[Feature] support TextVQA dataset (#1596)

* [Support] Suport TextVQA dataset

* add folder structure

* fix readme

configs/blip/README.md

@@ -48,9 +48,9 @@ python tools/test.py configs/blip/blip-base_8xb32_caption.py https://download.op
 
 ### Image Caption on NoCaps
 
-| Model                          | Params (M) | SPICE | CIDER  |                Config                |                                                     Download                                                      |
-| :----------------------------- | :--------: | :---: | :----: | :----------------------------------: | :---------------------------------------------------------------------------------------------------------------: |
-| `blip-base_3rdparty_caption`\* |   223.97   | 14.69 | 109.12 | [config](./blip-base_8x32_nocaps.py) | [model](https://download.openmmlab.com/mmclassification/v1/blip/blip-base_3rdparty_coco-caption_20230419-a5b71af3.pth) |
+| Model                          | Params (M) | SPICE | CIDER  |                Config                 |                                                     Download                                                     |
+| :----------------------------- | :--------: | :---: | :----: | :-----------------------------------: | :--------------------------------------------------------------------------------------------------------------: |
+| `blip-base_3rdparty_caption`\* |   223.97   | 14.69 | 109.12 | [config](./blip-base_8xb32_nocaps.py) | [model](https://download.openmmlab.com/mmclassification/v1/blip/blip-base_3rdparty_coco-caption_20230419-a5b71af3.pth) |
 
 ### Visual Grounding on RefCOCO
 

mmpretrain/datasets/__init__.py

@@ -42,17 +42,12 @@ if WITH_MULTIMODAL:
     from .nocaps import NoCaps
     from .refcoco import RefCOCO
     from .scienceqa import ScienceQA
+    from .textvqa import TextVQA
     from .visual_genome import VisualGenomeQA
 
     __all__.extend([
-        'COCOCaption',
-        'COCORetrieval',
-        'COCOVQA',
-        'FlamingoEvalCOCOCaption',
-        'FlamingoEvalCOCOVQA',
-        'RefCOCO',
-        'VisualGenomeQA',
-        'ScienceQA',
+        'COCOCaption', 'COCORetrieval', 'COCOVQA', 'FlamingoEvalCOCOCaption',
+        'FlamingoEvalCOCOVQA', 'RefCOCO', 'VisualGenomeQA', 'ScienceQA',
         'NoCaps'
-        'GQA',
+        'GQA', 'TextVQA'
     ])

mmpretrain/datasets/textvqa.py

@@ -0,0 +1,105 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from collections import Counter
+from typing import List
+
+import mmengine
+from mmengine.dataset import BaseDataset
+
+from mmpretrain.registry import DATASETS
+
+
+@DATASETS.register_module()
+class TextVQA(BaseDataset):
+    """TextVQA dataset.
+
+    val image:
+        https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip
+    test image:
+        https://dl.fbaipublicfiles.com/textvqa/images/test_images.zip
+    val json:
+        https://dl.fbaipublicfiles.com/textvqa/data/TextVQA_0.5.1_val.json
+    test json:
+        https://dl.fbaipublicfiles.com/textvqa/data/TextVQA_0.5.1_test.json
+
+    folder structure:
+    data/textvqa
+        ├── annotations
+        │   ├── TextVQA_0.5.1_test.json
+        │   └── TextVQA_0.5.1_val.json
+        └── images
+            ├── test_images
+            └── train_images
+
+    Args:
+        data_root (str): The root directory for ``data_prefix``, ``ann_file``
+            and ``question_file``.
+        data_prefix (str): The directory of images.
+        question_file (str): Question file path.
+        ann_file (str, optional): Annotation file path for training and
+            validation. Defaults to an empty string.
+        **kwargs: Other keyword arguments in :class:`BaseDataset`.
+    """
+
+    def __init__(self,
+                 data_root: str,
+                 data_prefix: str,
+                 ann_file: str = '',
+                 **kwarg):
+        super().__init__(
+            data_root=data_root,
+            data_prefix=dict(img_path=data_prefix),
+            ann_file=ann_file,
+            **kwarg,
+        )
+
+    def load_data_list(self) -> List[dict]:
+        """Load data list."""
+        annotations = mmengine.load(self.ann_file)['data']
+
+        data_list = []
+
+        for ann in annotations:
+
+            # ann example
+            # {
+            #     'question': 'what is the brand of...is camera?',
+            #     'image_id': '003a8ae2ef43b901',
+            #     'image_classes': [
+            #         'Cassette deck', 'Printer', ...
+            #         ],
+            #     'flickr_original_url': 'https://farm2.static...04a6_o.jpg',
+            #     'flickr_300k_url': 'https://farm2.static...04a6_o.jpg',
+            #     'image_width': 1024,
+            #     'image_height': 664,
+            #     'answers': [
+            #         'nous les gosses',
+            #         'dakota',
+            #         'clos culombu',
+            #         'dakota digital' ...
+            #        ],
+            #     'question_tokens':
+            #         ['what', 'is', 'the', 'brand', 'of', 'this', 'camera'],
+            #     'question_id': 34602,
+            #     'set_name': 'val'
+            # }
+
+            data_info = dict(question=ann['question'])
+            data_info['question_id'] = ann['question_id']
+            data_info['image_id'] = ann['image_id']
+
+            img_path = mmengine.join_path(self.data_prefix['img_path'],
+                                          ann['image_id'] + '.jpg')
+            data_info['img_path'] = img_path
+
+            data_info['question_id'] = ann['question_id']
+
+            if 'answers' in ann:
+                answers = [item for item in ann.pop('answers')]
+                count = Counter(answers)
+                answer_weight = [i / len(answers) for i in count.values()]
+                data_info['gt_answer'] = list(count.keys())
+                data_info['gt_answer_weight'] = answer_weight
+
+            data_list.append(data_info)
+
+        return data_list