Remove code for jupyter notebooks

There was some partially commented out code to create matplotlib
figures. Remove it altogether.

CLIP_explainability/utils.py

@@ -69,7 +69,7 @@ def interpret(image, texts, model, device):
     return text_relevance, image_relevance
 
 
-def show_image_relevance(image_relevance, image, orig_image, device, show=True):
+def show_image_relevance(image_relevance, image, orig_image, device):
     # create heatmap from mask on image
     def show_cam_on_image(img, mask):
         heatmap = cv2.applyColorMap(np.uint8(255 * mask), cv2.COLORMAP_JET)
@@ -78,15 +78,6 @@ def show_image_relevance(image_relevance, image, orig_image, device, show=True):
         cam = cam / np.max(cam)
         return cam
 
-    # plt.axis('off')
-    # f, axarr = plt.subplots(1,2)
-    # axarr[0].imshow(orig_image)
-    
-    if show:
-        fig, axs = plt.subplots(1, 2)
-        axs[0].imshow(orig_image);
-        axs[0].axis('off');
-
     image_relevance = image_relevance.reshape(1, 1, 7, 7)
     image_relevance = torch.nn.functional.interpolate(image_relevance, size=224, mode='bilinear')
     image_relevance = image_relevance.reshape(224, 224).to(device).data.cpu().numpy()
@@ -97,16 +88,10 @@ def show_image_relevance(image_relevance, image, orig_image, device, show=True):
     vis = np.uint8(255 * vis)
     vis = cv2.cvtColor(np.array(vis), cv2.COLOR_RGB2BGR)
 
-    if show:
-        # axar[1].imshow(vis)
-        axs[1].imshow(vis);
-        axs[1].axis('off');
-        # plt.imshow(vis)
-
     return image_relevance
 
 
-def show_heatmap_on_text(text, text_encoding, R_text, show=True):
+def show_heatmap_on_text(text, text_encoding, R_text):
     CLS_idx = text_encoding.argmax(dim=-1)
     R_text = R_text[CLS_idx, 1:CLS_idx]
     text_scores = R_text / R_text.sum()
@@ -115,19 +100,16 @@ def show_heatmap_on_text(text, text_encoding, R_text, show=True):
     text_tokens=_tokenizer.encode(text)
     text_tokens_decoded=[_tokenizer.decode([a]) for a in text_tokens]
     vis_data_records = [visualization.VisualizationDataRecord(text_scores,0,0,0,0,0,text_tokens_decoded,1)]
-    
-    if show:
-        visualization.visualize_text(vis_data_records)
 
     return text_scores, text_tokens_decoded
 
 
-def show_img_heatmap(image_relevance, image, orig_image, device, show=True):
-    return show_image_relevance(image_relevance, image, orig_image, device, show=show)
+def show_img_heatmap(image_relevance, image, orig_image, device):
+    return show_image_relevance(image_relevance, image, orig_image, device)
 
 
-def show_txt_heatmap(text, text_encoding, R_text, show=True):
-    return show_heatmap_on_text(text, text_encoding, R_text, show=show)
+def show_txt_heatmap(text, text_encoding, R_text):
+    return show_heatmap_on_text(text, text_encoding, R_text)
 
 
 def load_dataset():
@@ -149,4 +131,4 @@ class color:
     RED = '\033[91m'
     BOLD = '\033[1m'
     UNDERLINE = '\033[4m'
-    END = '\033[0m'
+    END = '\033[0m'

app.py

@@ -59,10 +59,10 @@ def run_demo(image, text):
 
     R_text, R_image = interpret(model=model, image=img, texts=text_input, device=device)
 
-    image_relevance = show_img_heatmap(R_image[0], img, orig_image=orig_image, device=device, show=False)
+    image_relevance = show_img_heatmap(R_image[0], img, orig_image=orig_image, device=device)
     overlapped = overlay_relevance_map_on_image(image, image_relevance)
 
-    text_scores, text_tokens_decoded = show_heatmap_on_text(text, text_input, R_text[0], show=False)
+    text_scores, text_tokens_decoded = show_heatmap_on_text(text, text_input, R_text[0])
 
     highlighted_text = []
     for i, token in enumerate(text_tokens_decoded):