I have a dataset of around 200 images. However, the format of my image is not standard because it has 6 dimensions.
One image would look like this (before being cast to tensor and being stack in a 3d Tensor):
[img0, img1, img2, img3, img4, img5]. (They are all grayscale PIL Image )
However my dataset is quite small and I want to do upsampling.  I wanted to use torchvisions.transforms to do that. My problem is that I need to perform the exact same list of transformation (like RandomHorizontalFlip, Random Rotation, etc…) for all the dimension of my image and but I do not want to do the same transformation for every image. Is there a way to do so?