Add grad cam checkbox

app.py

@@ -39,7 +39,7 @@ def resize_image_pil(image, new_width, new_height):
 
     return resized
 
-def inference(input_img, transparency = 0.5, target_layer_number = -1):
+def inference(input_img, transparency = 0.5, is_grad_cam=True, target_layer_number = -1):
     input_img = resize_image_pil(input_img, 32, 32)
     
     input_img = np.array(input_img)
@@ -54,11 +54,14 @@ def inference(input_img, transparency = 0.5, target_layer_number = -1):
     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_img, targets=None)
-    grayscale_cam = grayscale_cam[0, :]
-    visualization = show_cam_on_image(org_img/255, grayscale_cam, use_rgb=True, image_weight=transparency)
+    if is_grad_cam:
+        target_layers = [model.layer2[target_layer_number]]
+        cam = GradCAM(model=model, target_layers=target_layers)
+        grayscale_cam = cam(input_tensor=input_img, targets=None)
+        grayscale_cam = grayscale_cam[0, :]
+        visualization = show_cam_on_image(org_img/255, grayscale_cam, use_rgb=True, image_weight=transparency)
+    else:
+        visualization = None
     return classes[prediction[0].item()], visualization, confidences
 
 title = "CIFAR10 trained on ResNet18 Model with GradCAM"
@@ -67,8 +70,9 @@ examples = [["cat.jpg", 0.5, -1], ["dog.jpg", 0.5, -1]]
 demo = gr.Interface(
     inference, 
     inputs = [
-        gr.Image(width=256, height=256, label="Input Image"), gr.Slider
-        (0, 1, value = 0.5, label="Overall Opacity of Image"), 
+        gr.Image(width=256, height=256, label="Input Image"),
+        gr.Slider(0, 1, value = 0.5, label="Overall Opacity of Image"), 
+        gr.Checkbox(label="Show GradCAM"),
         gr.Slider(-2, -1, value = -2, step=1, label="Which Layer?")
         ], 
     outputs = [