Spaces:
Runtime error
Runtime error
File size: 3,929 Bytes
3b96cb1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 |
# 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
|