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| import torch, 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 custom_ResNet | |
| import gradio as gr | |
| import os | |
| model = custom_ResNet() | |
| model.load_state_dict(torch.load("custom_resnet_model.pth", map_location=torch.device('cpu')), strict=False) | |
| model.setup(stage="test") | |
| 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 inference(input_img, transparency=0.5, target_layer_number=-1, top_classes=3): | |
| transform = transforms.ToTensor() | |
| org_img = input_img | |
| input_img = transform(input_img) | |
| input_img = input_img.unsqueeze(0) | |
| outputs = model(input_img) | |
| softmax = torch.nn.Softmax(dim=1) # Use dim=1 to compute softmax along the classes dimension | |
| o = softmax(outputs) | |
| confidences = {classes[i]: float(o[0, i]) for i in range(10)} | |
| _, prediction = torch.max(outputs, 1) | |
| target_layers = [model.convblock2_l1] | |
| cam = GradCAM(model=model, target_layers=target_layers, use_cuda=False) | |
| grayscale_cam = cam(input_tensor=input_img, targets=None) | |
| grayscale_cam = grayscale_cam[0, :] | |
| img = input_img.squeeze(0) | |
| img = inv_normalize(img) | |
| rgb_img = np.transpose(img, (1, 2, 0)) | |
| rgb_img = rgb_img.numpy() | |
| visualization = show_cam_on_image(org_img / 255, grayscale_cam, use_rgb=True, image_weight=transparency) | |
| # Sort the confidences dictionary by values in descending order | |
| sorted_confidences = {k: v for k, v in sorted(confidences.items(), key=lambda item: item[1], reverse=True)} | |
| # Take the top `top_classes` elements from the sorted_confidences | |
| top_classes_confidences = {k: sorted_confidences[k] for k in list(sorted_confidences)[:top_classes]} | |
| return top_classes_confidences, visualization | |
| # Create a wrapper function for show_misclassified_images() | |
| def show_misclassified_images_wrapper(num_images=10, use_gradcam=False, gradcam_layer=-1, transparency=0.5): | |
| transparency = float(transparency) | |
| num_images = int(num_images) | |
| if use_gradcam == "Yes": | |
| use_gradcam = True | |
| else: | |
| use_gradcam = False | |
| return model.show_misclassified_images(num_images, use_gradcam, gradcam_layer, transparency) | |
| description1 = "Test the model's prediction. Currently the model only supports the following classes - plane, car, bird, cat, deer, dog, frog, horse, ship, truck." | |
| # Define the full path to the images folder | |
| images_folder = "examples" | |
| # Define the examples list with full paths | |
| examples = [[os.path.join(images_folder, "plane.jpg"), 0.5, -1], | |
| [os.path.join(images_folder, "car.jpg"), 0.5, -1], | |
| [os.path.join(images_folder, "bird.jpeg"), 0.5, -1], | |
| [os.path.join(images_folder, "cat.jpeg"), 0.5, -1], | |
| [os.path.join(images_folder, "deer.jpeg"), 0.5, -1], | |
| [os.path.join(images_folder, "dog.jpeg"), 0.5, -1], | |
| [os.path.join(images_folder, "frog.jpeg"), 0.5, -1], | |
| [os.path.join(images_folder, "horse.jpeg"), 0.5, -1], | |
| [os.path.join(images_folder, "ship.jpeg"), 0.5, -1], | |
| [os.path.join(images_folder, "truck.jpeg"), 0.5, -1]] | |
| # Create a separate interface for the "Input an image" tab | |
| input_interface = gr.Interface(inference, | |
| inputs=[gr.Image(shape=(32, 32), label="Input Image"), | |
| gr.Slider(0, 1, value=0.5, label="Opacity of GradCAM"), | |
| gr.Slider(-2, -1, value=-2, step=1, label="Which Layer?"), | |
| gr.Slider(1, 10, value=3, step=1, label="How many top confidence classes to be shown?")], | |
| outputs=[gr.Label(), | |
| gr.Image(shape=(32, 32), label="Model Prediction").style(width=300, height=300)], | |
| description=description1,examples=examples) | |
| description2 = "Displays misclassified image of the model" | |
| # Create a separate interface for the "Misclassified Images" tab | |
| misclassified_interface = gr.Interface(show_misclassified_images_wrapper, | |
| inputs=[gr.Number(value=10, label="Number of images to display"), | |
| gr.Radio(["Yes", "No"], value="No" , label="Show GradCAM outputs"), | |
| gr.Slider(-2, -1, value=-1, step=1, label="Which layer for GradCAM?"), | |
| gr.Slider(0, 1, value=0.5, label="Opacity of GradCAM")], | |
| outputs=gr.Plot(), description=description2) | |
| demo = gr.TabbedInterface([input_interface, misclassified_interface], tab_names=["Input an image", "Misclassified Images"], | |
| title="Custom Resnet on CIFAR10 using GradCAM") | |
| demo.launch() |