Thank you for the reply. At a high level, I’m just looking at shuffling data. I know that there is randomization in the order of samples selected (with shuffle=True in the data loader) via the sampling procedure but due to how I implement __getitem__, there is a likely correlation between the mini-batch of images loaded. A random shuffling of my image set after every epoch would break this correlation.
I’ll look into Ignite. I did come across Torchsample 1 but it wasn’t obvious how to do what I wanted in it the way it seems to be in Ignite. But I think the native pytorch solution of just calling it after the iterator of train_loader completes is what I’ll go with, for now.
Thanks again for your help !