update the model with wrapper.

Signed-off-by: wxywb <xy.wang@zilliz.com>

clip.py

@@ -24,26 +24,53 @@ from towhee import register
 from transformers import CLIPTokenizer, CLIPTextModel ,CLIPModel,CLIPProcessor
 #from towhee.dc2 import accelerate
 
-#@accelerate
+
+def create_model(model_name, modality, checkpoint_path, device):
+    hf_clip_model = CLIPModel.from_pretrained(model_name)
+    if checkpoint_path:
+        try:
+            state_dict = torch.load(checkpoint_path, map_location=device)
+            hf_clip_model.load_state_dict(state_dict)
+        except Exception as e:
+            log.error(f"Fail to load state dict from {checkpoint_path}: {e}")
+    hf_clip_model.to(device)
+    hf_clip_model.eval()
+
+    if modality == 'image':
+        clip = CLIPModelVision(hf_clip_model)
+    elif modality == 'text':
+        clip = CLIPModelText(hf_clip_model)
+    else:
+        raise ValueError("modality[{}] not implemented.".format(modality))
+    model = Model(clip)
+    return model
+
 class CLIPModelVision(nn.Module):
     def __init__(self, model):
         super().__init__()
-        self.model = model
+        self.backbone = model
     
     def forward(self, pixel_values):
-        image_embeds = self.model.get_image_features(pixel_values)
+        image_embeds = self.backbone.get_image_features(pixel_values)
         return image_embeds
     
-#@accelerate
 class CLIPModelText(nn.Module):
     def __init__(self, model):
         super().__init__()
-        self.model = model
+        self.backbone = model
 
     def forward(self, input_ids, attention_mask):
-        text_embeds = self.model.get_text_features(input_ids, attention_mask)
+        text_embeds = self.backbone.get_text_features(input_ids, attention_mask)
         return text_embeds
     
+#@accelerate
+class Model:
+    def __init__(self, model):
+        self.model = model
+    
+    def __call__(self, *args, **kwargs):
+        outs = self.model(*args, **kwargs)
+        return outs
 
 @register(output_schema=['vec'])
 class Clip(NNOperator): 
@@ -56,18 +83,8 @@ class Clip(NNOperator):
         self.device = device
         self.checkpoint_path = checkpoint_path
         cfg = self._configs()[model_name]
-        try:
-            clip_model = CLIPModel.from_pretrained(cfg)
-        except Exception as e:
-            log.error(f"Fail to load model by name: {self.model_name}")
-            raise e
 
-        if self.modality == 'image':
-            self.model = CLIPModelVision(self._model)
-        elif self.modality == 'text':
-            self.model = CLIPModelText(self._model)
-        else:
-            raise ValueError("modality[{}] not implemented.".format(self.modality))
+        self.model = create_model(cfg, modality, checkpoint_path, device)
         self.tokenizer = CLIPTokenizer.from_pretrained(cfg)
         self.processor =  CLIPProcessor.from_pretrained(cfg)
 
@@ -115,7 +132,7 @@ class Clip(NNOperator):
         from train_clip_with_hf_trainer import train_with_hf_trainer
         data_args = kwargs.pop('data_args', None)
         training_args = kwargs.pop('training_args', None)
-        train_with_hf_trainer(self.model, self.tokenizer, data_args, training_args)
+        train_with_hf_trainer(self._model.backbone, self.tokenizer, data_args, training_args)
     
     def _configs(self):
         config = {}
@@ -148,21 +165,7 @@ class Clip(NNOperator):
 
     @property
     def _model(self):
-        cfg = self._configs()[self.model_name]
-        try:
-            hf_clip_model = CLIPModel.from_pretrained(cfg)
-        except Exception as e:
-            log.error(f"Fail to load model by name: {self.model_name}")
-            raise e
-        if self.checkpoint_path:
-            try:
-                state_dict = torch.load(self.checkpoint_path, map_location=self.device)
-                hf_clip_model.load_state_dict(state_dict)
-            except Exception as e:
-                log.error(f"Fail to load state dict from {checkpoint_path}: {e}")
-        hf_clip_model.to(self.device)
-        hf_clip_model.eval()
-        return hf_clip_model
+        return self.model.model 
 
     def save_model(self, model_type: str = 'pytorch', output_file: str = 'default'):
         import os
@@ -222,7 +225,7 @@ class Clip(NNOperator):
             else:
                 raise ValueError("modality[{}] not implemented.".format(self.modality))
 
-            onnx_export(self.model,
+            onnx_export(self._model,
                 (dict(inputs),),
                 f=Path(output_file),
                 input_names= input_names,