| | 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 custom_resnet import Net |
| |
|
| | model = Net('batch') |
| | model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')), strict=False) |
| |
|
| | classes = ('plane', 'car', 'bird', 'cat', 'deer', |
| | 'dog', 'frog', 'horse', 'ship', 'truck') |
| |
|
| | def inference(input_img, transparency = 0.5, target_layer_number = -1, num_top_classes = 5): |
| | """This function take input as an image and generate Grad Cam image of it. |
| | |
| | Args: |
| | input_img (_type_): Input image provided by user. |
| | transparency (float, optional): _description_. Defaults to 0.5. |
| | target_layer_number (int, optional): Output of layer which will be given to Grad Cam. Defaults to -1. |
| | num_top_classes (int, optional): To show number of classes to show in the output. Defaults to 5. |
| | |
| | Returns: |
| | top: Top Classes and Confidence level of the prediction |
| | visualization: Grad Cam output |
| | """ |
| | |
| | transform = transforms.Compose([ |
| | transforms.ToTensor(), |
| | transforms.Normalize((0.1307,), (0.3081,)) |
| | ]) |
| | org_img = input_img |
| | input_img = transform(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.layer3_r3[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) |
| | 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) |
| |
|
| | |
| | sorted_confidences = {k: v for k, v in sorted(confidences.items(), key=lambda item: item[1], reverse=True)} |
| | top_classes = list(sorted_confidences.keys())[:num_top_classes] |
| | top = dict((k,v) for k, v in sorted_confidences.items() if k in top_classes) |
| |
|
| | return top, visualization |
| |
|
| | title = "CIFAR10 trained on ResNet18 Model with GradCAM" |
| | description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results" |
| | examples = [["airplane.png", 0.5, -1, 5],["bird.jpeg", 0.5, -1, 5], ["car.jpeg", 0.5, -1, 5], ["cat.png", 0.5, -1, 5], |
| | ["deer.jpeg", 0.5, -1, 6], ["dog.png", 0.5, -1, 7], ["frog.jpeg", 0.5, -1, 4], ["horse.png", 0.5, -1, 7], |
| | ["ship.png", 0.5, -1, 3], ["truck.jpeg", 0.5, -1, 8]] |
| |
|
| | 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?"), |
| | gr.Slider(0, 10, value = 1, step=1, label="Number of Top Classes")], |
| | outputs = [gr.Label(num_top_classes=10), gr.Image(shape=(32, 32), label="Output", style={"width": "128px", "height": "128px"})], |
| | title = title, |
| | description = description, |
| | examples = examples, |
| | ) |
| |
|
| | demo.launch() |
| |
|
