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os\nimport torch\nimport cv2\nfrom torch.utils.data import Dataset, DataLoader, default_collate\nfrom torchvision import transforms\nfrom transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation\nfrom torch.amp import GradScaler, autocast\nimport torch.nn as nn\nimport torch.optim as optim\nfrom tqdm import tqdm\nimport albumentations as A\nfrom albumentations.pytorch import ToTensorV2\nimport numpy as np\nfrom IPython.display import FileLink\nimport pandas as pd","metadata":{"execution":{"iopub.execute_input":"2025-02-25T21:18:31.062949Z","iopub.status.busy":"2025-02-25T21:18:31.062679Z","iopub.status.idle":"2025-02-25T21:18:54.511982Z","shell.execute_reply":"2025-02-25T21:18:54.511082Z"},"papermill":{"duration":23.455478,"end_time":"2025-02-25T21:18:54.513391","exception":false,"start_time":"2025-02-25T21:18:31.057913","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"cc5579b6","cell_type":"markdown","source":"# *Constants and Parameters*","metadata":{"papermill":{"duration":0.002817,"end_time":"2025-02-25T21:18:54.519612","exception":false,"start_time":"2025-02-25T21:18:54.516795","status":"completed"},"tags":[]}},{"id":"4a733ad9","cell_type":"code","source":"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")","metadata":{"execution":{"iopub.execute_input":"2025-02-25T21:18:54.526309Z","iopub.status.busy":"2025-02-25T21:18:54.525804Z","iopub.status.idle":"2025-02-25T21:18:54.576319Z","shell.execute_reply":"2025-02-25T21:18:54.575507Z"},"papermill":{"duration":0.055251,"end_time":"2025-02-25T21:18:54.577681","exception":false,"start_time":"2025-02-25T21:18:54.522430","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"b74e6009","cell_type":"code","source":"submission = []\ndice_scores = []\npredictions = []","metadata":{"execution":{"iopub.execute_input":"2025-02-25T21:18:54.584378Z","iopub.status.busy":"2025-02-25T21:18:54.584160Z","iopub.status.idle":"2025-02-25T21:18:54.587297Z","shell.execute_reply":"2025-02-25T21:18:54.586631Z"},"papermill":{"duration":0.00777,"end_time":"2025-02-25T21:18:54.588480","exception":false,"start_time":"2025-02-25T21:18:54.580710","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"09ece25c","cell_type":"code","source":"#SegFormer's Defination\nfeature_extractor = SegformerFeatureExtractor.from_pretrained(\"nvidia/segformer-b4-finetuned-ade-512-512\")\nmodel = SegformerForSemanticSegmentation.from_pretrained(\"nvidia/segformer-b4-finetuned-ade-512-512\").to(device)","metadata":{"execution":{"iopub.execute_input":"2025-02-25T21:18:54.594872Z","iopub.status.busy":"2025-02-25T21:18:54.594665Z","iopub.status.idle":"2025-02-25T21:18:58.191880Z","shell.execute_reply":"2025-02-25T21:18:58.190593Z"},"papermill":{"duration":3.602248,"end_time":"2025-02-25T21:18:58.193642","exception":false,"start_time":"2025-02-25T21:18:54.591394","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"0db0a8b6","cell_type":"code","source":"#Parameters which control training \nnum_epochs = 5\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-4)\nscheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=5)\naccumulation_steps = 4\nscaler = GradScaler('cuda')","metadata":{"execution":{"iopub.execute_input":"2025-02-25T21:18:58.202153Z","iopub.status.busy":"2025-02-25T21:18:58.201847Z","iopub.status.idle":"2025-02-25T21:18:58.210507Z","shell.execute_reply":"2025-02-25T21:18:58.209699Z"},"papermill":{"duration":0.014224,"end_time":"2025-02-25T21:18:58.211817","exception":false,"start_time":"2025-02-25T21:18:58.197593","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"f30769b5","cell_type":"markdown","source":"# *Dataset Class,Transformations and Dataloaders*","metadata":{"papermill":{"duration":0.003146,"end_time":"2025-02-25T21:18:58.218465","exception":false,"start_time":"2025-02-25T21:18:58.215319","status":"completed"},"tags":[]}},{"id":"a28fdf63","cell_type":"code","source":"class FaceSegmentationDataset(Dataset):\n    def __init__(self, image_dir, mask_dir, feature_extractor, transform=None, is_test=False):\n        self.image_dir = image_dir\n        self.mask_dir = mask_dir\n        self.image_filenames = os.listdir(image_dir)\n        self.transform = transform\n        self.feature_extractor = feature_extractor\n        self.is_test = is_test\n    \n    def __len__(self):\n        return len(self.image_filenames)\n    \n    def __getitem__(self, idx):\n        img_path = os.path.join(self.image_dir, self.image_filenames[idx])\n        image = cv2.imread(img_path)\n        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n        \n        if self.is_test:\n            encoded_inputs = self.feature_extractor(images=image, return_tensors=\"pt\", do_rescale=False)\n            return encoded_inputs, self.image_filenames[idx]\n        \n        mask_path = os.path.join(self.mask_dir, self.image_filenames[idx].replace('.jpg', '.png'))\n        mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)\n        \n        if self.transform:\n            augmented = self.transform(image=image, mask=mask)\n            image = augmented['image']\n            mask = augmented['mask']\n        \n        encoded_inputs = self.feature_extractor(images=image, return_tensors=\"pt\", do_rescale=False)\n        encoded_inputs['labels'] = torch.tensor(mask, dtype=torch.long)\n        return encoded_inputs\n\n\ntrain_transform = A.Compose([\n    A.Resize(1024,1024),\n    A.HorizontalFlip(p=0.5),\n    A.Rotate(limit=20, p=0.5),\n    A.RandomBrightnessContrast(p=0.2),\n    A.ElasticTransform(p=0.2, alpha=120, sigma=120 * 0.05, alpha_affine=120 * 0.03),\n    A.GaussianBlur(p=0.1),\n    ToTensorV2()\n])\n\ndataset = FaceSegmentationDataset(\"/kaggle/input/slicee-my-face/images/train\", \"/kaggle/input/slicee-my-face/annotations/train\", feature_extractor, transform=train_transform)\ndataloader = DataLoader(dataset, batch_size=2, shuffle=True)\ntest_dataset = FaceSegmentationDataset(\"/kaggle/input/slicee-my-face/images/test\", None, feature_extractor, is_test=True)\ntest_dataloader = DataLoader(test_dataset, batch_size=1, shuffle=False)","metadata":{"execution":{"iopub.execute_input":"2025-02-25T21:18:58.225886Z","iopub.status.busy":"2025-02-25T21:18:58.225672Z","iopub.status.idle":"2025-02-25T21:18:58.393230Z","shell.execute_reply":"2025-02-25T21:18:58.392322Z"},"papermill":{"duration":0.172885,"end_time":"2025-02-25T21:18:58.394706","exception":false,"start_time":"2025-02-25T21:18:58.221821","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"5bee3521","cell_type":"markdown","source":"# *Definations of all the classes in my code*","metadata":{"papermill":{"duration":0.003355,"end_time":"2025-02-25T21:18:58.401798","exception":false,"start_time":"2025-02-25T21:18:58.398443","status":"completed"},"tags":[]}},{"id":"e8215fb6","cell_type":"code","source":"def train_class(model, dataloader, criterion, optimizer, accumulation_steps):\n    model.train()\n    total_loss = 0\n    optimizer.zero_grad()\n\n    for i, batch in enumerate(tqdm(dataloader, desc=\"Training\")):\n        if batch is None:\n            continue\n        \n        with autocast('cuda'):\n            inputs = {k: v.squeeze(0).to(device) if k != 'pixel_values' else v.squeeze(1).to(device) for k, v in batch.items()}\n            outputs = model(**inputs)\n            loss = outputs.loss\n            loss = loss / accumulation_steps\n            scaler.scale(loss).backward()\n\n        if (i + 1) % accumulation_steps == 0:\n            scaler.step(optimizer)\n            scaler.update()\n            optimizer.zero_grad()\n            \n        total_loss += loss.item() * accumulation_steps\n    return total_loss / len(dataloader)","metadata":{"execution":{"iopub.execute_input":"2025-02-25T21:18:58.409509Z","iopub.status.busy":"2025-02-25T21:18:58.409275Z","iopub.status.idle":"2025-02-25T21:18:58.414684Z","shell.execute_reply":"2025-02-25T21:18:58.414032Z"},"papermill":{"duration":0.010669,"end_time":"2025-02-25T21:18:58.415897","exception":false,"start_time":"2025-02-25T21:18:58.405228","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"03c49238","cell_type":"code","source":"def rle_encode(mask):\n    pixels = mask.flatten()\n    pixels = np.concatenate([[0], pixels, [0]])\n    runs = np.where(pixels[1:] != pixels[:-1])[0] + 1\n    if len(runs) % 2 != 0:\n        runs = np.append(runs, len(pixels) - 1)\n    runs[1::2] -= runs[::2]\n    return \" \".join(str(x) for x in runs)","metadata":{"execution":{"iopub.execute_input":"2025-02-25T21:18:58.423717Z","iopub.status.busy":"2025-02-25T21:18:58.423496Z","iopub.status.idle":"2025-02-25T21:18:58.427460Z","shell.execute_reply":"2025-02-25T21:18:58.426835Z"},"papermill":{"duration":0.009018,"end_time":"2025-02-25T21:18:58.428545","exception":false,"start_time":"2025-02-25T21:18:58.419527","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"dae6a400","cell_type":"code","source":"def dice_coefficient(pred_mask, true_mask):\n    intersection = np.sum(pred_mask * true_mask)\n    union = np.sum(pred_mask) + np.sum(true_mask)\n    if union == 0:\n        return 1.0\n    return 2 * intersection / union","metadata":{"execution":{"iopub.execute_input":"2025-02-25T21:18:58.436413Z","iopub.status.busy":"2025-02-25T21:18:58.436185Z","iopub.status.idle":"2025-02-25T21:18:58.439784Z","shell.execute_reply":"2025-02-25T21:18:58.439001Z"},"papermill":{"duration":0.008912,"end_time":"2025-02-25T21:18:58.441081","exception":false,"start_time":"2025-02-25T21:18:58.432169","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"00e0fc0d","cell_type":"code","source":"def tta_inference(model, image):\n    augments = [\n        lambda x: x,\n        lambda x: cv2.flip(x, 1),\n        lambda x: cv2.GaussianBlur(x, (5, 5), 0),\n    ]\n    \n    for augment in augments:\n            augmented_img = augment(image)\n            inputs = feature_extractor(images=augmented_img, return_tensors=\"pt\").to(device)\n            outputs = model(**inputs)\n            pred_mask = torch.argmax(outputs.logits, dim=1).cpu().numpy()\n            predictions.append(pred_mask)\n    \n    return np.mean(predictions, axis=0) > 0.5 ","metadata":{"execution":{"iopub.execute_input":"2025-02-25T21:18:58.448874Z","iopub.status.busy":"2025-02-25T21:18:58.448666Z","iopub.status.idle":"2025-02-25T21:18:58.452779Z","shell.execute_reply":"2025-02-25T21:18:58.452239Z"},"papermill":{"duration":0.009431,"end_time":"2025-02-25T21:18:58.454011","exception":false,"start_time":"2025-02-25T21:18:58.444580","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"c877310c","cell_type":"markdown","source":"# *Training and Evaluating*","metadata":{"papermill":{"duration":0.003374,"end_time":"2025-02-25T21:18:58.460920","exception":false,"start_time":"2025-02-25T21:18:58.457546","status":"completed"},"tags":[]}},{"id":"39808d5f","cell_type":"code","source":"model.train()","metadata":{"papermill":{"duration":0.02805,"end_time":"2025-02-25T21:18:58.492552","exception":false,"start_time":"2025-02-25T21:18:58.464502","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"09edd82b","cell_type":"code","source":"for epoch in range(num_epochs):\n    loss = train_class(model, dataloader, criterion, optimizer, accumulation_steps)\n    print(f\"Epoch {epoch+1}/{num_epochs}, Loss: {loss:.4f}\")\n    scheduler.step()","metadata":{"execution":{"iopub.execute_input":"2025-02-25T21:18:58.502888Z","iopub.status.busy":"2025-02-25T21:18:58.502680Z","iopub.status.idle":"2025-02-25T23:14:20.458916Z","shell.execute_reply":"2025-02-25T23:14:20.457829Z"},"papermill":{"duration":6921.962978,"end_time":"2025-02-25T23:14:20.460440","exception":false,"start_time":"2025-02-25T21:18:58.497462","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"77391670","cell_type":"code","source":"model.eval()","metadata":{"papermill":{"duration":0.405052,"end_time":"2025-02-25T23:14:21.306362","exception":false,"start_time":"2025-02-25T23:14:20.901310","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"28665a60","cell_type":"code","source":"with torch.no_grad():\n    for batch, image_name in tqdm(test_dataloader, desc=\"Inference\"):\n        inputs = {k: v.squeeze(0).to(device) for k, v in batch.items()}\n        outputs = model(**inputs)\n        pred_mask = torch.argmax(outputs.logits, dim=1).cpu().numpy().squeeze()\n        \n        true_mask_path = f\"/kaggle/input/slicee-my-face/annotations/test/{image_name[0].replace('.jpg', '.png')}\"\n        true_mask = cv2.imread(true_mask_path, cv2.IMREAD_GRAYSCALE)\n        \n        binary_pred_mask = cv2.resize((pred_mask > 0).astype(np.uint8), (true_mask.shape[1], true_mask.shape[0]))\n        binary_true_mask = (true_mask > 0).astype(np.uint8)\n\n        dice_score = dice_coefficient(binary_pred_mask, binary_true_mask)\n        dice_scores.append(dice_score)\n        \n        kernel = np.ones((3,3), np.uint8)\n        clean_mask = cv2.morphologyEx(binary_pred_mask, cv2.MORPH_CLOSE, kernel)\n        clean_mask = cv2.morphologyEx(clean_mask, cv2.MORPH_OPEN, kernel)\n        clean_mask[clean_mask > 0] = 1\n\n        rle_mask = rle_encode(clean_mask)\n        submission.append([image_name[0].replace(\".jpg\", \"\"), rle_mask])","metadata":{"execution":{"iopub.execute_input":"2025-02-25T23:14:22.075041Z","iopub.status.busy":"2025-02-25T23:14:22.074678Z","iopub.status.idle":"2025-02-25T23:17:06.065359Z","shell.execute_reply":"2025-02-25T23:17:06.064417Z"},"papermill":{"duration":164.37559,"end_time":"2025-02-25T23:17:06.066941","exception":false,"start_time":"2025-02-25T23:14:21.691351","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"dd4558f0","cell_type":"markdown","source":"# *Managing CSV file*","metadata":{"papermill":{"duration":0.426363,"end_time":"2025-02-25T23:17:06.925883","exception":false,"start_time":"2025-02-25T23:17:06.499520","status":"completed"},"tags":[]}},{"id":"777e5fd8","cell_type":"code","source":"submission_df = pd.DataFrame(submission, columns=[\"id\", \"predicted\"])\nsubmission_df = submission_df.sort_values(by=\"id\")\nsubmission_df.to_csv(\"submission.csv\", index=False)\nprint(f\"Average Dice Coefficient: {np.mean(dice_scores):.4f}\")\ndisplay(FileLink('submission.csv'))","metadata":{"execution":{"iopub.execute_input":"2025-02-25T23:17:07.829074Z","iopub.status.busy":"2025-02-25T23:17:07.828719Z","iopub.status.idle":"2025-02-25T23:17:08.099738Z","shell.execute_reply":"2025-02-25T23:17:08.098996Z"},"papermill":{"duration":0.697336,"end_time":"2025-02-25T23:17:08.100914","exception":false,"start_time":"2025-02-25T23:17:07.403578","status":"completed"},"tags":[]},"outputs":[],"execution_count":null}]}