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@soumith @SsnL @moskomule @lukeyeager @pmeier @activatedgeek @zhoumingjun @xenosoz @tomas1808 @chsasank @rtqichen @NicolasHug
from PIL import Image
import os
import os.path
import numpy as np
import pickle
import torch
from typing import Any, Callable, Optional, Tuple
from .vision import VisionDataset
from .utils import check_integrity, download_and_extract_archive
class CIFAR10(VisionDataset):
"""`CIFAR10 <>`_ Dataset.
root (string): Root directory of dataset where directory
``cifar-10-batches-py`` exists or will be saved to if download is set to True.
train (bool, optional): If True, creates dataset from training set, otherwise
creates from test set.
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
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.
base_folder = 'cifar-10-batches-py'
url = ""
filename = "cifar-10-python.tar.gz"
tgz_md5 = 'c58f30108f718f92721af3b95e74349a'
train_list = [
['data_batch_1', 'c99cafc152244af753f735de768cd75f'],
['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'],
['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'],
['data_batch_4', '634d18415352ddfa80567beed471001a'],
['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'],
test_list = [
['test_batch', '40351d587109b95175f43aff81a1287e'],
meta = {
'filename': 'batches.meta',
'key': 'label_names',
'md5': '5ff9c542aee3614f3951f8cda6e48888',
def __init__(
root: str,
train: bool = True,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
download: bool = False,
) -> None:
super(CIFAR10, self).__init__(root, transform=transform,
self.train = train # training set or test set
if download:
if not self._check_integrity():
raise RuntimeError('Dataset not found or corrupted.' +
' You can use download=True to download it')
if self.train:
downloaded_list = self.train_list
downloaded_list = self.test_list Any = []
self.targets = []
# now load the picked numpy arrays
for file_name, checksum in downloaded_list:
file_path = os.path.join(self.root, self.base_folder, file_name)
with open(file_path, 'rb') as f:
entry = pickle.load(f, encoding='latin1')['data'])
if 'labels' in entry:
self.targets.extend(entry['fine_labels']) = np.vstack(, 3, 32, 32) =, 2, 3, 1)) # convert to HWC
def _load_meta(self) -> None:
path = os.path.join(self.root, self.base_folder, self.meta['filename'])
if not check_integrity(path, self.meta['md5']):
raise RuntimeError('Dataset metadata file not found or corrupted.' +
' You can use download=True to download it')
with open(path, 'rb') as infile:
data = pickle.load(infile, encoding='latin1')
self.classes = data[self.meta['key']]
self.class_to_idx = {_class: i for i, _class in enumerate(self.classes)}
def __getitem__(self, index: int) -> Tuple[Any, Any]:
index (int): Index
tuple: (image, target) where target is index of the target class.
img, target =[index], self.targets[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
def __len__(self) -> int:
return len(
def _check_integrity(self) -> bool:
root = self.root
for fentry in (self.train_list + self.test_list):
filename, md5 = fentry[0], fentry[1]
fpath = os.path.join(root, self.base_folder, filename)
if not check_integrity(fpath, md5):
return False
return True
def download(self) -> None:
if self._check_integrity():
print('Files already downloaded and verified')
download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.tgz_md5)
def extra_repr(self) -> str:
return "Split: {}".format("Train" if self.train is True else "Test")
class CIFAR100(CIFAR10):
"""`CIFAR100 <>`_ Dataset.
This is a subclass of the `CIFAR10` Dataset.
base_folder = 'cifar-100-python'
url = ""
filename = "cifar-100-python.tar.gz"
tgz_md5 = 'eb9058c3a382ffc7106e4002c42a8d85'
train_list = [
['train', '16019d7e3df5f24257cddd939b257f8d'],
test_list = [
['test', 'f0ef6b0ae62326f3e7ffdfab6717acfc'],
meta = {
'filename': 'meta',
'key': 'fine_label_names',
'md5': '7973b15100ade9c7d40fb424638fde48',