Let n denote the size of the original dataset. In ordinary augmentation (i.e. precomputed and static, not on the fly), if we have 5 transformations then the size of the augmented data is 5n, which means at each epoch the number of iterations is also 5n. Now, if we augment the data on the fly (with random transformations) using PyTorch, then each epoch has the same number of iterations n. If we concatenate 5 epochs consécutive to create a large epoch (or call it whatever you want), then the total number of iterations in this large epoch is 5n. Thus it is roughly equivalent to static augmentation. (Note that this large epoch is a valid epoch because there’s no duplicate in the 5n iteration since the transformations are random.)