For clarification:
If you have random transformations, after each epoch, you will receive a new set of randomly transformed samples (e.g. rotated random num of deg).
In this case, it’s enough to multiply the number of epochs to get more samples.
But you may want to concat all of generated samples if your transformations
performs determined operations. (e.g. add padding)
Then just use ConcatDataset like here: Concatenation while using Data Loader
In my opinion, the first approach is slightly better to avoid overfitting.
After all, you can create a transformation that will be randomly applied or not.