|
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 |
|
|