Source code for torch_uncertainty.datasets.classification.cifar.cifar_n
from collections.abc import Callable
from pathlib import Path
from typing import Any, Literal
import torch
from torchvision.datasets import CIFAR10, CIFAR100
from torchvision.datasets.utils import (
check_integrity,
download_and_extract_archive,
)
[docs]class CIFAR10N(CIFAR10):
"""`CIFAR-10N <https://github.com/UCSC-REAL/cifar-10-100n>`_ Dataset.
Args:
root (string): Root directory of dataset where file
``cifar-10h-probs.npy`` exists or will be saved to if download
is set to True.
train (bool, optional): For API consistency, not used.
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 root directory. If dataset is already
downloaded, it is not downloaded again. Defaults to False.
"""
n_test_list = ["CIFAR-N-1.zip", "666bf3cff3a944c245f2b6f62af4b919"]
n_url = "http://www.yliuu.com/web-cifarN/files/CIFAR-N-1.zip"
filename = "CIFAR-N/CIFAR-10_human.pt"
file_arg = ""
def __init__(
self,
root: str | Path,
train: bool = True,
file_arg: Literal[
"aggre_label",
"worse_label",
"random_label1",
"random_label2",
"random_label3",
] = "aggre_label",
transform: Callable[..., Any] | None = None,
target_transform: Callable[..., Any] | None = None,
download: bool = False,
) -> None:
super().__init__(
Path(root),
train=train,
transform=transform,
target_transform=target_transform,
download=download,
)
if download:
self.download_n()
if not self._check_specific_integrity():
raise RuntimeError(
"Dataset not found or corrupted. You can use download=True to " "download it."
)
self.targets = list(torch.load(self.root / self.filename)[file_arg])
def _check_specific_integrity(self) -> bool:
filename, md5 = self.n_test_list
fpath = self.root / filename
return check_integrity(fpath, md5)
def download_n(self) -> None:
download_and_extract_archive(
self.n_url,
self.root,
filename=self.n_test_list[0],
md5=self.n_test_list[1],
)
[docs]class CIFAR100N(CIFAR100):
n_test_list = ["CIFAR-N-1.zip", "666bf3cff3a944c245f2b6f62af4b919"]
n_url = "http://www.yliuu.com/web-cifarN/files/CIFAR-N-1.zip"
filename = "CIFAR-N/CIFAR-100_human.pt"
file_arg = ""
def __init__(
self,
root: str,
train: bool = True,
file_arg: Literal[
"fine_label",
"coarse_label",
] = "fine_label",
transform: Callable[..., Any] | None = None,
target_transform: Callable[..., Any] | None = None,
download: bool = False,
) -> None:
super().__init__(
root,
train=train,
transform=transform,
target_transform=target_transform,
download=download,
)
self.root = Path(self.root)
if download:
self.download_n()
if not self._check_specific_integrity():
raise RuntimeError(
"Dataset not found or corrupted. You can use download=True to " "download it."
)
self.targets = list(torch.load(self.root / self.filename)[file_arg])
def _check_specific_integrity(self) -> bool:
filename, md5 = self.n_test_list
fpath = self.root / filename
return check_integrity(fpath, md5)
def download_n(self) -> None:
download_and_extract_archive(
self.n_url,
self.root,
filename=self.n_test_list[0],
md5=self.n_test_list[1],
)