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from PIL import Image
from torchvision import transforms
from torchvision.datasets import CIFAR10
from augmentations.augmentations_cifar import aug_cifar
from augmentations.augmentations_tiny import aug_tiny
from augmentations.augmentations_stl import aug_stl
# for cifar10 / cifar100 (32x32)
class CifarPairTransform:
def __init__(self, train_transform = True, pair_transform = True):
if train_transform is True:
self.transform = transforms.Compose([
transforms.RandomResizedCrop(32),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomApply([transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)], p=0.8),
transforms.RandomGrayscale(p=0.2),
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])])
else:
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])])
self.pair_transform = pair_transform
def __call__(self, x):
if self.pair_transform is True:
y1 = self.transform(x)
y2 = self.transform(x)
return y1, y2
else:
return self.transform(x)
# for tiny_imagenet (64x64)
class TinyImageNetPairTransform:
def __init__(self, train_transform = True, pair_transform = True):
if train_transform is True:
self.transform = transforms.Compose([
transforms.RandomApply(
[transforms.ColorJitter(brightness=0.4, contrast=0.4,
saturation=0.4, hue=0.1)],
p=0.8
),
transforms.RandomGrayscale(p=0.1),
transforms.RandomResizedCrop(
64,
scale=(0.2, 1.0),
ratio=(0.75, (4 / 3)),
interpolation=Image.BICUBIC,
),
transforms.RandomHorizontalFlip(p=0.5),
transforms.ToTensor(),
transforms.Normalize((0.480, 0.448, 0.398), (0.277, 0.269, 0.282))
])
else:
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.480, 0.448, 0.398), (0.277, 0.269, 0.282))
])
self.pair_transform = pair_transform
def __call__(self, x):
if self.pair_transform is True:
y1 = self.transform(x)
y2 = self.transform(x)
return y1, y2
else:
return self.transform(x)
# for stl10 (96x96)
class StlPairTransform:
def __init__(self, train_transform = True, pair_transform = True):
if train_transform is True:
self.transform = transforms.Compose([
transforms.RandomApply(
[transforms.ColorJitter(brightness=0.4, contrast=0.4,
saturation=0.4, hue=0.1)],
p=0.8
),
transforms.RandomGrayscale(p=0.1),
transforms.RandomResizedCrop(
64,
scale=(0.2, 1.0),
ratio=(0.75, (4 / 3)),
interpolation=Image.BICUBIC,
),
transforms.RandomHorizontalFlip(p=0.5),
transforms.ToTensor(),
transforms.Normalize((0.43, 0.42, 0.39), (0.27, 0.26, 0.27))
])
else:
self.transform = transforms.Compose([
transforms.Resize(70, interpolation=Image.BICUBIC),
transforms.CenterCrop(64),
transforms.ToTensor(),
transforms.Normalize((0.43, 0.42, 0.39), (0.27, 0.26, 0.27))
])
self.pair_transform = pair_transform
def __call__(self, x):
if self.pair_transform is True:
y1 = self.transform(x)
y2 = self.transform(x)
return y1, y2
else:
return self.transform(x) |