Update app.py

app.py

@@ -59,8 +59,8 @@ def show_cam_on_image(img: np.ndarray,
         raise Exception(
             "The input image should np.float32 in the range [0, 1]")
 
-    # cam = 0.7*heatmap + 0.3*img
-    cam = heatmap    
+    cam = 0.7*heatmap + 0.3*img
+    # cam = heatmap    
     # cam = cam / np.max(cam)
     return np.uint8(255 * cam)
 
@@ -73,19 +73,19 @@ def classify_image(inp):
     prediction = model(img_t.unsqueeze(0)).softmax(-1).flatten()
 
     modulator = model.layers[0].blocks[11].modulation.modulator.norm(2, 1, keepdim=True)
-    # modulator = nn.Upsample(size=img_t.shape[1:], mode='bilinear')(modulator)
+    modulator = nn.Upsample(size=img_t.shape[1:], mode='bilinear')(modulator)
     modulator = modulator.squeeze(1).detach().permute(1, 2, 0).numpy()
     modulator = (modulator - modulator.min()) / (modulator.max() - modulator.min())
     cam0 = show_cam_on_image(img_d, modulator, use_rgb=True)
 
     modulator = model.layers[0].blocks[8].modulation.modulator.norm(2, 1, keepdim=True)
-    # modulator = nn.Upsample(size=img_t.shape[1:], mode='bilinear')(modulator)
+    modulator = nn.Upsample(size=img_t.shape[1:], mode='bilinear')(modulator)
     modulator = modulator.squeeze(1).detach().permute(1, 2, 0).numpy()
     modulator = (modulator - modulator.min()) / (modulator.max() - modulator.min())
     cam1 = show_cam_on_image(img_d, modulator, use_rgb=True)
 
     modulator = model.layers[0].blocks[5].modulation.modulator.norm(2, 1, keepdim=True)
-    # modulator = nn.Upsample(size=img_t.shape[1:], mode='bilinear')(modulator)
+    modulator = nn.Upsample(size=img_t.shape[1:], mode='bilinear')(modulator)
     modulator = modulator.squeeze(1).detach().permute(1, 2, 0).numpy()
     modulator = (modulator - modulator.min()) / (modulator.max() - modulator.min())
     cam2 = show_cam_on_image(img_d, modulator, use_rgb=True)