fix all comments

modelscope/metainfo.py

@@ -282,7 +282,7 @@ class Trainers(object):
 
     # multi-modal trainers
     clip_multi_modal_embedding = 'clip-multi-modal-embedding'
-    ofa_tasks = 'ofa'
+    ofa = 'ofa'
 
     # cv trainers
     image_instance_segmentation = 'image-instance-segmentation'

modelscope/preprocessors/multi_modal.py

@@ -74,9 +74,7 @@ class OfaPreprocessor(Preprocessor):
             data[key] = item
         return data
 
-    def _compatible_with_pretrain(self, data):
-        # 预训练的时候使用的image都是经过pil转换的，PIL save的时候一般会进行有损压缩，为了保证和预训练一致
-        # 所以增加了这个逻辑
+    def _ofa_input_compatibility_conversion(self, data):
         if 'image' in data and self.cfg.model.get('type', None) == 'ofa':
             if isinstance(data['image'], str):
                 image = load_image(data['image'])
@@ -95,7 +93,7 @@ class OfaPreprocessor(Preprocessor):
             data = input
         else:
             data = self._build_dict(input)
-        data = self._compatible_with_pretrain(data)
+        data = self._ofa_input_compatibility_conversion(data)
         sample = self.preprocess(data)
         str_data = dict()
         for k, v in data.items():

modelscope/trainers/multi_modal/ofa/ofa_trainer.py

@@ -27,7 +27,7 @@ from .ofa_trainer_utils import (AdjustLabelSmoothedCrossEntropyCriterion,
                                 get_schedule)
 
 
-@TRAINERS.register_module(module_name=Trainers.ofa_tasks)
+@TRAINERS.register_module(module_name=Trainers.ofa)
 class OFATrainer(EpochBasedTrainer):
 
     def __init__(

tests/trainers/test_ofa_trainer.py

@@ -93,7 +93,7 @@ class TestOfaTrainer(unittest.TestCase):
                 split='validation[:10]'),
             metrics=[Metrics.BLEU],
             cfg_file=config_file)
-        trainer = build_trainer(name=Trainers.ofa_tasks, default_args=args)
+        trainer = build_trainer(name=Trainers.ofa, default_args=args)
         trainer.train()
 
         self.assertIn(ModelFile.TORCH_MODEL_BIN_FILE,