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# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import re
from collections import Counter
from typing import List
import mmengine
from mmengine.dataset import BaseDataset
from mmpretrain.registry import DATASETS
@DATASETS.register_module()
class COCOVQA(BaseDataset):
"""VQAv2 dataset.
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,
question_file: str,
ann_file: str = '',
**kwarg):
self.question_file = question_file
super().__init__(
data_root=data_root,
data_prefix=dict(img_path=data_prefix),
ann_file=ann_file,
**kwarg,
)
def _join_prefix(self):
if not mmengine.is_abs(self.question_file) and self.question_file:
self.question_file = osp.join(self.data_root, self.question_file)
return super()._join_prefix()
def _create_image_index(self):
img_prefix = self.data_prefix['img_path']
files = mmengine.list_dir_or_file(img_prefix, list_dir=False)
image_index = {}
for file in files:
image_id = re.findall(r'\d{12}', file)
if len(image_id) > 0:
image_id = int(image_id[-1])
image_index[image_id] = mmengine.join_path(img_prefix, file)
return image_index
def load_data_list(self) -> List[dict]:
"""Load data list."""
questions = mmengine.load(self.question_file)['questions']
if self.ann_file:
annotations = mmengine.load(self.ann_file)['annotations']
assert len(questions) == len(annotations)
else:
annotations = [None] * len(questions)
# The original VQAv2 annotation file and question file includes
# only image id but no image file paths.
self.image_index = self._create_image_index()
data_list = []
for question, ann in zip(questions, annotations):
# question example
# {
# 'image_id': 262144,
# 'question': "Is the ball flying towards the batter?",
# 'question_id': 262144000
# }
#
# ann example
# {
# 'question_type': "what are the",
# 'answer_type': "other",
# 'answers': [
# {'answer': 'watching',
# 'answer_id': 1,
# 'answer_confidence': 'yes'},
# ...
# ],
# 'image_id': 262148,
# 'question_id': 262148000,
# 'multiple_choice_answer': 'watching',
# 'answer_type': 'other',
# }
data_info = question
data_info['img_path'] = self.image_index[question['image_id']]
if ann is not None:
assert ann['question_id'] == question['question_id']
# add answer_weight & answer_count, delete duplicate answer
answers = [item['answer'] 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_info.update(ann)
data_list.append(data_info)
return data_list
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