Update app.py

app.py

@@ -180,25 +180,25 @@ def model_process(image, mask, alpha_channel, ext):
     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]} / {res_np_img.dtype}")
+    print(f"process time_1_: {(time.time() - start) * 1000}ms,  {res_np_img.shape} / {res_np_img[250][250]} / {res_np_img.dtype}")
 
     torch.cuda.empty_cache()
 
     alpha_channel = None
     if alpha_channel is not None:
-        print(f"liuyz_here_10_: {alpha_channel.shape} / {alpha_channel.dtype} / {res_np_img.dtype}") 
+        print(f"liuyz_here_10_: {alpha_channel.shape} /  {res_np_img.dtype}") 
         if alpha_channel.shape[:2] != res_np_img.shape[:2]:
-            print(f"liuyz_here_20_: {alpha_channel.shape} / {res_np_img.shape}")
+            print(f"liuyz_here_20_:  {res_np_img.shape}")
             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} / {alpha_channel.dtype} / {res_np_img.dtype}")
+        print(f"liuyz_here_30_: {res_np_img.dtype}")
         res_np_img = np.concatenate(
             (res_np_img, alpha_channel[:, :, np.newaxis]), axis=-1
         )
-        print(f"liuyz_here_40_: {alpha_channel.shape} / {res_np_img.shape} / {alpha_channel.dtype} / {res_np_img.dtype}")
+        print(f"liuyz_here_40_:  {res_np_img.dtype}")
     
-    print(f"process time_2_: {(time.time() - start) * 1000}ms, {alpha_channel.shape}, {res_np_img.shape} / {res_np_img[250][250]} / {res_np_img.dtype} /{ext}")
+    print(f"process time_2_: {(time.time() - start) * 1000}ms, {res_np_img.shape} / {res_np_img[250][250]} / {res_np_img.dtype} /{ext}")
   
     image = Image.open(io.BytesIO(numpy_to_bytes(res_np_img, ext)))
     return  image # image