mmagic.datasets.cifar10_dataset

Module Contents

Classes

CIFAR10

CIFAR10 Dataset.

class mmagic.datasets.cifar10_dataset.CIFAR10(data_prefix: str, test_mode: bool, metainfo: Optional[dict] = None, data_root: str = '', download: bool = True, **kwargs)

Bases: mmagic.datasets.basic_conditional_dataset.BasicConditionalDataset

CIFAR10 Dataset.

This implementation is modified from https://github.com/pytorch/vision/blob/master/torchvision/datasets/cifar.py

参数

• data_prefix (str) – Prefix for data.

• test_mode (bool) – test_mode=True means in test phase. It determines to use the training set or test set.

• metainfo (dict, optional) – Meta information for dataset, such as categories information. Defaults to None.

• data_root (str) – The root directory for data_prefix. Defaults to ‘’.

• download (bool) – Whether to download the dataset if not exists. Defaults to True.

• **kwargs – Other keyword arguments in BaseDataset.

base_folder = 'cifar-10-batches-py'

url = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'

filename = 'cifar-10-python.tar.gz'

tgz_md5 = 'c58f30108f718f92721af3b95e74349a'

train_list = [['data_batch_1', 'c99cafc152244af753f735de768cd75f'], ['data_batch_2',...

test_list = [['test_batch', '40351d587109b95175f43aff81a1287e']]

meta

METAINFO

load_data_list()

Load images and ground truth labels.

_load_meta()

Load categories information from metafile.

_check_integrity()

Check the integrity of data files.

extra_repr() → List[str]

The extra repr information of the dataset.