update blip.

Signed-off-by: jinlingxu06 <jinling.xu@zilliz.com>

blip.py

@@ -16,6 +16,7 @@ import sys
 import os
 from pathlib import Path
 from typing import Optional, Union, Tuple
+from types import MethodType
 
 import torch
 import logging
@@ -36,32 +37,7 @@ warnings.filterwarnings('ignore')
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 t_logging.set_verbosity_error()
 
-class BlipForImageTextRetrievalFinetune():
-    def __init__(self, blip_itm_model):
-        self.proxied = blip_itm_model
-        self.proxied.logit_scale = torch.nn.Parameter(torch.ones([]) * blip_itm_model.config.logit_scale_init_value)
-
-    def __getattr__(self, attr):
-        if attr == 'proxied':
-            return self.proxied
-        elif attr != 'forward':
-            if getattr(self.proxied, attr) is True:
-                def wrapped_method(*args, **kwargs):
-                    result = getattr(self.proxied, attr)(*args, **kwargs)
-                    return result
-                return wrapped_method
-            else:
-                return getattr(self.proxied, attr)
-        else:
-            return self._forward
-
-    def forward(self, *args, **kwargs):
-        raise NotImplemented
-    
-    def __call__(self, *args, **kwargs):
-        return self._forward(*args, **kwargs)
-
-    def _forward(
+def _forward(
     self,
     input_ids: Optional[torch.LongTensor] = None,
     pixel_values: Optional[torch.FloatTensor] = None,
@@ -258,10 +234,13 @@ class Blip(NNOperator):
         import pathlib
         path = str(pathlib.Path(__file__).parent)
         sys.path.append(path)
-        from train_blip_with_hf_trainer import train_with_hf_trainer
+        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)
-        model_finetune = BlipForImageTextRetrievalFinetune(self._model.backbone)
+        model_finetune = self._model.backbone
+        model_finetune.forward =  MethodType(_forward, model_finetune)
+        model_finetune.logit_scale = torch.nn.Parameter(torch.ones([]) * model_finetune.config.logit_scale_init_value)
+
         train_with_hf_trainer(model_finetune, self.processor.tokenizer, data_args, training_args)