Datasets:
Tasks:
Image Classification
Modalities:
Image
Languages:
English
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10K<n<100K
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"""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"):
tag = "v1.0.0"
else:
raise ValueError("Unsupported version.")
_URL = (
f"https://github.com/testingautomated-usi/fashion-mnist-c/blob/{tag}/generated/npy/"
)
_URLS = {
"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):
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
images = np.load(filepath[0])
labels = np.load(filepath[1])
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])}
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