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5457a22 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | import json
import os
from pathlib import Path
import datasets
_CITATION = """
@misc{gurari2018vizwiz,
title={VizWiz Grand Challenge: Answering Visual Questions from Blind People},
author={Danna Gurari and Qing Li and Abigale J. Stangl and Anhong Guo and Chi Lin and Kristen Grauman and Jiebo Luo and Jeffrey P. Bigham},
year={2018},
eprint={1802.08218},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
"""
_HOMEPAGE = "https://vizwiz.org/tasks-and-datasets/vqa/"
_DESCRIPTION = """
The VizWiz-VQA dataset originates from a natural visual question answering setting where blind people
each took an image and recorded a spoken question about it, together with 10 crowdsourced answers per
visual question. The proposed challenge addresses the following two tasks for this dataset: predict the
answer to a visual question and (2) predict whether a visual question cannot be answered.
"""
_LICENSE = " Creative Commons Attribution 4.0 International License."
_DATA_URL = {"train" : "https://vizwiz.cs.colorado.edu/VizWiz_final/images/train.zip",
"test" : "https://vizwiz.cs.colorado.edu/VizWiz_final/images/test.zip",
"val" : "https://vizwiz.cs.colorado.edu/VizWiz_final/images/val.zip" }
_ANNOTATION_URL = "https://vizwiz.cs.colorado.edu/VizWiz_final/vqa_data/Annotations.zip"
_FEATURES = datasets.Features(
{
"id" : datasets.Value("int32"),
"image": datasets.Image(),
"filename": datasets.Value("string"),
"question": datasets.Value("string"),
"answers": datasets.Sequence(datasets.Value("string")),
"answers_original": [
{
"answer": datasets.Value("string"),
"answer_confidence": datasets.Value("string"),
}
],
"answer_type": datasets.Value("string"),
"answerable": datasets.Value("int32")
}
)
class VizWiz(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description = _DESCRIPTION,
features = _FEATURES,
homepage = _HOMEPAGE,
license = _LICENSE,
citation = _CITATION,
)
def _split_generators(self, dl_manager):
ann_file_train = os.path.join(dl_manager.download_and_extract(_ANNOTATION_URL), "train.json")
ann_file_val = os.path.join(dl_manager.download_and_extract(_ANNOTATION_URL), "val.json")
ann_file_test = os.path.join(dl_manager.download_and_extract(_ANNOTATION_URL), "test.json")
image_folders = {k: Path(v) for k, v in dl_manager.download_and_extract(_DATA_URL).items()}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"annotation_file": ann_file_train,
"image_folders": image_folders,
"split_key": 'train'
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"annotation_file": ann_file_val,
"image_folders": image_folders,
"split_key": "val"
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"annotation_file": ann_file_test,
"image_folders": image_folders,
"split_key": "test"
},
),
]
def _generate_examples(self, annotation_file,image_folders,split_key):
counter = 0
annotations = json.load(open(annotation_file))
for ann in annotations:
if split_key in ['train','val']:
answers = [answer["answer"] for answer in ann["answers"]]
answers_original = ann['answers']
answer_type = ann["answer_type"]
answerable = ann["answerable"]
else:
answers = None
answers_original = None
answer_type = None
answerable = None
yield counter, {
"id" : counter,
"image": str(image_folders[split_key]/split_key/ann['image']),
"filename" : ann['image'],
"question" : ann["question"],
"answers" : answers,
"answers_original" : answers_original,
"answer_type" : answer_type,
"answerable" : answerable
}
counter += 1
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