Source code for torchvision.datasets.cifar

from PIL import Image
import os
import os.path
import numpy as np
import pickle
from typing import Any, Callable, Optional, Tuple

from .vision import VisionDataset
from .utils import check_integrity, download_and_extract_archive

[docs]class CIFAR10(VisionDataset): """`CIFAR10 <>`_ Dataset. Args: 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__( self, 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, target_transform=target_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 else: 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['labels']) else: self.targets.extend(entry['fine_labels']) = np.vstack(, 3, 32, 32) =, 2, 3, 1)) # convert to HWC self._load_meta() 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)}
[docs] def __getitem__(self, index: int) -> Tuple[Any, Any]: """ Args: index (int): Index Returns: 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') return 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")
[docs]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', }


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