[ { "path": ".gitignore", "content": "#cmake related files\nCMakeCache.txt\nCMakeFiles\nCMakeScripts\nMakefile\ncmake_install.cmake\ninstall_manifest.txt\nCTestTestfile.cmake\n#ignore all bin dirs\nbin/\n.DS_Store\n*.vtp\n*.pathlinesinfo\n*.pyc\n*.pyo\n*~\n*.bak\n*.swp\n*.o\n*.nii.gz\n*.mha\n*.raw\n*.mhd\n*.a\n*.orig # git merge conflict file\n# model file\n*.pth\n*.p\n\n### following copied from https://gist.github.com/octocat/9257657\n# Compiled source #\n###################\n*.com\n*.class\n*.dll\n*.exe\n*.o\n*.so\n\n# Packages #\n############\n# it's better to unpack these files and commit the raw source\n# git has its own built in compression methods\n*.7z\n*.dmg\n*.gz\n*.iso\n*.jar\n*.rar\n*.tar\n*.zip\n\n# Logs and databases #\n######################\n*.log\n*.sql\n*.sqlite\n\n# OS generated files #\n######################\n.DS_Store\n.DS_Store?\n._*\n.Spotlight-V100\n.Trashes\nehthumbs.db\nThumbs.db\n\n# Ignore data and experiments dirs, but keep folder organizations#\n######################\nexperiments\n" }, { "path": "LICENSE", "content": "“Commons Clause” License Condition v1.0\n\nThe Software is provided to you by the Licensor under the\nLicense, as defined below, subject to the following condition.\n\nWithout limiting other conditions in the License, the grant\nof rights under the License will not include, and the License\ndoes not grant to you, the right to Sell the Software.\n\nFor purposes of the foregoing, “Sell” means practicing any or\nall of the rights granted to you under the License to provide\nto third parties, for a fee or other consideration (including\nwithout limitation fees for hosting or consulting/ support\nservices related to the Software), a product or service whose\nvalue derives, entirely or substantially, from the functionality\nof the Software. Any license notice or attribution required by\nthe License must also include this Commons Clause License\nCondition notice.\n\nSoftware: COVNet\n\nLicense: Apache 2.0\n\nLicensor: CuraCloud Corporation\n\n\n---------------------------------------------------------------------\n\n\n Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. Definitions.\n\n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n\n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n\n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. 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We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\n Copyright [yyyy] [name of copyright owner]\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n\n" }, { "path": "README.md", "content": "# COVID-19 Detection Neural Network (COVNet)\nThis is a PyTorch implementation of the paper \"[Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT](https://pubs.rsna.org/doi/10.1148/radiol.2020200905)\". It supports training, validation and testing for COVNet.\n\n\n\n### Updates & Notices\n- 2020-03-30: Thanks for the interest in our work. Unfortunately, we do not own the data, and we have to get permission from our collaborators before we share the data and model. We will update later.\n\n## Citation\nIf you find this code is useful for your research, please consider citing:\n```\n@article{li2020artificial,\n title={Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT},\n author={Li, Lin and Qin, Lixin and Xu, Zeguo and Yin, Youbing and Wang, Xin and Kong, Bin and Bai, Junjie and Lu, Yi and Fang, Zhenghan and Song, Qi and Cao, Kunlin and others},\n journal={Radiology},\n year={2020}\n}\n```\n\n## Setup\n### Prerequisites\n- Anaconda 3.7\n- PyTorch 1.4\n- SimpleITK\n- batchgenerators\n- tensorboardX\n\n### Prepare data\nPreprocess the data according to the [Appendix E1 section](https://pubs.rsna.org/doi/suppl/10.1148/radiol.2020200905/suppl_file/ry_200905_supp_in%20press.pdf) of the paper and organize them as the following. A example of train.csv and val.csv are also provided.\n```\ndata\n├── caseid1\n| ├── masked_ct.nii\n| └── mask.nii.gz\n├── caseid2\n| ├── masked_ct.nii\n| └── mask.nii.gz\n├── caseid3\n| ├── masked_ct.nii\n| └── mask.nii.gz\n├── caseid4\n| ├── masked_ct.nii\n| └── mask.nii.gz\n├── train.csv\n└── val.csv\n```\n\n## COVNet\n\n\n### Training\nTraining a COVNet with default arguments. Model checkpoints and tensorboard logs are written out to a unique directory created by default within `experiments/models` and `experiments/logs` respectively after starting training.\n```\npython main.py\n```\n\n### Validation and Testing\nYou can run validation and testing on the checkpointed best model by:\n```\npython test.py\n```\n" }, { "path": "config.py", "content": "import argparse\n\n\ndef parse_arguments():\n \"\"\"Argument Parser for the commandline argments\n :returns: command line arguments\n\n \"\"\"\n ##########################################################################\n # Training setting #\n ##########################################################################\n parser = argparse.ArgumentParser()\n parser.add_argument('--n_classes', help=\"Number of classes\", type=int,\n default=3)\n parser.add_argument('--lr', type=float, default=1e-5)\n parser.add_argument('--epochs', type=int, default=50)\n parser.add_argument('--lr_scheduler', type=str,\n default='plateau', choices=['plateau', 'step'])\n parser.add_argument('--gamma', type=float,\n help='LR Multiplicative factor if lr_scheduler is step',\n default=0.1)\n parser.add_argument('--patience', type=int, default=9)\n parser.add_argument('--log-every', type=int, default=100)\n parser.add_argument('--save-model', type=bool, default=True)\n args = parser.parse_args()\n\n return args\n" }, { "path": "data/train.csv", "content": "case,label\ncaseid1,0\ncaseid2,1\ncaseid3,2\n" }, { "path": "data/val.csv", "content": "case,label\ncaseid4,0\n" }, { "path": "dataset.py", "content": "import os\nimport pandas as pd\nimport numpy as np\n\nimport torch\nimport torch.utils.data as data\nimport SimpleITK as sitk\nfrom batchgenerators.transforms import noise_transforms\nfrom batchgenerators.transforms import spatial_transforms\n\n\nclass NCovDataset(data.Dataset):\n def __init__(self, root_dir, stage='train'):\n super().__init__()\n self.root_dir = root_dir\n self.stage = stage\n assert stage in ['train', 'val', 'test']\n\n if stage == 'train':\n split_file = 'train.csv'\n elif stage == 'val':\n split_file = 'val.csv'\n elif stage == 'test':\n # We just assume validation set is the same as test set\n split_file = 'val.csv'\n\n df = pd.read_csv(os.path.join(root_dir, split_file),\n converters={'case': str, 'label': int})\n df = df.set_index('case')\n self.case_ids = list(df.index)\n self.labels = df['label'].values.tolist()\n\n def __len__(self):\n return len(self.case_ids)\n\n def __getitem__(self, index):\n fn = os.path.join(self.root_dir, self.case_ids[index], 'masked_ct.nii')\n image = sitk.ReadImage(fn)\n array = sitk.GetArrayFromImage(image)\n\n mask_fn = os.path.join(self.root_dir, self.case_ids[index],\n 'mask.nii.gz')\n mask_image = sitk.ReadImage(mask_fn)\n mask = sitk.GetArrayFromImage(mask_image)\n\n array, mask = array[None, ...], mask[None, ...]\n if self.stage == 'train':\n # Default randomly mirroring the second and third axes\n array, mask = spatial_transforms.augment_mirroring(\n array, sample_seg=mask, axes=(1, 2))\n array, mask = array[0], mask[0]\n\n ######################################################\n # Preprocessing for both train and validation data #\n ######################################################\n min_value, max_value = -1250, 250\n np.clip(array, min_value, max_value, out=array)\n array = (array - min_value) / (max_value - min_value)\n\n # data should be a numpy array with shape [x, y, z] or [c, x, y, z]\n # seg should be a numpy array with shape [x, y, z]\n full_channel = np.stack([array, array, array])\n\n if self.stage == 'train':\n full_channel, mask = self.do_augmentation(full_channel, mask)\n else:\n mask = mask[None, ...]\n\n # remove the noise in the non-lung regions\n mask = mask[0]\n full_channel[0][mask == 0] = 0\n full_channel[1][mask == 0] = 0\n full_channel[2][mask == 0] = 0\n label = self.labels[index]\n full_channel = torch.FloatTensor(full_channel).permute((1, 0, 2, 3))\n\n return full_channel, label, self.case_ids[index]\n\n def do_augmentation(self, array, mask):\n \"\"\"Augmentation for the training data.\n\n :array: A numpy array of size [c, x, y, z]\n :returns: augmented image and the corresponding mask\n\n \"\"\"\n # normalize image to range [0, 1], then apply this transform\n patch_size = np.asarray(array.shape)[1:]\n augmented = noise_transforms.augment_gaussian_noise(\n array, noise_variance=(0, .015))\n\n # need to become [bs, c, x, y, z] before augment_spatial\n augmented = augmented[None, ...]\n mask = mask[None, None, ...]\n r_range = (0, (3 / 360.) * 2 * np.pi)\n cval = 0.\n\n augmented, mask = spatial_transforms.augment_spatial(\n augmented, seg=mask, patch_size=patch_size,\n do_elastic_deform=True, alpha=(0., 100.), sigma=(8., 13.),\n do_rotation=True, angle_x=r_range, angle_y=r_range, angle_z=r_range,\n do_scale=True, scale=(.9, 1.1),\n border_mode_data='constant', border_cval_data=cval,\n order_data=3,\n p_el_per_sample=0.5,\n p_scale_per_sample=.5,\n p_rot_per_sample=.5,\n random_crop=False\n )\n mask = mask[0]\n return augmented[0], mask\n\n def make_weights_for_balanced_classes(self):\n \"\"\"Making sampling weights for the data samples\n :returns: sampling weigghts for dealing with class imbalance problem\n\n \"\"\"\n n_samples = len(self.labels)\n unique, cnts = np.unique(self.labels, return_counts=True)\n cnt_dict = dict(zip(unique, cnts))\n\n weights = []\n for label in self.labels:\n weights.append(n_samples / float(cnt_dict[label]))\n return weights\n" }, { "path": "main.py", "content": "import os\nimport numpy as np\nimport time\n\nfrom tensorboardX import SummaryWriter\nimport torch\nimport torch.optim as optim\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nimport dataset\nimport model\nimport config\nimport utils\n\n\ndef get_lr(optimizer):\n for param_group in optimizer.param_groups:\n return param_group['lr']\n\n\ndef train_model(model, train_loader, epoch, num_epochs, optimizer, writer,\n current_lr, log_every=100):\n n_classes = model.n_classes\n metric = torch.nn.CrossEntropyLoss()\n\n y_probs = np.zeros((0, n_classes), np.float)\n losses, y_trues = [], []\n model.train()\n\n for m in model.modules():\n if isinstance(m, nn.BatchNorm2d):\n m.train()\n m.weight.requires_grad = False\n m.bias.requires_grad = False\n\n for i, (image, label, case_id) in enumerate(train_loader):\n optimizer.zero_grad()\n if torch.cuda.is_available():\n image = image.cuda()\n label = label.cuda()\n\n prediction = model.forward(image.float())\n loss = metric(prediction, label.long())\n loss.backward()\n optimizer.step()\n\n loss_value = loss.item()\n losses.append(loss_value)\n y_prob = F.softmax(prediction, dim=1)\n y_probs = np.concatenate([y_probs, y_prob.detach().cpu().numpy()])\n y_trues.append(label.item())\n\n metric_collects = utils.calc_multi_cls_measures(y_probs, y_trues)\n n_iter = epoch * len(train_loader) + i\n writer.add_scalar('Train/Loss', loss_value, n_iter)\n\n if (i % log_every == 0) & (i > 0):\n utils.print_progress(epoch + 1, num_epochs, i, len(train_loader),\n np.mean(losses), current_lr, metric_collects)\n\n train_loss_epoch = np.round(np.mean(losses), 4)\n return train_loss_epoch, metric_collects\n\n\ndef evaluate_model(model, val_loader, epoch, num_epochs, writer, current_lr,\n log_every=20):\n n_classes = model.n_classes\n metric = torch.nn.CrossEntropyLoss()\n\n model.eval()\n for m in model.modules():\n if isinstance(m, nn.BatchNorm2d):\n m.train()\n m.weight.requires_grad = False\n m.bias.requires_grad = False\n\n y_probs = np.zeros((0, n_classes), np.float)\n losses, y_trues = [], []\n\n for i, (image, label, case_id) in enumerate(val_loader):\n\n if torch.cuda.is_available():\n image = image.cuda()\n label = label.cuda()\n\n prediction = model.forward(image.float())\n loss = metric(prediction, label.long())\n\n loss_value = loss.item()\n losses.append(loss_value)\n y_prob = F.softmax(prediction, dim=1)\n y_probs = np.concatenate([y_probs, y_prob.detach().cpu().numpy()])\n y_trues.append(label.item())\n\n metric_collects = utils.calc_multi_cls_measures(y_probs, y_trues)\n\n n_iter = epoch * len(val_loader) + i\n writer.add_scalar('Val/Loss', loss_value, n_iter)\n\n if (i % log_every == 0) & (i > 0):\n prefix = '*Val|'\n utils.print_progress(epoch + 1, num_epochs, i, len(val_loader),\n np.mean(losses), current_lr, metric_collects,\n prefix=prefix)\n\n val_loss_epoch = np.round(np.mean(losses), 4)\n return val_loss_epoch, metric_collects\n\n\ndef main(args):\n \"\"\"Main function for the training pipeline\n\n :args: commandlien arguments\n :returns: None\n\n \"\"\"\n ##########################################################################\n # Basic settings #\n ##########################################################################\n exp_dir = 'experiments'\n log_dir = os.path.join(exp_dir, 'logs')\n model_dir = os.path.join(exp_dir, 'models')\n os.makedirs(model_dir, exist_ok=True)\n\n ##########################################################################\n # Define all the necessary variables for model training and evaluation #\n ##########################################################################\n writer = SummaryWriter(log_dir)\n train_dataset = dataset.NCovDataset('data/', stage='train')\n weights = train_dataset.make_weights_for_balanced_classes()\n weights = torch.DoubleTensor(weights)\n sampler = torch.utils.data.sampler.WeightedRandomSampler(\n weights, len(train_dataset.case_ids))\n\n train_loader = torch.utils.data.DataLoader(\n train_dataset, batch_size=1, num_workers=20,\n drop_last=False, sampler=sampler)\n\n val_dataset = dataset.NCovDataset('data/', stage='val')\n val_loader = torch.utils.data.DataLoader(\n val_dataset, batch_size=1, shuffle=False, num_workers=11,\n drop_last=False)\n\n cov_net = model.COVNet(n_classes=args.n_classes)\n if torch.cuda.is_available():\n cov_net = cov_net.cuda()\n optimizer = optim.Adam(cov_net.parameters(), lr=args.lr, weight_decay=0.1)\n\n if args.lr_scheduler == \"plateau\":\n scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n optimizer, patience=3, factor=.3, threshold=1e-4, verbose=True)\n elif args.lr_scheduler == \"step\":\n scheduler = torch.optim.lr_scheduler.StepLR(\n optimizer, step_size=3, gamma=args.gamma)\n\n best_val_loss = float('inf')\n best_val_accu = float(0)\n\n iteration_change_loss = 0\n t_start_training = time.time()\n ##########################################################################\n # Main training loop #\n ##########################################################################\n for epoch in range(args.epochs):\n current_lr = get_lr(optimizer)\n t_start = time.time()\n\n ############################################################\n # The actual training and validation step for each epoch #\n ############################################################\n train_loss, train_metric = train_model(\n cov_net, train_loader, epoch, args.epochs, optimizer, writer,\n current_lr, args.log_every)\n\n with torch.no_grad():\n val_loss, val_metric = evaluate_model(\n cov_net, val_loader, epoch, args.epochs, writer, current_lr)\n\n ##############################\n # Adjust the learning rate #\n ##############################\n if args.lr_scheduler == 'plateau':\n scheduler.step(val_loss)\n elif args.lr_scheduler == 'step':\n scheduler.step()\n\n t_end = time.time()\n delta = t_end - t_start\n\n utils.print_epoch_progress(train_loss, val_loss, delta, train_metric,\n val_metric)\n iteration_change_loss += 1\n print('-' * 30)\n\n train_acc, val_acc = train_metric['accuracy'], val_metric['accuracy']\n file_name = ('train_acc_{}_val_acc_{}_epoch_{}.pth'.\n format(train_acc, val_acc, epoch))\n torch.save(cov_net, os.path.join(model_dir, file_name))\n\n if val_acc > best_val_accu:\n best_val_accu = val_acc\n if bool(args.save_model):\n torch.save(cov_net, os.path.join(model_dir, 'best.pth'))\n\n if val_loss < best_val_loss:\n best_val_loss = val_loss\n iteration_change_loss = 0\n\n if iteration_change_loss == args.patience:\n print(('Early stopping after {0} iterations without the decrease ' +\n 'of the val loss').format(iteration_change_loss))\n break\n t_end_training = time.time()\n print('training took {}s'.\n format(t_end_training - t_start_training))\n\n\nif __name__ == \"__main__\":\n args = config.parse_arguments()\n main(args)\n" }, { "path": "model.py", "content": "import torch\nimport torch.nn as nn\nfrom torchvision import models\n\n\nclass COVNet(nn.Module):\n def __init__(self, n_classes):\n super().__init__()\n model = models.resnet50(pretrained=True)\n layer_list = list(model.children())[:-2]\n self.pretrained_model = nn.Sequential(*layer_list)\n\n self.pooling_layer = nn.AdaptiveAvgPool2d(1)\n self.classifer = nn.Linear(2048, n_classes)\n self.n_classes = n_classes\n\n def forward(self, x):\n x = torch.squeeze(x, dim=0)\n features = self.pretrained_model(x)\n pooled_features = self.pooling_layer(features)\n pooled_features = pooled_features.view(pooled_features.size(0), -1)\n flattened_features = torch.max(pooled_features, 0, keepdim=True)[0]\n output = self.classifer(flattened_features)\n return output\n" }, { "path": "test.py", "content": "import os\nfrom tqdm import tqdm\nimport numpy as np\n\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch\n\nimport model\nimport config\nimport dataset\nimport utils\n\n\ndef evaluate_model(model, val_loader):\n metric = torch.nn.CrossEntropyLoss()\n model.eval()\n\n for m in model.modules():\n if isinstance(m, nn.BatchNorm2d):\n m.train()\n m.weight.requires_grad = False\n m.bias.requires_grad = False\n\n y_probs = np.zeros((0, 3), np.float)\n losses, y_trues = [], []\n\n for i, (image, label, case_id) in enumerate(tqdm(val_loader)):\n if torch.cuda.is_available():\n image = image.cuda()\n label = label.cuda()\n\n prediction = model.forward(image.float())\n loss = metric(prediction, label.long())\n\n loss_value = loss.item()\n losses.append(loss_value)\n y_prob = F.softmax(prediction, dim=1).detach().cpu().numpy()\n\n y_probs = np.concatenate([y_probs, y_prob])\n y_trues.append(label.item())\n metric_collects = utils.calc_multi_cls_measures(y_probs, y_trues)\n val_loss_epoch = np.mean(losses)\n return val_loss_epoch, metric_collects\n\n\ndef main(args):\n \"\"\"Main function for the testing pipeline\n\n :args: commandline arguments\n :returns: None\n\n \"\"\"\n ##########################################################################\n # Basic settings #\n ##########################################################################\n exp_dir = 'experiments'\n model_dir = os.path.join(exp_dir, 'models')\n model_file = os.path.join(model_dir, 'best.pth')\n val_dataset = dataset.NCovDataset('data/', stage='val')\n val_loader = torch.utils.data.DataLoader(\n val_dataset, batch_size=1, shuffle=False, num_workers=11,\n drop_last=False)\n\n cov_net = model.COVNet(n_classes=args.n_classes)\n if torch.cuda.is_available():\n cov_net.cuda()\n\n state = torch.load(model_file)\n cov_net.load_state_dict(state.state_dict())\n\n with torch.no_grad():\n val_loss, metric_collects = evaluate_model(cov_net, val_loader)\n prefix = '******Evaluate******'\n utils.print_progress(mean_loss=val_loss, metric_collects=metric_collects,\n prefix=prefix)\n\n\nif __name__ == \"__main__\":\n args = config.parse_arguments()\n main(args)\n" }, { "path": "utils.py", "content": "import numpy as np\nimport pandas as pd\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score\n\n\ndef calc_multi_cls_measures(probs, label):\n \"\"\"Calculate multi-class classification measures (Accuracy, precision,\n Recall, AUC.\n\n :probs: NxC numpy array storing probabilities for each case\n :label: ground truth label\n :returns: a dictionary of accuracy, precision and recall\n\n \"\"\"\n n_classes = probs.shape[1]\n preds = np.argmax(probs, axis=1)\n accuracy = accuracy_score(label, preds)\n precisions = precision_score(label, preds, average=None,\n labels=range(n_classes), zero_division=0.)\n recalls = recall_score(label, preds, average=None, labels=range(n_classes),\n zero_division=0.)\n\n metric_collects = {'accuracy': accuracy, 'precisions': precisions,\n 'recalls': recalls}\n return metric_collects\n\n\ndef print_progress(epoch=None, n_epoch=None, n_iter=None, iters_one_batch=None,\n mean_loss=None, cur_lr=None, metric_collects=None,\n prefix=None):\n \"\"\"Print the training progress.\n\n :epoch: epoch number\n :n_epoch: total number of epochs\n :n_iter: current iteration number\n :mean_loss: mean loss of current batch\n :iters_one_batch: number of iterations per batch\n :cur_lr: current learning rate\n :metric_collects: dictionary returned by function calc_multi_cls_measures\n :returns: None\n\n \"\"\"\n accuracy = metric_collects['accuracy']\n precisions = metric_collects['precisions']\n recalls = metric_collects['recalls']\n\n n_classes = len(precisions)\n\n log_str = ''\n if epoch is not None:\n log_str += 'Ep: {0}/{1}|'.format(epoch, n_epoch)\n\n if n_iter is not None:\n log_str += 'It: {0}/{1}|'.format(n_iter, iters_one_batch)\n\n if mean_loss is not None:\n log_str += 'Loss: {0:.4f}|'.format(mean_loss)\n\n log_str += 'Acc: {:.4f}|'.format(accuracy)\n templ = 'Pr: ' + ', '.join(['{:.4f}'] * (n_classes-1)) + '|'\n log_str += templ.format(*(precisions[1:].tolist()))\n templ = 'Re: ' + ', '.join(['{:.4f}'] * (n_classes-1)) + '|'\n log_str += templ.format(*(recalls[1:].tolist()))\n\n if cur_lr is not None:\n log_str += 'lr: {0}'.format(cur_lr)\n log_str = log_str if prefix is None else prefix + log_str\n print(log_str)\n\n\ndef print_epoch_progress(train_loss, val_loss, time_duration, train_metric,\n val_metric):\n \"\"\"Print all the information after each epoch.\n\n :train_loss: average training loss\n :val_loss: average validation loss\n :time_duration: time duration for current epoch\n :train_metric_collects: a performance dictionary for training\n :val_metric_collects: a performance dictionary for validation\n :returns: None\n\n \"\"\"\n train_acc, val_acc = train_metric['accuracy'], val_metric['accuracy']\n train_prec, val_prec = train_metric['precisions'], val_metric['precisions']\n train_recalls, val_recalls = train_metric['recalls'], val_metric['recalls']\n log_str = 'Train/Val| Loss: {:.4f}/{:.4f}|'.format(train_loss, val_loss)\n log_str += 'Acc: {:.4f}/{:.4f}|'.format(train_acc, val_acc)\n\n n_classes = len(train_prec)\n\n templ = 'Pr: ' + ', '.join(['{:.4f}'] * (n_classes-1)) + '/'\n log_str += templ.format(*(train_prec[1:].tolist()))\n templ = ', '.join(['{:.4f}'] * (n_classes-1)) + '|'\n log_str += templ.format(*(val_prec[1:].tolist()))\n\n templ = 'Re: ' + ', '.join(['{:.4f}'] * (n_classes - 1)) + '/'\n log_str += templ.format(*(train_recalls[1:].tolist()))\n templ = ', '.join(['{:.4f}'] * (n_classes - 1)) + '|'\n log_str += templ.format(*(val_recalls[1:].tolist()))\n log_str += 'T(s) {:.2f}'.format(time_duration)\n print(log_str)\n" } ]