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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 ResNet18 | |
| import gradio as gr | |
| 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 inference(input_img, transparency = 0.5, target_layer_number = -1): | |
| transform = transforms.ToTensor() | |
| org_img = input_img | |
| input_img = transform(input_img) | |
| input_img = input_img | |
| input_img = input_img.unsqueeze(0) | |
| outputs = model(input_img) | |
| softmax = torch.nn.Softmax(dim=0) | |
| o = softmax(outputs.flatten()) | |
| confidences = {classes[i]: float(o[i]) for i in range(10)} | |
| _, prediction = torch.max(outputs, 1) | |
| target_layers = [model.layer2[target_layer_number]] | |
| 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) | |
| return confidences, visualization | |
| title = "CIFAR10 trained on ResNet18 Model with GradCAM" | |
| description = "Gradio interface to infer on ResNet18 model, and get GradCAM results" | |
| examples = [["cat.jpg", 0.5, -1], ["dog.jpg", 0.5, -1]] | |
| demo = 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?")], | |
| # outputs = [gr.Label(num_top_classes=3), gr.Image(shape=(32, 32), label="Output").style(width=128, height=128)], | |
| # title = title, | |
| # description = description, | |
| # examples = examples, | |
| fn=None, # We'll add the function later after defining all functions | |
| inputs=[ | |
| gr.InterfaceChoice(["Yes", "No"], label="View GradCAM images?"), | |
| gr.InterfaceNumber(label="Number of GradCAM images to view", default=5, min=1, max=10), | |
| gr.InterfaceText(label="Layer name for GradCAM visualization", default="layer4", lines=1), | |
| gr.InterfaceSlider(label="Opacity", min=0.1, max=1.0, default=0.5, step=0.1), | |
| gr.InterfaceChoice(["Yes", "No"], label="View misclassified images?"), | |
| gr.InterfaceNumber(label="Number of misclassified images to view", default=5, min=1, max=10), | |
| gr.InterfaceChoice(["Upload New Images", "Example Images"], label="Select images source"), | |
| gr.InterfaceImage("file" if "Interface" in gr.__file__ else "image", label="Uploaded image" if "Interface" in gr.__file__ else "Image"), | |
| gr.InterfaceButton("Submit", label="View Images") | |
| ], | |
| outputs = [gr.Label(num_top_classes=3), gr.Image(shape=(32, 32), label="Output").style(width=128, height=128)], | |
| live=True | |
| ) | |
| demo.launch() | |