# -*- coding: utf-8 -*-
"""CIFAR-100 DeepOBS dataset."""
from . import dataset
from deepobs import config
from torch.utils import data as dat
from torchvision import datasets
from torchvision import transforms
from .datasets_utils import train_eval_sampler
training_transform_augmented = transforms.Compose([
transforms.Pad(padding=2),
transforms.RandomCrop(size=(32, 32)),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=63. / 255., saturation=[0.5, 1.5], contrast=[0.2, 1.8]),
transforms.ToTensor(),
transforms.Normalize((0.50707516, 0.48654887, 0.44091784), (0.26733429, 0.25643846, 0.27615047))
])
training_transform_not_augmented = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.50707516, 0.48654887, 0.44091784), (0.26733429, 0.25643846, 0.27615047))
])
[docs]class cifar100(dataset.DataSet):
"""DeepOBS data set class for the `CIFAR-100\
<https://www.cs.toronto.edu/~kriz/cifar.html>`_ data set.
Args:
batch_size (int): The mini-batch size to use. Note that, if ``batch_size``
is not a divider of the dataset size (``50 000`` for train, ``10 000``
for test) the remainder is dropped in each epoch (after shuffling).
data_augmentation (bool): If ``True`` some data augmentation operations
(random crop window, horizontal flipping, lighting augmentation) are
applied to the training data (but not the test data).
train_eval_size (int): Size of the train eval data set.
Defaults to ``10 000`` the size of the test set.
Methods:
_make_dataloader: A helper that is shared by all three data loader methods.
"""
def __init__(self,
batch_size,
data_augmentation=True,
train_eval_size=10000):
"""Creates a new CIFAR-100 instance.
Args:
batch_size (int): The mini-batch size to use. Note that, if ``batch_size``
is not a divider of the dataset size (``50 000`` for train, ``10 000``
for test) the remainder is dropped in each epoch (after shuffling).
data_augmentation (bool): If ``True`` some data augmentation operations
(random crop window, horizontal flipping, lighting augmentation) are
applied to the training data (but not the test data).
train_eval_size (int): Size of the train eval data set.
Defaults to ``10 000`` the size of the test set.
"""
self._name = "cifar100"
self._data_augmentation = data_augmentation
self._train_eval_size = train_eval_size
super(cifar100, self).__init__(batch_size)
def _make_train_and_valid_dataloader(self):
if self._data_augmentation:
transform = training_transform_augmented
else:
transform = training_transform_not_augmented
train_dataset = datasets.CIFAR100(root=config.get_data_dir(), train=True, download=True, transform=transform)
valid_dataset = datasets.CIFAR100(root=config.get_data_dir(), train=True, download=True, transform=training_transform_not_augmented)
train_loader, valid_loader = self._make_train_and_valid_dataloader_helper(train_dataset, valid_dataset)
return train_loader, valid_loader
def _make_test_dataloader(self):
transform = training_transform_not_augmented
test_dataset = datasets.CIFAR100(root=config.get_data_dir(), train=False, download=True, transform=transform)
return self._make_dataloader(test_dataset, sampler=None)