Datasets:

mweiss
/

fashion_mnist_corrupted

	"""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 os.path

	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"):
	tag = "v1.0.0"
	else:
	raise ValueError("Unsupported version.")

	# Downloaded from: f"https://raw.githubusercontent.com/testingautomated-usi/fashion-mnist-c/{tag}/generated/npy/
	_URL = "data.zip"
	_FILENAMES = {
	"train_images": "fmnist-c-train.npy",
	"train_labels": "fmnist-c-train-labels.npy",
	"test_images": "fmnist-c-test.npy",
	"test_labels": "fmnist-c-test-labels.npy",
	}

	_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):
	data_dir = dl_manager.download_and_extract(_URL)
	downloaded_files = {
	key: os.path.join(data_dir, fname) for key, fname in _FILENAMES.items()
	}

	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:
	images = np.load(f)
	with open(filepath[1], "rb") as f:
	labels = np.load(f)

	if images.shape[0] != labels.shape[0]:
	raise ValueError(
	f"Number of images {images.shape[0]} and labels {labels.shape[0]} do not match."
	)

	for idx in range(images.shape[0]):
	yield idx, {"image": images[idx], "label": int(labels[idx])}