Datasets:
Tasks:
Visual Question Answering
Sub-tasks:
visual-question-answering
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
License:
File size: 5,024 Bytes
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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TextVQA dataset"""
import copy
import json
import os
import datasets
_CITATION = """
@inproceedings{singh2019towards,
title={Towards VQA Models That Can Read},
author={Singh, Amanpreet and Natarjan, Vivek and Shah, Meet and Jiang, Yu and Chen, Xinlei and Batra, Dhruv and Parikh, Devi and Rohrbach, Marcus},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={8317-8326},
year={2019}
}
"""
_DESCRIPTION = """\
TextVQA requires models to read and reason about text in images to answer questions about them.
Specifically, models need to incorporate a new modality of text present in the images and reason
over it to answer TextVQA questions. TextVQA dataset contains 45,336 questions over 28,408 images
from the OpenImages dataset.
"""
_HOMEPAGE = "https://textvqa.org"
_LICENSE = "CC BY 4.0"
_SPLITS = ["train", "val", "test"]
_URLS = {
f"{split}_annotations": f"https://dl.fbaipublicfiles.com/textvqa/data/TextVQA_0.5.1_{split}.json"
for split in _SPLITS
}
# TextVQA val and train images are packed together
_URLS["train_val_images"] = "https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip"
_URLS["test_images"] = "https://dl.fbaipublicfiles.com/textvqa/images/test_images.zip"
_NUM_ANSWERS_PER_QUESTION = 10
class Textvqa(datasets.GeneratorBasedBuilder):
"""TextVQA dataset."""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="textvqa",
version=datasets.Version("0.5.1"),
description=_DESCRIPTION,
)
]
DEFAULT_CONFIG_NAME = "textvqa"
def _info(self):
features = datasets.Features(
{
"image_id": datasets.Value("string"),
"question_id": datasets.Value("int32"),
"question": datasets.Value("string"),
"question_tokens": datasets.Sequence(datasets.Value("string")),
"image": datasets.Image(),
"image_width": datasets.Value("int32"),
"image_height": datasets.Value("int32"),
"flickr_original_url": datasets.Value("string"),
"flickr_300k_url": datasets.Value("string"),
"answers": datasets.Sequence(datasets.Value("string")),
"image_classes": datasets.Sequence(datasets.Value("string")),
"set_name": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"annotations_path": downloaded_files["train_annotations"],
"images_path": downloaded_files["train_val_images"],
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"annotations_path": downloaded_files["val_annotations"],
"images_path": downloaded_files["train_val_images"],
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"annotations_path": downloaded_files["test_annotations"],
"images_path": downloaded_files["test_images"],
},
),
]
def _generate_examples(self, annotations_path: str, images_path: str):
with open(annotations_path, "r", encoding="utf-8") as f:
data = json.load(f)["data"]
idx = 0
for item in data:
item = copy.deepcopy(item)
item["answers"] = item.get("answers", ["" for _ in range(_NUM_ANSWERS_PER_QUESTION)])
image_id = item["image_id"]
image_subfolder = "train_images" if item["set_name"] != "test" else "test_images"
item["image"] = os.path.join(images_path, image_subfolder, f"{image_id}.jpg")
yield idx, item
idx += 1
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