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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "13af5935-ba3f-4c00-bf56-c76da3c380db",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import cv2\n",
"import numpy as np\n",
"from torchvision import transforms\n",
"import matplotlib.pyplot as plt\n",
"from PIL import Image"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d04ef85e-3ca6-4e4f-baf9-d5a8a01fac15",
"metadata": {},
"outputs": [],
"source": [
"def preprocess_image(image_path):\n",
" \"\"\"\n",
" Load and preprocess an image for inference.\n",
" \"\"\"\n",
" transform = transforms.Compose([\n",
" transforms.Resize((224, 224)),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
" ])\n",
" \n",
" img = Image.open(image_path).convert('RGB')\n",
" tensor = transform(img)\n",
" return tensor.unsqueeze(0), img"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4f59b6a8-7969-4754-a418-126242bc8bf6",
"metadata": {},
"outputs": [],
"source": [
"def get_last_conv_layer(model):\n",
" \"\"\"\n",
" Get the last convolutional layer in the model.\n",
" \"\"\"\n",
" # For ResNet architecture\n",
" for name, module in reversed(list(model.named_modules())):\n",
" if isinstance(module, nn.Conv2d):\n",
" return name\n",
" raise ValueError(\"No Conv2d layers found in the model.\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5509721f-0ee5-49fc-ab39-37b03c34fb3f",
"metadata": {},
"outputs": [],
"source": [
"def apply_gradcam(model, image_tensor, target_class=None):\n",
" \"\"\"\n",
" Apply Grad-CAM to an image.\n",
" \"\"\"\n",
" device = next(model.parameters()).device\n",
" image_tensor = image_tensor.to(device)\n",
"\n",
" # Register hooks to get activations and gradients\n",
" features = []\n",
" gradients = []\n",
"\n",
" def forward_hook(module, input, output):\n",
" features.append(output.detach())\n",
"\n",
" def backward_hook(module, grad_input, grad_output):\n",
" gradients.append(grad_output[0].detach())\n",
"\n",
" last_conv_layer_name = get_last_conv_layer(model)\n",
" last_conv_layer = dict(model.named_modules())[last_conv_layer_name]\n",
" handle_forward = last_conv_layer.register_forward_hook(forward_hook)\n",
" handle_backward = last_conv_layer.register_backward_hook(backward_hook)\n",
"\n",
" # Forward pass\n",
" model.eval()\n",
" output = model(image_tensor)\n",
" if target_class is None:\n",
" target_class = output.argmax(dim=1).item()\n",
"\n",
" # Zero out all gradients\n",
" model.zero_grad()\n",
"\n",
" # Backward pass\n",
" one_hot = torch.zeros_like(output)\n",
" one_hot[0][target_class] = 1\n",
" output.backward(gradient=one_hot)\n",
"\n",
" # Remove hooks\n",
" handle_forward.remove()\n",
" handle_backward.remove()\n",
"\n",
" # Get feature maps and gradients\n",
" feature_map = features[-1].squeeze().cpu().numpy()\n",
" gradient = gradients[-1].squeeze().cpu().numpy()\n",
"\n",
" # Global Average Pooling on gradients\n",
" pooled_gradients = np.mean(gradient, axis=(1, 2), keepdims=True)\n",
" cam = feature_map * pooled_gradients\n",
" cam = np.sum(cam, axis=0)\n",
"\n",
" # Apply ReLU\n",
" cam = np.maximum(cam, 0)\n",
"\n",
" # Normalize the CAM\n",
" cam = cam - np.min(cam)\n",
" cam = cam / np.max(cam)\n",
"\n",
" # Resize CAM to match the original image size\n",
" cam = cv2.resize(cam, (224, 224))\n",
"\n",
" return cam"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "96e420e6-2359-4712-b247-7deec782920f",
"metadata": {},
"outputs": [],
"source": [
"def overlay_heatmap(original_image, heatmap, alpha=0.5):\n",
" \"\"\"\n",
" Overlay the heatmap on the original image.\n",
" \n",
" Args:\n",
" original_image (np.ndarray): Original image (H, W, 3), uint8\n",
" heatmap (np.ndarray): Grad-CAM heatmap (H', W'), float between 0 and 1\n",
" alpha (float): Weight for the heatmap\n",
" \n",
" Returns:\n",
" np.ndarray: Overlayed image\n",
" \"\"\"\n",
" # Ensure heatmap is 2D\n",
" if heatmap.ndim == 3:\n",
" heatmap = np.mean(heatmap, axis=2)\n",
"\n",
" # Resize heatmap to match original image size\n",
" heatmap_resized = cv2.resize(heatmap, (original_image.shape[1], original_image.shape[0]))\n",
"\n",
" # Normalize heatmap to [0, 255]\n",
" heatmap_resized = np.uint8(255 * heatmap_resized)\n",
"\n",
" # Apply colormap\n",
" heatmap_colored = cv2.applyColorMap(heatmap_resized, cv2.COLORMAP_JET)\n",
"\n",
" # Convert from BGR to RGB\n",
" heatmap_colored = cv2.cvtColor(heatmap_colored, cv2.COLOR_BGR2RGB)\n",
"\n",
" # Superimpose: blend heatmap and original image\n",
" superimposed_img = heatmap_colored * alpha + original_image * (1 - alpha)\n",
" return np.uint8(superimposed_img)\n",
"\n",
"def visualize_gradcam(model, image_path):\n",
" \"\"\"\n",
" Visualize Grad-CAM for a given image.\n",
" \"\"\"\n",
" # Preprocess image\n",
" image_tensor, original_image = preprocess_image(image_path) # (1, 3, 224, 224), (H, W, 3)\n",
" original_image_np = np.array(original_image) # PIL -> numpy array\n",
"\n",
" # Apply Grad-CAM\n",
" cam = apply_gradcam(model, image_tensor) # returns (224, 224)\n",
"\n",
" # Overlay heatmap on original image\n",
" heatmap_overlay = overlay_heatmap(original_image_np, cam)\n",
"\n",
" # Display results\n",
" plt.figure(figsize=(10, 5))\n",
" plt.subplot(1, 2, 1)\n",
" plt.imshow(original_image_np)\n",
" plt.title(\"Original Image\")\n",
" plt.axis(\"off\")\n",
"\n",
" plt.subplot(1, 2, 2)\n",
" plt.imshow(heatmap_overlay)\n",
" plt.title(\"Grad-CAM Heatmap\")\n",
" plt.axis(\"off\")\n",
"\n",
" plt.tight_layout()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "94610c70-994e-4b2d-ab37-20f2a70d1c18",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.12/site-packages/torch/nn/modules/module.py:1842: FutureWarning: Using a non-full backward hook when the forward contains multiple autograd Nodes is deprecated and will be removed in future versions. This hook will be missing some grad_input. Please use register_full_backward_hook to get the documented behavior.\n",
" self._maybe_warn_non_full_backward_hook(args, result, grad_fn)\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1000x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"if __name__ == \"__main__\":\n",
"\n",
" from models.resnet_model import MalariaResNet50\n",
" # Load your trained model\n",
" model = MalariaResNet50(num_classes=2)\n",
" model.load_state_dict(torch.load(\"models/malaria_model.pth\"))\n",
" model.eval()\n",
"\n",
" # Path to an image\n",
" image_path = \"malaria_ds/split_dataset/test/Parasitized/C33P1thinF_IMG_20150619_114756a_cell_181.png\"\n",
"\n",
" # Visualize Grad-CAM\n",
" visualize_gradcam(model, image_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "07450501-8fcf-49e3-ab90-93aabc0394e9",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.17"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|