Update app.py

app.py

@@ -31,19 +31,61 @@ inference_model = LitResnet.load_from_checkpoint("cifar10_customresnet_20_epoch.
 classes = ('plane', 'car', 'bird', 'cat', 'deer',
            'dog', 'frog', 'horse', 'ship', 'truck')
 
-def inference(input_img, see_misclassified=False,num_misclassified_imgs=0,see_gradcam=False,num_gradcam_imgs=0,transparency = 0.85, target_layer_number = -1,top_classes=3):
+# def inference(input_img, see_misclassified=False,num_misclassified_imgs=0,see_gradcam=False,num_gradcam_imgs=0,transparency = 0.85, target_layer_number = -1,top_classes=3):
 
-    if see_misclassified: # show misclassified images
-      org_img   =  np.asarray(Image.open('misclassified_images/mis_eg_0.jpg'))
-      input_img = org_img
+#     if see_misclassified: # show misclassified images
+#       org_img   =  np.asarray(Image.open('misclassified_images/mis_eg_0.jpg'))
+#       input_img = org_img
 
-    elif num_gradcam_imgs > 0: # show gradcam on example images
-      org_img   =  np.asarray(Image.open('examples/car.jpg'))
-      input_img = org_img
+#     elif num_gradcam_imgs > 0: # show gradcam on example images
+#       org_img   =  np.asarray(Image.open('examples/car.jpg'))
+#       input_img = org_img
+
+#     else: # nothing chosen - misclassified or gradcam
+#         org_img = input_img
+
+#     # model inference
+#     transform = transforms.ToTensor()
+#     input_img = transform(input_img)
+#     input_img = input_img.unsqueeze(0)
+#     outputs = inference_model.model(input_img)
+#     softmax = torch.nn.Softmax(dim=0)
+#     o = softmax(outputs.flatten())
+#     confidences = {classes[i]: float(o[i]) for i in range(10)}
+#     sorted_confidences = dict(sorted(confidences.items(), key=lambda x:x[1], reverse=True))
+    
+#     _, prediction = torch.max(outputs, 1)
 
-    else: # nothing chosen - misclassified or gradcam
-        org_img = input_img
+#     # gradcam
+#     if see_gradcam:
+#       target_layers = [inference_model.model.layer2[target_layer_number]]
+#       cam = GradCAM(model=inference_model.model, target_layers=target_layers, use_cuda=False)
+#       grayscale_cam = cam(input_tensor=input_img, targets=None)
+#       grayscale_cam = grayscale_cam[0, :]
+#       visualization = show_cam_on_image(org_img/255.0, grayscale_cam, use_rgb=True, image_weight=transparency)
+#     else:
+#       visualization = org_img
 
+#     # top n classes only
+#     sorted_confidences = {k: sorted_confidences[k] for k in list(sorted_confidences)[:top_classes]}
+#     return sorted_confidences, [visualization]
+
+# title = "CIFAR10 trained on Custom ResNet Model with GradCAM"
+# description = "A Gradio interface to infer on ResNet model, and get GradCAM results"
+# examples = [["examples/cat.jpg"], ["examples/plane.jpg"],["examples/dog.jpg"],["examples/truck.jpg"],["examples/bird.jpg"],["examples/ship.jpg"],["examples/horse.jpg"],["examples/frog.jpg"],["examples/deer.jpg"],["examples/car.jpg"]]
+
+# demo = gr.Interface(
+#     inference,
+#     inputs =  [gr.Image(shape=(32, 32), label="Input Image"), gr.Checkbox(label="Misclassified"),gr.Number(value=2,minimum=0,maximum=10,label="Total Misclassified Images"),gr.Checkbox(label="Gradcam"),gr.Number(value=2,minimum=0,maximum=10,label="Total GradCam Images"),gr.Slider(0, 1, value = 0.5, label="Opacity of GradCAM"), gr.Slider(-2, -1, value = -1, step=1, label="Which Layer?"), gr.Slider(1, 10, value=3, step=1, label="How many top classes?")],
+#     outputs = [gr.Label(), gr.Gallery(label="Output Images", show_label=False, elem_id="gallery").style(columns=[2], rows=[5], object_fit="contain", height="auto")],
+#     title = title,
+#     description = description,
+#     examples = examples)
+
+# demo.launch()
+
+def inference_up_img(input_img,see_gradcam= True,target_layer_number = -1,transparency = 0.85,top_classes=3):
+    org_img = input_img
     # model inference
     transform = transforms.ToTensor()
     input_img = transform(input_img)
@@ -53,7 +95,6 @@ def inference(input_img, see_misclassified=False,num_misclassified_imgs=0,see_gr
     o = softmax(outputs.flatten())
     confidences = {classes[i]: float(o[i]) for i in range(10)}
     sorted_confidences = dict(sorted(confidences.items(), key=lambda x:x[1], reverse=True))
-    
     _, prediction = torch.max(outputs, 1)
 
     # gradcam
@@ -68,18 +109,84 @@ def inference(input_img, see_misclassified=False,num_misclassified_imgs=0,see_gr
 
     # top n classes only
     sorted_confidences = {k: sorted_confidences[k] for k in list(sorted_confidences)[:top_classes]}
-    return sorted_confidences, [visualization]
+    return sorted_confidences, visualization
+
+def misclass_fn(misclassified_check,num_misclassified=1,see_gradcam=True,num_gradcam=1,gradcam_layer=-2,gradcam_opa= 0.50):
+  img_gallery = []
+
+  if misclassified_check:
+    for i in range(int(num_misclassified)):
+      org_img   =  np.asarray(Image.open('misclassified_images/mis_eg_' + str(i) + '.jpg'))
+      input_img = org_img
+
+      if see_gradcam:
+        transform = transforms.ToTensor()
+        input_img = transform(input_img)
+        input_img = input_img.unsqueeze(0)
+        target_layers = [inference_model.model.layer2[gradcam_layer]]
+        cam = GradCAM(model=inference_model.model, target_layers=target_layers, use_cuda=False)
+        grayscale_cam = cam(input_tensor=input_img, targets=None)
+        grayscale_cam = grayscale_cam[0, :]
+        visualization = show_cam_on_image(org_img/255.0, grayscale_cam, use_rgb=True, image_weight=gradcam_opa)
+        img_gallery.append(visualization)
+      else:
+        img_gallery.append(org_img)
+
+  elif see_gradcam:
+    for i in range(int(num_gradcam)):
+      org_img   =  np.asarray(Image.open('misclassified_images/mis_eg_' + str(i) + '.jpg'))
+      input_img = org_img
+      transform = transforms.ToTensor()
+      input_img = transform(input_img)
+      input_img = input_img.unsqueeze(0)
+      target_layers = [inference_model.model.layer2[gradcam_layer]]
+      cam = GradCAM(model=inference_model.model, target_layers=target_layers, use_cuda=False)
+      grayscale_cam = cam(input_tensor=input_img, targets=None)
+      grayscale_cam = grayscale_cam[0, :]
+      visualization = show_cam_on_image(org_img/255.0, grayscale_cam, use_rgb=True, image_weight=gradcam_opa)
+      img_gallery.append(visualization)
+
+  return img_gallery
 
-title = "CIFAR10 trained on Custom ResNet Model with GradCAM"
-description = "A Gradio interface to infer on ResNet model, and get GradCAM results"
 examples = [["examples/cat.jpg"], ["examples/plane.jpg"],["examples/dog.jpg"],["examples/truck.jpg"],["examples/bird.jpg"],["examples/ship.jpg"],["examples/horse.jpg"],["examples/frog.jpg"],["examples/deer.jpg"],["examples/car.jpg"]]
 
-demo = gr.Interface(
-    inference,
-    inputs =  [gr.Image(shape=(32, 32), label="Input Image"), gr.Checkbox(label="Misclassified"),gr.Number(value=2,minimum=0,maximum=10,label="Total Misclassified Images"),gr.Checkbox(label="Gradcam"),gr.Number(value=2,minimum=0,maximum=10,label="Total GradCam Images"),gr.Slider(0, 1, value = 0.5, label="Opacity of GradCAM"), gr.Slider(-2, -1, value = -1, step=1, label="Which Layer?"), gr.Slider(1, 10, value=3, step=1, label="How many top classes?")],
-    outputs = [gr.Label(), gr.Gallery(label="Output Images", show_label=False, elem_id="gallery").style(columns=[2], rows=[5], object_fit="contain", height="auto")],
-    title = title,
-    description = description,
-    examples = examples)
+with gr.Blocks() as demo:
+  gr.Markdown("Explore Custom ResNet model for CIFAR10.")
+  with gr.Tab("Upload your own image"):
+    with gr.Row():
+      image_input   = gr.Image(shape=(32, 32), label="Input Image")
+      image_label   = gr.Label()
+    with gr.Row():
+      with gr.Column():
+        gradcam_check = gr.Checkbox(label="Gradcam")
+        gradcam_layer = gr.Slider(-2, -1, value = -1, step=1, label="Which Layer?")
+        gradcam_opa = gr.Slider(0, 1, value = 0.5, label="Opacity of GradCAM")
+        top_classes = gr.Slider(1, 10, value=3, step=1, label="How many top classes?")
+      image_output  = gr.Image(shape=(32, 32), label="Output").style(width=128, height=128)
+    with gr.Row():
+      examples = gr.Examples(examples=examples, 
+                             inputs=[image_input,gradcam_check,gradcam_layer,gradcam_opa,top_classes,image_label],
+                             outputs=[image_output],
+                             fn=inference_up_img, cache_examples=False)
+    with gr.Row():
+      tab_1_button = gr.Button("Submit")
+      tab_1_cl_button = gr.ClearButton([image_input,gradcam_check,gradcam_layer,gradcam_opa,top_classes,image_label,image_output])
+      
+  with gr.Tab("Explore Misclassified/Gradcam Images"):
+    with gr.Row():
+      with gr.Column():
+        misclassified_check = gr.Checkbox(label="Misclassified")
+        num_misclassified = gr.Number(value=2,minimum=1,maximum=10,label="Total Misclassified Images")
+        gradcam_check1 = gr.Checkbox(label="Gradcam")
+        num_gradcam = gr.Number(value=2,minimum=1,maximum=10,label="Total Gradcam Images")
+        gradcam_layer1 = gr.Slider(-2, -1, value = -1, step=1, label="Which Layer?")
+        gradcam_opa1 = gr.Slider(0, 1, value = 0.5, label="Opacity of GradCAM")
+      image_gallery_output = gr.Gallery(label="Output Images", show_label=False, elem_id="gallery").style(columns=[2], rows=[5], object_fit="contain", height="auto")
+    with gr.Row():
+      tab_2_button = gr.Button("Submit")
+      tab_2_cl_button = gr.ClearButton([misclassified_check,num_misclassified,gradcam_check1,num_gradcam,gradcam_layer1,gradcam_opa1,image_gallery_output])
+      
+  tab_1_button.click(inference_up_img, inputs=[image_input,gradcam_check,gradcam_layer,gradcam_opa,top_classes], outputs=[image_label,image_output])
+  tab_2_button.click(misclass_fn, inputs=[misclassified_check,num_misclassified,gradcam_check1,num_gradcam,gradcam_layer1,gradcam_opa1], outputs=[image_gallery_output])
+demo.launch(debug=True)
 
-demo.launch()