Create app.py

app.py

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+import warnings
+
+warnings.filterwarnings("ignore")
+import torch
+import cv2
+import numpy as np
+from torchvision import transforms
+from torchvision.models import resnet18, ResNet18_Weights
+import urllib.request
+from pytorch_grad_cam import GradCAMPlusPlus
+from pytorch_grad_cam.utils.image import show_cam_on_image, preprocess_image
+from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
+import gradio as gr
+
+IMG_SIZE = 224
+CLASSES = ResNet18_Weights.IMAGENET1K_V1.meta["categories"]
+TOP_NUM_CLASSES = 3
+
+url = (
+    "https://upload.wikimedia.org/wikipedia/commons/3/38/Adorable-animal-cat-20787.jpg"
+)
+path_input = "./cat.jpg"
+urllib.request.urlretrieve(url, filename=path_input)
+
+
+url = "https://upload.wikimedia.org/wikipedia/commons/4/43/Cute_dog.jpg"
+path_input = "./dog.jpg"
+urllib.request.urlretrieve(url, filename=path_input)
+
+device = "cpu"
+if torch.cuda.is_available():
+    device = "cuda"
+
+model = resnet18(pretrained=True)
+
+data_transforms = transforms.Compose(
+    [
+        transforms.Resize(IMG_SIZE),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ]
+)
+
+
+def grad_campp(img, cls_ids):
+    img_rz = cv2.resize(np.array(img), (IMG_SIZE, IMG_SIZE))
+    img = np.float32(img_rz) / 255
+    input_tensor = preprocess_image(img, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]).to(
+        device
+    )
+    # mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]
+
+    # Set target layers
+    target_layers = [model.layer4[-1]]
+
+    # Set target classes
+    # targets = [ClassifierOutputTarget(cls_id) for cls_id in cls_ids]
+
+    # GradCAM++
+    gradcampp = GradCAMPlusPlus(model=model, target_layers=target_layers)
+
+    lst_gradcam = []
+    for i in range(TOP_NUM_CLASSES):
+        targets = [ClassifierOutputTarget(cls_ids[i])]
+        grayscale_gradcampp = gradcampp(
+            input_tensor=input_tensor,
+            targets=targets,
+            eigen_smooth=False,
+            aug_smooth=False,
+        )
+        grayscale_gradcampp = grayscale_gradcampp[0, :]
+        gradcampp_image = show_cam_on_image(img, grayscale_gradcampp, use_rgb=True)
+        lst_gradcam.append(gradcampp_image)
+
+    return img_rz, lst_gradcam
+
+
+def do_inference(img):
+    img_t = data_transforms(img)
+    batch_t = torch.unsqueeze(img_t, 0)
+    model.eval()
+    # We don't need gradients for test, so wrap in
+    # no_grad to save memory
+    with torch.no_grad():
+        batch_t = batch_t.to(device)
+        # forward propagation
+        output = model(batch_t)
+        # get prediction
+        probs = torch.nn.functional.softmax(output, dim=1)
+        cls_ids = (
+            torch.argsort(probs, dim=1, descending=True).cpu().numpy()[0].astype(int)
+        )[:TOP_NUM_CLASSES]
+        probs = probs.cpu().numpy()[0]
+        probs = probs[cls_ids]
+        labels = np.array(CLASSES)[cls_ids]
+    img_rz, lst_gradcam = grad_campp(img, cls_ids)
+    return (
+        {labels[i]: round(float(probs[i]), 2) for i in range(len(labels))},
+        img_rz,
+        lst_gradcam[0],
+        lst_gradcam[1],
+        lst_gradcam[2],
+    )
+
+
+im = gr.inputs.Image(
+    shape=None, image_mode="RGB", invert_colors=False, source="upload", type="pil"
+)
+
+title = "Explainable AI - PyTorch"
+description = "Playground: GradCam Inferernce of Object Classification using ResNet18 model. Libraries: PyTorch, Gradio, Grad-Cam"
+examples = [["./cat.jpg"], ["./dog.jpg"]]
+article = "<p style='text-align: center'><a href='https://github.com/mawady' target='_blank'>By Dr. Mohamed Elawady</a></p>"
+iface = gr.Interface(
+    do_inference,
+    im,
+    outputs=[
+        gr.outputs.Label(num_top_classes=TOP_NUM_CLASSES),
+        gr.outputs.Image(label="Output image", type="pil"),
+        gr.outputs.Image(label="Output image", type="pil"),
+        gr.outputs.Image(label="Output image", type="pil"),
+        gr.outputs.Image(label="Output image", type="pil"),
+    ],
+    live=False,
+    interpretation=None,
+    title=title,
+    description=description,
+    examples=examples,
+)
+
+# iface.test_launch()
+
+iface.launch()