Delete fashion_mnist_c.py

fashion_mnist_c.py

@@ -1,127 +0,0 @@
-"""Corrupted Fashion-Mnist Data Set.
-
-This module contains the huggingface dataset adaptation of
-the Corrupted Fashion-Mnist Data Set.
-Find the full code at `https://github.com/testingautomated-usi/fashion-mnist-c`."""
-import struct
-
-import datasets
-import numpy as np
-from datasets.tasks import ImageClassification
-
-_CITATION = """\
-@inproceedings{Weiss2022SimpleTechniques,
-  title={Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning},
-  author={Weiss, Michael and Tonella, Paolo},
-  booktitle={Proceedings of the 31th ACM SIGSOFT International Symposium on Software Testing and Analysis},
-  year={2022}
-}
-"""
-
-_DESCRIPTION = """\
-Fashion-MNIST is dataset of fashion images, indended as a drop-in replacement for the MNIST dataset.
-This dataset (Fashion-Mnist-Corrupted) provides out-of-distribution data for the Fashion-Mnist
-dataset. Fashion-Mnist-Corrupted is based on a similar project for MNIST, called MNIST-C, by Mu et. al.
-"""
-
-CONFIG = datasets.BuilderConfig(name="fashion_mnist_corrupted",
-                                version=datasets.Version("1.0.0"),
-                                description=_DESCRIPTION, )
-
-_HOMEPAGE = "https://github.com/testingautomated-usi/fashion-mnist-c"
-_LICENSE = "https://github.com/testingautomated-usi/fashion-mnist-c/blob/main/LICENSE"
-
-if CONFIG.version == datasets.Version("1.0.0"):
-    _CURRENT_VERSION_TAG = "e31d36a102cdd8c5e2690533eb2aaec7c296fcb6"
-else:
-    raise ValueError("Unsupported version.")
-
-_URL = f"https://github.com/testingautomated-usi/fashion-mnist-c/blob/{_CURRENT_VERSION_TAG}/generated/ubyte/"
-
-_URLS = {
-    "train_images": "fmnist-c-train-ubyte.gz",
-    "train_labels": "fmnist-c-train-labels-ubyte.gz",
-    "test_images": "fmnist-c-test-ubyte.gz",
-    "test_labels": "fmnist-c-test-labels-ubyte.gz",
-}
-
-_NAMES = [
-    "T - shirt / top",
-    "Trouser",
-    "Pullover",
-    "Dress",
-    "Coat",
-    "Sandal",
-    "Shirt",
-    "Sneaker",
-    "Bag",
-    "Ankle boot",
-]
-
-
-class FashionMnistCorrupted(datasets.GeneratorBasedBuilder):
-    """FashionMNIST-Corrupted Data Set"""
-
-    BUILDER_CONFIGS = [
-        CONFIG
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "image": datasets.Image(),
-                    "label": datasets.features.ClassLabel(names=_NAMES),
-                }
-            ),
-            supervised_keys=("image", "label"),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            task_templates=[ImageClassification(image_column="image", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        urls_to_download = {key: _URL + fname + "?raw=true" for key, fname in _URLS.items()}
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": [downloaded_files["train_images"], downloaded_files["train_labels"]],
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": [downloaded_files["test_images"], downloaded_files["test_labels"]],
-                    "split": "test",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """This function returns the examples in the raw form."""
-        # Images
-        with open(filepath[0], "rb") as f:
-            # First 16 bytes contain some metadata
-            _ = f.read(4)
-            size = struct.unpack(">I", f.read(4))[0]
-            _ = f.read(8)
-            images = np.frombuffer(f.read(), dtype=np.uint8).reshape(size, 28, 28)
-
-        # Labels
-        with open(filepath[1], "rb") as f:
-            # First 8 bytes contain some metadata
-            _ = f.read(8)
-            labels = np.frombuffer(f.read(), dtype=np.uint8)
-
-        for idx in range(size):
-            yield idx, {"image": images[idx], "label": int(labels[idx])}
-
-
-# For local development / debugger support only
-if __name__ == '__main__':
-    FashionMnistCorrupted().download_and_prepare()