app.py

import torch
import torchvision
from torchvision import transforms
import gradio as gr
import numpy as np
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):

    # convert to PIL IMage
    img = Image.fromarray(np.array(image))
    # get original size
    width, height = img.size

    # calculate scale
    width_scale = new_width/width
    height_scale = new_height/height

    scale = min(width_scale, height_scale)

    # resize
    resized = img.resize(size=(int(width*scale), int(height*scale)), resample=Image.NEAREST)

    # crop resized image
    resized = resized.crop((0, 0, new_width, new_height))

    return resized

def inference(input_img, transparency):
    transform = transforms.ToTensor()
    input_img = transform(input_img)
    input_img = input_img.to(device)
    input_img = input_img.unsqueeze(0)
    outputs = model(input_img)
    _, prediction = torch.max(outputs, 1)
    target_layers = [model.layer2[-2]]
    cam = GradCAM(model=model, target_layers=target_layers, use_cuda=True)
    grayscale_cam = cam(input_tensor=input_img, targets=targets)
    grayscale_cam = grayscale_cam[0, :]
    img = input_img.squeeze(0).to('cpu')
    img = inv_normalize(img)
    rgb_img = np.transpose(img, (1, 2, 0))
    rgb_img = rgb_img.numpy()
    visualization = show_cam_on_image(rgb_img, grayscale_cam, use_rgb=True, image_weight=transparency)
    return classes[prediction[0].item()], visualization

demo = gr.Interface(
    inference,
    inputs = [
        gr.Image(width=256, height=256, label="Input Image"),
        gr.Slider(0, 1, value=0.5, label="Overall opacity fo the overlay"),
        gr.Slider(-2, -1, value=-2, step=1, label="Which GradCAM layer?")    
    ],
    outputs = [
        "text",
        gr.Image(width=256, height=256, label="Output"),
        gr.Label(num_top_classes=3)
    ],
    title="CIFAR10 trained on ResNet18 with GradCAM feature",
    description = "A simple Gradio app for checking GradCAM outputs from results of ResNet18 model.",
    examples = [["cat.jpg", 0.5, -1], ["dog.jpg", 0.7, -2]]
)

demo.launch()