Merge 292c69f into 7581b76

configs/_base_/datasets/vizwiz.py

@@ -0,0 +1,80 @@
+# data settings
+
+data_preprocessor = dict(
+    mean=[122.770938, 116.7460125, 104.09373615],
+    std=[68.5005327, 66.6321579, 70.32316305],
+    to_rgb=True,
+)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        scale=384,
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(
+        type='PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    ),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='Resize',
+        scale=(480, 480),
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(
+        type='CleanCaption',
+        keys=['question'],
+    ),
+    dict(
+        type='PackInputs',
+        algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
+        meta_keys=['question_id', 'image_id'],
+    ),
+]
+
+train_dataloader = dict(
+    batch_size=16,
+    num_workers=8,
+    dataset=dict(
+        type='VizWiz',
+        data_root='data/vizwiz/Images',
+        data_prefix='',
+        ann_file='Annotations/train.json',
+        pipeline=train_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    persistent_workers=True,
+    drop_last=True,
+)
+
+val_dataloader = dict(
+    batch_size=16,
+    num_workers=8,
+    dataset=dict(
+        type='VizWiz',
+        data_root='data/vizwiz/Images',
+        data_prefix='',
+        ann_file='Annotations/val.json',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    persistent_workers=True,
+)
+val_evaluator = dict(type='VizWizAcc')
+
+test_dataloader = dict(
+    batch_size=16,
+    num_workers=8,
+    dataset=dict(
+        type='VizWiz',
+        data_root='data/vizwiz/Images',
+        data_prefix='',
+        ann_file='Annotations/test.json',
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+test_evaluator = dict(type='ReportVQA', file_path='vqa_test.json')

mmpretrain/datasets/__init__.py

@@ -46,9 +46,10 @@
     from .textvqa import TextVQA
     from .visual_genome import VisualGenomeQA
     from .vsr import VSR
+    from .vizwiz import VizWiz
 
     __all__.extend([
         'COCOCaption', 'COCORetrieval', 'COCOVQA', 'FlamingoEvalCOCOCaption',
         'FlamingoEvalCOCOVQA', 'OCRVQA', 'RefCOCO', 'VisualGenomeQA',
-        'ScienceQA', 'NoCaps', 'GQA', 'TextVQA', 'VSR'
+        'ScienceQA', 'NoCaps', 'GQA', 'TextVQA', 'VSR', 'VizWiz'
     ])

mmpretrain/datasets/vizwiz.py

@@ -0,0 +1,112 @@
+# 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 VizWiz(BaseDataset):
+    """VizWiz dataset.
+
+    Args:
+        data_root (str): The root directory for ``data_prefix``, ``ann_file``
+            and ``question_file``.
+        data_prefix (str): The directory of images.
+        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_list = []
+        for ann in annotations:
+            # {
+            #     "image": "VizWiz_val_00000001.jpg",
+            #     "question": "Can you tell me what this medicine is please?",
+            #     "answers": [
+            #     {
+            #         "answer": "no",
+            #         "answer_confidence": "yes"
+            #     },
+            #     {
+            #         "answer": "unanswerable",
+            #         "answer_confidence": "yes"
+            #     },
+            #     {
+            #         "answer": "night time",
+            #         "answer_confidence": "maybe"
+            #     },
+            #     {
+            #         "answer": "unanswerable",
+            #         "answer_confidence": "yes"
+            #     },
+            #     {
+            #         "answer": "night time",
+            #         "answer_confidence": "maybe"
+            #     },
+            #     {
+            #         "answer": "night time cold medicine",
+            #         "answer_confidence": "maybe"
+            #     },
+            #     {
+            #         "answer": "night time",
+            #         "answer_confidence": "maybe"
+            #     },
+            #     {
+            #         "answer": "night time",
+            #         "answer_confidence": "maybe"
+            #     },
+            #     {
+            #         "answer": "night time",
+            #         "answer_confidence": "maybe"
+            #     },
+            #     {
+            #         "answer": "night time medicine",
+            #         "answer_confidence": "yes"
+            #     }
+            #     ],
+            #     "answer_type": "other",
+            #     "answerable": 1
+            # },
+            data_info = dict()
+            data_info['question'] = ann['question']
+            data_info['img_path'] = mmengine.join_path(
+                self.data_prefix['img_path'], ann['image'])
+
+            if 'answerable' not in ann:
+                data_list.append(data_info)
+            else:
+                if ann['answerable'] == 1:
+                    # add answer_weight & answer_count, delete duplicate answer
+                    answers = []
+                    for item in ann.pop('answers'):
+                        if item['answer_confidence'] == 'yes' and item[
+                                'answer'] != 'unanswerable':
+                            answers.append(item['answer'])
+                    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_info.update(ann)
+                    data_list.append(data_info)
+
+        return data_list