import pickle from pathlib import Path from typing import List import datasets logger = datasets.logging.get_logger(__name__) _HOMEPAGE = "https://www.kaggle.com/datasets/msambare/fer2013" _URL = "https://huggingface.co/datasets/Jeneral/fer-2013/resolve/main/" _URLS = { "train": _URL + "train.pt", "test": _URL + "test.pt", } _DESCRIPTION = "A large set of images of faces with seven emotional classes" _CITATION = """\ @TECHREPORT{FER2013 dataset, author = {Prince Awuah Baffour}, title = {Facial Emotion Detection}, institution = {}, year = {2022} } """ class fer2013(datasets.GeneratorBasedBuilder): def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "img_bytes": datasets.Value("binary"), "labels": datasets.features.ClassLabel(names=["angry", "disgust", "fear", "happy", "neutral", "sad", "surprise"]), } ), supervised_keys=("img_bytes", "labels"), homepage=_HOMEPAGE, citation=_CITATION, ) def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: downloaded_files = dl_manager.download_and_extract(_URLS) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"] } ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": downloaded_files["test"], }, ), ] def _generate_examples(self, filepath): """This function returns the examples in the raw (text) form.""" logger.info("generating examples from = %s", filepath) with Path(filepath).open("rb") as f: examples = pickle.load(f) for i, ex in enumerate(examples): yield str(i), ex