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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 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install pretrainedmodels\n!unzip -qq /kaggle/input/panda256/train","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:44:25.049746Z","iopub.execute_input":"2023-06-02T22:44:25.050128Z","iopub.status.idle":"2023-06-02T22:45:19.062379Z","shell.execute_reply.started":"2023-06-02T22:44:25.050096Z","shell.execute_reply":"2023-06-02T22:45:19.060943Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Collecting pretrainedmodels\n  Downloading pretrainedmodels-0.7.4.tar.gz (58 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.8/58.8 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25ldone\n\u001b[?25hRequirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (from pretrainedmodels) (2.0.0+cpu)\nRequirement already satisfied: torchvision in /opt/conda/lib/python3.10/site-packages (from pretrainedmodels) (0.15.1+cpu)\nRequirement already satisfied: munch in /opt/conda/lib/python3.10/site-packages (from pretrainedmodels) (3.0.0)\nRequirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from pretrainedmodels) (4.64.1)\nRequirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from munch->pretrainedmodels) (1.16.0)\nRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch->pretrainedmodels) (3.12.0)\nRequirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from torch->pretrainedmodels) (4.5.0)\nRequirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch->pretrainedmodels) (1.12)\nRequirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch->pretrainedmodels) (3.1)\nRequirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch->pretrainedmodels) (3.1.2)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision->pretrainedmodels) (1.23.5)\nRequirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision->pretrainedmodels) (2.28.2)\nRequirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision->pretrainedmodels) (9.5.0)\nRequirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch->pretrainedmodels) (2.1.2)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision->pretrainedmodels) (2.1.1)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision->pretrainedmodels) (3.4)\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision->pretrainedmodels) (1.26.15)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision->pretrainedmodels) (2023.5.7)\nRequirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch->pretrainedmodels) (1.3.0)\nBuilding wheels for collected packages: pretrainedmodels\n  Building wheel for pretrainedmodels (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for pretrainedmodels: filename=pretrainedmodels-0.7.4-py3-none-any.whl size=60966 sha256=6c1d500e1c804c81f011c7a8a328b7a01d89b21bdf9f5ee9b1e21b0988980035\n  Stored in directory: /root/.cache/pip/wheels/35/cb/a5/8f534c60142835bfc889f9a482e4a67e0b817032d9c6883b64\nSuccessfully built pretrainedmodels\nInstalling collected packages: pretrainedmodels\nSuccessfully installed pretrainedmodels-0.7.4\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"}]},{"cell_type":"code","source":"import os\nimport openslide\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\n\nimport torch\nimport cv2\nfrom tqdm import tqdm\nimport albumentations\nimport pretrainedmodels\n\nimport torch.nn as nn\nfrom torch.utils.data import Dataset, DataLoader\nimport torch.utils.data as data_utils\nfrom torch.nn import functional as F\n\nfrom sklearn.metrics import cohen_kappa_score\nfrom fastai.losses import LabelSmoothingCrossEntropyFlat\nfrom matplotlib import pyplot as plt\n","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:45:19.06429Z","iopub.execute_input":"2023-06-02T22:45:19.064703Z","iopub.status.idle":"2023-06-02T22:45:27.313962Z","shell.execute_reply.started":"2023-06-02T22:45:19.064657Z","shell.execute_reply":"2023-06-02T22:45:27.31276Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"BASE_DIR = '/kaggle/input/panda256'\nDATA_DIR = '/kaggle/working/kaggle/working/train_images'\nDEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\nBATCH_SIZE = 16\nEPOCHS = 30\nLEARNING_RATE = 0.02\n####\ncheckpoint_dir = '/kaggle/working/checkpoints'\nos.makedirs(checkpoint_dir, exist_ok=True)","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:45:27.317049Z","iopub.execute_input":"2023-06-02T22:45:27.317875Z","iopub.status.idle":"2023-06-02T22:45:27.325729Z","shell.execute_reply.started":"2023-06-02T22:45:27.317832Z","shell.execute_reply":"2023-06-02T22:45:27.3241Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"train_df = pd.read_csv(os.path.join(BASE_DIR, 'train.csv'))\ntrain_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:45:27.327661Z","iopub.execute_input":"2023-06-02T22:45:27.328014Z","iopub.status.idle":"2023-06-02T22:45:27.648745Z","shell.execute_reply.started":"2023-06-02T22:45:27.327978Z","shell.execute_reply":"2023-06-02T22:45:27.646886Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"                              image_id data_provider  isup_grade  \\\n0   1b04602fffd6117606d1a05efa96daaf_4       radboud           5   \n1  e2726cffee3499287c6f87b561dd228d_10       radboud           5   \n2   d908ac781e507048dca9b45d93fcec44_1    karolinska           3   \n3   56e5d931e0c46a942e76aaa95485b9cd_6       radboud           5   \n4   effe920ec2c4a10fe08d237a806d04d5_1    karolinska           0   \n\n  gleason_score  kfold  \n0           5+4      3  \n1           5+4      0  \n2           4+3      4  \n3           4+5      1  \n4           0+0      0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>image_id</th>\n      <th>data_provider</th>\n      <th>isup_grade</th>\n      <th>gleason_score</th>\n      <th>kfold</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1b04602fffd6117606d1a05efa96daaf_4</td>\n      <td>radboud</td>\n      <td>5</td>\n      <td>5+4</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>e2726cffee3499287c6f87b561dd228d_10</td>\n      <td>radboud</td>\n      <td>5</td>\n      <td>5+4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>d908ac781e507048dca9b45d93fcec44_1</td>\n      <td>karolinska</td>\n      <td>3</td>\n      <td>4+3</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>56e5d931e0c46a942e76aaa95485b9cd_6</td>\n      <td>radboud</td>\n      <td>5</td>\n      <td>4+5</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>effe920ec2c4a10fe08d237a806d04d5_1</td>\n      <td>karolinska</td>\n      <td>0</td>\n      <td>0+0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"train_df.shape\n","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:45:27.651275Z","iopub.execute_input":"2023-06-02T22:45:27.652768Z","iopub.status.idle":"2023-06-02T22:45:27.666999Z","shell.execute_reply.started":"2023-06-02T22:45:27.652705Z","shell.execute_reply":"2023-06-02T22:45:27.664309Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"(92427, 5)"},"metadata":{}}]},{"cell_type":"code","source":"train_df['gleason_score'].unique()\n","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:45:27.668795Z","iopub.execute_input":"2023-06-02T22:45:27.671622Z","iopub.status.idle":"2023-06-02T22:45:27.720255Z","shell.execute_reply.started":"2023-06-02T22:45:27.671566Z","shell.execute_reply":"2023-06-02T22:45:27.718951Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"array(['5+4', '4+3', '4+5', '0+0', '3+3', '3+4', '4+4', 'negative', '5+5',\n       '3+5', '5+3'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"train_df['gleason_score'] = train_df['gleason_score'].str.replace('negative', '0+0')\ntrain_df['gleason_score'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:45:27.722318Z","iopub.execute_input":"2023-06-02T22:45:27.722709Z","iopub.status.idle":"2023-06-02T22:45:27.858919Z","shell.execute_reply.started":"2023-06-02T22:45:27.72268Z","shell.execute_reply":"2023-06-02T22:45:27.857444Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"array(['5+4', '4+3', '4+5', '0+0', '3+3', '3+4', '4+4', '5+5', '3+5',\n       '5+3'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"train_df['gleason_score'] = train_df['gleason_score'].astype('category')\ntrain_df.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:45:27.861105Z","iopub.execute_input":"2023-06-02T22:45:27.863093Z","iopub.status.idle":"2023-06-02T22:45:27.888562Z","shell.execute_reply.started":"2023-06-02T22:45:27.863031Z","shell.execute_reply":"2023-06-02T22:45:27.887014Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"image_id           object\ndata_provider      object\nisup_grade          int64\ngleason_score    category\nkfold               int64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"mappings = dict(enumerate(train_df['gleason_score'].cat.categories))\nmappings","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:45:27.894764Z","iopub.execute_input":"2023-06-02T22:45:27.895565Z","iopub.status.idle":"2023-06-02T22:45:27.904766Z","shell.execute_reply.started":"2023-06-02T22:45:27.895519Z","shell.execute_reply":"2023-06-02T22:45:27.903525Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"{0: '0+0',\n 1: '3+3',\n 2: '3+4',\n 3: '3+5',\n 4: '4+3',\n 5: '4+4',\n 6: '4+5',\n 7: '5+3',\n 8: '5+4',\n 9: '5+5'}"},"metadata":{}}]},{"cell_type":"code","source":"train_df['gleason_score'] = train_df['gleason_score'].cat.codes\n","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:45:27.906342Z","iopub.execute_input":"2023-06-02T22:45:27.906695Z","iopub.status.idle":"2023-06-02T22:45:27.918952Z","shell.execute_reply.started":"2023-06-02T22:45:27.906665Z","shell.execute_reply":"2023-06-02T22:45:27.9175Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"train_df.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:45:27.92132Z","iopub.execute_input":"2023-06-02T22:45:27.922008Z","iopub.status.idle":"2023-06-02T22:45:27.935598Z","shell.execute_reply.started":"2023-06-02T22:45:27.921973Z","shell.execute_reply":"2023-06-02T22:45:27.934496Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"image_id         object\ndata_provider    object\nisup_grade        int64\ngleason_score      int8\nkfold             int64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"print(f'Number of classes: {len(train_df[\"gleason_score\"].unique())}')\n","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:45:27.936996Z","iopub.execute_input":"2023-06-02T22:45:27.938019Z","iopub.status.idle":"2023-06-02T22:45:27.953129Z","shell.execute_reply.started":"2023-06-02T22:45:27.937984Z","shell.execute_reply":"2023-06-02T22:45:27.952111Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"Number of classes: 10\n","output_type":"stream"}]},{"cell_type":"code","source":"class PandaDataset(Dataset):\n    \"\"\"Custom dataset for PANDA\"\"\"\n    \n    def __init__(self, df, folds, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):\n        self.df = df\n        self.df = self.df[self.df.kfold.isin(folds)].reset_index(drop=True)\n        \n        # In case of validation dataset, don't apply transformations\n        if len(folds) == 1:\n            self.aug = albumentations.Compose([\n                albumentations.Normalize(mean, std, always_apply=True)\n            ])\n        else:\n            self.aug = albumentations.Compose([\n                albumentations.ShiftScaleRotate(shift_limit=0.0625,\n                                               scale_limit=0.15, \n                                               rotate_limit=10,\n                                               p=0.9),\n                albumentations.HorizontalFlip(p=0.5),\n                albumentations.VerticalFlip(p=0.5),\n                albumentations.Normalize(mean, std, always_apply=True)\n            ])\n        \n    def __len__(self):\n        return len(self.df)\n    \n    def __getitem__(self, index):\n        image_id = self.df.loc[index]['image_id']\n        image = cv2.imread(os.path.join(DATA_DIR, f'{image_id}.jpg'))\n        image = self.aug(image=image)['image']\n        \n        # Convert from NHWC to NCHW as pytorch expects images in NCHW format\n        image = np.transpose(image, (2, 0, 1))\n        \n        # For now, just return image and ISUP grades\n        return image, self.df.loc[index]['gleason_score']","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:45:27.954952Z","iopub.execute_input":"2023-06-02T22:45:27.955746Z","iopub.status.idle":"2023-06-02T22:45:27.970195Z","shell.execute_reply.started":"2023-06-02T22:45:27.955687Z","shell.execute_reply":"2023-06-02T22:45:27.968939Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"train_dataset = PandaDataset(train_df, folds=[0, 1, 2, 3])\ntrain_loader = data_utils.DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n\nval_dataset = PandaDataset(train_df, folds=[4])\nval_loader = data_utils.DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=True)\n","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:45:27.971561Z","iopub.execute_input":"2023-06-02T22:45:27.972373Z","iopub.status.idle":"2023-06-02T22:45:28.002357Z","shell.execute_reply.started":"2023-06-02T22:45:27.972302Z","shell.execute_reply":"2023-06-02T22:45:28.001304Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"!pip install opencv-python\n!pip install numpy\n!pip install tqdm\n!pip install matplotlib\n!pip install easydict\n!pip install scipy\n!pip install scikit-learn","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:45:28.004066Z","iopub.execute_input":"2023-06-02T22:45:28.004521Z","iopub.status.idle":"2023-06-02T22:46:59.89361Z","shell.execute_reply.started":"2023-06-02T22:45:28.004489Z","shell.execute_reply":"2023-06-02T22:46:59.892223Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"Requirement already satisfied: opencv-python in /opt/conda/lib/python3.10/site-packages (4.5.4.60)\nRequirement already satisfied: numpy>=1.21.2 in /opt/conda/lib/python3.10/site-packages (from opencv-python) (1.23.5)\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[0mRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (1.23.5)\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[0mRequirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (4.64.1)\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[0mRequirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (3.6.3)\nRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.0.7)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (0.11.0)\nRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (4.39.3)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.4.4)\nRequirement already satisfied: numpy>=1.19 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.23.5)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (21.3)\nRequirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (9.5.0)\nRequirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (3.0.9)\nRequirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (2.8.2)\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\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[0mRequirement already satisfied: easydict in /opt/conda/lib/python3.10/site-packages (1.10)\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[0mRequirement already satisfied: scipy in /opt/conda/lib/python3.10/site-packages (1.10.1)\nRequirement already satisfied: numpy<1.27.0,>=1.19.5 in /opt/conda/lib/python3.10/site-packages (from scipy) (1.23.5)\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[0mRequirement already satisfied: scikit-learn in /opt/conda/lib/python3.10/site-packages (1.2.2)\nRequirement already satisfied: numpy>=1.17.3 in /opt/conda/lib/python3.10/site-packages (from scikit-learn) (1.23.5)\nRequirement already satisfied: scipy>=1.3.2 in /opt/conda/lib/python3.10/site-packages (from scikit-learn) (1.10.1)\nRequirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn) (1.2.0)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn) (3.1.0)\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"}]},{"cell_type":"code","source":"!pip install timm","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:46:59.896412Z","iopub.execute_input":"2023-06-02T22:46:59.896911Z","iopub.status.idle":"2023-06-02T22:47:13.103084Z","shell.execute_reply.started":"2023-06-02T22:46:59.896858Z","shell.execute_reply":"2023-06-02T22:47:13.101949Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"Requirement already satisfied: timm in /opt/conda/lib/python3.10/site-packages (0.9.2)\nRequirement already satisfied: torch>=1.7 in /opt/conda/lib/python3.10/site-packages (from timm) (2.0.0+cpu)\nRequirement already satisfied: torchvision in /opt/conda/lib/python3.10/site-packages (from timm) (0.15.1+cpu)\nRequirement already satisfied: pyyaml in /opt/conda/lib/python3.10/site-packages (from timm) (5.4.1)\nRequirement already satisfied: huggingface-hub in /opt/conda/lib/python3.10/site-packages (from timm) (0.14.1)\nRequirement already satisfied: safetensors in /opt/conda/lib/python3.10/site-packages (from timm) (0.3.1)\nRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch>=1.7->timm) (3.12.0)\nRequirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from torch>=1.7->timm) (4.5.0)\nRequirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1.7->timm) (1.12)\nRequirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.7->timm) (3.1)\nRequirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.7->timm) (3.1.2)\nRequirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from huggingface-hub->timm) (2023.5.0)\nRequirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from huggingface-hub->timm) (2.28.2)\nRequirement already satisfied: tqdm>=4.42.1 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub->timm) (4.64.1)\nRequirement already satisfied: packaging>=20.9 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub->timm) (21.3)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision->timm) (1.23.5)\nRequirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision->timm) (9.5.0)\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.9->huggingface-hub->timm) (3.0.9)\nRequirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.7->timm) (2.1.2)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub->timm) (2.1.1)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub->timm) (3.4)\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub->timm) (1.26.15)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub->timm) (2023.5.7)\nRequirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.7->timm) (1.3.0)\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"}]},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nfrom torchvision.models import resnet\nfrom timm.models.vision_transformer import vit_base_patch16_224\n\nclass LionModel(nn.Module):\n    \"\"\"\n    Define LionModel based on Lion architecture with 10 output classes based on Gleason scores\n    \"\"\"\n    def __init__(self, pretrained=True):\n        super(LionModel, self).__init__()\n        if pretrained:\n            self.model = vit_base_patch16_224(pretrained=True)\n        else:\n            self.model = vit_base_patch16_224(pretrained=False)\n        \n        self.model.head = nn.Linear(self.model.head.in_features, 10)\n    \n    def forward(self, x):\n        bs, _, _, _ = x.shape\n        x = self.model(x)\n        return x\n\n\n# Usage:\npretrained = True  # Set to True if you want to use pre-trained weights\nmodel = LionModel(pretrained)\ninput_tensor = torch.randn(1, 3, 224, 224)  # Example input tensor\noutput = model(input_tensor)\nprint(output.shape)  # Print the shape of the output tensor\n","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:47:13.104589Z","iopub.execute_input":"2023-06-02T22:47:13.104972Z","iopub.status.idle":"2023-06-02T22:47:21.951151Z","shell.execute_reply.started":"2023-06-02T22:47:13.104935Z","shell.execute_reply":"2023-06-02T22:47:21.949965Z"},"trusted":true},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":"Downloading model.safetensors:   0%|          | 0.00/346M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c8c321f3441546f5b3799a0dc2137de3"}},"metadata":{}},{"name":"stdout","text":"torch.Size([1, 10])\n","output_type":"stream"}]},{"cell_type":"code","source":"\nmodel = LionModel(pretrained)\nmodel.to(DEVICE)\n","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:47:21.952707Z","iopub.execute_input":"2023-06-02T22:47:21.95303Z","iopub.status.idle":"2023-06-02T22:47:24.477715Z","shell.execute_reply.started":"2023-06-02T22:47:21.953003Z","shell.execute_reply":"2023-06-02T22:47:24.476501Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"LionModel(\n  (model): VisionTransformer(\n    (patch_embed): PatchEmbed(\n      (proj): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n      (norm): Identity()\n    )\n    (pos_drop): Dropout(p=0.0, inplace=False)\n    (patch_drop): Identity()\n    (norm_pre): Identity()\n    (blocks): Sequential(\n      (0): Block(\n        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (attn): Attention(\n          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n          (q_norm): Identity()\n          (k_norm): Identity()\n          (attn_drop): Dropout(p=0.0, inplace=False)\n          (proj): Linear(in_features=768, out_features=768, bias=True)\n          (proj_drop): Dropout(p=0.0, inplace=False)\n        )\n        (ls1): Identity()\n        (drop_path1): Identity()\n        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (mlp): Mlp(\n          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n          (act): GELU(approximate='none')\n          (drop1): Dropout(p=0.0, inplace=False)\n          (norm): Identity()\n          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n          (drop2): Dropout(p=0.0, inplace=False)\n        )\n        (ls2): Identity()\n        (drop_path2): Identity()\n      )\n      (1): Block(\n        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (attn): Attention(\n          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n          (q_norm): Identity()\n          (k_norm): Identity()\n          (attn_drop): Dropout(p=0.0, inplace=False)\n          (proj): Linear(in_features=768, out_features=768, bias=True)\n          (proj_drop): Dropout(p=0.0, inplace=False)\n        )\n        (ls1): Identity()\n        (drop_path1): Identity()\n        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (mlp): Mlp(\n          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n          (act): GELU(approximate='none')\n          (drop1): Dropout(p=0.0, inplace=False)\n          (norm): Identity()\n          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n          (drop2): Dropout(p=0.0, inplace=False)\n        )\n        (ls2): Identity()\n        (drop_path2): Identity()\n      )\n      (2): Block(\n        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (attn): Attention(\n          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n          (q_norm): Identity()\n          (k_norm): Identity()\n          (attn_drop): Dropout(p=0.0, inplace=False)\n          (proj): Linear(in_features=768, out_features=768, bias=True)\n          (proj_drop): Dropout(p=0.0, inplace=False)\n        )\n        (ls1): Identity()\n        (drop_path1): Identity()\n        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (mlp): Mlp(\n          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n          (act): GELU(approximate='none')\n          (drop1): Dropout(p=0.0, inplace=False)\n          (norm): Identity()\n          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n          (drop2): Dropout(p=0.0, inplace=False)\n        )\n        (ls2): Identity()\n        (drop_path2): Identity()\n      )\n      (3): Block(\n        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (attn): Attention(\n          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n          (q_norm): Identity()\n          (k_norm): Identity()\n          (attn_drop): Dropout(p=0.0, inplace=False)\n          (proj): Linear(in_features=768, out_features=768, bias=True)\n          (proj_drop): Dropout(p=0.0, inplace=False)\n        )\n        (ls1): Identity()\n        (drop_path1): Identity()\n        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (mlp): Mlp(\n          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n          (act): GELU(approximate='none')\n          (drop1): Dropout(p=0.0, inplace=False)\n          (norm): Identity()\n          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n          (drop2): Dropout(p=0.0, inplace=False)\n        )\n        (ls2): Identity()\n        (drop_path2): Identity()\n      )\n      (4): Block(\n        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (attn): Attention(\n          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n          (q_norm): Identity()\n          (k_norm): Identity()\n          (attn_drop): Dropout(p=0.0, inplace=False)\n          (proj): Linear(in_features=768, out_features=768, bias=True)\n          (proj_drop): Dropout(p=0.0, inplace=False)\n        )\n        (ls1): Identity()\n        (drop_path1): Identity()\n        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (mlp): Mlp(\n          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n          (act): GELU(approximate='none')\n          (drop1): Dropout(p=0.0, inplace=False)\n          (norm): Identity()\n          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n          (drop2): Dropout(p=0.0, inplace=False)\n        )\n        (ls2): Identity()\n        (drop_path2): Identity()\n      )\n      (5): Block(\n        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (attn): Attention(\n          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n          (q_norm): Identity()\n          (k_norm): Identity()\n          (attn_drop): Dropout(p=0.0, inplace=False)\n          (proj): Linear(in_features=768, out_features=768, bias=True)\n          (proj_drop): Dropout(p=0.0, inplace=False)\n        )\n        (ls1): Identity()\n        (drop_path1): Identity()\n        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (mlp): Mlp(\n          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n          (act): GELU(approximate='none')\n          (drop1): Dropout(p=0.0, inplace=False)\n          (norm): Identity()\n          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n          (drop2): Dropout(p=0.0, inplace=False)\n        )\n        (ls2): Identity()\n        (drop_path2): Identity()\n      )\n      (6): Block(\n        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (attn): Attention(\n          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n          (q_norm): Identity()\n          (k_norm): Identity()\n          (attn_drop): Dropout(p=0.0, inplace=False)\n          (proj): Linear(in_features=768, out_features=768, bias=True)\n          (proj_drop): Dropout(p=0.0, inplace=False)\n        )\n        (ls1): Identity()\n        (drop_path1): Identity()\n        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (mlp): Mlp(\n          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n          (act): GELU(approximate='none')\n          (drop1): Dropout(p=0.0, inplace=False)\n          (norm): Identity()\n          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n          (drop2): Dropout(p=0.0, inplace=False)\n        )\n        (ls2): Identity()\n        (drop_path2): Identity()\n      )\n      (7): Block(\n        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (attn): Attention(\n          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n          (q_norm): Identity()\n          (k_norm): Identity()\n          (attn_drop): Dropout(p=0.0, inplace=False)\n          (proj): Linear(in_features=768, out_features=768, bias=True)\n          (proj_drop): Dropout(p=0.0, inplace=False)\n        )\n        (ls1): Identity()\n        (drop_path1): Identity()\n        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (mlp): Mlp(\n          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n          (act): GELU(approximate='none')\n          (drop1): Dropout(p=0.0, inplace=False)\n          (norm): Identity()\n          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n          (drop2): Dropout(p=0.0, inplace=False)\n        )\n        (ls2): Identity()\n        (drop_path2): Identity()\n      )\n      (8): Block(\n        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (attn): Attention(\n          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n          (q_norm): Identity()\n          (k_norm): Identity()\n          (attn_drop): Dropout(p=0.0, inplace=False)\n          (proj): Linear(in_features=768, out_features=768, bias=True)\n          (proj_drop): Dropout(p=0.0, inplace=False)\n        )\n        (ls1): Identity()\n        (drop_path1): Identity()\n        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (mlp): Mlp(\n          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n          (act): GELU(approximate='none')\n          (drop1): Dropout(p=0.0, inplace=False)\n          (norm): Identity()\n          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n          (drop2): Dropout(p=0.0, inplace=False)\n        )\n        (ls2): Identity()\n        (drop_path2): Identity()\n      )\n      (9): Block(\n        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (attn): Attention(\n          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n          (q_norm): Identity()\n          (k_norm): Identity()\n          (attn_drop): Dropout(p=0.0, inplace=False)\n          (proj): Linear(in_features=768, out_features=768, bias=True)\n          (proj_drop): Dropout(p=0.0, inplace=False)\n        )\n        (ls1): Identity()\n        (drop_path1): Identity()\n        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (mlp): Mlp(\n          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n          (act): GELU(approximate='none')\n          (drop1): Dropout(p=0.0, inplace=False)\n          (norm): Identity()\n          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n          (drop2): Dropout(p=0.0, inplace=False)\n        )\n        (ls2): Identity()\n        (drop_path2): Identity()\n      )\n      (10): Block(\n        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (attn): Attention(\n          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n          (q_norm): Identity()\n          (k_norm): Identity()\n          (attn_drop): Dropout(p=0.0, inplace=False)\n          (proj): Linear(in_features=768, out_features=768, bias=True)\n          (proj_drop): Dropout(p=0.0, inplace=False)\n        )\n        (ls1): Identity()\n        (drop_path1): Identity()\n        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (mlp): Mlp(\n          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n          (act): GELU(approximate='none')\n          (drop1): Dropout(p=0.0, inplace=False)\n          (norm): Identity()\n          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n          (drop2): Dropout(p=0.0, inplace=False)\n        )\n        (ls2): Identity()\n        (drop_path2): Identity()\n      )\n      (11): Block(\n        (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (attn): Attention(\n          (qkv): Linear(in_features=768, out_features=2304, bias=True)\n          (q_norm): Identity()\n          (k_norm): Identity()\n          (attn_drop): Dropout(p=0.0, inplace=False)\n          (proj): Linear(in_features=768, out_features=768, bias=True)\n          (proj_drop): Dropout(p=0.0, inplace=False)\n        )\n        (ls1): Identity()\n        (drop_path1): Identity()\n        (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n        (mlp): Mlp(\n          (fc1): Linear(in_features=768, out_features=3072, bias=True)\n          (act): GELU(approximate='none')\n          (drop1): Dropout(p=0.0, inplace=False)\n          (norm): Identity()\n          (fc2): Linear(in_features=3072, out_features=768, bias=True)\n          (drop2): Dropout(p=0.0, inplace=False)\n        )\n        (ls2): Identity()\n        (drop_path2): Identity()\n      )\n    )\n    (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n    (fc_norm): Identity()\n    (head_drop): Dropout(p=0.0, inplace=False)\n    (head): Linear(in_features=768, out_features=10, bias=True)\n  )\n)"},"metadata":{}}]},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\n\nclass LabelSmoothingCrossEntropy(nn.Module):\n    def __init__(self, epsilon=0.1):\n        super(LabelSmoothingCrossEntropy, self).__init__()\n        self.epsilon = epsilon\n\n    def forward(self, outputs, targets):\n        num_classes = outputs.size(1)\n        device = outputs.device\n\n        one_hot = torch.zeros_like(outputs).scatter(1, targets.unsqueeze(1), 1)\n        smooth_labels = one_hot * (1 - self.epsilon) + torch.ones_like(outputs) * self.epsilon / num_classes\n\n        log_prob = torch.log_softmax(outputs, dim=1)\n\n        loss = torch.sum(-smooth_labels * log_prob, dim=1).mean()\n        return loss","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:47:24.479318Z","iopub.execute_input":"2023-06-02T22:47:24.479724Z","iopub.status.idle":"2023-06-02T22:47:24.488278Z","shell.execute_reply.started":"2023-06-02T22:47:24.479693Z","shell.execute_reply":"2023-06-02T22:47:24.487475Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"optimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE)\nlr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=3, verbose=False, threshold=0.0001, threshold_mode='rel', cooldown=0, min_lr=0.00001, eps=1e-08)\ncriterion = LabelSmoothingCrossEntropy(0.1)  # Using label smoothing loss instead of normal cross entropy loss","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:47:24.489612Z","iopub.execute_input":"2023-06-02T22:47:24.490193Z","iopub.status.idle":"2023-06-02T22:47:24.508598Z","shell.execute_reply.started":"2023-06-02T22:47:24.490162Z","shell.execute_reply":"2023-06-02T22:47:24.507119Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"# Make use of parallel GPUs if available\nif torch.cuda.device_count() > 1:\n    model = nn.DataParallel(model)","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:47:24.509888Z","iopub.execute_input":"2023-06-02T22:47:24.510231Z","iopub.status.idle":"2023-06-02T22:47:24.522619Z","shell.execute_reply.started":"2023-06-02T22:47:24.510203Z","shell.execute_reply":"2023-06-02T22:47:24.521807Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"code","source":"def get_accuracy(preds, targets):\n    assert len(preds) == len(targets)\n    \n    total = len(preds)\n    _, preds = torch.max(preds.data, axis=1)\n\n    correct = (preds == targets).sum().item()\n    return correct / total","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:47:24.523707Z","iopub.execute_input":"2023-06-02T22:47:24.524303Z","iopub.status.idle":"2023-06-02T22:47:24.536094Z","shell.execute_reply.started":"2023-06-02T22:47:24.524272Z","shell.execute_reply":"2023-06-02T22:47:24.534672Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"import torch\nfrom torchvision.transforms import Resize\nfrom torch.nn import CrossEntropyLoss\nfrom torch.optim import SGD\nfrom torch.optim.lr_scheduler import StepLR\nfrom tqdm import tqdm\n\n# Define other variables and parameters\n\n\ntransform = Resize((224, 224), antialias=True)   # Resize images to (224, 224)\n\nval_acc_list = []\n####\nbest_val_acc = 0.0  # Variable to track the best validation accuracy\n\nfor epoch in range(EPOCHS):\n    model.train()\n    for i, (x_train, y_train) in tqdm(enumerate(train_loader), total=int(len(train_dataset)/train_loader.batch_size)):\n        x_train = transform(x_train)  # Resize the input images\n        x_train = x_train.to(DEVICE, dtype=torch.float32) / 255\n        y_train = y_train.to(DEVICE, dtype=torch.long)\n        \n        # Forward pass\n        preds = model(x_train)\n        loss = criterion(preds, y_train)\n        \n        # Backpropagate\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n    \n    lr_scheduler.step(loss.item())\n    \n    # Calculate validation accuracy after each epoch\n    # Predict on validation set\n    \n    with torch.no_grad():\n        model.eval()\n        \n        correct = 0\n        val_size = len(val_loader)\n        for x_val, y_val in tqdm(val_loader, total=int(len(val_dataset)/val_loader.batch_size)):\n            x_val = transform(x_val)  # Resize the input images\n            x_val = x_val.to(DEVICE, dtype=torch.float32) / 255\n            y_val = y_val.to(DEVICE, dtype=torch.long)\n            \n            val_preds = model(x_val)\n            _, preds = torch.max(val_preds.data, axis=1)\n            correct += (preds == y_val).sum().item()\n        \n        val_acc = correct / len(val_dataset)\n        val_acc_list.append(val_acc)\n    \n     # Save checkpoint if the current validation accuracy is the best so far\n        if val_acc > best_val_acc:\n            best_val_acc = val_acc\n            checkpoint_path = os.path.join(checkpoint_dir, 'best_checkpoint.pth')\n            torch.save(model.state_dict(), checkpoint_path)\n    \n\n    print('Epoch [{}/{}], Loss: {:.4f}, Validation accuracy: {:.2f}%'\n          .format(epoch + 1, EPOCHS, loss.item(), val_acc * 100))","metadata":{"execution":{"iopub.status.busy":"2023-06-02T22:47:24.53778Z","iopub.execute_input":"2023-06-02T22:47:24.538235Z"},"trusted":true},"execution_count":null,"outputs":[{"name":"stderr","text":"  0%|          | 20/4621 [04:58<18:12:10, 14.24s/it]","output_type":"stream"}]}]}