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