avalanche.training.plugins.LwFPlugin
- class avalanche.training.plugins.LwFPlugin(alpha=1, temperature=2)[source]
Learning without Forgetting plugin.
LwF uses distillation to regularize the current loss with soft targets taken from a previous version of the model. When used with multi-headed models, all heads are distilled.
- __init__(alpha=1, temperature=2)[source]
- Parameters
alpha – distillation hyperparameter. It can be either a float number or a list containing alpha for each experience.
temperature – softmax temperature for distillation
Methods
__init__
([alpha, temperature])- param alpha
distillation hyperparameter. It can be either a float
after_backward
(strategy, *args, **kwargs)Called after criterion.backward() by the BaseTemplate.
after_eval
(strategy, *args, **kwargs)Called after eval by the BaseTemplate.
after_eval_dataset_adaptation
(strategy, ...)Called after eval_dataset_adaptation by the BaseTemplate.
after_eval_exp
(strategy, *args, **kwargs)Called after eval_exp by the BaseTemplate.
after_eval_forward
(strategy, *args, **kwargs)Called after model.forward() by the BaseTemplate.
after_eval_iteration
(strategy, *args, **kwargs)Called after the end of an iteration by the BaseTemplate.
after_forward
(strategy, *args, **kwargs)Called after model.forward() by the BaseTemplate.
after_train_dataset_adaptation
(strategy, ...)Called after train_dataset_adapatation by the BaseTemplate.
after_training
(strategy, *args, **kwargs)Called after train by the BaseTemplate.
after_training_epoch
(strategy, *args, **kwargs)Called after train_epoch by the BaseTemplate.
after_training_exp
(strategy, **kwargs)Save a copy of the model after each experience and update self.prev_classes to include the newly learned classes.
after_training_iteration
(strategy, *args, ...)Called after the end of a training iteration by the BaseTemplate.
after_update
(strategy, *args, **kwargs)Called after optimizer.update() by the BaseTemplate.
before_backward
(strategy, **kwargs)Add distillation loss
before_eval
(strategy, *args, **kwargs)Called before eval by the BaseTemplate.
before_eval_dataset_adaptation
(strategy, ...)Called before eval_dataset_adaptation by the BaseTemplate.
before_eval_exp
(strategy, *args, **kwargs)Called before eval_exp by the BaseTemplate.
before_eval_forward
(strategy, *args, **kwargs)Called before model.forward() by the BaseTemplate.
before_eval_iteration
(strategy, *args, **kwargs)Called before the start of a training iteration by the BaseTemplate.
before_forward
(strategy, *args, **kwargs)Called before model.forward() by the BaseTemplate.
before_train_dataset_adaptation
(strategy, ...)Called before train_dataset_adapatation by the BaseTemplate.
before_training
(strategy, *args, **kwargs)Called before train by the BaseTemplate.
before_training_epoch
(strategy, *args, **kwargs)Called before train_epoch by the BaseTemplate.
before_training_exp
(strategy, *args, **kwargs)Called before train_exp by the BaseTemplate.
before_training_iteration
(strategy, *args, ...)Called before the start of a training iteration by the BaseTemplate.
before_update
(strategy, *args, **kwargs)Called before optimizer.update() by the BaseTemplate.