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

from .base import ImageNetVariation


[docs] class ImageNetR(ImageNetVariation): url = "https://people.eecs.berkeley.edu/~hendrycks/imagenet-r.tar" filename = "imagenet-r.tar" tgz_md5 = "a61312130a589d0ca1a8fca1f2bd3337" dataset_name = "imagenet-r" def __init__(self, **kwargs) -> None: """Initializes the ImageNetR 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)