upd model

model.py

@@ -1,8 +1,9 @@
 import torch
 import torch.nn as nn
 import torchvision.models as models
+from modelscope.msdatasets import MsDataset
 from datasets import load_dataset
-from utils import MODEL_DIR
+from utils import MODEL_DIR, EN_US
 
 
 class EvalNet:
@@ -17,7 +18,7 @@ class EvalNet:
         self.m_type, self.input_size = self._model_info(m_ver)
 
         if not hasattr(models, m_ver):
-            raise Exception("Unsupported model.")
+            raise ValueError("不支持的模型")
 
         self.model = eval("models.%s()" % m_ver)
         linear_output = self._set_outsize()
@@ -34,11 +35,15 @@ class EvalNet:
             if ver == bb["ver"]:
                 return bb
 
-        print("Backbone name not found, using default option - alexnet.")
+        print("未找到骨干网络名称，使用默认选项 - alexnet。")
         return backbone_list[0]
 
     def _model_info(self, m_ver: str):
-        backbone_list = load_dataset("monetjoe/cv_backbones", split="train")
+        backbone_list = (
+            load_dataset("monetjoe/cv_backbones", split="train")
+            if EN_US
+            else MsDataset.load("monetjoe/cv_backbones", split="v1")
+        )
         backbone = self._get_backbone(m_ver, backbone_list)
         m_type = str(backbone["type"])
         input_size = int(backbone["input_size"])