Datasets:

youssef101
/

artelingo

	import json
	import os
	import datasets
	import pandas as pd
	from PIL import Image


	class ArtelingoBuilderConfig(datasets.BuilderConfig):

	def __init__(self, name, splits, **kwargs):
	super().__init__(name, **kwargs)
	self.splits = splits


	# Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@inproceedings{mohamed2022artelingo,
	title={ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture},
	author={Mohamed, Youssef and Abdelfattah, Mohamed and Alhuwaider, Shyma and Li, Feifan and Zhang, Xiangliang and Church, Kenneth and Elhoseiny, Mohamed},
	booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
	pages={8770--8785},
	year={2022}
	}
	"""

	# Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	ArtELingo is a benchmark and dataset having a collection of 80,000 artworks from WikiArt with 1.2 Million annotations in English, Arabic, and Chinese.
	"""

	# Add a link to an official homepage for the dataset here
	_HOMEPAGE = "https://www.artelingo.org/"

	# Add the licence for the dataset here if you can find it
	_LICENSE = "Terms of Use: Before we are able to offer you access to the database, \
	please agree to the following terms of use. After approval, you (the 'Researcher') \
	receive permission to use the ArtELingo database (the 'Database') at King Abdullah \
	University of Science and Technology (KAUST). In exchange for being able to join the \
	ArtELingo community and receive such permission, Researcher hereby agrees to the \
	following terms and conditions: [1.] The Researcher shall use the Database only for \
	non-commercial research and educational purposes. [2.] The Universities make no \
	representations or warranties regarding the Database, including but not limited to \
	warranties of non-infringement or fitness for a particular purpose. [3.] Researcher \
	accepts full responsibility for his or her use of the Database and shall defend and \
	indemnify the Universities, including their employees, Trustees, officers and agents, \
	against any and all claims arising from Researcher's use of the Database, and \
	Researcher's use of any copies of copyrighted 2D artworks originally uploaded to \
	http://www.wikiart.org that the Researcher may use in connection with the Database. \
	[4.] Researcher may provide research associates and colleagues with access to the \
	Database provided that they first agree to be bound by these terms and conditions. \
	[5.] The Universities reserve the right to terminate Researcher's access to the Database \
	at any time. [6.] If Researcher is employed by a for-profit, commercial entity, \
	Researcher's employer shall also be bound by these terms and conditions, and Researcher \
	hereby represents that he or she is fully authorized to enter into this agreement on \
	behalf of such employer. [7.] The international copyright laws shall apply to all \
	disputes under this agreement."

	# Add link to the official dataset URLs here
	# The HuggingFace dataset library don't host the datasets but only point to the original files
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)

	# This script can work with local (downloaded) files.
	_URLs = {
	'val': 'https://artelingo.s3.amazonaws.com/val.zip',
	'test': 'https://artelingo.s3.amazonaws.com/test.zip',
	'train': 'https://artelingo.s3.amazonaws.com/train.zip',
	'wecia-emo_dev': 'https://artelingo.s3.amazonaws.com/wecia_emo_dev.zip',
	'wecia-cap_dev': 'https://artelingo.s3.amazonaws.com/wecia_cap_dev.zip',
	'wecia-emo_hidden': 'https://artelingo.s3.amazonaws.com/wecia_emo_hidden.zip',
	'wecia-cap_hidden': 'https://artelingo.s3.amazonaws.com/wecia_cap_hidden.zip',
	}

	# _URL_ANN = "https://artelingo.s3.amazonaws.com/artelingo_release_lite.csv"


	_EMOTIONS = ['contentment', 'awe', 'amusement', 'excitement', 'sadness', 'fear', 'anger', 'disgust', 'something else']

	# Name of the dataset usually match the script name with CamelCase instead of snake_case
	class Artelingo(datasets.GeneratorBasedBuilder):
	"""An example dataset script to work with ArtELingo dataset"""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	ArtelingoBuilderConfig(name='artelingo', splits=['train', 'val', 'test'],
	version=VERSION, description="The full ArtELingo dataset"),
	ArtelingoBuilderConfig(name='dev', splits=['val', 'test'],
	version=VERSION, description="The Test and Val subsets of ArtELingo"),
	ArtelingoBuilderConfig(name='wecia-emo', splits=['dev', 'hidden'],
	version=VERSION, description="The Dev set of the WECIA Emotion Prediction challenge"),
	ArtelingoBuilderConfig(name='wecia-cap', splits=['dev', 'hidden'],
	version=VERSION, description="The Dev set of the WECIA Affective Caption Generation challenge"),
	]
	DEFAULT_CONFIG_NAME = "artelingo"

	def _info(self):
	# This method specifies the datasets. DatasetInfo object which contains informations and typings for the dataset

	feature_dict = {
	"uid": datasets.Value("int32"),
	'image': datasets.Image(),
	"art_style": datasets.Value("string"),
	"painting": datasets.Value("string"),
	# "emotion": datasets.ClassLabel(names=_EMOTIONS),
	"emotion": datasets.Value("string"),
	"language": datasets.Value("string"),
	"text": datasets.Value("string"),
	}

	features = datasets.Features(feature_dict)

	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
	data_dir = self.config.data_dir
	if data_dir is None:
	data_dir = {}
	prefix = self.config.name + '_' if 'wecia' in self.config.name else ''
	for split in self.config.splits:
	data_dir[split] = dl_manager.download_and_extract(_URLs[prefix + split])
	# data_dir = dl_manager.download_and_extract(_URLs)

	splits = []
	for split in self.config.splits:
	dataset = datasets.SplitGenerator(
	name=split,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"metadata": os.path.join(data_dir[split], split, "metadata.csv"),
	"image_dir": os.path.join(data_dir[split], split),
	}
	)
	splits.append(dataset)
	return splits

	def _generate_examples(
	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	self, metadata, image_dir
	):
	""" Yields examples as (key, example) tuples. """
	# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is here for legacy reason (tfds) and is not important in itself.

	name = self.config.name

	df = pd.read_csv(metadata)
	uids = range(len(df))

	if name == 'wecia-emo':
	for uid, entry in zip(uids, df.itertuples()):
	result = {
	"uid": entry.uid,
	"image": Image.open(os.path.join(image_dir, entry.file_name)),
	"art_style": entry.art_style,
	"painting": entry.painting,
	"text": entry.text,
	"emotion": None,
	'language': None,
	}
	yield (uid, result)
	elif name == 'wecia-cap':
	for uid, entry in zip(uids, df.itertuples()):
	result = {
	"uid": entry.uid,
	"image": Image.open(os.path.join(image_dir, entry.file_name)),
	"art_style": entry.art_style,
	"painting": entry.painting,
	"emotion": entry.emotion,
	"language": entry.language,
	"text": None,
	}
	yield (uid, result)
	else:
	for uid, entry in zip(uids, df.itertuples()):
	result = {
	"uid": uid,
	"image": Image.open(os.path.join(image_dir, entry.file_name)),
	"art_style": entry.art_style,
	"painting": entry.painting,
	"emotion": entry.emotion,
	"language": entry.language,
	"text": entry.text,
	}
	yield (uid, result)