v1.5.4

• [Breaking] train_utils.perform_one_epoch() now returns a dictionary instead of list.
• Model evaluation / prediction methods now accept return_keys as an argument to pre-specify what items are to be returned.
  • This results in huge memory advantages by not having to store unnecessary objects.
• Added option to only perform training without any evaluation, by simply not providing any validation dataloader / logger arguments.
  • [Breaking] As part of this change, for the sake of simplicity, the ReduceLROnPlateau scheduler is now no longer supported (which requires validation loss in order to take each step).
• Added feature to add sample weighting during training and evaluation.
• Added several unit tests in accordance with the aforementioned features.
• Changed default early stopping criterion to accuracy (instead of f1).
• Several other time, memory, and logging improvements.
• In sync withc3809cf7