Hello,
I am working on an optical flow algorithm, where the input is 2 images of size HxWx3 and the target is a tensor of size  HxWx2. To do data augmentation, I need to apply the same random transformation to all the 3 tensors. Take this augmentation for example:
        aug_transforms = transforms.Compose([
            transforms.RandomHorizontalFlip(),
            transforms.RandomResizedCrop((614, 216), scale=(0.1, 1.0)),
            transforms.RandomRotation(17),
            transforms.RandomVerticalFlip(),
            transforms.RandomApply([transforms.ColorJitter()])
        ])

The torchvision transforms accept PIL object as an input. To solve this problem I have two questions:

Can we save the random state of a given transform, so that we can apply the same randomness to img1, img2 and the flow ?
Can we combine all the date into a PIL object, apply transformation and retrieve back the same tensors values range we had at the beginning ?