artelingo.py

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)