Source code for torch_uncertainty.datasets.classification.imagenet.imagenet_c

from .base import ImageNetVariation


[docs] class ImageNetC(ImageNetVariation): """The corrupted ImageNet-C dataset. References: Benchmarking neural network robustness to common corruptions and perturbations. Dan Hendrycks and Thomas Dietterich. In ICLR, 2019. """ url = [ "https://zenodo.org/record/2235448/files/blur.tar", "https://zenodo.org/record/2235448/files/digital.tar", "https://zenodo.org/record/2235448/files/extra.tar", "https://zenodo.org/record/2235448/files/noise.tar", "https://zenodo.org/record/2235448/files/weather.tar", ] filename = [ "blur.tar", "digital.tar", "extra.tar", "noise.tar", "weather.tar", ] tgz_md5 = [ "2d8e81fdd8e07fef67b9334fa635e45c", "89157860d7b10d5797849337ca2e5c03", "d492dfba5fc162d8ec2c3cd8ee672984", "e80562d7f6c3f8834afb1ecf27252745", "33ffea4db4d93fe4a428c40a6ce0c25d", ] dataset_name = "imagenet-c" root_appendix = "imagenet-c" def __init__(self, **kwargs) -> None: """Initializes the ImageNetC dataset class. This is a subclass of ImageNetVariation that supports additional keyword arguments. Args: kwargs: Additional keyword arguments passed to the superclass, including: - root (str): Root directory of the datasets. - split (str, optional): For API consistency. Defaults to ``None``. - transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed version. E.g., transforms.RandomCrop. Defaults to ``None``. - target_transform (callable, optional): A function/transform that takes in the target and transforms it. Defaults to ``None``. - download (bool, optional): If ``True``, downloads the dataset from the internet and puts it in the root directory. If the dataset is already downloaded, it is not downloaded again. Defaults to ``False``. """ super().__init__(**kwargs)