fix bug for downloading hugging face pretrained model http error

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10750369

modelscope/models/cv/referring_video_object_segmentation/model.py

@@ -31,7 +31,10 @@ class ReferringVideoObjectSegmentation(TorchModel):
 
         config_path = osp.join(model_dir, ModelFile.CONFIGURATION)
         self.cfg = Config.from_file(config_path)
-        self.model = MTTR(**self.cfg.model)
+        transformer_cfg_dir = osp.join(model_dir, 'transformer_cfg_dir')
+
+        self.model = MTTR(
+            transformer_cfg_dir=transformer_cfg_dir, **self.cfg.model)
 
         model_path = osp.join(model_dir, ModelFile.TORCH_MODEL_FILE)
         params_dict = torch.load(model_path, map_location='cpu')

modelscope/models/cv/referring_video_object_segmentation/utils/mttr.py

@@ -19,6 +19,7 @@ class MTTR(nn.Module):
                  num_queries,
                  mask_kernels_dim=8,
                  aux_loss=False,
+                 transformer_cfg_dir=None,
                  **kwargs):
         """
         Parameters:
@@ -29,7 +30,9 @@ class MTTR(nn.Module):
         """
         super().__init__()
         self.backbone = init_backbone(**kwargs)
-        self.transformer = MultimodalTransformer(**kwargs)
+        assert transformer_cfg_dir is not None
+        self.transformer = MultimodalTransformer(
+            transformer_cfg_dir=transformer_cfg_dir, **kwargs)
         d_model = self.transformer.d_model
         self.is_referred_head = nn.Linear(
             d_model,

modelscope/models/cv/referring_video_object_segmentation/utils/multimodal_transformer.py

@@ -26,6 +26,7 @@ class MultimodalTransformer(nn.Module):
                  num_decoder_layers=3,
                  text_encoder_type='roberta-base',
                  freeze_text_encoder=True,
+                 transformer_cfg_dir=None,
                  **kwargs):
         super().__init__()
         self.d_model = kwargs['d_model']
@@ -40,10 +41,12 @@ class MultimodalTransformer(nn.Module):
         self.pos_encoder_2d = PositionEmbeddingSine2D()
         self._reset_parameters()
 
-        self.text_encoder = RobertaModel.from_pretrained(text_encoder_type)
+        if text_encoder_type != 'roberta-base':
+            transformer_cfg_dir = text_encoder_type
+        self.text_encoder = RobertaModel.from_pretrained(transformer_cfg_dir)
         self.text_encoder.pooler = None  # this pooler is never used, this is a hack to avoid DDP problems...
         self.tokenizer = RobertaTokenizerFast.from_pretrained(
-            text_encoder_type)
+            transformer_cfg_dir)
         self.freeze_text_encoder = freeze_text_encoder
         if freeze_text_encoder:
             for p in self.text_encoder.parameters():