fix ocr prepreocess

modelscope/preprocessors/multi_modal.py

@@ -93,7 +93,6 @@ class OfaPreprocessor(Preprocessor):
             data = input
         else:
             data = self._build_dict(input)
-        data = self._ofa_input_compatibility_conversion(data)
         sample = self.preprocess(data)
         str_data = dict()
         for k, v in data.items():

modelscope/preprocessors/ofa/ocr_recognition.py

@@ -2,12 +2,12 @@
 from typing import Any, Dict
 
 import torch
-from PIL import Image
+import unicodedata2
 from torchvision import transforms
 from torchvision.transforms import InterpolationMode
 from torchvision.transforms import functional as F
+from zhconv import convert
 
-from modelscope.preprocessors.image import load_image
 from modelscope.utils.constant import ModeKeys
 from .base import OfaBasePreprocessor
 
@@ -98,8 +98,7 @@ class OfaOcrRecognitionPreprocessor(OfaBasePreprocessor):
 
     def _build_train_sample(self, data: Dict[str, Any]) -> Dict[str, Any]:
         sample = self._build_infer_sample(data)
-        target = data[self.column_map['text']]
-        target = target.translate(self.transtab).strip()
+        target = sample['label']
         target_token_list = target.strip().split()
         target = ' '.join(target_token_list[:self.max_tgt_length])
         sample['target'] = self.tokenize_text(target, add_bos=False)
@@ -119,5 +118,7 @@ class OfaOcrRecognitionPreprocessor(OfaBasePreprocessor):
             'patch_mask': torch.tensor([True])
         }
         if 'text' in self.column_map and self.column_map['text'] in data:
-            sample['label'] = data[self.column_map['text']]
+            target = data[self.column_map['text']]
+            target = unicodedata2.normalize('NFKC', convert(target, 'zh-hans'))
+            sample['label'] = target
         return sample

requirements/multi-modal.txt

@@ -11,3 +11,5 @@ timm
 tokenizers
 torchvision
 transformers>=4.12.0
+unicodedata2
+zhconv

tests/trainers/test_ofa_trainer.py

@@ -5,7 +5,7 @@ import unittest
 
 import json
 
-from modelscope.metainfo import Trainers
+from modelscope.metainfo import Metrics, Trainers
 from modelscope.msdatasets import MsDataset
 from modelscope.trainers import build_trainer
 from modelscope.utils.constant import DownloadMode, ModelFile
@@ -85,7 +85,7 @@ class TestOfaTrainer(unittest.TestCase):
                 'ocr_fudanvi_zh',
                 subset_name='scene',
                 namespace='modelscope',
-                split='train[:200]',
+                split='train[800:900]',
                 download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS),
             eval_dataset=MsDataset.load(
                 'ocr_fudanvi_zh',
@@ -96,7 +96,6 @@ class TestOfaTrainer(unittest.TestCase):
             cfg_file=config_file)
         trainer = build_trainer(name=Trainers.ofa, default_args=args)
         trainer.train()
-
         self.assertIn(
             ModelFile.TORCH_MODEL_BIN_FILE,
             os.listdir(os.path.join(WORKSPACE, ModelFile.TRAIN_OUTPUT_DIR)))