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gqa / gqa.py

Jinchen Ge

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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,
	}