fix colormaps

app.py

@@ -2186,7 +2186,7 @@ demo = gr.Blocks(
 )
 with demo:
 
-    with gr.Tab('Hierarchical (dev)'):
+    with gr.Tab('PlayGround'):
         eigvecs = gr.State(np.array([]))
         tsne3d_rgb = gr.State(np.array([]))
         with gr.Row():
@@ -2535,7 +2535,7 @@ with demo:
             right = F.normalize(right, p=2, dim=-1)
             similarity = left @ right.T
             similarity = similarity.max(dim=-1).values  # B H W
-            hot_map = matplotlib.cm.get_cmap('hot')
+            hot_map = matplotlib.colormaps['hot']
             heatmap = hot_map(similarity)[..., :3]  # B H W 3
             heatmap_images = to_pil_images(torch.tensor(heatmap), target_size=256, force_size=True)
             # overlay input images on the heatmap
@@ -3976,7 +3976,7 @@ with demo:
                 return heatmap
             # apply hot colormap and covert to PIL image 256x256
             heatmap = heatmap.cpu().numpy()
-            hot_map = matplotlib.cm.get_cmap('hot')
+            hot_map = matplotlib.colormaps['hot']
             heatmap = hot_map(heatmap)
             pil_images = to_pil_images(torch.tensor(heatmap), target_size=256, force_size=True)
             if overlay_image:
@@ -4063,7 +4063,7 @@ with demo:
                 # convert [-1, 1] to [0, 1]
                 heatmap = (heatmap + 1) / 2
                 heatmap = heatmap.cpu().numpy()
-                cm = matplotlib.cm.get_cmap('bwr')
+                cm = matplotlib.colormaps['bwr']
                 heatmap = cm(heatmap)
                 # bwr with contrast
                 pil_images1 = to_pil_images(torch.tensor(heatmap), resize=256)
@@ -4119,7 +4119,7 @@ with demo:
                 outputs=[n_eig, current_idx, parent_plot, current_plot, *child_plots, child_idx],
             )
             
-    with gr.Tab('PlayGround', visible=True) as test_playground_tab2:
+    with gr.Tab('PlayGround (eig)', visible=True) as test_playground_tab2:
         eigvecs = gr.State(np.array([]))
         with gr.Row():
             with gr.Column(scale=5, min_width=200):
@@ -4247,7 +4247,7 @@ with demo:
             # apply hot colormap and covert to PIL image 256x256
             # gr.Info(f"heatmap vmin: {heatmap.min()}, vmax: {heatmap.max()}, mean: {heatmap.mean()}")
             heatmap = heatmap.cpu().numpy()
-            hot_map = matplotlib.cm.get_cmap('hot')
+            hot_map = matplotlib.colormaps['hot']
             heatmap = hot_map(heatmap)
             pil_images = to_pil_images(torch.tensor(heatmap), target_size=256, force_size=True)
             if overlay_image: