanceNorm2d\n\n model = [nn.ReflectionPad2d(3),\n nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0,\n bias=use_bias),\n norm_layer(ngf),\n nn.ReLU(True)]\n\n n_downsampling = 2\n for i in range(n_downsampling):\n mult = 2**i\n model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3,\n stride=2, padding=1, bias=use_bias),\n norm_layer(ngf * mult * 2),\n nn.ReLU(True)]\n\n mult = 2**n_downsampling\n for i in range(n_blocks):\n model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)]\n\n for i in range(n_downsampling):\n mult = 2**(n_downsampling - i)\n model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),\n kernel_size=3, stride=2,\n padding=1, output_padding=1,\n bias=use_bias),\n norm_layer(int(ngf * mult / 2)),\n nn.ReLU(True)]\n model += [nn.ReflectionPad2d(3)]\n model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]\n model += [nn.Tanh()]\n\n self.model = nn.Sequential(*model)\n\n def forward(self, input):\n return self.model(input)\n\n\n# Define a resnet block\nclass ResnetBlock(nn.Module):\n def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias):\n super(ResnetBlock, self).__init__()\n self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias)\n\n def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias):\n conv_block = []\n p = 0\n if padding_type == 'reflect':\n conv_block += [nn.ReflectionPad2d(1)]\n elif padding_type == 'replicate':\n conv_block += [nn.ReplicationPad2d(1)]\n elif padding_type == 'zero':\n p = 1\n else:\n raise NotImplementedError('padding [%s] is not implemented' % padding_type)\n\n conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias),\n norm_layer(dim),\n nn.ReLU(True)]\n if use_dropout:\n conv_block += [nn.Dropout(0.5)]\n\n p = 0\n if padding_type == 'reflect':\n conv_block += [nn.ReflectionPad2d(1)]\n elif padding_type == 'replicate':\n conv_block += [nn.ReplicationPad2d(1)]\n elif padding_type == 'zero':\n p = 1\n else:\n raise NotImplementedError('padding [%s] is not implemented' % padding_type)\n conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias),\n norm_layer(dim)]\n\n return nn.Sequential(*conv_block)\n\n def forward(self, x):\n out = x + self.conv_block(x)\n return out\n\n\n# Defines the Unet generator.\n# |num_downs|: number of downsamplings in UNet. For example,\n# if |num_downs| == 7, image of size 128x128 will become of size 1x1\n# at the bottleneck\nclass UnetGenerator(nn.Module):\n def __init__(self, input_nc, output_nc, num_downs, ngf=64,\n norm_layer=nn.BatchNorm2d, use_dropout=False):\n super(UnetGenerator, self).__init__()\n\n # construct unet structure\n unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True)\n for i in range(num_downs - 5):\n unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout)\n unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer)\n unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer)\n unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer)\n unet_block = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer)\n\n self.model = unet_block\n\n def forward(self, input):\n return self.model(input)\n\n\n# Defines the submodule with skip connection.\n# X -------------------identity---------------------- X\n# |-- downsampling -- |submodule| -- upsampling --|\nclass UnetSkipConnectionBlock(nn.Module):\n def __init__(self, outer_nc, inner_nc, input_nc=None,\n submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False):\n super(UnetSkipConnectionBlock, self).__init__()\n self.outermost = outermost\n if type(norm_layer) == functools.partial:\n use_bias = norm_layer.func == nn.InstanceNorm2d\n else:\n use_bias = norm_layer == nn.InstanceNorm2d\n if input_nc is None:\n input_nc = outer_nc\n downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4,\n stride=2, padding=1, bias=use_bias)\n downrelu = nn.LeakyReLU(0.2, True)\n downnorm = norm_layer(inner_nc)\n uprelu = nn.ReLU(True)\n upnorm = norm_layer(outer_nc)\n\n if outermost:\n upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,\n kernel_size=4, stride=2,\n padding=1)\n down = [downconv]\n up = [uprelu, upconv] # , nn.Tanh()]\n model = down + [submodule] + up\n elif innermost:\n upconv = nn.ConvTranspose2d(inner_nc, outer_nc,\n kernel_size=4, stride=2,\n padding=1, bias=use_bias)\n down = [downrelu, downconv]\n up = [uprelu, upconv, upnorm]\n model = down + up\n else:\n upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,\n kernel_size=4, stride=2,\n padding=1, bias=use_bias)\n down = [downrelu, downconv, downnorm]\n up = [uprelu, upconv, upnorm]\n\n if use_dropout:\n model = down + [submodule] + up + [nn.Dropout(0.5)]\n else:\n model = down + [submodule] + up\n\n self.model = nn.Sequential(*model)\n\n def forward(self, x):\n if self.outermost:\n return self.model(x)\n else:\n return torch.cat([x, self.model(x)], 1)\n\n\n# Defines the PatchGAN discriminator with the specified arguments.\nclass NLayerDiscriminator(nn.Module):\n def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False):\n super(NLayerDiscriminator, self).__init__()\n if type(norm_layer) == functools.partial:\n use_bias = norm_layer.func == nn.InstanceNorm2d\n else:\n use_bias = norm_layer == nn.InstanceNorm2d\n\n kw = 4\n padw = 1\n sequence = [\n nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),\n nn.LeakyReLU(0.2, True)\n ]\n\n nf_mult = 1\n nf_mult_prev = 1\n for n in range(1, n_layers):\n nf_mult_prev = nf_mult\n nf_mult = min(2**n, 8)\n sequence += [\n nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult,\n kernel_size=kw, stride=2, padding=padw, bias=use_bias),\n norm_layer(ndf * nf_mult),\n nn.LeakyReLU(0.2, True)\n ]\n\n nf_mult_prev = nf_mult\n nf_mult = min(2**n_layers, 8)\n sequence += [\n nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult,\n kernel_size=kw, stride=1, padding=padw, bias=use_bias),\n norm_layer(ndf * nf_mult),\n nn.LeakyReLU(0.2, True)\n ]\n\n sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]\n\n if use_sigmoid:\n sequence += [nn.Sigmoid()]\n\n self.model = nn.Sequential(*sequence)\n\n def forward(self, input):\n return self.model(input)\n\n\nclass PixelDiscriminator(nn.Module):\n def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d, use_sigmoid=False):\n super(PixelDiscriminator, self).__init__()\n if type(norm_layer) == functools.partial:\n use_bias = norm_layer.func == nn.InstanceNorm2d\n else:\n use_bias = norm_layer == nn.InstanceNorm2d\n\n self.net = [\n nn.Conv2d(input_nc, ndf, kernel_size=1, stride=1, padding=0),\n nn.LeakyReLU(0.2, True),\n nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias),\n norm_layer(ndf * 2),\n nn.LeakyReLU(0.2, True),\n nn.Conv2d(ndf * 2, 1, kernel_size=1, stride=1, padding=0, bias=use_bias)]\n\n if use_sigmoid:\n self.net.append(nn.Sigmoid())\n\n self.net = nn.Sequential(*self.net)\n\n def forward(self, input):\n return self.net(input)\n" }, { "path": "models/resnet.py", "content": "import torch\nimport torch.nn as nn\n#from .utils import load_state_dict_from_url\ntry:\n from torch.hub import load_state_dict_from_url\nexcept ImportError:\n from torch.utils.model_zoo import load_url as load_state_dict_from_url\n\n__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',\n 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',\n 'wide_resnet50_2', 'wide_resnet101_2']\n\n\nmodel_urls = {\n 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',\n 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',\n 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',\n 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',\n 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',\n 'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',\n 'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',\n 'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',\n 'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',\n}\n\n\ndef conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):\n \"\"\"3x3 convolution with padding\"\"\"\n return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,\n padding=dilation, groups=groups, bias=False, dilation=dilation)\n\n\ndef conv1x1(in_planes, out_planes, stride=1):\n \"\"\"1x1 convolution\"\"\"\n return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)\n\n\nclass BasicBlock(nn.Module):\n expansion = 1\n\n def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,\n base_width=64, dilation=1, norm_layer=None):\n super(BasicBlock, self).__init__()\n if norm_layer is None:\n norm_layer = nn.BatchNorm2d\n if groups != 1 or base_width != 64:\n raise ValueError('BasicBlock only supports groups=1 and base_width=64')\n if dilation > 1:\n raise NotImplementedError(\"Dilation > 1 not supported in BasicBlock\")\n # Both self.conv1 and self.downsample layers downsample the input when stride != 1\n self.conv1 = conv3x3(inplanes, planes, stride)\n self.bn1 = norm_layer(planes)\n self.relu = nn.ReLU(inplace=True)\n self.conv2 = conv3x3(planes, planes)\n self.bn2 = norm_layer(planes)\n self.downsample = downsample\n self.stride = stride\n\n def forward(self, x):\n identity = x\n\n out = self.conv1(x)\n out = self.bn1(out)\n out = self.relu(out)\n\n out = self.conv2(out)\n out = self.bn2(out)\n\n if self.downsample is not None:\n identity = self.downsample(x)\n\n out += identity\n out = self.relu(out)\n\n return out\n\n\nclass Bottleneck(nn.Module):\n # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)\n # while original implementation places the stride at the first 1x1 convolution(self.conv1)\n # according to \"Deep residual learning for image recognition\"https://arxiv.org/abs/1512.03385.\n # This variant is also known as ResNet V1.5 and improves accuracy according to\n # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.\n\n expansion = 4\n\n def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,\n base_width=64, dilation=1, norm_layer=None):\n super(Bottleneck, self).__init__()\n if norm_layer is None:\n norm_layer = nn.BatchNorm2d\n width = int(planes * (base_width / 64.)) * groups\n # Both self.conv2 and self.downsample layers downsample the input when stride != 1\n self.conv1 = conv1x1(inplanes, width)\n self.bn1 = norm_layer(width)\n self.conv2 = conv3x3(width, width, stride, groups, dilation)\n self.bn2 = norm_layer(width)\n self.conv3 = conv1x1(width, planes * self.expansion)\n self.bn3 = norm_layer(planes * self.expansion)\n self.relu = nn.ReLU(inplace=True)\n self.downsample = downsample\n self.stride = stride\n\n def forward(self, x):\n identity = x\n\n out = self.conv1(x)\n out = self.bn1(out)\n out = self.relu(out)\n\n out = self.conv2(out)\n out = self.bn2(out)\n out = self.relu(out)\n\n out = self.conv3(out)\n out = self.bn3(out)\n\n if self.downsample is not None:\n identity = self.downsample(x)\n\n out += identity\n out = self.relu(out)\n\n return out\n\n\nclass ResNet(nn.Module):\n def my_load_state_dict(self,state_dict):\n own_state = self.state_dict()\n for name,param in state_dict.items():\n if name not in own_state:\n continue\n if isinstance(param, torch.Tensor):\n param = param.data\n try:\n own_state[name].copy_(param)\n except:\n if len(own_state[name].shape)>=2:\n torch.nn.init.xavier_normal_(own_state[name])\n continue\n\n def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,\n groups=1, width_per_group=64, replace_stride_with_dilation=None,\n norm_layer=None,num_inputchannels=3):\n super(ResNet, self).__init__()\n if norm_layer is None:\n norm_layer = nn.BatchNorm2d\n self._norm_layer = norm_layer\n\n self.inplanes = 64\n self.dilation = 1\n if replace_stride_with_dilation is None:\n # each element in the tuple indicates if we should replace\n # the 2x2 stride with a dilated convolution instead\n replace_stride_with_dilation = [False, False, False]\n if len(replace_stride_with_dilation) != 3:\n raise ValueError(\"replace_stride_with_dilation should be None \"\n \"or a 3-element tuple, got {}\".format(replace_stride_with_dilation))\n self.groups = groups\n self.base_width = width_per_group\n self.conv1 = nn.Conv2d(num_inputchannels, self.inplanes, kernel_size=7, stride=2, padding=3,\n bias=False)\n self.bn1 = norm_layer(self.inplanes)\n self.relu = nn.ReLU(inplace=True)\n self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n self.layer1 = self._make_layer(block, 64, layers[0])\n self.layer2 = self._make_layer(block, 128, layers[1], stride=2,\n dilate=replace_stride_with_dilation[0])\n self.layer3 = self._make_layer(block, 256, layers[2], stride=2,\n dilate=replace_stride_with_dilation[1])\n self.layer4 = self._make_layer(block, 512, layers[3], stride=2,\n dilate=replace_stride_with_dilation[2])\n self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n self.fc = nn.Linear(512 * block.expansion, num_classes)\n\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):\n nn.init.constant_(m.weight, 1)\n nn.init.constant_(m.bias, 0)\n\n # Zero-initialize the last BN in each residual branch,\n # so that the residual branch starts with zeros, and each residual block behaves like an identity.\n # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677\n if zero_init_residual:\n for m in self.modules():\n if isinstance(m, Bottleneck):\n nn.init.constant_(m.bn3.weight, 0)\n elif isinstance(m, BasicBlock):\n nn.init.constant_(m.bn2.weight, 0)\n\n def _make_layer(self, block, planes, blocks, stride=1, dilate=False):\n norm_layer = self._norm_layer\n downsample = None\n previous_dilation = self.dilation\n if dilate:\n self.dilation *= stride\n stride = 1\n if stride != 1 or self.inplanes != planes * block.expansion:\n downsample = nn.Sequential(\n conv1x1(self.inplanes, planes * block.expansion, stride),\n norm_layer(planes * block.expansion),\n )\n\n layers = []\n layers.append(block(self.inplanes, planes, stride, downsample, self.groups,\n self.base_width, previous_dilation, norm_layer))\n self.inplanes = planes * block.expansion\n for _ in range(1, blocks):\n layers.append(block(self.inplanes, planes, groups=self.groups,\n base_width=self.base_width, dilation=self.dilation,\n norm_layer=norm_layer))\n\n return nn.Sequential(*layers)\n\n def _forward_impl(self, x):\n # See note [TorchScript super()]\n x = self.conv1(x)\n x = self.bn1(x)\n x = self.relu(x)\n x = self.maxpool(x)\n\n x = self.layer1(x)\n x = self.layer2(x)\n x = self.layer3(x)\n x = self.layer4(x)\n\n x = self.avgpool(x)\n x = torch.flatten(x, 1)\n x = self.fc(x)\n\n return x\n\n def forward(self, x):\n return self._forward_impl(x)\n\n\ndef _resnet(arch, block, layers, pretrained, progress, **kwargs):\n model = ResNet(block, layers, **kwargs)\n if pretrained:\n state_dict = load_state_dict_from_url(model_urls[arch],\n progress=progress)\n model.my_load_state_dict(state_dict)\n return model\n\n\ndef resnet18(pretrained=False, progress=True, **kwargs):\n r\"\"\"ResNet-18 model from\n `\"Deep Residual Learning for Image Recognition\" `_\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n \"\"\"\n return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,\n **kwargs)\n\n\ndef resnet34(pretrained=False, progress=True, **kwargs):\n r\"\"\"ResNet-34 model from\n `\"Deep Residual Learning for Image Recognition\" `_\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n \"\"\"\n return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,\n **kwargs)\n\n\ndef resnet50(pretrained=False, progress=True, **kwargs):\n r\"\"\"ResNet-50 model from\n `\"Deep Residual Learning for Image Recognition\" `_\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n \"\"\"\n return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,\n **kwargs)\n\n\ndef resnet101(pretrained=False, progress=True, **kwargs):\n r\"\"\"ResNet-101 model from\n `\"Deep Residual Learning for Image Recognition\" `_\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n \"\"\"\n return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,\n **kwargs)\n\n\ndef resnet152(pretrained=False, progress=True, **kwargs):\n r\"\"\"ResNet-152 model from\n `\"Deep Residual Learning for Image Recognition\" `_\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n \"\"\"\n return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,\n **kwargs)\n\n\ndef resnext50_32x4d(pretrained=False, progress=True, **kwargs):\n r\"\"\"ResNeXt-50 32x4d model from\n `\"Aggregated Residual Transformation for Deep Neural Networks\" `_\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n \"\"\"\n kwargs['groups'] = 32\n kwargs['width_per_group'] = 4\n return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],\n pretrained, progress, **kwargs)\n\n\ndef resnext101_32x8d(pretrained=False, progress=True, **kwargs):\n r\"\"\"ResNeXt-101 32x8d model from\n `\"Aggregated Residual Transformation for Deep Neural Networks\" `_\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n \"\"\"\n kwargs['groups'] = 32\n kwargs['width_per_group'] = 8\n return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],\n pretrained, progress, **kwargs)\n\n\ndef wide_resnet50_2(pretrained=False, progress=True, **kwargs):\n r\"\"\"Wide ResNet-50-2 model from\n `\"Wide Residual Networks\" `_\n\n The model is the same as ResNet except for the bottleneck number of channels\n which is twice larger in every block. The number of channels in outer 1x1\n convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048\n channels, and in Wide ResNet-50-2 has 2048-1024-2048.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n \"\"\"\n kwargs['width_per_group'] = 64 * 2\n return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],\n pretrained, progress, **kwargs)\n\n\ndef wide_resnet101_2(pretrained=False, progress=True, **kwargs):\n r\"\"\"Wide ResNet-101-2 model from\n `\"Wide Residual Networks\" `_\n\n The model is the same as ResNet except for the bottleneck number of channels\n which is twice larger in every block. The number of channels in outer 1x1\n convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048\n channels, and in Wide ResNet-50-2 has 2048-1024-2048.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n \"\"\"\n kwargs['width_per_group'] = 64 * 2\n return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],\n pretrained, progress, **kwargs)\n\n\nif __name__=='__main__':\n a = resnext101_32x8d(pretrained=True,num_classes=6,num_inputchannels=4)\n print(a)\n" }, { "path": "models/vgg.py", "content": "from __future__ import print_function\nfrom __future__ import division\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport numpy as np\nimport torchvision\nfrom torchvision import datasets, models, transforms\nimport matplotlib.pyplot as plt\nimport time\nimport os\nimport copy\n\ndef set_parameter_requires_grad(model, feature_extracting):\n if feature_extracting:\n for param in model.parameters():\n param.requires_grad = False\n\ndef create_vgg(num_ic,num_classes,use_pretrained=False,feature_extract=False):\n model_ft = models.vgg16(pretrained=use_pretrained)\n set_parameter_requires_grad(model_ft, feature_extract)\n num_ftrs = model_ft.classifier[6].in_features\n \n model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)\n modules =[]\n for i in model_ft.features:\n modules.append(i)\n modules[0] = nn.Conv2d(num_ic, 64, kernel_size=3, padding=1) \n model_ft.features=nn.Sequential(*modules)\n\n\n modules2=[]\n for i in model_ft.classifier:\n modules2.append(i)\n modules2.append(nn.Tanh())\n model_ft.classifier = nn.Sequential(*modules2)\n input_size = 224 \n return model_ft\nif __name__=='__main__':\n a = create_vgg(4,6)\n print(a)\n inp = torch.ones((1,4,128,128))\n print(a(inp).shape)\n\n" }, { "path": "options/PAMI_options.py", "content": "from .base_options import BaseOptions\n\n\nclass TrainOptions(BaseOptions):\n def initialize(self, parser):\n parser = BaseOptions.initialize(self, parser)\n parser.add_argument('--display_freq', type=int, default=40, help='frequency of showing training results on screen')\n parser.add_argument('--display_ncols', type=int, default=4, help='if positive, display all images in a single visdom web panel with certain number of images per row.')\n parser.add_argument('--display_id', type=int, default=1, help='window id of the web display')\n parser.add_argument('--display_server', type=str, default=\"http://bigeye.cs.stonybrook.edu\", help='visdom server of the web display')\n parser.add_argument('--display_env', type=str, default='main', help='visdom display environment name (default is \"main\")')\n parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display')\n parser.add_argument('--update_html_freq', type=int, default=1000, help='frequency of saving training results to html')\n parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console')\n parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results')\n parser.add_argument('--save_epoch_freq', type=int, default=5, help='frequency of saving checkpoints at the end of epochs')\n parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model')\n parser.add_argument('--epoch_count', type=int, default=1, help='the starting epoch count, we save the model by , +, ...')\n parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')\n parser.add_argument('--niter', type=int, default=100, help='# of iter at starting learning rate')\n parser.add_argument('--niter_decay', type=int, default=100, help='# of iter to linearly decay learning rate to zero')\n parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam')\n parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam')\n parser.add_argument('--no_lsgan', action='store_true', help='do *not* use least square GAN, if false, use vanilla GAN')\n parser.add_argument('--pool_size', type=int, default=50, help='the size of image buffer that stores previously generated images')\n parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/')\n parser.add_argument('--lr_policy', type=str, default='lambda', help='learning rate policy: lambda|step|plateau|cosine')\n parser.add_argument('--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations')\n\n self.isTrain = True\n return parser\n" }, { "path": "options/__init__.py", "content": "" }, { "path": "options/base_options.py", "content": "import argparse\nimport os\nfrom util import util\nimport torch\nimport models\nimport data\n\n\nclass BaseOptions():\n def __init__(self):\n self.initialized = False\n\n def initialize(self, parser):\n parser.add_argument('--dataroot', help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')\n parser.add_argument('--batch_size', type=int, default=1, help='input batch size')\n parser.add_argument('--loadSize', type=int, default=286, help='scale images to this size')\n parser.add_argument('--fineSize', type=int, default=256, help='then crop to this size')\n parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML')\n parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels')\n parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels')\n parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer')\n parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer')\n parser.add_argument('--netD', type=str, default='basic', help='selects model to use for netD')\n parser.add_argument('--netG', type=str, default='resnet_9blocks', help='selects model to use for netG')\n parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers')\n parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU')\n parser.add_argument('--name', type=str, default='experiment_name', help='name of the experiment. It decides where to store samples and models')\n parser.add_argument('--dataset_mode', type=str, default='unaligned', help='chooses how datasets are loaded. [unaligned | aligned | single]')\n parser.add_argument('--model', type=str, default='cycle_gan',\n help='chooses which model to use. cycle_gan, pix2pix, test')\n parser.add_argument('--direction', type=str, default='AtoB', help='AtoB or BtoA')\n parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')\n parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data')\n parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')\n parser.add_argument('--randomSize', action='store_true', help='if specified, do not flip the images for data augmentation')\n parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization')\n parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')\n parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator')\n parser.add_argument('--max_dataset_size', type=int, default=float(\"inf\"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')\n parser.add_argument('--resize_or_crop', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop|none]')\n parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation')\n parser.add_argument('--no_crop', action='store_true', help='if specified, do not flip the images for data augmentation')\n parser.add_argument('--init_type', type=str, default='xavier', help='network initialization [normal|xavier|kaiming|orthogonal]')\n parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.')\n parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information')\n parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{loadSize}')\n parser.add_argument('--lambda_GAN', type=float, default=0.0)\n parser.add_argument('--lambda_smooth', type=float, default=0.0)\n parser.add_argument('--lambda_L1', type=float, default=0.0)\n parser.add_argument('--lambda_bd', type=float, default=0.0)\n parser.add_argument('--keep_ratio', action='store_true')\n parser.add_argument('--norm_mean', type=list, default=[0.5,0.5,0.5])\n parser.add_argument('--norm_std', type=list, default=[0.5,0.5,0.5])\n parser.add_argument('--finetuning', action='store_true')\n parser.add_argument('--finetuning_name', type=str)\n parser.add_argument('--finetuning_epoch', type=str)\n parser.add_argument('--finetuning_dir', type=str)\n\n self.initialized = True\n return parser\n\n def gather_options(self):\n # initialize parser with basic options\n if not self.initialized:\n parser = argparse.ArgumentParser(\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser = self.initialize(parser)\n\n # get the basic options\n opt, _ = parser.parse_known_args()\n\n # modify model-related parser options\n model_name = opt.model\n model_option_setter = models.get_option_setter(model_name)\n parser = model_option_setter(parser, self.isTrain)\n opt, _ = parser.parse_known_args() # parse again with the new defaults\n\n # modify dataset-related parser options\n dataset_name = opt.dataset_mode\n dataset_option_setter = data.get_option_setter(dataset_name)\n parser = dataset_option_setter(parser, self.isTrain)\n\n self.parser = parser\n\n return parser.parse_args()\n\n def print_options(self, opt):\n message = ''\n message += '----------------- Options ---------------\\n'\n for k, v in sorted(vars(opt).items()):\n comment = ''\n default = self.parser.get_default(k)\n if v != default:\n comment = '\\t[default: %s]' % str(default)\n message += '{:>25}: {:<30}{}\\n'.format(str(k), str(v), comment)\n message += '----------------- End -------------------'\n print(message)\n\n # save to the disk\n expr_dir = os.path.join(opt.checkpoints_dir, opt.name)\n util.mkdirs(expr_dir)\n file_name = os.path.join(expr_dir, 'opt.txt')\n with open(file_name, 'wt') as opt_file:\n opt_file.write(message)\n opt_file.write('\\n')\n\n def parse(self):\n\n opt = self.gather_options()\n opt.isTrain = self.isTrain # train or test\n\n # process opt.suffix\n if opt.suffix:\n suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else ''\n opt.name = opt.name + suffix\n\n self.print_options(opt)\n\n # set gpu ids\n str_ids = opt.gpu_ids.split(',')\n opt.gpu_ids = []\n for str_id in str_ids:\n id = int(str_id)\n if id >= 0:\n opt.gpu_ids.append(id)\n print(opt.gpu_ids)\n if len(opt.gpu_ids) > 0:\n torch.cuda.set_device(opt.gpu_ids[0])\n\n self.opt = opt\n return self.opt\n" }, { "path": "options/test_options.py", "content": "from .base_options import BaseOptions\n\n\nclass TestOptions(BaseOptions):\n def initialize(self, parser):\n parser = BaseOptions.initialize(self, parser)\n parser.add_argument('--ntest', type=int, default=float(\"inf\"), help='# of test examples.')\n parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.')\n parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images')\n parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')\n # Dropout and Batchnorm has different behavioir during training and test.\n parser.add_argument('--eval', action='store_true', help='use eval mode during test time.')\n parser.add_argument('--num_test', type=int, default=50, help='how many test images to run')\n\n parser.set_defaults(model='test')\n # To avoid cropping, the loadSize should be the same as fineSize\n parser.set_defaults(loadSize=parser.get_default('fineSize'))\n self.isTrain = False\n return parser\n" }, { "path": "options/train_options.py", "content": "from .base_options import BaseOptions\n\n\nclass TrainOptions(BaseOptions):\n def initialize(self, parser):\n parser = BaseOptions.initialize(self, parser)\n parser.add_argument('--display_freq', type=int, default=40, help='frequency of showing training results on screen')\n parser.add_argument('--display_ncols', type=int, default=4, help='if positive, display all images in a single visdom web panel with certain number of images per row.')\n parser.add_argument('--display_id', type=int, default=1, help='window id of the web display')\n parser.add_argument('--display_server', type=str, default=\"http://bigiris.cs.stonybrook.edu\", help='visdom server of the web display')\n parser.add_argument('--display_env', type=str, default='main', help='visdom display environment name (default is \"main\")')\n parser.add_argument('--param_path', type=str)\n parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display')\n parser.add_argument('--update_html_freq', type=int, default=1000, help='frequency of saving training results to html')\n parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console')\n parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results')\n parser.add_argument('--save_epoch_freq', type=int, default=5, help='frequency of saving checkpoints at the end of epochs')\n parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model')\n parser.add_argument('--epoch_count', type=int, default=1, help='the starting epoch count, we save the model by , +, ...')\n parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')\n parser.add_argument('--niter', type=int, default=100, help='# of iter at starting learning rate')\n parser.add_argument('--niter_decay', type=int, default=100, help='# of iter to linearly decay learning rate to zero')\n parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam')\n parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam')\n parser.add_argument('--no_lsgan', action='store_true', help='do *not* use least square GAN, if false, use vanilla GAN')\n parser.add_argument('--pool_size', type=int, default=50, help='the size of image buffer that stores previously generated images')\n parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/')\n parser.add_argument('--lr_policy', type=str, default='lambda', help='learning rate policy: lambda|step|plateau|cosine')\n parser.add_argument('--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations')\n\n parser.add_argument('--n', type=int, default=3)\n parser.add_argument('--ks', type=int, default=3)\n parser.add_argument('--rks', type=int, default=3)\n parser.add_argument('--shadow_loss', type=float, default=0.0)\n parser.add_argument('--tv_loss', type=float, default=0.0)\n parser.add_argument('--grad_loss', type=float, default=0.0)\n parser.add_argument('--pgrad_loss', type=float, default=0.0)\n parser.add_argument('--load_dir', type=str, default='')\n parser.add_argument('--optimizer', type=str, default='adam')\n\n self.isTrain = True\n return parser\n" }, { "path": "util/__init__.py", "content": "" }, { "path": "util/get_data.py", "content": "from __future__ import print_function\nimport os\nimport tarfile\nimport requests\nfrom warnings import warn\nfrom zipfile import ZipFile\nfrom bs4 import BeautifulSoup\nfrom os.path import abspath, isdir, join, basename\n\n\nclass GetData(object):\n \"\"\"\n\n Download CycleGAN or Pix2Pix Data.\n\n Args:\n technique : str\n One of: 'cyclegan' or 'pix2pix'.\n verbose : bool\n If True, print additional information.\n\n Examples:\n >>> from util.get_data import GetData\n >>> gd = GetData(technique='cyclegan')\n >>> new_data_path = gd.get(save_path='./datasets') # options will be displayed.\n\n \"\"\"\n\n def __init__(self, technique='cyclegan', verbose=True):\n url_dict = {\n 'pix2pix': 'https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets',\n 'cyclegan': 'https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets'\n }\n self.url = url_dict.get(technique.lower())\n self._verbose = verbose\n\n def _print(self, text):\n if self._verbose:\n print(text)\n\n @staticmethod\n def _get_options(r):\n soup = BeautifulSoup(r.text, 'lxml')\n options = [h.text for h in soup.find_all('a', href=True)\n if h.text.endswith(('.zip', 'tar.gz'))]\n return options\n\n def _present_options(self):\n r = requests.get(self.url)\n options = self._get_options(r)\n print('Options:\\n')\n for i, o in enumerate(options):\n print(\"{0}: {1}\".format(i, o))\n choice = input(\"\\nPlease enter the number of the \"\n \"dataset above you wish to download:\")\n return options[int(choice)]\n\n def _download_data(self, dataset_url, save_path):\n if not isdir(save_path):\n os.makedirs(save_path)\n\n base = basename(dataset_url)\n temp_save_path = join(save_path, base)\n\n with open(temp_save_path, \"wb\") as f:\n r = requests.get(dataset_url)\n f.write(r.content)\n\n if base.endswith('.tar.gz'):\n obj = tarfile.open(temp_save_path)\n elif base.endswith('.zip'):\n obj = ZipFile(temp_save_path, 'r')\n else:\n raise ValueError(\"Unknown File Type: {0}.\".format(base))\n\n self._print(\"Unpacking Data...\")\n obj.extractall(save_path)\n obj.close()\n os.remove(temp_save_path)\n\n def get(self, save_path, dataset=None):\n \"\"\"\n\n Download a dataset.\n\n Args:\n save_path : str\n A directory to save the data to.\n dataset : str, optional\n A specific dataset to download.\n Note: this must include the file extension.\n If None, options will be presented for you\n to choose from.\n\n Returns:\n save_path_full : str\n The absolute path to the downloaded data.\n\n \"\"\"\n if dataset is None:\n selected_dataset = self._present_options()\n else:\n selected_dataset = dataset\n\n save_path_full = join(save_path, selected_dataset.split('.')[0])\n\n if isdir(save_path_full):\n warn(\"\\n'{0}' already exists. Voiding Download.\".format(\n save_path_full))\n else:\n self._print('Downloading Data...')\n url = \"{0}/{1}\".format(self.url, selected_dataset)\n self._download_data(url, save_path=save_path)\n\n return abspath(save_path_full)\n" }, { "path": "util/html.py", "content": "import dominate\nfrom dominate.tags import *\nimport os\n\n\nclass HTML:\n def __init__(self, web_dir, title, reflesh=0):\n self.title = title\n self.web_dir = web_dir\n self.img_dir = os.path.join(self.web_dir, 'images')\n if not os.path.exists(self.web_dir):\n os.makedirs(self.web_dir)\n if not os.path.exists(self.img_dir):\n os.makedirs(self.img_dir)\n # print(self.img_dir)\n\n self.doc = dominate.document(title=title)\n if reflesh > 0:\n with self.doc.head:\n meta(http_equiv=\"reflesh\", content=str(reflesh))\n\n def get_image_dir(self):\n return self.img_dir\n\n def add_header(self, str):\n with self.doc:\n h3(str)\n\n def add_table(self, border=1):\n self.t = table(border=border, style=\"table-layout: fixed;\")\n self.doc.add(self.t)\n\n def add_images(self, ims, txts, links, width=400):\n self.add_table()\n with self.t:\n with tr():\n for im, txt, link in zip(ims, txts, links):\n with td(style=\"word-wrap: break-word;\", halign=\"center\", valign=\"top\"):\n with p():\n with a(href=os.path.join('images', link)):\n img(style=\"width:%dpx\" % width, src=os.path.join('images', im))\n br()\n p(txt)\n\n def save(self):\n html_file = '%s/index.html' % self.web_dir\n f = open(html_file, 'wt')\n f.write(self.doc.render())\n f.close()\n\n\nif __name__ == '__main__':\n html = HTML('web/', 'test_html')\n html.add_header('hello world')\n\n ims = []\n txts = []\n links = []\n for n in range(4):\n ims.append('image_%d.png' % n)\n txts.append('text_%d' % n)\n links.append('image_%d.png' % n)\n html.add_images(ims, txts, links)\n html.save()\n" }, { "path": "util/image_pool.py", "content": "import random\nimport torch\n\n\nclass ImagePool():\n def __init__(self, pool_size):\n self.pool_size = pool_size\n if self.pool_size > 0:\n self.num_imgs = 0\n self.images = []\n\n def query(self, images):\n if self.pool_size == 0:\n return images\n return_images = []\n for image in images:\n image = torch.unsqueeze(image.data, 0)\n if self.num_imgs < self.pool_size:\n self.num_imgs = self.num_imgs + 1\n self.images.append(image)\n return_images.append(image)\n else:\n p = random.uniform(0, 1)\n if p > 0.5:\n random_id = random.randint(0, self.pool_size - 1) # randint is inclusive\n tmp = self.images[random_id].clone()\n self.images[random_id] = image\n return_images.append(tmp)\n else:\n return_images.append(image)\n return_images = torch.cat(return_images, 0)\n return return_images\n" }, { "path": "util/util.py", "content": "from __future__ import print_function\nimport torch\nimport numpy as np\nfrom PIL import Image\nimport os\n\n\ndef sdmkdir(dir_name):\n if not os.path.exists(dir_name):\n os.mkdir(dir_name)\n# Converts a Tensor into an image array (numpy)\n# |imtype|: the desired type of the converted numpy array\ndef tensor2im(input_image, imtype=np.uint8,scale=None):\n \n if len(input_image.shape)<3: return None\n # if scale>0 and input_image.size()[1]==3:\n # return tensor2im_logc(input_image, imtype=np.uint8,scale=scale)\n\n if isinstance(input_image, torch.Tensor):\n image_tensor = input_image.data\n else:\n return input_image\n image_numpy = image_tensor.data[0].cpu().float().numpy()\n if image_numpy.shape[0] == 1:\n image_numpy = np.tile(image_numpy, (3, 1, 1))\n image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0\n image_numpy[image_numpy<0] = 0\n image_numpy[image_numpy>255] = 255\n return image_numpy.astype(imtype)\n\ndef tensor2im_logc(image_tensor, imtype=np.uint8,scale=255):\n image_numpy = image_tensor.data[0].cpu().double().numpy()\n image_numpy = np.transpose(image_numpy,(1,2,0))\n image_numpy = (image_numpy+1) /2.0 \n image_numpy = image_numpy * (np.log(scale+1)) \n image_numpy = np.exp(image_numpy) -1\n image_numpy = image_numpy.astype(np.uint8)\n\n return image_numpy.astype(np.uint8)\n\n\ndef diagnose_network(net, name='network'):\n mean = 0.0\n count = 0\n for param in net.parameters():\n if param.grad is not None:\n mean += torch.mean(torch.abs(param.grad.data))\n count += 1\n if count > 0:\n mean = mean / count\n print(name)\n print(mean)\n\n\ndef save_image(image_numpy, image_path):\n image_pil = Image.fromarray(image_numpy)\n image_pil.save(image_path)\n\n\ndef print_numpy(x, val=True, shp=False):\n x = x.astype(np.float64)\n if shp:\n print('shape,', x.shape)\n if val:\n x = x.flatten()\n print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % (\n np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x)))\n\n\ndef mkdirs(paths):\n if isinstance(paths, list) and not isinstance(paths, str):\n for path in paths:\n mkdir(path)\n else:\n mkdir(paths)\n\n\ndef mkdir(path):\n if not os.path.exists(path):\n os.makedirs(path)\n" }, { "path": "util/visualizer.py", "content": "import numpy as np\nimport os\nimport ntpath\nimport time\nfrom . import util\nfrom . import html\nfrom pdb import set_trace as st\nclass Visualizer():\n def __init__(self, opt):\n # self.opt = opt\n self.display_id = opt.display_id\n self.use_html = (opt.isTrain or opt.isTrainMatte) and not opt.no_html\n\n self.win_size = opt.display_winsize\n self.name = opt.name\n if self.display_id > 0:\n import visdom\n self.vis = visdom.Visdom(server = opt.display_server,port = opt.display_port)\n #self.ncols = opt.ncols\n self.ncols = opt.display_ncols\n if self.use_html:\n self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web')\n self.img_dir = os.path.join(self.web_dir, 'images')\n print('create web directory %s...' % self.web_dir)\n util.mkdirs([self.web_dir, self.img_dir])\n self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')\n self.log_name_test = os.path.join(opt.checkpoints_dir, opt.name, 'test_log.txt')\n with open(self.log_name, \"a\") as log_file:\n now = time.strftime(\"%c\")\n log_file.write('================ Training Loss (%s) ================\\n' % now)\n\n # |visuals|: dictionary of images to display or save\n def display_current_results(self, visuals, epoch):\n if self.display_id > 0: # show images in the browser\n ncols = self.ncols\n if self.ncols > 0:\n h, w = next(iter(visuals.values())).shape[:2]\n table_css = \"\"\"\"\"\" % (w, h)\n ncols = self.ncols\n title = self.name\n label_html = ''\n label_html_row = ''\n nrows = int(np.ceil(len(visuals.items()) / ncols))\n images = []\n idx = 0\n for label, image_numpy in visuals.items():\n label_html_row += '%s' % label\n images.append(image_numpy.transpose([2, 0, 1]))\n idx += 1\n if idx % ncols == 0:\n label_html += '%s' % label_html_row\n label_html_row = ''\n '''\n white_image = np.ones_like(image_numpy.transpose([2, 0, 1]))*255\n while idx % ncols != 0:\n images.append(white_image)\n label_html_row += ''\n idx += 1\n '''\n if label_html_row != '':\n label_html += '%s' % label_html_row\n # pane col = image row\n self.vis.images(images, nrow=ncols, win=self.display_id + 1,\n padding=2, opts=dict(title=title + ' images'))\n #label_html = '%s
' % label_html\n #self.vis.text(table_css + label_html, win = self.display_id + 2,\n # opts=dict(title=title + ' labels'))\n else:\n idx = 1\n for label, image_numpy in visuals.items():\n self.vis.image(image_numpy.transpose([2,0,1]), opts=dict(title=label),\n win=self.display_id + idx)\n idx += 1\n\n if self.use_html: # save images to a html file\n for label, image_numpy in visuals.items():\n img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.png' % (epoch, label))\n util.save_image(image_numpy, img_path)\n # update website\n webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, reflesh=1)\n for n in range(epoch, 0, -1):\n webpage.add_header('epoch [%d]' % n)\n ims = []\n txts = []\n links = []\n\n for label, image_numpy in visuals.items():\n img_path = 'epoch%.3d_%s.png' % (n, label)\n ims.append(img_path)\n txts.append(label)\n links.append(img_path)\n webpage.add_images(ims, txts, links, width=self.win_size)\n webpage.save()\n\n # errors: dictionary of error labels and values\n def plot_current_losses(self, epoch, counter_ratio, opt, errors):\n if not hasattr(self, 'plot_data_train'):\n self.plot_data_train = {'X':[],'Y':[], 'legend':list(errors.keys())}\n self.plot_data_train['X'].append(epoch + counter_ratio)\n self.plot_data_train['Y'].append([errors[k] for k in self.plot_data_train['legend']])\n self.vis.line(\n X=np.stack([np.array(self.plot_data_train['X'])]*len(self.plot_data_train['legend']),1),\n Y=np.array(self.plot_data_train['Y']),\n opts={\n 'title': self.name + ' loss over time',\n 'legend': self.plot_data_train['legend'],\n 'xlabel': 'epoch',\n 'ylabel': 'loss'},\n win=self.display_id)\n\n def plot_test_errors(self, epoch, counter_ratio, opt, errors):\n if not hasattr(self, 'plot_data'):\n self.plot_data = {'X':[],'Y':[], 'legend':list(errors.keys())}\n self.plot_data['X'].append(epoch + counter_ratio)\n self.plot_data['Y'].append([errors[k] for k in self.plot_data['legend']])\n self.vis.line(\n X=np.stack([np.array(self.plot_data['X'])]*len(self.plot_data['legend']),1),\n Y=np.array(self.plot_data['Y']),\n opts={\n 'title': self.name + ' loss over time',\n 'legend': self.plot_data['legend'],\n 'xlabel': 'epoch',\n 'ylabel': 'loss'},\n win=self.display_id+10)\n message = '(epoch: %d)' %(epoch)\n for k, v in errors.items():\n message += '%s: %.3f ' % (k, v)\n\n print(message)\n with open(self.log_name_test, \"a\") as log_file:\n log_file.write('%s\\n' % message)\n \n # errors: same format as |errors| of plotCurrentErrors\n def print_current_errors(self, epoch, i, errors, t):\n message = '(epoch: %d, iters: %d, time: %.3f) ' % (epoch, i, t)\n for k, v in errors.items():\n message += '%s: %.3f ' % (k, v)\n\n print(message)\n with open(self.log_name, \"a\") as log_file:\n log_file.write('%s\\n' % message)\n\n # save image to the disk\n def save_images(self, webpage, visuals, image_path):\n image_dir = webpage.get_image_dir()\n short_path = ntpath.basename(image_path[0])\n name = os.path.splitext(short_path)[0]\n\n webpage.add_header(name)\n ims = []\n txts = []\n links = []\n\n for label, image_numpy in visuals.items():\n image_name = '%s_%s.png' % (name, label)\n save_path = os.path.join(image_dir, image_name)\n util.save_image(image_numpy, save_path)\n\n ims.append(image_name)\n txts.append(label)\n links.append(image_name)\n webpage.add_images(ims, txts, links, width=self.win_size)\n" } ]