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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\nimport os\nfor 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 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input/aptos2019-blindness-detection/test_images/f6578f3dfafe.png\n/kaggle/input/aptos2019-blindness-detection/test_images/8a2b969112a7.png\n/kaggle/input/aptos2019-blindness-detection/test_images/b82e6271eaa5.png\n/kaggle/input/aptos2019-blindness-detection/test_images/38496a64a658.png\n/kaggle/input/aptos2019-blindness-detection/test_images/57d5d0d42cac.png\n/kaggle/input/aptos2019-blindness-detection/test_images/490c7597892e.png\n/kaggle/input/aptos2019-blindness-detection/test_images/bafc8f7940f0.png\n/kaggle/input/aptos2019-blindness-detection/test_images/925967c408c7.png\n/kaggle/input/aptos2019-blindness-detection/test_images/48b280688d0d.png\n/kaggle/input/aptos2019-blindness-detection/test_images/77f1ffe777b7.png\n/kaggle/input/aptos2019-blindness-detection/test_images/7b17a0d85204.png\n/kaggle/input/aptos2019-blindness-detection/test_images/7baa6e053baf.png\n/kaggle/input/aptos2019-blindness-detection/test_images/fc3935afc788.png\n/kaggle/input/aptos2019-blindness-detection/test_images/30376d8cde4e.png\n/kaggle/input/aptos2019-blindness-detection/test_images/72dd7ca97f55.png\n/kaggle/input/aptos2019-blindness-detection/test_images/0a262e8b2a5a.png\n/kaggle/input/aptos2019-blindness-detection/test_images/1b79278c8cb5.png\n/kaggle/input/aptos2019-blindness-detection/test_images/afb7907b77d9.png\n/kaggle/input/aptos2019-blindness-detection/test_images/0f61878bfd0e.png\n/kaggle/input/aptos2019-blindness-detection/test_images/896c75533897.png\n/kaggle/input/aptos2019-blindness-detection/test_images/f32e563daf7b.png\n/kaggle/input/aptos2019-blindness-detection/test_images/4890c8e4f897.png\n/kaggle/input/aptos2019-blindness-detection/test_images/3ef3056cc3d0.png\n/kaggle/input/aptos2019-blindness-detection/test_images/ef5f4ffdd75c.png\n/kaggle/input/aptos2019-blindness-detection/test_images/29112729cdc0.png\n/kaggle/input/aptos2019-blindness-detection/test_images/ac6c7462887b.png\n/kaggle/input/aptos2019-blindness-detection/test_images/f524093e34ea.png\n/kaggle/input/aptos2019-blindness-detection/test_images/dff147e8c2f9.png\n/kaggle/input/aptos2019-blindness-detection/test_images/c38bfcb69bac.png\n/kaggle/input/aptos2019-blindness-detection/test_images/1a6b7f58d488.png\n/kaggle/input/aptos2019-blindness-detection/test_images/24289104b70b.png\n/kaggle/input/aptos2019-blindness-detection/test_images/f3daf369a103.png\n/kaggle/input/aptos2019-blindness-detection/test_images/e177610cf7a1.png\n/kaggle/input/aptos2019-blindness-detection/test_images/246efa653ba4.png\n/kaggle/input/aptos2019-blindness-detection/test_images/45f34a152021.png\n/kaggle/input/aptos2019-blindness-detection/test_images/427110b0a05e.png\n/kaggle/input/aptos2019-blindness-detection/test_images/cd45c48db97d.png\n/kaggle/input/aptos2019-blindness-detection/test_images/7eeed2e26c9f.png\n/kaggle/input/aptos2019-blindness-detection/test_images/fa09c97a2887.png\n/kaggle/input/aptos2019-blindness-detection/test_images/995eb7091756.png\n","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"import os\n\n# Walk through the input directory to see the file structure\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames[:5]: # Print just the first 5 files to check\n        print(os.path.join(dirname, filename))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-24T06:48:52.955882Z","iopub.execute_input":"2026-01-24T06:48:52.956797Z","iopub.status.idle":"2026-01-24T06:48:54.665844Z","shell.execute_reply.started":"2026-01-24T06:48:52.956758Z","shell.execute_reply":"2026-01-24T06:48:54.665106Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input/aptos2019-blindness-detection/sample_submission.csv\n/kaggle/input/aptos2019-blindness-detection/train.csv\n/kaggle/input/aptos2019-blindness-detection/test.csv\n/kaggle/input/aptos2019-blindness-detection/train_images/ef476be214d4.png\n/kaggle/input/aptos2019-blindness-detection/train_images/6dcde47060f9.png\n/kaggle/input/aptos2019-blindness-detection/train_images/ec363f48867b.png\n/kaggle/input/aptos2019-blindness-detection/train_images/17f6c7072f61.png\n/kaggle/input/aptos2019-blindness-detection/train_images/b49b2fac2514.png\n/kaggle/input/aptos2019-blindness-detection/test_images/755615db51d3.png\n/kaggle/input/aptos2019-blindness-detection/test_images/d8f6c660bf2d.png\n/kaggle/input/aptos2019-blindness-detection/test_images/619ce2c77917.png\n/kaggle/input/aptos2019-blindness-detection/test_images/94a6e4f3c625.png\n/kaggle/input/aptos2019-blindness-detection/test_images/ee506d853270.png\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"!pip install timm","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-24T06:48:54.666865Z","iopub.execute_input":"2026-01-24T06:48:54.667176Z","iopub.status.idle":"2026-01-24T06:48:58.674575Z","shell.execute_reply.started":"2026-01-24T06:48:54.667145Z","shell.execute_reply":"2026-01-24T06:48:58.673708Z"}},"outputs":[{"name":"stdout","text":"Requirement already satisfied: timm in /usr/local/lib/python3.12/dist-packages (1.0.20)\nRequirement already satisfied: torch in /usr/local/lib/python3.12/dist-packages (from timm) (2.8.0+cu126)\nRequirement already satisfied: torchvision in /usr/local/lib/python3.12/dist-packages (from timm) (0.23.0+cu126)\nRequirement already satisfied: pyyaml in /usr/local/lib/python3.12/dist-packages (from timm) (6.0.3)\nRequirement already satisfied: huggingface_hub in /usr/local/lib/python3.12/dist-packages (from timm) (0.36.0)\nRequirement already satisfied: safetensors in /usr/local/lib/python3.12/dist-packages (from timm) (0.6.2)\nRequirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from huggingface_hub->timm) (3.20.3)\nRequirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub->timm) (2025.10.0)\nRequirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub->timm) (26.0rc2)\nRequirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from huggingface_hub->timm) (2.32.5)\nRequirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub->timm) (4.67.1)\nRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub->timm) (4.15.0)\nRequirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub->timm) (1.2.1rc0)\nRequirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch->timm) (75.2.0)\nRequirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (1.13.3)\nRequirement already satisfied: networkx in /usr/local/lib/python3.12/dist-packages (from torch->timm) (3.5)\nRequirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (3.1.6)\nRequirement already satisfied: nvidia-cuda-nvrtc-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (12.6.77)\nRequirement already satisfied: nvidia-cuda-runtime-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (12.6.77)\nRequirement already satisfied: nvidia-cuda-cupti-cu12==12.6.80 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (12.6.80)\nRequirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (9.10.2.21)\nRequirement already satisfied: nvidia-cublas-cu12==12.6.4.1 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (12.6.4.1)\nRequirement already satisfied: nvidia-cufft-cu12==11.3.0.4 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (11.3.0.4)\nRequirement already satisfied: nvidia-curand-cu12==10.3.7.77 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (10.3.7.77)\nRequirement already satisfied: nvidia-cusolver-cu12==11.7.1.2 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (11.7.1.2)\nRequirement already satisfied: nvidia-cusparse-cu12==12.5.4.2 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (12.5.4.2)\nRequirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (0.7.1)\nRequirement already satisfied: nvidia-nccl-cu12==2.27.3 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (2.27.3)\nRequirement already satisfied: nvidia-nvtx-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (12.6.77)\nRequirement already satisfied: nvidia-nvjitlink-cu12==12.6.85 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (12.6.85)\nRequirement already satisfied: nvidia-cufile-cu12==1.11.1.6 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (1.11.1.6)\nRequirement already satisfied: triton==3.4.0 in /usr/local/lib/python3.12/dist-packages (from torch->timm) (3.4.0)\nRequirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from torchvision->timm) (2.0.2)\nRequirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.12/dist-packages (from torchvision->timm) (11.3.0)\nRequirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch->timm) (1.3.0)\nRequirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch->timm) (3.0.3)\nRequirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub->timm) (3.4.4)\nRequirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub->timm) (3.11)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub->timm) (2.6.3)\nRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub->timm) (2026.1.4)\n","output_type":"stream"}],"execution_count":3},{"cell_type":"code","source":"import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef load_ben_color(path, sigmaX=10):\n    image = cv2.imread(path)\n    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n    \n    # 1. Crop to the circle of the eye (remove black borders)\n    # (Simple cropping logic often works, or use standard resize)\n    image = cv2.resize(image, (512, 512))\n    \n    # 2. Ben Graham's Method: Gaussian Blur subtraction\n    # This acts as a high-pass filter to highlight blood vessels\n    image = cv2.addWeighted(image, 4, cv2.GaussianBlur(image, (0,0), sigmaX), -4, 128)\n    \n    return image\n\n# Quick Visualization\n# plt.imshow(load_ben_color(\"../input/aptos2019-blindness-detection/train_images/000c1434d8d7.png\"))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-24T06:48:58.675784Z","iopub.execute_input":"2026-01-24T06:48:58.676201Z","iopub.status.idle":"2026-01-24T06:48:58.939432Z","shell.execute_reply.started":"2026-01-24T06:48:58.676172Z","shell.execute_reply":"2026-01-24T06:48:58.938909Z"}},"outputs":[],"execution_count":4},{"cell_type":"code","source":"import torch\nfrom torch.utils.data import Dataset, DataLoader\nimport pandas as pd\nfrom torchvision import transforms\n\nclass AptosDataset(Dataset):\n    def __init__(self, csv_file, root_dir, transform=None):\n        self.df = pd.read_csv(csv_file)\n        self.root_dir = root_dir\n        self.transform = transform\n        \n    def __len__(self):\n        return len(self.df)\n    \n    def __getitem__(self, idx):\n        img_name = self.df.iloc[idx, 0]\n        img_path = f\"{self.root_dir}/{img_name}.png\"\n        \n        # Load with Ben's Preprocessing\n        image = load_ben_color(img_path)\n        \n        if self.transform:\n            image = self.transform(image)\n            \n        label = self.df.iloc[idx, 1]\n        return image, torch.tensor(label, dtype=torch.long)\n\n# Transforms: Standard Normalize for ImageNet\ntransform = transforms.Compose([\n    transforms.ToPILImage(),\n    transforms.Resize((224, 224)), # ViT usually likes 224 or 384\n    transforms.ToTensor(),\n    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-24T06:48:58.941017Z","iopub.execute_input":"2026-01-24T06:48:58.941249Z","iopub.status.idle":"2026-01-24T06:49:05.511086Z","shell.execute_reply.started":"2026-01-24T06:48:58.941229Z","shell.execute_reply":"2026-01-24T06:49:05.510454Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"import torch.nn as nn\nimport timm\nimport torch.nn.functional as F\n\nclass CrossAttentionFusion(nn.Module):\n    def __init__(self, cnn_dim, vit_dim, embed_dim=512, num_heads=8):\n        super().__init__()\n        # Project both CNN and ViT features to the same dimension\n        self.cnn_proj = nn.Linear(cnn_dim, embed_dim)\n        self.vit_proj = nn.Linear(vit_dim, embed_dim)\n        \n        # Multi-Head Attention\n        self.mha = nn.MultiheadAttention(embed_dim, num_heads, batch_first=True)\n        \n        # Layer Norms\n        self.norm_cnn = nn.LayerNorm(embed_dim)\n        self.norm_vit = nn.LayerNorm(embed_dim)\n        \n    def forward(self, cnn_feats, vit_cls):\n        # cnn_feats: [Batch, Channels, Height, Width] -> Flatten to [Batch, Seq_Len, Channels]\n        B, C, H, W = cnn_feats.shape\n        cnn_flat = cnn_feats.view(B, C, -1).permute(0, 2, 1) # [B, H*W, C]\n        \n        # Project to common dimension\n        cnn_emb = self.cnn_proj(cnn_flat)  # This will be our Key (K) and Value (V)\n        vit_emb = self.vit_proj(vit_cls).unsqueeze(1) # This is Query (Q) [B, 1, Dim]\n        \n        # CROSS ATTENTION: ViT asks CNN \"Where should I look?\"\n        # query=ViT, key=CNN, value=CNN\n        attn_out, _ = self.mha(query=self.norm_vit(vit_emb), \n                               key=self.norm_cnn(cnn_emb), \n                               value=self.norm_cnn(cnn_emb))\n        \n        return attn_out.squeeze(1) # Return [B, Dim]\n\nclass HybridModel(nn.Module):\n    def __init__(self, num_classes=5):\n        super().__init__()\n        \n        # 1. CNN Branch (ResNet50)\n        # remove_pooling=True gives us the spatial feature map (7x7), not the pooled vector\n        self.cnn = timm.create_model('resnet50', pretrained=True, features_only=True)\n        # ResNet50 last layer has 2048 channels\n        \n        # 2. ViT Branch (ViT-Small)\n        self.vit = timm.create_model('vit_small_patch16_224', pretrained=True)\n        # ViT-Small embedding dim is 384\n        \n        # 3. Fusion Layer\n        self.fusion = CrossAttentionFusion(cnn_dim=2048, vit_dim=384, embed_dim=512)\n        \n        # 4. Final Classifier\n        self.head = nn.Linear(512, num_classes)\n        \n    def forward(self, x):\n        # --- CNN Forward ---\n        # features_only=True returns a list of feature maps. We want the last one.\n        cnn_out = self.cnn(x)[-1] # Shape: [Batch, 2048, 7, 7]\n        \n        # --- ViT Forward ---\n        # forward_features returns all tokens. We usually just want the CLS token (index 0)\n        # OR the pooled output. Let's grab the CLS token.\n        vit_out = self.vit.forward_features(x) \n        vit_cls = vit_out[:, 0] # Shape: [Batch, 384]\n        \n        # --- Fusion ---\n        fused_vec = self.fusion(cnn_out, vit_cls)\n        \n        # --- Classification ---\n        logits = self.head(fused_vec)\n        return logits\n\n# Initialize\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel = HybridModel(num_classes=5).to(device)\nprint(\"Model created!\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-24T06:49:05.511912Z","iopub.execute_input":"2026-01-24T06:49:05.512265Z","iopub.status.idle":"2026-01-24T06:49:13.539447Z","shell.execute_reply.started":"2026-01-24T06:49:05.512237Z","shell.execute_reply":"2026-01-24T06:49:13.538721Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.12/dist-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\n  warnings.warn(\n/usr/local/lib/python3.12/dist-packages/pydantic/_internal/_generate_schema.py:2249: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\n  warnings.warn(\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"model.safetensors:   0%|          | 0.00/102M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c2c404d667cc4862a76590ca5f1415fe"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model.safetensors:   0%|          | 0.00/88.2M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a5c42a0ffc054c1fabe6a532620698f9"}},"metadata":{}},{"name":"stdout","text":"Model created!\n","output_type":"stream"}],"execution_count":6},{"cell_type":"code","source":"import torch.optim as optim\n\n# Hyperparameters\nEPOCHS = 10 \nLR = 1e-4\nBATCH_SIZE = 16 # Reduce if you get OOM (Out of Memory)\n\n# Loaders\ntrain_dataset = AptosDataset(\n    csv_file='../input/aptos2019-blindness-detection/train.csv',\n    root_dir='../input/aptos2019-blindness-detection/train_images',\n    transform=transform\n)\ntrain_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2)\n\n# Loss and Optimizer\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.AdamW(model.parameters(), lr=LR)\nscheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS)\n\n# Training Loop\nfor epoch in range(EPOCHS):\n    model.train()\n    running_loss = 0.0\n    \n    for images, labels in train_loader:\n        images, labels = images.to(device), labels.to(device)\n        \n        optimizer.zero_grad()\n        \n        # Forward\n        outputs = model(images)\n        loss = criterion(outputs, labels)\n        \n        # Backward\n        loss.backward()\n        optimizer.step()\n        \n        running_loss += loss.item()\n        \n    scheduler.step()\n    print(f\"Epoch {epoch+1}/{EPOCHS}, Loss: {running_loss/len(train_loader):.4f}\")\n\n# Save the model\ntorch.save(model.state_dict(), \"hybrid_cnn_vit.pth\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-24T06:49:13.540263Z","iopub.execute_input":"2026-01-24T06:49:13.54047Z","iopub.status.idle":"2026-01-24T07:31:07.087896Z","shell.execute_reply.started":"2026-01-24T06:49:13.54045Z","shell.execute_reply":"2026-01-24T07:31:07.087196Z"}},"outputs":[{"name":"stdout","text":"Epoch 1/10, Loss: 0.7151\nEpoch 2/10, Loss: 0.5195\nEpoch 3/10, Loss: 0.4006\nEpoch 4/10, Loss: 0.3045\nEpoch 5/10, Loss: 0.1996\nEpoch 6/10, Loss: 0.1307\nEpoch 7/10, Loss: 0.0911\nEpoch 8/10, Loss: 0.0593\nEpoch 9/10, Loss: 0.0444\nEpoch 10/10, Loss: 0.0479\n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix, classification_report\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndef evaluate_model(model, loader, device):\n    # 1. Setup\n    model.eval()  # Set model to evaluation mode (disables Dropout/BatchNorm training)\n    all_preds = []\n    all_labels = []\n    \n    print(\"Starting evaluation... (this might take a minute)\")\n    \n    # 2. Evaluation Loop (No Gradients needed)\n    with torch.no_grad():\n        for images, labels in loader:\n            images = images.to(device)\n            labels = labels.to(device)\n            \n            # Forward pass\n            outputs = model(images)\n            \n            # Get predictions (highest probability)\n            _, preds = torch.max(outputs, 1)\n            \n            # Store results (move to CPU and convert to numpy)\n            all_preds.extend(preds.cpu().numpy())\n            all_labels.extend(labels.cpu().numpy())\n    \n    # 3. Calculate Accuracy\n    all_preds = np.array(all_preds)\n    all_labels = np.array(all_labels)\n    accuracy = (all_preds == all_labels).mean() * 100\n    print(f\"Overall Accuracy: {accuracy:.2f}%\")\n    \n    return all_labels, all_preds\n\n# --- Run the Evaluation ---\n# Note: Ideally, you should use a separate 'val_loader' here. \n# If you only have 'train_loader', this checks how well the model memorized the training data.\ntrue_labels, predicted_labels = evaluate_model(model, train_loader, device)\n\n# --- Plot Confusion Matrix ---\n# APTOS Classes: 0: No DR, 1: Mild, 2: Moderate, 3: Severe, 4: Proliferative\nclass_names = [\"No DR\", \"Mild\", \"Moderate\", \"Severe\", \"Proliferative\"]\n\ncm = confusion_matrix(true_labels, predicted_labels)\n\nplt.figure(figsize=(10, 8))\nsns.heatmap(cm, annot=True, fmt='d', cmap='Blues', \n            xticklabels=class_names, yticklabels=class_names)\nplt.ylabel('Actual Label', fontsize=12)\nplt.xlabel('Predicted Label', fontsize=12)\nplt.title('Confusion Matrix: Hybrid CNN+ViT', fontsize=14)\nplt.show()\n\n# --- (Optional) Detailed Text Report ---\nprint(\"\\nClassification Report:\\n\")\nprint(classification_report(true_labels, predicted_labels, target_names=class_names))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-24T07:33:33.150376Z","iopub.execute_input":"2026-01-24T07:33:33.151165Z","iopub.status.idle":"2026-01-24T07:37:33.158084Z","shell.execute_reply.started":"2026-01-24T07:33:33.151124Z","shell.execute_reply":"2026-01-24T07:37:33.157396Z"}},"outputs":[{"name":"stdout","text":"Starting evaluation... (this might take a minute)\nOverall Accuracy: 99.07%\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x800 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\nClassification Report:\n\n               precision    recall  f1-score   support\n\n        No DR       1.00      1.00      1.00      1805\n         Mild       0.99      0.98      0.99       370\n     Moderate       0.99      0.99      0.99       999\n       Severe       0.96      0.96      0.96       193\nProliferative       0.97      0.98      0.97       295\n\n     accuracy                           0.99      3662\n    macro avg       0.98      0.98      0.98      3662\n weighted avg       0.99      0.99      0.99      3662\n\n","output_type":"stream"}],"execution_count":8},{"cell_type":"code","source":"import pandas as pd\nfrom torch.utils.data import Dataset, DataLoader\n\n# 1. Define the Test Dataset Class\n# (We need a specific class because test.csv has no 'diagnosis' column)\nclass TestDataset(Dataset):\n    def __init__(self, csv_file, root_dir, transform=None):\n        self.df = pd.read_csv(csv_file)\n        self.root_dir = root_dir\n        self.transform = transform\n        \n    def __len__(self):\n        return len(self.df)\n    \n    def __getitem__(self, idx):\n        # Get the ID from the csv\n        img_name = self.df.iloc[idx, 0]\n        # Construct the full image path\n        img_path = f\"{self.root_dir}/{img_name}.png\"\n        \n        # Apply the SAME preprocessing as training\n        image = load_ben_color(img_path)\n        \n        if self.transform:\n            image = self.transform(image)\n            \n        # Return image and the ID (so we can map them in the CSV)\n        return image, img_name\n\n# 2. Setup the Test Loader\ntest_dataset = TestDataset(\n    csv_file='../input/aptos2019-blindness-detection/test.csv',\n    root_dir='../input/aptos2019-blindness-detection/test_images',\n    transform=transform\n)\n\n# shuffle=False is CRITICAL for submission! \ntest_loader = DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=2)\n\n# 3. Generate Predictions\nmodel.eval()\nresults = []\nprint(f\"Generating predictions for {len(test_dataset)} images...\")\n\nwith torch.no_grad():\n    for images, ids in test_loader:\n        images = images.to(device)\n        \n        # Forward pass\n        outputs = model(images)\n        \n        # Get predicted class (index of max probability)\n        _, preds = torch.max(outputs, 1)\n        \n        # Collect results\n        for id_code, prediction in zip(ids, preds):\n            results.append([id_code, prediction.item()])\n\n# 4. Save to CSV\nsubmission_df = pd.DataFrame(results, columns=['id_code', 'diagnosis'])\nsubmission_df.to_csv('submission.csv', index=False)\n\nprint(\"Success! 'submission.csv' saved. Ready to submit.\")\nprint(submission_df.head()) # Show first 5 rows to verify","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-24T07:52:47.064966Z","iopub.execute_input":"2026-01-24T07:52:47.065573Z","iopub.status.idle":"2026-01-24T07:53:59.359832Z","shell.execute_reply.started":"2026-01-24T07:52:47.065539Z","shell.execute_reply":"2026-01-24T07:53:59.359026Z"}},"outputs":[{"name":"stdout","text":"Generating predictions for 1928 images...\nSuccess! 'submission.csv' saved. Ready to submit.\n        id_code  diagnosis\n0  0005cfc8afb6          2\n1  003f0afdcd15          3\n2  006efc72b638          2\n3  00836aaacf06          2\n4  009245722fa4          2\n","output_type":"stream"}],"execution_count":9}]}