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import torch |
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import torchvision |
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from torchvision import transforms |
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import numpy as np |
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import gradio as gr |
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from PIL import Image |
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from pytorch_grad_cam import GradCAM |
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from pytorch_grad_cam.utils.image import show_cam_on_image |
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from resnet import ResNet18 |
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model = ResNet18() |
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model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')), strict=False) |
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inv_normalize = transforms.Normalize( |
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mean=[-0.50/0.23, -0.50/0.23, -0.50/0.23], |
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std=[1/0.23, 1/0.23, 1/0.23] |
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) |
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classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck') |
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def resize_image_pil(image, new_width, new_height): |
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img = Image.fromarray(np.array(image)) |
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width, height = img.size |
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width_scale = new_width / width |
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height_scale = new_height / height |
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scale = min(width_scale, height_scale) |
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resized = img.resize((int(width*scale), int(height*scale)), Image.NEAREST) |
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resized = resized.crop((0, 0, new_width, new_height)) |
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return resized |
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def inference(input_img, show_gradcam, num_gradcam, target_layer_number, opacity, show_misclassified, num_misclassified, num_top_classes): |
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input_img = resize_image_pil(input_img, 32, 32) |
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input_img = np.array(input_img) |
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org_img = input_img |
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input_img = input_img.reshape((32, 32, 3)) |
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transform = transforms.ToTensor() |
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input_img = transform(input_img) |
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input_img = input_img.unsqueeze(0) |
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outputs = model(input_img) |
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softmax = torch.nn.Softmax(dim=1) |
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probs = softmax(outputs) |
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top_probs, top_labels = torch.topk(probs, k=min(num_top_classes, 10)) |
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top_classes = [classes[idx] for idx in top_labels[0]] |
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confidences = {cls: float(prob) for cls, prob in zip(top_classes, top_probs[0])} |
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_, prediction = torch.max(outputs, 1) |
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predicted_class = classes[prediction[0].item()] |
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results = [predicted_class, confidences] |
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if show_gradcam: |
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target_layers = [model.layer2[target_layer_number]] |
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cam = GradCAM(model=model, target_layers=target_layers) |
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grayscale_cam = cam(input_tensor=input_img, targets=None) |
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grayscale_cam = grayscale_cam[0, :] |
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visualization = show_cam_on_image(org_img/255, grayscale_cam, use_rgb=True, image_weight=opacity) |
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results.append(visualization) |
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if show_misclassified: |
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results.append("Misclassified images feature would be implemented here") |
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return results |
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def launch(): |
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with gr.Blocks() as demo: |
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gr.Markdown("# CIFAR10 ResNet18 Model with GradCAM") |
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with gr.Row(): |
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input_image = gr.Image(width=256, height=256, label="Input Image") |
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output_image = gr.Image(width=256, height=256, label="GradCAM Output") |
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with gr.Row(): |
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prediction = gr.Textbox(label="Predicted Class") |
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confidences = gr.Label(label="Top Class Confidences") |
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with gr.Row(): |
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show_gradcam = gr.Checkbox(label="Show GradCAM") |
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num_gradcam = gr.Slider(1, 5, value=1, step=1, label="Number of GradCAM images") |
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target_layer = gr.Slider(-2, -1, value=-2, step=1, label="Target Layer") |
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opacity = gr.Slider(0, 1, value=0.5, label="GradCAM Opacity") |
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with gr.Row(): |
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show_misclassified = gr.Checkbox(label="Show Misclassified Images") |
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num_misclassified = gr.Slider(1, 10, value=5, step=1, label="Number of Misclassified Images") |
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num_top_classes = gr.Slider(1, 10, value=3, step=1, label="Number of Top Classes to Show") |
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submit_btn = gr.Button("Submit") |
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example_images = gr.Dataset( |
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components=[input_image], |
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samples=[["cat.jpg"], ["dog.jpg"], ["bird.jpg"], ["plane.jpg"], ["car.jpg"], |
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["deer.jpg"], ["frog.jpg"], ["horse.jpg"], ["ship.jpg"], ["truck.jpg"]] |
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) |
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submit_btn.click( |
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inference, |
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inputs=[input_image, show_gradcam, num_gradcam, target_layer, opacity, |
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show_misclassified, num_misclassified, num_top_classes], |
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outputs=[prediction, confidences, output_image] |
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) |
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demo.launch() |
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if __name__ == "__main__": |
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launch() |
