[Feature]: Add scienceqa

mmpretrain/datasets/__init__.py

@@ -40,6 +40,7 @@
     from .flamingo import FlamingoEvalCOCOCaption, FlamingoEvalCOCOVQA
     from .refcoco import RefCOCO
     from .visual_genome import VisualGenomeQA
+    from .scienceqa import ScienceQA
 
     __all__.extend([
         'COCOCaption',
@@ -49,4 +50,5 @@
         'FlamingoEvalCOCOVQA',
         'RefCOCO',
         'VisualGenomeQA',
+        'ScienceQA',
     ])

mmpretrain/datasets/scienceqa.py

@@ -0,0 +1,104 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+from typing import Callable, List, Sequence
+
+import mmengine
+from mmengine.dataset import BaseDataset
+from mmengine.fileio import get_file_backend
+
+from mmpretrain.registry import DATASETS
+
+
+@DATASETS.register_module()
+class ScienceQA(BaseDataset):
+    """ScienceQA dataset.
+
+    This dataset is used to load the multimodal data of ScienceQA dataset.
+
+    Args:
+        data_root (str): The root directory for ``data_prefix`` and
+            ``ann_file``.
+        data_split (str): The split of dataset. Options: ``train``, ``val``,
+            ``test``, ``trainval``, ``minival``, and ``minitest``.
+        data_split_file (str): The split file of dataset, which contains the
+            ids of data samples in the split.
+        ann_file (str): Annotation file path.
+        data_prefix (dict): Prefix for data field. Defaults to
+            ``dict(img_path='')``.
+        pipeline (Sequence): Processing pipeline. Defaults to an empty tuple.
+        **kwargs: Other keyword arguments in :class:`BaseDataset`.
+    """
+
+    def __init__(self,
+                 data_root: str,
+                 data_split: str,
+                 data_split_file: str,
+                 ann_file: str,
+                 data_prefix: dict = dict(img_path=''),
+                 pipeline: Sequence[Callable] = (),
+                 **kwargs):
+
+        assert data_split in [
+            'train', 'val', 'test', 'trainval', 'minival', 'minitest'
+        ], f'Invalid split {data_split}'
+        self.data_split = data_split
+        self.data_split_file = os.path.join(data_root, data_split_file)
+
+        super().__init__(
+            data_root=data_root,
+            ann_file=ann_file,
+            data_prefix=data_prefix,
+            pipeline=pipeline,
+            **kwargs)
+
+    def load_data_list(self) -> List[dict]:
+        """Load data list."""
+        img_prefix = self.data_prefix['img_path']
+        annotations = mmengine.load(self.ann_file)
+        current_data_split = mmengine.load(
+            self.data_split_file)[self.data_split]  # noqa
+
+        file_backend = get_file_backend(img_prefix)
+
+        data_list = []
+        for data_id in current_data_split:
+            ann = annotations[data_id]
+            if ann['image'] is not None:
+                data_info = {
+                    'image_id':
+                    data_id,
+                    'question':
+                    ann['question'],
+                    'choices':
+                    ann['choices'],
+                    'answer':
+                    ann['answer'],
+                    'hint':
+                    ann['hint'],
+                    'image_name':
+                    ann['image'],
+                    'task':
+                    ann['task'],
+                    'grade':
+                    ann['grade'],
+                    'subject':
+                    ann['subject'],
+                    'topic':
+                    ann['topic'],
+                    'category':
+                    ann['category'],
+                    'skill':
+                    ann['skill'],
+                    'lecture':
+                    ann['lecture'],
+                    'solution':
+                    ann['solution'],
+                    'split':
+                    ann['split'],
+                    'img_path':
+                    file_backend.join_path(img_prefix, data_id,
+                                           ann['image']),  # noqa
+                }
+                data_list.append(data_info)
+
+        return data_list