Update app.py

app.py

@@ -13,7 +13,7 @@ from data_transforms import normal_transforms, no_shift_transforms, ig_transform
 from utils import overlay_heatmap, viz_map, show_image, deprocess, get_ssl_model, fig2img
 from methods import occlusion, occlusion_context_agnositc, pairwise_occlusion
 from methods import create_mixed_images, averaged_transforms, sailency, smooth_grad 
-from methods import get_sample_dataset, pixel_invariance, get_gradcam, get_interactioncam
+from methods import get_gradcam, get_interactioncam
 
 matplotlib.use('Agg')
 
@@ -148,63 +148,24 @@ def get_cams():
 
   gradcam1, gradcam2 = get_gradcam(ssl_model, img1, img2)
   intcam1_mean, intcam2_mean = get_interactioncam(ssl_model, img1, img2, reduction = 'mean')
-  intcam1_maxmax, intcam2_maxmax = get_interactioncam(ssl_model, img1, img2, reduction = 'max', grad_interact = True)
-  intcam1_attnmax, intcam2_attnmax = get_interactioncam(ssl_model, img1, img2, reduction = 'attn', grad_interact = True)
 
-  fig, axs = plt.subplots(2, 5, figsize=(20,8))
+  fig, axs = plt.subplots(2, 3, figsize=(20,8))
   np.vectorize(lambda ax:ax.axis('off'))(axs)
 
   axs[0,0].imshow(show_image(img1[0], squeeze = False, denormalize = denorm))
   axs[0,1].imshow(overlay_heatmap(img1, gradcam1, denormalize = denorm))
   axs[0,1].set_title("Grad-CAM")
   axs[0,2].imshow(overlay_heatmap(img1, intcam1_mean, denormalize = denorm))
-  axs[0,2].set_title("IntCAM Mean")
-  axs[0,3].imshow(overlay_heatmap(img1, intcam1_maxmax, denormalize = denorm))
-  axs[0,3].set_title("IntCAM Max + IntGradMax")
-  axs[0,4].imshow(overlay_heatmap(img1, intcam1_attnmax, denormalize = denorm))
-  axs[0,4].set_title("IntCAM Attn + IntGradMax")
+  axs[0,2].set_title("IntCAM")
 
   axs[1,0].imshow(show_image(img2[0], squeeze = False, denormalize = denorm))
   axs[1,1].imshow(overlay_heatmap(img2, gradcam2, denormalize = denorm))
   axs[1,2].imshow(overlay_heatmap(img2, intcam2_mean, denormalize = denorm))
-  axs[1,3].imshow(overlay_heatmap(img2, intcam2_maxmax, denormalize = denorm))
-  axs[1,4].imshow(overlay_heatmap(img2, intcam2_attnmax, denormalize = denorm))
 
   plt.subplots_adjust(wspace=0.01, hspace = 0.01)
   pil_output = fig2img(fig)
   return pil_output
 
-def get_pixel_invariance():
-
-  data_samples1, data_samples2, data_labels, labels_invariance = get_sample_dataset(img_path = img_main, 
-                                                                                    num_augments = 1000, 
-                                                                                    batch_size =  32, 
-                                                                                    no_shift_transforms = no_shift_transforms, 
-                                                                                    ssl_model = ssl_model, 
-                                                                                    n_components = 10)
-
-  inv_heatmap = pixel_invariance(data_samples1 = data_samples1, data_samples2 = data_samples2, data_labels = data_labels,
-                                labels_invariance = labels_invariance, resize_transform = transforms.Resize, size = 64, 
-                                epochs = 1000, learning_rate = 0.1, l1_weight = 0.2, zero_small_values = True, 
-                                blur_output = True, nmf_weight = 0)
-
-  inv_heatmap_nmf = pixel_invariance(data_samples1 = data_samples1, data_samples2 = data_samples2, data_labels = data_labels,
-                                    labels_invariance = labels_invariance, resize_transform = transforms.Resize, size = 64, 
-                                    epochs = 100, learning_rate = 0.1, l1_weight = 0.2, zero_small_values = True, 
-                                    blur_output = True, nmf_weight = 1)
-
-  fig, axs = plt.subplots(1, 2, figsize=(10,5))
-  np.vectorize(lambda ax:ax.axis('off'))(axs)
-
-  axs[0].imshow(viz_map(img_main, inv_heatmap))
-  axs[0].set_title("Heatmap w/o NMF")
-  axs[1].imshow(viz_map(img_main, inv_heatmap_nmf))
-  axs[1].set_title("Heatmap w/ NMF")
-  plt.subplots_adjust(wspace=0.01, hspace = 0.01)
-
-  pil_output = fig2img(fig)
-  return pil_output
-
 xai = gr.Blocks()
 
 with xai:
@@ -240,9 +201,6 @@ with xai:
           blur_output = gr.Checkbox(value = True, label = "Blur Output")
           guided = gr.Checkbox(value = True, label = "Guided Backprop")
           avgtransform_button = gr.Button("Get Saliency")
-        with gr.TabItem("Pixel Invariance"):
-          gr.Markdown("Note: Invariance map will be obtained for the first image")
-          pixel_invariance_button = gr.Button("Get Invariance Map")
 
     with gr.Column():
       output_image = gr.Image(type='pil', show_label = False)
@@ -252,7 +210,6 @@ with xai:
   occlusion_button.click(run_occlusion, inputs=[w_size,stride], outputs=output_image)
   avgtransform_button.click(get_avg_trasforms, inputs = [transform_type, add_noise, blur_output, guided], outputs = output_image)
   cams_button.click(get_cams, inputs = [], outputs = output_image) 
-  pixel_invariance_button.click(get_pixel_invariance, inputs = [], outputs = output_image)
 
 xai.launch()