{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":26680,"databundleVersionId":2283525,"sourceType":"competition"},{"sourceId":1677248,"sourceType":"datasetVersion","datasetId":993628}],"dockerImageVersionId":30407,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\n# import os\n# for dirname, _, filenames in os.walk('/kaggle/input'):\n# for filename in filenames:\n# print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-09-28T15:12:19.209493Z","iopub.execute_input":"2023-09-28T15:12:19.209825Z","iopub.status.idle":"2023-09-28T15:12:19.242454Z","shell.execute_reply.started":"2023-09-28T15:12:19.209793Z","shell.execute_reply":"2023-09-28T15:12:19.241467Z"},"trusted":true},"outputs":[],"execution_count":1},{"cell_type":"code","source":"!pip install -U pylibjpeg pylibjpeg-openjpeg pylibjpeg-libjpeg pydicom ","metadata":{"execution":{"iopub.status.busy":"2023-09-24T13:51:20.140298Z","iopub.execute_input":"2023-09-24T13:51:20.140701Z","iopub.status.idle":"2023-09-24T13:51:37.182938Z","shell.execute_reply.started":"2023-09-24T13:51:20.140645Z","shell.execute_reply":"2023-09-24T13:51:37.181613Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Collecting pylibjpeg\n Downloading pylibjpeg-1.4.0-py3-none-any.whl (28 kB)\nCollecting pylibjpeg-openjpeg\n Downloading pylibjpeg_openjpeg-1.3.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.4 MB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/1.4 MB\u001b[0m \u001b[31m26.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n\u001b[?25hCollecting pylibjpeg-libjpeg\n Downloading pylibjpeg_libjpeg-1.3.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.3 MB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.3/4.3 MB\u001b[0m \u001b[31m51.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hRequirement already satisfied: pydicom in /opt/conda/lib/python3.7/site-packages (2.3.1)\nCollecting pydicom\n Downloading pydicom-2.4.3-py3-none-any.whl (1.8 MB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m50.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hRequirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from pylibjpeg) (1.21.6)\nInstalling collected packages: pylibjpeg-openjpeg, pylibjpeg-libjpeg, pylibjpeg, pydicom\n Attempting uninstall: pydicom\n Found existing installation: pydicom 2.3.1\n Uninstalling pydicom-2.3.1:\n Successfully uninstalled pydicom-2.3.1\nSuccessfully installed pydicom-2.4.3 pylibjpeg-1.4.0 pylibjpeg-libjpeg-1.3.4 pylibjpeg-openjpeg-1.3.2\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\npd.set_option('display.max_rows',100)\npd.set_option('display.max_columns',100)\n\nimport torch\nimport torch.nn as nn\nimport torchvision.transforms as transforms\n\n\nimport timm\nimport gc\nimport os\nimport time\nimport random\nfrom datetime import datetime\n\nfrom PIL import Image\n# from tqdm.notebook import tqdm\nfrom sklearn import model_selection, metrics\n\nimport sys\n\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nimport pydicom\nimport glob\nfrom tqdm.notebook import tqdm\nfrom pydicom.pixel_data_handlers.util import apply_voi_lut\nimport matplotlib.pyplot as plt\n\nimport cv2\nimport warnings\nfrom fastai.vision.all import *\nfrom fastai.medical.imaging import *\nwarnings.filterwarnings('ignore')\nimport vtk\n# numba\nimport numba\nfrom numba import jit\nfrom vtk.util import numpy_support\n\nreader = vtk.vtkDICOMImageReader()\n\n############\nimport seaborn as sns\n\nimport pydicom\nimport scipy.ndimage\n# import gdcm\n\n# import glob\n\nfrom skimage import measure\nfrom mpl_toolkits.mplot3d.art3d import Poly3DCollection\nfrom skimage.morphology import disk, opening, closing\nfrom tqdm import tqdm\n\nfrom IPython.display import HTML\nfrom PIL import Image\n\nimport warnings\nwarnings.filterwarnings(\"ignore\", category=DeprecationWarning)\nwarnings.filterwarnings(\"ignore\", category=UserWarning)\n\n## leaf\nimport numpy as np\nimport pandas as pd\n\nimport torch\nimport torch.nn as nn\nimport torchvision.transforms as transforms\n\n\n\n\n\nimport time\nimport random\nfrom datetime import datetime\n\nfrom PIL import Image\n\n\n# from tqdm.notebook import tqdm\nfrom sklearn import model_selection, metrics\n\nimport sys\nimport os\n\nimport pandas as pd\nimport numpy as np\nimport cv2\nimport os\nimport re\nimport pydicom\nimport warnings\n\n\nimport albumentations as A\nfrom albumentations.pytorch.transforms import ToTensorV2\nfrom albumentations.core.transforms_interface import ImageOnlyTransform\n\nimport torch\nimport torchvision\n\nfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictor\nfrom torchvision.models.detection import FasterRCNN\nfrom torchvision.models.detection.rpn import AnchorGenerator\n\nfrom torch.utils.data import DataLoader, Dataset\nfrom torch.utils.data.sampler import SequentialSampler\nfrom pydicom import dcmread\n\nfrom matplotlib import pyplot as plt\nimport random\n\npaddingSize = 0\n","metadata":{"execution":{"iopub.status.busy":"2023-09-24T13:51:37.184696Z","iopub.execute_input":"2023-09-24T13:51:37.185098Z","iopub.status.idle":"2023-09-24T13:51:45.925579Z","shell.execute_reply.started":"2023-09-24T13:51:37.185056Z","shell.execute_reply":"2023-09-24T13:51:45.924392Z"},"trusted":true},"outputs":[],"execution_count":3},{"cell_type":"code","source":"def seed_everything(seed):\n \"\"\"\n Seeds basic parameters for reproductibility of results\n\n Arguments:\n seed {int} -- Number of the seed\n \"\"\"\n random.seed(seed)\n os.environ[\"PYTHONHASHSEED\"] = str(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n torch.cuda.manual_seed(seed)\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n\n\nseed_everything(1001)","metadata":{"execution":{"iopub.status.busy":"2023-09-24T13:51:45.93107Z","iopub.execute_input":"2023-09-24T13:51:45.932331Z","iopub.status.idle":"2023-09-24T13:51:45.942216Z","shell.execute_reply.started":"2023-09-24T13:51:45.932251Z","shell.execute_reply":"2023-09-24T13:51:45.941149Z"},"trusted":true},"outputs":[],"execution_count":4},{"cell_type":"code","source":"DATA_PATH = Path('../input/siim-covid19-detection')\nMODEL_PATH = (\"../input/vit-base-models-pretrained-pytorch/jx_vit_base_p16_224-80ecf9dd.pth\")\n\nIMG_SIZE = 224\nBATCH_SIZE = 16\nLR = 2e-05\nGAMMA = 0.7\nN_EPOCHS = 1","metadata":{"execution":{"iopub.status.busy":"2023-09-24T13:51:45.943641Z","iopub.execute_input":"2023-09-24T13:51:45.944724Z","iopub.status.idle":"2023-09-24T13:51:45.958661Z","shell.execute_reply.started":"2023-09-24T13:51:45.944659Z","shell.execute_reply":"2023-09-24T13:51:45.957528Z"},"trusted":true},"outputs":[],"execution_count":5},{"cell_type":"code","source":"## Support Functions\ndef dicom2array(path, voi_lut=True, fix_monochrome=True):\n # Original from: https://www.kaggle.com/raddar/convert-dicom-to-np-array-the-correct-way\n dicom = pydicom.read_file(path)\n # VOI LUT (if available by DICOM device) is used to transform raw DICOM data to\n # \"human-friendly\" view\n if voi_lut:\n data = apply_voi_lut(dicom.pixel_array, dicom)\n else:\n data = dicom.pixel_array\n\n # depending on this value, X-ray may look inverted - fix that:\n if fix_monochrome and dicom.PhotometricInterpretation == \"MONOCHROME1\":\n data = np.amax(data) - data\n\n data = data - np.min(data)\n data = data / np.max(data)\n data = (data * 255).astype(np.uint8)\n\n return data\ndef plot_imgs(imgs, cols=4, size=7, is_rgb=True, title=\"\", cmap='gray', img_size=(500,500)):\n rows = len(imgs)//cols + 1\n fig = plt.figure(figsize=(cols*size, rows*size))\n for i, img in enumerate(imgs):\n if img_size is not None:\n img = cv2.resize(img, img_size)\n fig.add_subplot(rows, cols, i+1)\n plt.imshow(img, cmap=cmap)\n plt.suptitle(title)\n # plt.show()\ndef image_path(row):\n study_path = DATA_PATH/'train'/row.StudyInstanceUID\n for i in get_dicom_files(study_path):\n if row.id.split('_')[0] == i.stem: return i","metadata":{"execution":{"iopub.status.busy":"2023-09-24T13:51:45.9619Z","iopub.execute_input":"2023-09-24T13:51:45.963081Z","iopub.status.idle":"2023-09-24T13:51:45.97572Z","shell.execute_reply.started":"2023-09-24T13:51:45.96304Z","shell.execute_reply":"2023-09-24T13:51:45.974566Z"},"trusted":true},"outputs":[],"execution_count":6},{"cell_type":"code","source":"train_image_df = pd.read_csv(DATA_PATH/'train_image_level.csv') #6322\ntrain_study_df = pd.read_csv(DATA_PATH/'train_study_level.csv') #6054\ntrain_study_df.loc[:, \"StudyInstanceUID\"] = train_study_df.id.str.split(\"_\", expand=True).loc[:, 0]\ntrain_study_df_drop = train_study_df.drop(columns=['id'])\ndf_concate = pd.merge( train_image_df,train_study_df_drop, on ='StudyInstanceUID')\n\ns1 = pd.Series.tolist(train_image_df['StudyInstanceUID'] )\ns2 = pd.Series.tolist(train_study_df_drop['StudyInstanceUID'] )\ns3 = [x for x in s2 if x in s1]\ndf_concate_copy = pd.DataFrame(columns=df_concate.columns,index=train_study_df_drop.index)\n\nfor x in s3:\n df_concate_copy.loc[df_concate['StudyInstanceUID']== x]=df_concate.loc[df_concate['StudyInstanceUID']== x]\n\ndf_concate_copy.loc[df_concate_copy[\"Negative for Pneumonia\"]== 1,'degree']=1\ndf_concate_copy.loc[df_concate_copy[\"Typical Appearance\"]== 1,'degree']=4\ndf_concate_copy.loc[df_concate_copy[\"Indeterminate Appearance\"]== 1,'degree']=2\ndf_concate_copy.loc[df_concate_copy[\"Atypical Appearance\"]== 1,'degree']=3\n\ndf_concate_copy['image_path'] = df_concate_copy.apply(image_path, axis=1)\n\ntrain_ids = df_concate_copy[-5000:] # Train and Validation Split\nvalid_ids = df_concate_copy[:-5000]\n","metadata":{"execution":{"iopub.status.busy":"2023-09-24T13:51:45.978837Z","iopub.execute_input":"2023-09-24T13:51:45.979857Z","iopub.status.idle":"2023-09-24T13:53:51.637116Z","shell.execute_reply.started":"2023-09-24T13:51:45.979816Z","shell.execute_reply":"2023-09-24T13:53:51.635926Z"},"trusted":true},"outputs":[],"execution_count":7},{"cell_type":"code","source":"###################\nimgs = []\nimage_paths = train_ids['image_path'].values\n\nthickness = 3\nscale = 5\n\n# single image, resize and image show\nimage_path = random.choice(image_paths)\nimg = dicom2array(str(image_path)) #(2800, 3408)\nimg = cv2.resize(img, None, fx=1 / scale, fy=1 / scale) #(560, 682)\nimg = np.stack([img, img, img], axis=-3) #(3,560, 682)\nlabel = train_ids.loc[train_ids['image_path'] == image_path, ['degree']].values","metadata":{"execution":{"iopub.status.busy":"2023-09-24T13:53:51.640141Z","iopub.execute_input":"2023-09-24T13:53:51.64091Z","iopub.status.idle":"2023-09-24T13:53:51.844044Z","shell.execute_reply.started":"2023-09-24T13:53:51.640862Z","shell.execute_reply":"2023-09-24T13:53:51.842915Z"},"trusted":true},"outputs":[],"execution_count":8},{"cell_type":"code","source":"class COVIDTrainDataset(Dataset): # Class to load Training Data, Feb 20 继承dataset\n\n def __init__(self, df, data_path=DATA_PATH, transforms=None, stat='Train'):\n super().__init__()\n\n self.df = df\n self.image_paths = df[\"image_path\"].values #TODO: 需要的\"id\"是什么\n self.transforms = transforms\n self.stat = stat\n\n def __getitem__(self, index):\n if self.stat == 'Train':\n\n image_path = self.image_paths[index]\n img = dicom2array(str(image_path)) #(2800, 3408)\n # img = cv2.resize(img, None, fx=1/scale, fy=1/scale) # (560, 682)\n img = cv2.resize(img, (224,224)) # (560, 682)\n label = torch.tensor(self.df.loc[self.df['image_path'] == image_path, ['degree']].values[0][0], dtype=torch.int64)\n\n\n\n '''if \"PhotometricInterpretation\" in dicom:\n if dicom.PhotometricInterpretation == \"MONOCHROME1\":\n image = np.amax(image) - image'''\n\n intercept = 0.0\n slope = 1.0\n\n if slope != 1:\n img = slope * img.astype(np.float64)\n img = img.astype(np.int16)\n\n img += np.int16(intercept)\n\n img = np.stack([img, img, img], axis=-1) # (560, 682, 3)\n\n img = img.astype('float32')\n img = img - img.min()\n img = img/img.max()\n img = img * 255.0\n\n\n\n if self.transforms is not None:\n # pdb.set_trace()\n\n transformed = self.transforms(image=img)\n image = transformed[\"image\"]\n\n\n return image, label\n\n else:\n pass\n # image_id = self.image_ids[index]\n # records = self.df[(self.df['id'] == image_id)]\n # records = records.reset_index(drop=True)\n #\n # image = dicom2array(self.df[(self.df['id'] == image_id)]['image_path'].values[0]) # dcmread\n #\n # # image = ds.pixel_array\n #\n # intercept = 0.0\n # slope = 1.0\n #\n # if slope != 1:\n # image = slope * image.astype(np.float64)\n # image = image.astype(np.int16)\n #\n # image += np.int16(intercept)\n #\n # image = np.stack([image, image, image])\n # image = image.astype('float32')\n # image = image - image.min()\n # image = image / image.max()\n # image = image * 255.0\n # image = image.transpose(1, 2, 0)\n #\n # if self.transforms:\n # sample = {\n # 'image': image,\n # }\n # sample = self.transforms(**sample)\n # image = sample['image']\n\n # return image, image_id\n\n def __len__(self):\n return len(self.image_paths)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-09-24T13:53:51.845964Z","iopub.execute_input":"2023-09-24T13:53:51.846369Z","iopub.status.idle":"2023-09-24T13:53:51.865119Z","shell.execute_reply.started":"2023-09-24T13:53:51.846327Z","shell.execute_reply":"2023-09-24T13:53:51.86392Z"},"trusted":true},"outputs":[],"execution_count":9},{"cell_type":"code","source":"def get_train_transform():\n return A.Compose([\n A.ShiftScaleRotate(scale_limit=0.1, rotate_limit=45, p=0.25),\n # A.LongestMaxSize(max_size=800, p=1.0),\n # A.Normalize(mean=(0, 0, 0), std=(1, 1, 1), max_pixel_value=255.0, p=1.0),\n ToTensorV2(p=1.0)\n ])\n\ndef get_valid_transform():\n return A.Compose([\n A.Normalize(mean=(0, 0, 0), std=(1, 1, 1), max_pixel_value=255.0, p=1.0),\n ToTensorV2(p=1.0)\n ])\n\ndef get_test_transform():\n return A.Compose([\n A.Normalize(mean=(0, 0, 0), std=(1, 1, 1), max_pixel_value=255.0, p=1.0),\n ToTensorV2(p=1.0)\n ])\n\ndef collate_fn(batch):\n return tuple(zip(*batch))","metadata":{"execution":{"iopub.status.busy":"2023-09-24T13:53:51.870068Z","iopub.execute_input":"2023-09-24T13:53:51.870353Z","iopub.status.idle":"2023-09-24T13:53:51.880488Z","shell.execute_reply.started":"2023-09-24T13:53:51.870324Z","shell.execute_reply":"2023-09-24T13:53:51.879198Z"},"trusted":true},"outputs":[],"execution_count":10},{"cell_type":"code","source":"class ViTBase16(nn.Module):\n def __init__(self, n_classes, pretrained=False):\n\n super(ViTBase16, self).__init__()\n\n self.model = timm.create_model(\"vit_base_patch16_224\", pretrained=False)\n if pretrained:\n self.model.load_state_dict(torch.load(MODEL_PATH))\n\n self.model.head = nn.Linear(self.model.head.in_features, n_classes)\n\n def forward(self, x):\n x = self.model(x)\n return x\n\n def train_one_epoch(self, train_loader, criterion, optimizer, device):\n # keep track of training loss\n epoch_loss = 0.0\n epoch_accuracy = 0.0\n\n ###################\n # train the model #\n ###################\n self.model.train()\n # data: {Tensor:(16,3,224,224)}\n for i, dataloader in enumerate(train_loader):\n inputs, labels = dataloader\n # move tensors to GPU if CUDA is available\n if device.type == \"cuda\":\n\n inputs, labels = inputs.cuda(), labels.cuda()\n elif device.type == \"xla\":\n inputs = inputs.to(device, dtype=torch.float32)\n labels = labels.to(device, dtype=torch.int64)\n\n # clear the gradients of all optimized variables\n optimizer.zero_grad()\n # forward pass: compute predicted outputs by passing inputs to the model\n output = self.forward(inputs)\n # calculate the batch loss\n loss = criterion(output, labels)\n # backward pass: compute gradient of the loss with respect to model parameters\n loss.backward()\n # Calculate Accuracy\n accuracy = (output.argmax(dim=1) == labels ).float().mean()\n # update training loss and accuracy\n epoch_loss += loss\n epoch_accuracy += accuracy\n\n # perform a single optimization step (parameter update)\n if device.type == \"xla\":\n xm.optimizer_step(optimizer)\n\n if i % 20 == 0:\n xm.master_print(f\"\\tBATCH {i+1}/{len(train_loader)} - LOSS: {loss}\")\n\n else:\n optimizer.step()\n\n # wandb.log({'epoch': epoch, 'loss': epoch_loss, 'train_loader': train_loader}) #wandb\n return epoch_loss / len(train_loader), epoch_accuracy / len(train_loader)\n\n def validate_one_epoch(self, valid_loader, criterion, device):\n # keep track of validation loss\n valid_loss = 0.0\n valid_accuracy = 0.0\n\n\n ######################\n # validate the model #\n ######################\n self.model.eval()\n for i, dataloader in enumerate(valid_loader):\n inputs, labels = dataloader\n # move tensors to GPU if CUDA is available\n if device.type == \"cuda\":\n inputs, labels = inputs.cuda(), labels.cuda()\n elif device.type == \"xla\":\n inputs = inputs.to(device, dtype=torch.float32)\n labels = labels.to(device, dtype=torch.int64)\n\n\n\n with torch.no_grad():\n # forward pass: compute predicted outputs by passing inputs to the model\n output = self.model(inputs)\n # calculate the batch loss\n loss = criterion(output, labels)\n # Calculate Accuracy\n accuracy = (output.argmax(dim=1) == labels).float().mean()\n # update average validation loss and accuracy\n valid_loss += loss\n valid_accuracy += accuracy\n\n return valid_loss / len(valid_loader), valid_accuracy / len(valid_loader)","metadata":{"execution":{"iopub.status.busy":"2023-09-24T13:53:51.882412Z","iopub.execute_input":"2023-09-24T13:53:51.88286Z","iopub.status.idle":"2023-09-24T13:53:51.903944Z","shell.execute_reply.started":"2023-09-24T13:53:51.882822Z","shell.execute_reply":"2023-09-24T13:53:51.902887Z"},"trusted":true},"outputs":[],"execution_count":11},{"cell_type":"code","source":"\n\ndef fit_tpu(model, epochs, device, criterion, optimizer, train_loader, valid_loader=None):\n\n valid_loss_min = np.Inf # track change in validation loss\n\n # keeping track of losses as it happen\n train_losses = []\n valid_losses = []\n train_accs = []\n valid_accs = []\n\n for epoch in range(1, epochs + 1):\n gc.collect()\n# para_train_loader = pl.ParallelLoader(train_loader, [device])\n print(f\"{'='*50}\")\n print(f\"EPOCH {epoch} - TRAINING...\")\n\n\n train_loss, train_acc = model.train_one_epoch(train_loader, criterion, optimizer, device)\n print(\n f\"\\n\\t[TRAIN] EPOCH {epoch} - LOSS: {train_loss}, ACCURACY: {train_acc}\\n\"\n )\n\n train_losses.append(train_loss)\n train_accs.append(train_acc)\n gc.collect()\n\n if valid_loader is not None:\n gc.collect()\n# para_valid_loader = pl.ParallelLoader(valid_loader, [device])\n print(f\"EPOCH {epoch} - VALIDATING...\")\n valid_loss, valid_acc = model.validate_one_epoch(\n valid_loader, criterion,device)\n print(f\"\\t[VALID] LOSS: {valid_loss}, ACCURACY: {valid_acc}\\n\")\n valid_losses.append(valid_loss)\n valid_accs.append(valid_acc)\n gc.collect()\n\n # save model if validation loss has decreased\n if valid_loss <= valid_loss_min and epoch != 1:\n print(\n \"Validation loss decreased ({:.4f} --> {:.4f}). Saving model ...\".format(\n valid_loss_min, valid_loss\n )\n )\n # save(model.state_dict(), 'best_model.pth')\n\n valid_loss_min = valid_loss\n\n return {\n \"train_loss\": train_losses,\n \"valid_losses\": valid_losses,\n \"train_acc\": train_accs,\n \"valid_acc\": valid_accs,\n }\n\n\n","metadata":{"execution":{"iopub.status.busy":"2023-09-24T13:53:51.905537Z","iopub.execute_input":"2023-09-24T13:53:51.905948Z","iopub.status.idle":"2023-09-24T13:53:51.918886Z","shell.execute_reply.started":"2023-09-24T13:53:51.905909Z","shell.execute_reply":"2023-09-24T13:53:51.91762Z"},"trusted":true},"outputs":[],"execution_count":12},{"cell_type":"code","source":"train_dataset = COVIDTrainDataset(train_ids, transforms=get_train_transform())\nvalid_dataset = COVIDTrainDataset(valid_ids, transforms=get_train_transform())\ntrain_loader = torch.utils.data.DataLoader(\n dataset=train_dataset,\n batch_size=BATCH_SIZE,\n drop_last=True,\n num_workers=0,\n )\n\nvalid_loader = torch.utils.data.DataLoader(\n dataset=valid_dataset,\n batch_size=BATCH_SIZE,\n drop_last=True,\n num_workers=0,\n )\n\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel = ViTBase16(n_classes=5, pretrained=True).to(device)\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=LR)","metadata":{"execution":{"iopub.status.busy":"2023-09-24T13:53:51.920452Z","iopub.execute_input":"2023-09-24T13:53:51.920902Z","iopub.status.idle":"2023-09-24T13:53:59.514819Z","shell.execute_reply.started":"2023-09-24T13:53:51.920863Z","shell.execute_reply":"2023-09-24T13:53:59.51087Z"},"trusted":true},"outputs":[],"execution_count":13},{"cell_type":"code","source":"logs = fit_tpu(\n model=model,\n epochs=N_EPOCHS,\n device=device,\n criterion=criterion,\n optimizer=optimizer,\n train_loader=train_loader,\n valid_loader=valid_loader,\n )\n\nprint(f\"Execution time: {datetime.now() - start_time}\")\nprint(\"Saving Model\")\ntorch.save(\n model.state_dict(), f'vit_{datetime.now().strftime(\"%Y%m%d-%H%M\")}.pth'\n )","metadata":{"execution":{"iopub.status.busy":"2023-09-24T13:53:59.516815Z","iopub.execute_input":"2023-09-24T13:53:59.517239Z","iopub.status.idle":"2023-09-24T13:54:06.194047Z","shell.execute_reply.started":"2023-09-24T13:53:59.517198Z","shell.execute_reply":"2023-09-24T13:54:06.189968Z"},"trusted":true},"outputs":[{"name":"stdout","text":"==================================================\nEPOCH 1 - TRAINING...\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_23/3277629294.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mvalid_loader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalid_loader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m )\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/tmp/ipykernel_23/728361114.py\u001b[0m in \u001b[0;36mfit_tpu\u001b[0;34m(model, epochs, device, criterion, optimizer, train_loader, valid_loader)\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mtrain_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_one_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 19\u001b[0m print(\n\u001b[1;32m 20\u001b[0m \u001b[0;34mf\"\\n\\t[TRAIN] EPOCH {epoch} - LOSS: {train_loss}, ACCURACY: {train_acc}\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/tmp/ipykernel_23/1955153336.py\u001b[0m in \u001b[0;36mtrain_one_epoch\u001b[0;34m(self, train_loader, criterion, optimizer, device)\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;31m# data: {Tensor:(16,3,224,224)}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataloader\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 27\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataloader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;31m# move tensors to GPU if CUDA is available\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 626\u001b[0m \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 627\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 628\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 629\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 630\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 669\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 670\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 671\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 672\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 673\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m 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data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1630\u001b[0m \u001b[0;31m# Will raise an exception if no pixel data element\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1631\u001b[0;31m \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhandler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_pixeldata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1632\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pixel_array\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreshape_pixel_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1633\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pydicom/pixel_data_handlers/pylibjpeg_handler.py\u001b[0m in \u001b[0;36mget_pixeldata\u001b[0;34m(ds)\u001b[0m\n\u001b[1;32m 325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 326\u001b[0m \u001b[0mgenerate_offsets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpected_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframe_len\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 327\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerate_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgenerate_offsets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 328\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0moffset\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mframe_len\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 329\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pydicom/pixel_data_handlers/pylibjpeg_handler.py\u001b[0m in \u001b[0;36mgenerate_frames\u001b[0;34m(ds, reshape)\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 235\u001b[0m raise RuntimeError(\n\u001b[0;32m--> 236\u001b[0;31m \u001b[0;34mf\"Unable to convert the Pixel Data as the '{plugin}' plugin is \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 237\u001b[0m \u001b[0;34mf\"not installed\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 238\u001b[0m )\n","\u001b[0;31mRuntimeError\u001b[0m: Unable to convert the Pixel Data as the 'pylibjpeg-libjpeg' plugin is not installed"],"ename":"RuntimeError","evalue":"Unable to convert the Pixel Data as the 'pylibjpeg-libjpeg' plugin is not installed","output_type":"error"}],"execution_count":14},{"cell_type":"code","source":"torch.cuda.is_available()","metadata":{"execution":{"iopub.status.busy":"2023-09-24T13:54:06.195242Z","iopub.status.idle":"2023-09-24T13:54:06.195767Z","shell.execute_reply.started":"2023-09-24T13:54:06.195485Z","shell.execute_reply":"2023-09-24T13:54:06.195511Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"nvidia-smi","metadata":{"execution":{"iopub.status.busy":"2023-09-24T13:54:06.197587Z","iopub.status.idle":"2023-09-24T13:54:06.197987Z","shell.execute_reply.started":"2023-09-24T13:54:06.197796Z","shell.execute_reply":"2023-09-24T13:54:06.197815Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null}]}