Upload artelingo.py

artelingo.py

@@ -0,0 +1,204 @@
+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)