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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.
"""The GQA dataset."""
import json
import os
import datasets
_CITATION = """\
@inproceedings{hudson2019gqa,
title={Gqa: A new dataset for real-world visual reasoning and compositional question answering},
author={Hudson, Drew A and Manning, Christopher D},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={6700--6709},
year={2019}
}
"""
_DESCRIPTION = """\
GQA is a new dataset for real-world visual reasoning and compositional question answering,
seeking to address key shortcomings of previous visual question answering (VQA) datasets.
"""
_URLS = {
"train": "https://nlp.cs.unc.edu/data/lxmert_data/gqa/train.json",
"valid": "https://nlp.cs.unc.edu/data/lxmert_data/gqa/valid.json",
"testdev": "https://nlp.cs.unc.edu/data/lxmert_data/gqa/testdev.json",
"img": "https://downloads.cs.stanford.edu/nlp/data/gqa/images.zip",
}
_IMG_DIR = "images"
class Gqa(datasets.GeneratorBasedBuilder):
"""The GQA dataset."""
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="gqa", version=datasets.Version("1.0.0"), description="GQA dataset."),
]
def _info(self):
features = datasets.Features(
{
"question": datasets.Value("string"),
"question_id": datasets.Value("int32"),
"image_id": datasets.Value("string"),
"label": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_dir = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": dl_dir["train"], "img_dir": os.path.join(dl_dir["img"], _IMG_DIR)},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": dl_dir["valid"], "img_dir": os.path.join(dl_dir["img"], _IMG_DIR)},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": dl_dir["testdev"], "img_dir": os.path.join(dl_dir["img"], _IMG_DIR)},
),
]
def _generate_examples(self, filepath, img_dir):
""" Yields examples as (key, example) tuples. """
with open(filepath, encoding="utf-8") as f:
gqa = json.load(f)
for id_, d in enumerate(gqa):
img_id = os.path.join(img_dir, d["img_id"] + ".jpg")
label = next(iter(d["label"]))
yield id_, {
"question": d["sent"],
"question_id": d["question_id"],
"image_id": img_id,
"label": label,
} |