Update app.py

app.py

@@ -154,20 +154,21 @@ def model_process(image, mask):
     )
    
     # print(f'config = {config}')
-
+    
+    print(f'config/alpha_channel/size_limit = {config} / {alpha_channel} / {size_limit}')
     if config.sd_seed == -1:
         config.sd_seed = random.randint(1, 999999999)
     
     # logger.info(f"Origin image shape: {original_shape}")
-    print(f"Origin image shape: {original_shape} / {type(image)}")
+    print(f"Origin image shape: {original_shape} / {image[250][250]}")
     image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
     # logger.info(f"Resized image shape: {image.shape}")
-    print(f"Resized image shape: {image.shape} / {type(image)}")
+    print(f"Resized image shape: {image.shape} / {image[250][250]}")
     
     #mask, _ = load_img(input["mask"].read(), gray=True)
     mask = np.array(Image.fromarray(mask).convert("L"))
     mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation)
-    print(f"mask image shape: {mask.shape} / {type(mask)}")
+    print(f"mask image shape: {mask.shape} / {type(mask)} / {mask[250][250]}")
 
     if model is None:
         return None
@@ -175,6 +176,7 @@ def model_process(image, mask):
     start = time.time()
     res_np_img = model(image, mask, config)
     logger.info(f"process time: {(time.time() - start) * 1000}ms, {res_np_img.shape}")
+    print(f"process time: {(time.time() - start) * 1000}ms, {res_np_img.shape} / {res_np_img[250][250]}")
 
     torch.cuda.empty_cache()