image_transformer.py

import numpy as np
import torch
import torchvision.transforms as transforms


# Transformations
class TwoCropTransform:
    def __init__(self, transform, img_size):
        self.transform = transform
        self.img_size = img_size
        color_jitter = transforms.ColorJitter(0.8, 0.8, 0.8, 0.2)
        self.data_transforms = transforms.Compose([transforms.RandomResizedCrop(size=self.img_size),
                                              transforms.RandomHorizontalFlip(),
                                              transforms.RandomApply([color_jitter], p=0.8),
                                              transforms.RandomGrayscale(p=0.2),
                                              transforms.ToTensor()])

    def __call__(self, x):
        return [self.transform(x), self.data_transforms(x)]


def rotation(input):
    batch = input.shape[0]
    target = torch.tensor(np.random.permutation([0,1,2,3] * (int(batch / 4) + 1)), device = input.device)[:batch]
    target = target.long()
    image = torch.zeros_like(input)
    image.copy_(input)
    for i in range(batch):
        image[i, :, :, :] = torch.rot90(input[i, :, :, :], target[i], [1, 2])

    return image, target