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"""Corrupted Fashion-Mnist Data Set. |
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This module contains the huggingface dataset adaptation of |
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the Corrupted Fashion-Mnist Data Set. |
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Find the full code at `https://github.com/testingautomated-usi/fashion-mnist-c`.""" |
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import struct |
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import datasets |
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import numpy as np |
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from datasets.tasks import ImageClassification |
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_CITATION = """\ |
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@inproceedings{Weiss2022SimpleTechniques, |
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title={Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning}, |
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author={Weiss, Michael and Tonella, Paolo}, |
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booktitle={Proceedings of the 31th ACM SIGSOFT International Symposium on Software Testing and Analysis}, |
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year={2022} |
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} |
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""" |
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_DESCRIPTION = """\ |
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Fashion-MNIST is dataset of fashion images, indended as a drop-in replacement for the MNIST dataset. |
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This dataset (Fashion-Mnist-Corrupted) provides out-of-distribution data for the Fashion-Mnist |
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dataset. Fashion-Mnist-Corrupted is based on a similar project for MNIST, called MNIST-C, by Mu et. al. |
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""" |
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CONFIG = datasets.BuilderConfig( |
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name="fashion_mnist_corrupted", |
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version=datasets.Version("1.0.0"), |
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description=_DESCRIPTION, |
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) |
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_HOMEPAGE = "https://github.com/testingautomated-usi/fashion-mnist-c" |
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_LICENSE = "https://github.com/testingautomated-usi/fashion-mnist-c/blob/main/LICENSE" |
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if CONFIG.version == datasets.Version("1.0.0"): |
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tag = "v1.0.0" |
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else: |
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raise ValueError("Unsupported version.") |
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_URL = ( |
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f"https://raw.githubusercontent.com/testingautomated-usi/fashion-mnist-c/{tag}/generated/npy/" |
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) |
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_URLS = { |
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"train_images": "fmnist-c-train.npy", |
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"train_labels": "fmnist-c-train-labels.npy", |
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"test_images": "fmnist-c-test.npy", |
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"test_labels": "fmnist-c-test-labels.npy", |
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} |
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_NAMES = [ |
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"T - shirt / top", |
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"Trouser", |
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"Pullover", |
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"Dress", |
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"Coat", |
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"Sandal", |
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"Shirt", |
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"Sneaker", |
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"Bag", |
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"Ankle boot", |
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] |
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class FashionMnistCorrupted(datasets.GeneratorBasedBuilder): |
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"""FashionMNIST-Corrupted Data Set""" |
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BUILDER_CONFIGS = [CONFIG] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"label": datasets.features.ClassLabel(names=_NAMES), |
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} |
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), |
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supervised_keys=("image", "label"), |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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task_templates=[ |
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ImageClassification(image_column="image", label_column="label") |
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], |
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) |
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def _split_generators(self, dl_manager): |
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urls_to_download = { |
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key: _URL + fname for key, fname in _URLS.items() |
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} |
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downloaded_files = dl_manager.download(urls_to_download) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": [ |
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downloaded_files["train_images"], |
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downloaded_files["train_labels"], |
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], |
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"split": "train", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": [ |
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downloaded_files["test_images"], |
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downloaded_files["test_labels"], |
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], |
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"split": "test", |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, split): |
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"""This function returns the examples in the raw form.""" |
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images = np.load(filepath[0]) |
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labels = np.load(filepath[1]) |
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if images.shape[0] != labels.shape[0]: |
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raise ValueError( |
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f"Number of images {images.shape[0]} and labels {labels.shape[0]} do not match." |
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) |
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for idx in range(images.shape[0]): |
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yield idx, {"image": images[idx], "label": int(labels[idx])} |
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