Changing Indices during training leads to much slower training

[muradtuk]

Dear authors,

I have been using your framework to train on subsets of data, specifically for the ImageNet dataset using your code.
Since the training is done via QUASI_RANDOM ordering, when taking a subset of the data, I had to define a Numpy array of indices, used for changing the train_loader.indices. Going over some of the raised issues from the past, I have found that it is best to also change the traversal_order object, i.e., train_loader.traversal_order = QuasiRandom(train_loader) to obtain the correct subset in mind.

Such step, i.e., train_loader.traversal_order = QuasiRandom(train_loader) makes the code so much slower than it should be. How can I solve this problem?

Please advise.

[andrewilyas]

Hi @muradtuk ! I might be misunderstanding your question, but setting the traversal_order property isn't recommended, and the proper way to take subsets of the dataset is to just pass the indices= flag when creating a Loader. We haven't tested with changing indices during training - if you don't need to do it too many times I might suggest just re-creating the loader object from scratch.

[lucasresck]

According to #152 (comment), to change the indices during training it is enough to update both the indices attribute of the loader and the indices attribute of the transversal_order:

loader.indices = new_array_of_indices
loader.traversal_order.indices = new_array_of_indices

Does this make the code slower as well?