Upload fer2013.py

fer2013.py

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+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/fer2013/resolve/main/"
+_URLS = {
+    "train": _URL + "train.pt",
+}
+_DESCRIPTION = "A large set of images of faces with seven emotional classes"
+_CITATION = """\
+@TECHREPORT{Affectnet 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"]}),
+        ]
+
+    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