transformerbased-model-dawn.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":9414220,"sourceType":"datasetVersion","datasetId":5717256}],"dockerImageVersionId":30761,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install ultralytics","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-09-20T22:38:36.523799Z","iopub.execute_input":"2024-09-20T22:38:36.524109Z","iopub.status.idle":"2024-09-20T22:38:52.425841Z","shell.execute_reply.started":"2024-09-20T22:38:36.524075Z","shell.execute_reply":"2024-09-20T22:38:52.424716Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Collecting ultralytics\n  Downloading ultralytics-8.2.98-py3-none-any.whl.metadata (39 kB)\nRequirement already satisfied: numpy<2.0.0,>=1.23.0 in /opt/conda/lib/python3.10/site-packages (from ultralytics) (1.26.4)\nRequirement already satisfied: matplotlib>=3.3.0 in /opt/conda/lib/python3.10/site-packages (from ultralytics) (3.7.5)\nRequirement already satisfied: opencv-python>=4.6.0 in /opt/conda/lib/python3.10/site-packages (from ultralytics) (4.10.0.84)\nRequirement already satisfied: pillow>=7.1.2 in 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(2024.6.1)\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.16.0)\nRequirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.5)\nRequirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)\nDownloading ultralytics-8.2.98-py3-none-any.whl (873 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m873.6/873.6 kB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n\u001b[?25hDownloading ultralytics_thop-2.0.6-py3-none-any.whl (26 kB)\nInstalling collected packages: ultralytics-thop, ultralytics\nSuccessfully installed ultralytics-8.2.98 ultralytics-thop-2.0.6\n","output_type":"stream"}]},{"cell_type":"code","source":"!pip install albumentations==0.4.6","metadata":{"execution":{"iopub.status.busy":"2024-09-20T22:38:52.427812Z","iopub.execute_input":"2024-09-20T22:38:52.428132Z","iopub.status.idle":"2024-09-20T22:39:09.128386Z","shell.execute_reply.started":"2024-09-20T22:38:52.428099Z","shell.execute_reply":"2024-09-20T22:39:09.127363Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Collecting albumentations==0.4.6\n  Downloading albumentations-0.4.6.tar.gz (117 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.2/117.2 kB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25ldone\n\u001b[?25hRequirement already satisfied: numpy>=1.11.1 in /opt/conda/lib/python3.10/site-packages (from albumentations==0.4.6) (1.26.4)\nRequirement already satisfied: scipy in /opt/conda/lib/python3.10/site-packages (from albumentations==0.4.6) (1.14.0)\nRequirement 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/opt/conda/lib/python3.10/site-packages (from imgaug>=0.4.0->albumentations==0.4.6) (2.34.1)\nRequirement already satisfied: Shapely in /opt/conda/lib/python3.10/site-packages (from imgaug>=0.4.0->albumentations==0.4.6) (1.8.5.post1)\nRequirement already satisfied: networkx>=2.8 in /opt/conda/lib/python3.10/site-packages (from scikit-image>=0.14.2->imgaug>=0.4.0->albumentations==0.4.6) (3.3)\nRequirement already satisfied: tifffile>=2022.8.12 in /opt/conda/lib/python3.10/site-packages (from scikit-image>=0.14.2->imgaug>=0.4.0->albumentations==0.4.6) (2024.5.22)\nRequirement already satisfied: packaging>=21 in /opt/conda/lib/python3.10/site-packages (from scikit-image>=0.14.2->imgaug>=0.4.0->albumentations==0.4.6) (21.3)\nRequirement already satisfied: lazy-loader>=0.4 in /opt/conda/lib/python3.10/site-packages (from scikit-image>=0.14.2->imgaug>=0.4.0->albumentations==0.4.6) (0.4)\nRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.4.6) (1.2.1)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.4.6) (0.12.1)\nRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.4.6) (4.53.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.4.6) (1.4.5)\nRequirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.4.6) (3.1.2)\nRequirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib->imgaug>=0.4.0->albumentations==0.4.6) (2.9.0.post0)\nBuilding wheels for collected packages: albumentations\n  Building wheel for albumentations (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for albumentations: filename=albumentations-0.4.6-py3-none-any.whl size=65153 sha256=412d455142b595607552477056dff1bc1e0cfb44157bc3d6d7a6c9977925c3c8\n  Stored in directory: /root/.cache/pip/wheels/f9/d7/0c/6ed42fd872f7d1af78b25045f8b16be330f2c70ae72c83e37d\nSuccessfully built albumentations\nInstalling collected packages: albumentations\n  Attempting uninstall: albumentations\n    Found existing installation: albumentations 1.4.14\n    Uninstalling albumentations-1.4.14:\n      Successfully uninstalled albumentations-1.4.14\nSuccessfully installed albumentations-0.4.6\n","output_type":"stream"}]},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms\nimport albumentations as A\nfrom albumentations.pytorch import ToTensorV2\nfrom ultralytics import YOLO\nimport os\nimport cv2\n\n","metadata":{"execution":{"iopub.status.busy":"2024-09-20T22:39:09.129780Z","iopub.execute_input":"2024-09-20T22:39:09.130088Z","iopub.status.idle":"2024-09-20T22:39:14.740320Z","shell.execute_reply.started":"2024-09-20T22:39:09.130056Z","shell.execute_reply":"2024-09-20T22:39:14.739532Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"# Data Augmentation Pipeline\ndef get_augmentations():\n    return A.Compose([\n        A.RandomBrightnessContrast(p=0.5),\n        A.HueSaturationValue(p=0.5),  # Replaces ColorJitter\n        A.ToGray(p=0.3),\n        A.RandomGamma(p=0.3),\n        A.GaussianBlur(blur_limit=7, p=0.2),\n        A.MotionBlur(blur_limit=7, p=0.2),\n        A.MedianBlur(blur_limit=7, p=0.2),\n        A.ISONoise(p=0.2),\n        A.ImageCompression(quality_lower=60, p=0.3),\n        A.HorizontalFlip(p=0.5),\n        A.VerticalFlip(p=0.2),\n        A.ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.2, rotate_limit=15, p=0.5),\n        A.RandomRain(p=0.3, blur_value=3, brightness_coefficient=0.9),\n        A.RandomFog(fog_coef_lower=0.3, fog_coef_upper=0.6, p=0.3),\n        A.RandomSnow(p=0.3, snow_point_lower=0.1, snow_point_upper=0.3),\n        A.Resize(640, 640),\n        A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),\n        ToTensorV2()\n    ])\n\n\n","metadata":{"execution":{"iopub.status.busy":"2024-09-20T22:39:14.742593Z","iopub.execute_input":"2024-09-20T22:39:14.743008Z","iopub.status.idle":"2024-09-20T22:39:14.752064Z","shell.execute_reply.started":"2024-09-20T22:39:14.742975Z","shell.execute_reply":"2024-09-20T22:39:14.751052Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"# Dataset class\nclass CombinedDataset(Dataset):\n    def __init__(self, root_dir, transform=None):\n        self.root_dir = root_dir\n        self.transform = transform\n        self.image_paths = []\n        self.label_paths = []\n\n        # Process the nested Fog directory\n        condition_path = os.path.join(root_dir, 'Fog', 'Fog')\n        label_path = os.path.join(condition_path, 'Fog_YOLO_darknet')  # Path to label files\n\n        print(f\"Processing condition: Fog\")\n        print(f\"Images directory: {condition_path}\")\n        print(f\"Labels directory: {label_path}\")\n\n        if os.path.isdir(condition_path):\n            # Iterate over the image files in the nested Fog folder\n            for img_file in os.listdir(condition_path):\n                if img_file.endswith('.jpg') or img_file.endswith('.png'):\n                    img_full_path = os.path.join(condition_path, img_file)\n                    self.image_paths.append(img_full_path)\n\n                    # Generate the corresponding label filename\n                    label_file = img_file.replace('.jpg', '.txt').replace('.png', '.txt')\n                    full_label_path = os.path.join(label_path, label_file)\n\n                    # Check if the label file exists\n                    if os.path.exists(full_label_path):\n                        self.label_paths.append(full_label_path)\n                    else:\n                        print(f\"Label file not found: {full_label_path}\")\n\n        print(f\"Total images found: {len(self.image_paths)}\")\n        print(f\"Total labels found: {len(self.label_paths)}\")\n\n    def __len__(self):\n        return len(self.image_paths)\n\n    def __getitem__(self, idx):\n        img_path = self.image_paths[idx]\n        label_path = self.label_paths[idx]\n\n        # Load image\n        image = cv2.imread(img_path)\n        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n\n        # Load label\n        with open(label_path, 'r') as f:\n            labels = f.readlines()\n\n        if self.transform:\n            augmented = self.transform(image=image)\n            image = augmented['image']\n\n        # Process labels to YOLO format\n        processed_labels = []\n        for label in labels:\n            label = list(map(float, label.strip().split()))\n            processed_labels.append(torch.tensor(label))\n\n        return image, torch.stack(processed_labels)\n\n","metadata":{"execution":{"iopub.status.busy":"2024-09-20T22:39:14.753392Z","iopub.execute_input":"2024-09-20T22:39:14.753703Z","iopub.status.idle":"2024-09-20T22:39:14.766215Z","shell.execute_reply.started":"2024-09-20T22:39:14.753672Z","shell.execute_reply":"2024-09-20T22:39:14.765512Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"\n# Transformer Model for Image Enhancement\nclass AllWeatherEnhancementTransformer(nn.Module):\n    def __init__(self, input_dim, num_heads, num_layers):\n        super().__init__()\n        self.transformer = nn.TransformerEncoder(\n            nn.TransformerEncoderLayer(d_model=input_dim, nhead=num_heads),\n            num_layers=num_layers\n        )\n        self.fc = nn.Linear(input_dim, 3 * 640 * 640)  # Output is 3 channels (RGB)\n\n    def forward(self, x):\n        x = self.transformer(x)\n        x = self.fc(x)\n        return x\n\n","metadata":{"execution":{"iopub.status.busy":"2024-09-20T22:39:14.767213Z","iopub.execute_input":"2024-09-20T22:39:14.767517Z","iopub.status.idle":"2024-09-20T22:39:14.777600Z","shell.execute_reply.started":"2024-09-20T22:39:14.767485Z","shell.execute_reply":"2024-09-20T22:39:14.776812Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"# Training function for enhancement model\ndef train_enhancement_network(model, dataloader, num_epochs, device):\n    optimizer = torch.optim.Adam(model.parameters())\n    criterion = nn.MSELoss()\n\n    for epoch in range(num_epochs):\n        for batch in dataloader:\n            inputs, _ = batch\n            inputs = inputs.to(device)\n\n            # Forward pass\n            outputs = model(inputs)\n            loss = criterion(outputs, inputs)\n\n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n\n        print(f\"Epoch {epoch+1}/{num_epochs}, Loss: {loss.item()}\")\n\n","metadata":{"execution":{"iopub.status.busy":"2024-09-20T22:39:14.778898Z","iopub.execute_input":"2024-09-20T22:39:14.779283Z","iopub.status.idle":"2024-09-20T22:39:14.788926Z","shell.execute_reply.started":"2024-09-20T22:39:14.779242Z","shell.execute_reply":"2024-09-20T22:39:14.788113Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"# Fine-tuning function for YOLOv8\ndef fine_tune_yolov8(enhancement_model, yolo_model, dataloader, num_epochs, device):\n    optimizer = torch.optim.Adam(list(enhancement_model.parameters()) + list(yolo_model.parameters()))\n\n    for epoch in range(num_epochs):\n        for batch in dataloader:\n            inputs, labels = batch\n            inputs, labels = inputs.to(device), labels.to(device)\n\n            # Enhance images\n            enhanced_inputs = enhancement_model(inputs)\n\n            # YOLOv8 forward pass and loss computation\n            loss = yolo_model(enhanced_inputs, labels)\n\n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n\n        print(f\"Epoch {epoch+1}/{num_epochs}, Loss: {loss.item()}\")\n\n","metadata":{"execution":{"iopub.status.busy":"2024-09-20T22:39:14.790009Z","iopub.execute_input":"2024-09-20T22:39:14.790410Z","iopub.status.idle":"2024-09-20T22:39:14.797537Z","shell.execute_reply.started":"2024-09-20T22:39:14.790343Z","shell.execute_reply":"2024-09-20T22:39:14.796631Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"def main():\n    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n    \n    data_dir = '/kaggle/input/dawn-low-visibilty-car-detection/DAWN'\n    \n    # Dataset and Dataloader with augmentation\n    combined_dataset = CombinedDataset(data_dir, transform=get_augmentations())\n    \n    # Debug: Print the number of samples in the dataset\n    print(f\"Number of samples in the dataset: {len(combined_dataset)}\")\n    \n    # If dataset length is 0, check file paths and structure.\n    if len(combined_dataset) == 0:\n        raise ValueError(\"The dataset is empty. Please check the dataset paths or contents.\")\n    \n    # Set pin_memory to True for faster data transfer to GPU\n    dataloader = DataLoader(combined_dataset, batch_size=16, shuffle=True, pin_memory=True)\n    \n    # Initialize models\n    enhancement_model = AllWeatherEnhancementTransformer(input_dim=3 * 512 * 512, num_heads=8, num_layers=4).to(device)\n    yolo_model = YOLO('yolov8n.pt').to(device)  # Load pre-trained YOLOv8 model and move it to GPU\n    \n    # Train enhancement network\n    train_enhancement_network(enhancement_model, dataloader, num_epochs=50, device=device)\n    \n    # Fine-tune YOLOv8 with enhancement network\n    fine_tune_yolov8(enhancement_model, yolo_model, dataloader, num_epochs=50, device=device)\n    \n    # Save the trained models\n    torch.save(enhancement_model.state_dict(), \"enhancement_model.pth\")\n    yolo_model.save(\"fine_tuned_yolov8.pt\")\n","metadata":{"execution":{"iopub.status.busy":"2024-09-20T22:39:14.798730Z","iopub.execute_input":"2024-09-20T22:39:14.799056Z","iopub.status.idle":"2024-09-20T22:39:14.807436Z","shell.execute_reply.started":"2024-09-20T22:39:14.799025Z","shell.execute_reply":"2024-09-20T22:39:14.806538Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"if __name__ == \"__main__\":\n    main()\n","metadata":{"execution":{"iopub.status.busy":"2024-09-20T22:39:14.811448Z","iopub.execute_input":"2024-09-20T22:39:14.811772Z"},"trusted":true},"execution_count":null,"outputs":[{"name":"stdout","text":"Processing condition: Fog\nImages directory: /kaggle/input/dawn-low-visibilty-car-detection/DAWN/Fog/Fog\nLabels directory: /kaggle/input/dawn-low-visibilty-car-detection/DAWN/Fog/Fog/Fog_YOLO_darknet\nTotal images found: 300\nTotal labels found: 300\nNumber of samples in the dataset: 300\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}