{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":20270,"databundleVersionId":1222630,"sourceType":"competition"},{"sourceId":9797223,"sourceType":"datasetVersion","datasetId":982170},{"sourceId":261002435,"sourceType":"kernelVersion"},{"sourceId":265636816,"sourceType":"kernelVersion"},{"sourceId":574351,"sourceType":"modelInstanceVersion","modelInstanceId":429902,"modelId":446851},{"sourceId":574363,"sourceType":"modelInstanceVersion","modelInstanceId":429911,"modelId":446860}],"dockerImageVersionId":31090,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import sys\nsys.path.append(\"../input/tez-lib/\")","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-10-11T18:19:37.582886Z","iopub.execute_input":"2025-10-11T18:19:37.583061Z","iopub.status.idle":"2025-10-11T18:19:37.589572Z","shell.execute_reply.started":"2025-10-11T18:19:37.583044Z","shell.execute_reply":"2025-10-11T18:19:37.588896Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import tez\nfrom tez import Tez, TezConfig\nimport albumentations\nimport pandas as pd\nimport cv2\nimport numpy as np\nimport timm\nimport torch.nn as nn\nfrom sklearn import metrics\nimport torch\nfrom tez.callbacks import EarlyStopping\nfrom tqdm import tqdm\nimport math","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-11T18:19:37.590282Z","iopub.execute_input":"2025-10-11T18:19:37.590493Z","iopub.status.idle":"2025-10-11T18:20:22.863084Z","shell.execute_reply.started":"2025-10-11T18:19:37.590477Z","shell.execute_reply":"2025-10-11T18:20:22.862456Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class args:\n    batch_size = 64#16\n    image_size = 384 #64","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-11T18:20:22.864824Z","iopub.execute_input":"2025-10-11T18:20:22.865253Z","iopub.status.idle":"2025-10-11T18:20:22.869090Z","shell.execute_reply.started":"2025-10-11T18:20:22.865217Z","shell.execute_reply":"2025-10-11T18:20:22.868200Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class CustomDataset:\n    def __init__(self, image_paths, dense_features, targets, augmentations):\n        self.image_paths = image_paths\n        self.dense_features = dense_features\n        self.targets = targets\n        self.augmentations = augmentations\n        \n    def __len__(self):\n        return len(self.image_paths)\n    \n    def __getitem__(self, item):\n        image = cv2.imread(self.image_paths[item])\n        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n        \n        if self.augmentations is not None:\n            augmented = self.augmentations(image=image)\n            image = augmented[\"image\"]\n            \n        image = np.transpose(image, (2, 0, 1)).astype(np.float32)\n        \n        features = self.dense_features[item, :]\n        targets = self.targets[item]\n        \n        return {\n            \"image\": torch.tensor(image, dtype=torch.float),\n            \"features\": torch.tensor(features, dtype=torch.float),\n            \"targets\": torch.tensor(targets, dtype=torch.float),\n        }","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-11T18:20:22.872351Z","iopub.execute_input":"2025-10-11T18:20:22.872577Z","iopub.status.idle":"2025-10-11T18:20:22.889688Z","shell.execute_reply.started":"2025-10-11T18:20:22.872558Z","shell.execute_reply":"2025-10-11T18:20:22.888813Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class CustomModel(nn.Module):\n    def __init__(self):\n        super().__init__()        \n        self.model = timm.create_model(\"resnet50\", pretrained=False, in_chans=3)#resnet50#resnet101#eca_nfnet_l1#resnest101e\n        self.dropout = nn.Dropout(0.5)# increase dropout\n        # self.out = nn.Linear(1280+12, 1)\n        self.out = nn.Linear(1000, 1)\n        # self.out_final = nn.Linear(512, 1)\n        \n        self.step_scheduler_after = \"epoch\"\n\n\n    def monitor_metrics(self, outputs, targets, loss):\n        # rmse = torch.sqrt(loss).cpu().detach().numpy()\n        rmse = loss\n        if str(rmse) == 'nan':\n            rmse = float('inf')\n        # return {\"rmse\": rmse}\n        return {\"rmse\": rmse}\n\n    def optimizer_scheduler(self):\n        opt = torch.optim.AdamW(self.parameters(), lr=2.5e-05, weight_decay=0.01)\n        sch = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(\n            opt, T_0=10, T_mult=1, eta_min=1e-6, last_epoch=-1\n        )\n        return opt,sch\n\n    def forward(self, image, features, targets=None):\n\n        x = self.model(image)\n        x = self.dropout(x)\n        # x = torch.cat([x, features], dim=1)\n        # x = self.dropout(x)\n        x = self.out(x)\n        # x = self.dropout(x)\n        # x = self.out_final(x)\n\n        if targets is not None:\n            loss = nn.MSELoss()(x, targets.view(-1, 1))\n            metrics = self.monitor_metrics(x, targets, loss)\n            return x, loss, metrics\n        return x, 0, {}\n\n# CustomModel()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-11T18:20:22.890485Z","iopub.execute_input":"2025-10-11T18:20:22.890697Z","iopub.status.idle":"2025-10-11T18:20:22.908618Z","shell.execute_reply.started":"2025-10-11T18:20:22.890680Z","shell.execute_reply":"2025-10-11T18:20:22.907789Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"test_aug = albumentations.Compose(\n    [\n        albumentations.Resize(args.image_size, args.image_size, p=1),\n        albumentations.Normalize(\n            mean=[0.485, 0.456, 0.406],\n            std=[0.229, 0.224, 0.225],\n            max_pixel_value=255.0,\n            p=1.0,\n        ),\n    ],\n    p=1.0,\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-11T18:20:22.909320Z","iopub.execute_input":"2025-10-11T18:20:22.909530Z","iopub.status.idle":"2025-10-11T18:20:22.929580Z","shell.execute_reply.started":"2025-10-11T18:20:22.909511Z","shell.execute_reply":"2025-10-11T18:20:22.928995Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"super_final_predictions = []\ni=0\nmodel = CustomModel()\nmodel = Tez(model)\nmodel.load(f\"/kaggle/input/training-siim-isic-melanoma-classification/model_f{i}.bin\", weights_only=True)\n\ndf_test = pd.read_csv(\"/kaggle/input/siim-isic-melanoma-classification/test.csv\")\ntest_img_paths = [f\"/kaggle/input/siim-isic-melanoma-classification/jpeg/test/{x}.jpg\" for x in df_test[\"image_name\"].values]\n\ndense_features = [\n    \n]\n\ntest_dataset = CustomDataset(\n    image_paths=test_img_paths,\n    dense_features=df_test[dense_features].values,\n    targets=np.ones(len(test_img_paths)),\n    augmentations=test_aug,\n)\ntest_predictions = model.predict(test_dataset, batch_size=2*args.batch_size, n_jobs=-1)\n\nfinal_test_predictions = []\nfor preds in tqdm(test_predictions):\n    final_test_predictions.extend(preds.ravel().tolist())\n\n# final_test_predictions = [sigmoid(x) * 100 for x in final_test_predictions]\nsuper_final_predictions.append(final_test_predictions)\n\nsuper_final_predictions = np.mean(np.column_stack(super_final_predictions), axis=1)\ndf_test[\"target\"] = super_final_predictions\ndf_test = df_test[[\"image_name\", \"target\"]]\ndf_test.to_csv(\"submission.csv\", index=False)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-11T18:20:22.930345Z","iopub.execute_input":"2025-10-11T18:20:22.930562Z","iopub.status.idle":"2025-10-11T18:27:35.363975Z","shell.execute_reply.started":"2025-10-11T18:20:22.930545Z","shell.execute_reply":"2025-10-11T18:27:35.363211Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df_test.head()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-11T18:27:35.366103Z","iopub.execute_input":"2025-10-11T18:27:35.366363Z","iopub.status.idle":"2025-10-11T18:27:35.383975Z","shell.execute_reply.started":"2025-10-11T18:27:35.366339Z","shell.execute_reply":"2025-10-11T18:27:35.383213Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}