Support IconQA dataset. (open-mmlab#1670)

mmpretrain/datasets/__init__.py

@@ -41,6 +41,7 @@
     from .flickr30k_caption import Flickr30kCaption
     from .flickr30k_retrieval import Flickr30kRetrieval
     from .gqa_dataset import GQA
+    from .iconqa import IconQA
     from .nocaps import NoCaps
     from .ocr_vqa import OCRVQA
     from .refcoco import RefCOCO
@@ -54,5 +55,5 @@
         'COCOCaption', 'COCORetrieval', 'COCOVQA', 'FlamingoEvalCOCOCaption',
         'FlamingoEvalCOCOVQA', 'Flickr30kCaption', 'Flickr30kRetrieval',
         'RefCOCO', 'VisualGenomeQA', 'ScienceQA', 'NoCaps', 'GQA', 'TextVQA',
-        'VSR', 'VizWiz', 'OCRVQA'
+        'VSR', 'VizWiz', 'OCRVQA', 'IconQA'
     ])

mmpretrain/datasets/iconqa.py

@@ -0,0 +1,63 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import List
+
+import mmengine
+from mmengine.dataset import BaseDataset
+from mmengine.fileio import list_dir_or_file
+from mmengine.utils import check_file_exist
+
+from mmpretrain.registry import DATASETS
+
+
+@DATASETS.register_module()
+class IconQA(BaseDataset):
+    """IconQA: A benchmark for abstract diagram understanding
+        and visual language reasoning.
+
+    Args:
+        data_root (str): The root directory for ``data_prefix``, ``ann_file``
+            and ``question_file``.
+        data_prefix (str): The directory of the specific task and split.
+            eg. ``iconqa/val/choose_text/``.
+        **kwargs: Other keyword arguments in :class:`BaseDataset`.
+    """
+
+    def __init__(self, data_root: str, data_prefix: str, **kwarg):
+        super().__init__(
+            data_root=data_root,
+            data_prefix=dict(img_path=data_prefix),
+            **kwarg,
+        )
+
+    def load_data_list(self) -> List[dict]:
+        """Load data list."""
+        sample_list = list(
+            list_dir_or_file(self.data_prefix['img_path'], list_file=False))
+
+        data_list = list()
+        for sample_id in sample_list:
+            # data json
+            # {
+            # "question": "How likely is it that you will pick a black one?",
+            # "choices": [
+            #     "certain",
+            #     "unlikely",
+            #     "impossible",
+            #     "probable"
+            # ],
+            # "answer": 2,
+            # "ques_type": "choose_txt",
+            # "grade": "grade1",
+            # "label": "S2"
+            # }
+            data_info = mmengine.load(
+                mmengine.join_path(self.data_prefix['img_path'], sample_id,
+                                   'data.json'))
+            data_info['gt_answer'] = data_info['choices'][int(
+                data_info['answer'])]
+            data_info['img_path'] = mmengine.join_path(
+                self.data_prefix['img_path'], sample_id, 'image.png')
+            check_file_exist(data_info['img_path'])
+            data_list.append(data_info)
+
+        return data_list