artelingo / artelingo.py
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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'],
version=VERSION, description="The Dev set of the WECIA Emotion Prediction challenge"),
ArtelingoBuilderConfig(name='wecia-cap', splits=['dev'],
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)