avalanche.training.plugins.LRSchedulerPlugin
- class avalanche.training.plugins.LRSchedulerPlugin(scheduler, reset_scheduler=True, reset_lr=True)[source]
Learning Rate Scheduler Plugin.
This plugin manages learning rate scheduling inside of a strategy using the PyTorch scheduler passed to the constructor. The step() method of the scheduler is called after each training epoch.
- __init__(scheduler, reset_scheduler=True, reset_lr=True)[source]
Creates a
LRSchedulerPlugin
instance.- Parameters
scheduler – a learning rate scheduler that can be updated through a step() method and can be reset by setting last_epoch=0
reset_scheduler – If True, the scheduler is reset at the end of the experience. Defaults to True.
reset_lr – If True, the optimizer learning rate is reset to its original value. Default to True.
Methods
__init__
(scheduler[, reset_scheduler, reset_lr])Creates a
LRSchedulerPlugin
instance.after_backward
(strategy, **kwargs)Called after criterion.backward() by the BaseStrategy.
after_eval
(strategy, **kwargs)Called after eval by the BaseStrategy.
after_eval_dataset_adaptation
(strategy, **kwargs)Called after eval_dataset_adaptation by the BaseStrategy.
after_eval_exp
(strategy, **kwargs)Called after eval_exp by the BaseStrategy.
after_eval_forward
(strategy, **kwargs)Called after model.forward() by the BaseStrategy.
after_eval_iteration
(strategy, **kwargs)Called after the end of an iteration by the BaseStrategy.
after_forward
(strategy, **kwargs)Called after model.forward() by the BaseStrategy.
after_train_dataset_adaptation
(strategy, ...)Called after train_dataset_adapatation by the BaseStrategy.
after_training
(strategy, **kwargs)Called after train by the BaseStrategy.
after_training_epoch
(strategy, **kwargs)Called after train_epoch by the BaseStrategy.
after_training_exp
(strategy, **kwargs)Called after train_exp by the BaseStrategy.
after_training_iteration
(strategy, **kwargs)Called after the end of a training iteration by the BaseStrategy.
after_update
(strategy, **kwargs)Called after optimizer.update() by the BaseStrategy.
before_backward
(strategy, **kwargs)Called before criterion.backward() by the BaseStrategy.
before_eval
(strategy, **kwargs)Called before eval by the BaseStrategy.
before_eval_dataset_adaptation
(strategy, ...)Called before eval_dataset_adaptation by the BaseStrategy.
before_eval_exp
(strategy, **kwargs)Called before eval_exp by the BaseStrategy.
before_eval_forward
(strategy, **kwargs)Called before model.forward() by the BaseStrategy.
before_eval_iteration
(strategy, **kwargs)Called before the start of a training iteration by the BaseStrategy.
before_forward
(strategy, **kwargs)Called before model.forward() by the BaseStrategy.
before_train_dataset_adaptation
(strategy, ...)Called before train_dataset_adapatation by the BaseStrategy.
before_training
(strategy, **kwargs)Called before train by the BaseStrategy.
before_training_epoch
(strategy, **kwargs)Called before train_epoch by the BaseStrategy.
before_training_exp
(strategy, **kwargs)Called before train_exp by the BaseStrategy.
before_training_iteration
(strategy, **kwargs)Called before the start of a training iteration by the BaseStrategy.
before_update
(strategy, **kwargs)Called before optimizer.update() by the BaseStrategy.