diff --git a/modelscope/models/cv/shop_segmentation/models.py b/modelscope/models/cv/shop_segmentation/models.py
index 8b82d1d1..171aafbd 100644
--- a/modelscope/models/cv/shop_segmentation/models.py
+++ b/modelscope/models/cv/shop_segmentation/models.py
@@ -552,7 +552,7 @@ class CLIPVisionTransformer(nn.Module):
                 nn.GroupNorm(1, embed_dim),
                 nn.ConvTranspose2d(
                     embed_dim, embed_dim, kernel_size=2, stride=2),
-                nn.SyncBatchNorm(embed_dim),
+                nn.BatchNorm2d(embed_dim),
                 nn.GELU(),
                 nn.ConvTranspose2d(
                     embed_dim, embed_dim, kernel_size=2, stride=2),
diff --git a/modelscope/models/cv/shop_segmentation/shop_seg_model.py b/modelscope/models/cv/shop_segmentation/shop_seg_model.py
index 409c583b..0aeeb1de 100644
--- a/modelscope/models/cv/shop_segmentation/shop_seg_model.py
+++ b/modelscope/models/cv/shop_segmentation/shop_seg_model.py
@@ -33,18 +33,18 @@ class ShopSegmentation(TorchModel):
             model_dir=model_dir, device_id=device_id, *args, **kwargs)
 
         self.model = SHOPSEG(model_dir=model_dir)
-        pretrained_params = torch.load('{}/{}'.format(
-            model_dir, ModelFile.TORCH_MODEL_BIN_FILE))
-
+        pretrained_params = torch.load(
+            '{}/{}'.format(model_dir, ModelFile.TORCH_MODEL_BIN_FILE),
+            map_location='cpu')
         self.model.load_state_dict(pretrained_params)
         self.model.eval()
-        self.device_id = device_id
-        if self.device_id >= 0 and torch.cuda.is_available():
-            self.model.to('cuda:{}'.format(self.device_id))
-            logger.info('Use GPU: {}'.format(self.device_id))
+        if device_id >= 0 and torch.cuda.is_available():
+            self.model.to('cuda:{}'.format(device_id))
+            logger.info('Use GPU: {}'.format(device_id))
         else:
-            self.device_id = -1
+            device_id = -1
             logger.info('Use CPU for inference')
+        self.device_id = device_id
 
     def preprocess(self, img, size=1024):
         mean = [0.48145466, 0.4578275, 0.40821073]