augmentation.py

from torchvision.transforms import transforms

class SimCLRTransform():
    def __init__(self):
        self.transform = transforms.Compose([
            transforms.RandomResizedCrop(size=32, scale=(0.2, 1.0)),
            transforms.RandomHorizontalFlip(),
            transforms.RandomApply([transforms.ColorJitter(0.8, 0.8, 0.8, 0.2)], p=0.8),
            transforms.RandomGrayscale(p=0.2),
            transforms.RandomApply([transforms.GaussianBlur(kernel_size=32//20*2+1, sigma=(0.1, 2.0))], p=0.5),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010])
        ])

    def __call__(self, x):
        x1 = self.transform(x)
        x2 = self.transform(x)
        return x1, x2 


class LeTransform():
    def __init__(self):
        self.transform = transforms.Compose([
            transforms.RandomResizedCrop(size=32, scale=(0.2, 1.0)),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010])
        ])
        
    def __call__(self, x):
        x = self.transform(x)
        return x