Update app.py

app.py

@@ -144,7 +144,7 @@ def model_process(input):
     interpolation = cv2.INTER_CUBIC    
     
     # form = request.form
-    print(f'liuyz_3_here_', original_shape, alpha_channel)
+    print(f'liuyz_3_here_', original_shape, alpha_channel, image.dtype, mask.dtype)
 
     size_limit = "Original" # image.shape[1] # : Union[int, str] = form.get("sizeLimit", "1080")
     if size_limit == "Original":
@@ -186,12 +186,12 @@ def model_process(input):
     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} / {image[250][250]}")
+    print(f"Resized image shape: {image.shape} / {image[250][250]} / {iamge.dtype}")
     
     # mask, _ = load_img(mask, gray=True)
     mask = np.array(mask_pil)
     mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation)
-    print(f"mask image shape: {mask.shape} / {type(mask)} / {mask[250][250]}")
+    print(f"mask image shape: {mask.shape} / {type(mask)} / {mask[250][250]} / {mask.dtype}")
 
     if model is None:
         return None
@@ -199,7 +199,7 @@ def model_process(input):
     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_1_: {(time.time() - start) * 1000}ms, {alpha_channel.shape}, {res_np_img.shape} / {res_np_img[250][250]}")
+    print(f"process time_1_: {(time.time() - start) * 1000}ms, {alpha_channel.shape}, {res_np_img.shape} / {res_np_img[250][250]} / {res_np_img.dtype}")
 
     torch.cuda.empty_cache()
 
@@ -210,7 +210,7 @@ def model_process(input):
             alpha_channel = cv2.resize(
                 alpha_channel, dsize=(res_np_img.shape[1], res_np_img.shape[0])
             )
-            print(f"liuyz_here_30_: {alpha_channel.shape} / {res_np_img.shape}")
+        print(f"liuyz_here_30_: {alpha_channel.shape} / {res_np_img.shape}")
         res_np_img = np.concatenate(
             (res_np_img, alpha_channel[:, :, np.newaxis]), axis=-1
         )