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eduvedras
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Img_Vars

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Img_Vars / Img_Vars.py

eduvedras

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	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# 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.

	# Lint as: python3
	"""Image Description Dataset"""


	import json

	import datasets
	from datasets.tasks import QuestionAnsweringExtractive
	import pandas as pd


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@article{2016arXiv160605250R,
	author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
	Konstantin and {Liang}, Percy},
	title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
	journal = {arXiv e-prints},
	year = 2016,
	eid = {arXiv:1606.05250},
	pages = {arXiv:1606.05250},
	archivePrefix = {arXiv},
	eprint = {1606.05250},
	}
	"""

	_DESCRIPTION = """\
	Image descriptions for charts
	"""

	_URL = "https://huggingface.co/datasets/eduvedras/Img_Vars/resolve/main/images.tar.gz"

	class Img_VarsTargz(datasets.GeneratorBasedBuilder):

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"Chart": datasets.Image(),
	"Description": datasets.Value("string"),
	"Chart_name": datasets.Value("string"),
	}
	),
	# No default supervised_keys (as we have to pass both question
	# and context as input).
	supervised_keys=None,
	homepage="https://huggingface.co/datasets/eduvedras/Img_Vars",
	citation=_CITATION,
	task_templates=[
	QuestionAnsweringExtractive(
	question_column="question", context_column="context", answers_column="answers"
	)
	],
	)

	def _split_generators(self, dl_manager):
	path = dl_manager.download(_URL)
	image_iters = dl_manager.iter_archive(path)
	metadata_train_path = "https://huggingface.co/datasets/eduvedras/Img_Vars/resolve/main/vars_dataset_train.csv"
	metadata_test_path = "https://huggingface.co/datasets/eduvedras/Img_Vars/resolve/main/vars_dataset_test.csv"

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"images": image_iters,
	"metadata_path": metadata_train_path}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"images": image_iters,
	"metadata_path": metadata_test_path}),
	]

	def _generate_examples(self, images, metadata_path):
	metadata = pd.read_csv(metadata_path, sep=';')
	idx = 0
	for index, row in metadata.iterrows():
	for filepath, image in images:
	filepath = filepath.split('/')[-1]
	if row['Chart'] in filepath:
	yield idx, {
	"Chart": {"path": filepath, "bytes": image.read()},
	"Description": row['description'],
	"Chart_name": row['Chart'],
	}
	break
	idx += 1