Create app.py

app.py

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+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_image, transparency=0.5, target_layer_number=-1):
+    input_image = resize_image_pil(input_image, 32, 32)
+    input_image = np.array(input_image)
+    org_img = input_image
+    input_image = input_image.reshape((32, 32, 3))
+    transforms = transforms.ToTensor()
+    input_image = transforms(input_image)
+    input_image = input_image.unsqueeze(0)
+    outputs = model(input_image)
+    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)
+    grayscale_cam = cam(input_tensor=input_image, target=None)
+    grayscale_cam = grayscale_cam[0, :]
+    visualization = show_cam_on_image(
+        org_img/255, 
+        grayscale_cam, 
+        use_rgb=True,
+        image_weight = transparency
+    )
+     
+    return classes[prediction[0].item(), visualization, confidences]
+
+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()