Hello all,
I would like to implement a custom Dataset that has a feedback loop connected to my training code. In specific, after each epoch I would like to return information about the training progress to the dataset so that I can make changes within the dataset that will (hopefully) improve the training.
Now my question is: Is this possible? I know that at the start of training the PyTorch Dataloader creates independent copies of my Dataset object and passes them to each worker. So changing something in the original dataset I created before the start of training would not work in my understanding as it does not alter the dataset copies in the worker processes. Is there some kind of workaround for this limitation or is the “dataset-training loop” connection meant as a strict one-way street in PyTorch?