Merge branch 'layoutlm' of https://github.com/KevinNuNu/mmocr into la…

…youtlm

mmocr/datasets/preparers/config_generators/base.py

@@ -83,11 +83,18 @@ def _prepare_anns(self, train_anns: Optional[List[Dict]],
                 assert 'ann_file' in ann_dict
                 suffix = ann_dict['ann_file'].split('.')[-1]
                 if suffix == 'json':
-                    dataset_type = 'OCRDataset'
+                    if self.task in ['ser', 're']:
+                        dataset_type = f'{self.task.upper()}Dataset'
+                    else:
+                        dataset_type = 'OCRDataset'
                 elif suffix == 'lmdb':
                     assert self.task == 'textrecog', \
                         'LMDB format only works for textrecog now.'
                     dataset_type = 'RecogLMDBDataset'
+                elif suffix == 'huggingface':
+                    assert self.task in ['ser', 're'], \
+                        'Huggingface format only works for ser or re now.'
+                    dataset_type = f'{self.task.upper()}HuggingfaceDataset'
                 else:
                     raise NotImplementedError(
                         'ann file only supports JSON file or LMDB file')

mmocr/datasets/preparers/config_generators/re_config_generator.py

@@ -87,8 +87,8 @@ def _gen_dataset_config(self) -> str:
         cfg = ''
         for key_name, ann_dict in self.anns.items():
             cfg += f'\n{key_name} = dict(\n'
-            cfg += '    type=\'REDataset\',\n'
-            cfg += '    data_root=' + f'{self.dataset_name}_{self.task}_data_root,\n'  # noqa: E501
+            cfg += f'    type=\'{ann_dict["dataset_type"]}\',\n'
+            cfg += f'    data_root={self.dataset_name}_{self.task}_data_root,\n'  # noqa: E501
             cfg += f'    ann_file=\'{ann_dict["ann_file"]}\',\n'
             if ann_dict['split'] in ['test', 'val']:
                 cfg += '    test_mode=True,\n'

mmocr/datasets/preparers/config_generators/ser_config_generator.py

@@ -87,8 +87,8 @@ def _gen_dataset_config(self) -> str:
         cfg = ''
         for key_name, ann_dict in self.anns.items():
             cfg += f'\n{key_name} = dict(\n'
-            cfg += '    type=\'SERDataset\',\n'
-            cfg += '    data_root=' + f'{self.dataset_name}_{self.task}_data_root,\n'  # noqa: E501
+            cfg += f'    type=\'{ann_dict["dataset_type"]}\',\n'
+            cfg += f'    data_root={self.dataset_name}_{self.task}_data_root,\n'  # noqa: E501
             cfg += f'    ann_file=\'{ann_dict["ann_file"]}\',\n'
             if ann_dict['split'] in ['test', 'val']:
                 cfg += '    test_mode=True,\n'

mmocr/datasets/preparers/config_generators/textdet_config_generator.py

@@ -86,7 +86,7 @@ def _gen_dataset_config(self) -> str:
         for key_name, ann_dict in self.anns.items():
             cfg += f'\n{key_name} = dict(\n'
             cfg += '    type=\'OCRDataset\',\n'
-            cfg += '    data_root=' + f'{self.dataset_name}_{self.task}_data_root,\n'  # noqa: E501
+            cfg += f'    data_root={self.dataset_name}_{self.task}_data_root,\n'  # noqa: E501
             cfg += f'    ann_file=\'{ann_dict["ann_file"]}\',\n'
             if ann_dict['split'] == 'train':
                 cfg += '    filter_cfg=dict(filter_empty_gt=True, min_size=32),\n'  # noqa: E501

mmocr/datasets/preparers/dumpers/__init__.py

@@ -1,10 +1,11 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .base import BaseDumper
+from .huggingface_dumper import HuggingfaceDumper
 from .json_dumper import JsonDumper
 from .lmdb_dumper import TextRecogLMDBDumper
 from .wild_receipt_openset_dumper import WildreceiptOpensetDumper
 
 __all__ = [
     'BaseDumper', 'JsonDumper', 'WildreceiptOpensetDumper',
-    'TextRecogLMDBDumper'
+    'TextRecogLMDBDumper', 'HuggingfaceDumper'
 ]

mmocr/datasets/preparers/dumpers/huggingface_dumper.py

@@ -0,0 +1,37 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+from collections import defaultdict
+from typing import Dict
+
+from datasets import Dataset, Image
+
+from mmocr.registry import DATA_DUMPERS
+from .base import BaseDumper
+
+
+@DATA_DUMPERS.register_module()
+class HuggingfaceDumper(BaseDumper):
+    """Semantic Entity Recognition and Relation Extraction huggingface datasets
+    format dumper."""
+
+    def dump(self, data: Dict) -> None:
+        """Dump data to datasets format to disk.
+
+        Args:
+            data (Dict): MMOCR format data to be dumped.
+        """
+        data_list = data.get('data_list', None)
+        filename = f'{self.task}_{self.split}.huggingface'
+        dst_file = osp.join(self.data_root, filename)
+
+        merged_dict = defaultdict(list)
+        for d in data_list:
+            instances = d['instances']
+            img_path = osp.join(self.data_root, d['img_path'])
+            merged_dict['image'].append(img_path)
+            for k, v in instances.items():
+                merged_dict[k].append(v)
+        ds = Dataset.from_dict(merged_dict)
+        ds = ds.cast_column('image', Image())
+        # save to disk
+        ds.save_to_disk(dst_file)

tools/dataset_converters/prepare_dataset.py

@@ -36,6 +36,13 @@ def parse_args():
         help='Whether to dump the textrecog dataset to LMDB format, It\'s a '
         'shortcut to force the dataset to be dumped in lmdb format. '
         'Applicable when --task=textrecog')
+    parser.add_argument(
+        '--huggingface',
+        action='store_true',
+        default=False,
+        help='Whether to dump the ser/re dataset to huggingface format,'
+        'It\'s a shortcut to force the dataset to be dumped in huggingface '
+        'format. Applicable when --task=ser or re')
     parser.add_argument(
         '--overwrite-cfg',
         action='store_true',
@@ -124,10 +131,56 @@ def force_lmdb(cfg):
     return cfg
 
 
+def force_huggingface(cfg):
+    """Force the dataset to be dumped in huggingface format.
+
+    Args:
+        cfg (Config): Config object.
+
+    Returns:
+        Config: Config object.
+    """
+    for split in ['train', 'val', 'test']:
+        preparer_cfg = cfg.get(f'{split}_preparer')
+        if preparer_cfg:
+            if preparer_cfg.get('dumper') is None:
+                raise ValueError(
+                    f'{split} split does not come with a dumper, '
+                    'so most likely the annotations are MMOCR-ready and do '
+                    'not need any adaptation, and it '
+                    'cannot be dumped in LMDB format.')
+            preparer_cfg.dumper['type'] = 'HuggingfaceDumper'
+
+    cfg.config_generator['dataset_name'] = f'{cfg.dataset_name}_huggingface'
+
+    for split in ['train_anns', 'val_anns', 'test_anns']:
+        if split in cfg.config_generator:
+            # It can be None when users want to clear out the default
+            # value
+            if not cfg.config_generator[split]:
+                continue
+            ann_list = cfg.config_generator[split]
+            for ann_dict in ann_list:
+                ann_dict['ann_file'] = (
+                    osp.splitext(ann_dict['ann_file'])[0] + '.huggingface')
+        else:
+            if split == 'train_anns':
+                ann_list = [dict(ann_file=f'{cfg.task}_train.huggingface')]
+            elif split == 'test_anns':
+                ann_list = [dict(ann_file=f'{cfg.task}_test.huggingface')]
+            else:
+                ann_list = []
+        cfg.config_generator[split] = ann_list
+
+    return cfg
+
+
 def main():
     args = parse_args()
     if args.lmdb and args.task != 'textrecog':
         raise ValueError('--lmdb only works with --task=textrecog')
+    if args.huggingface and args.task not in ['ser', 're']:
+        raise ValueError('--huggingface only works with --task=ser or re')
     for dataset in args.datasets:
         if not osp.isdir(osp.join(args.dataset_zoo_path, dataset)):
             warnings.warn(f'{dataset} is not supported yet. Please check '
@@ -145,6 +198,8 @@ def main():
         cfg.dataset_name = dataset
         if args.lmdb:
             cfg = force_lmdb(cfg)
+        if args.huggingface:
+            cfg = force_huggingface(cfg)
         preparer = DatasetPreparer.from_file(cfg)
         preparer.run(args.splits)