avalanche.training.plugins.FeatureDistillationPlugin

class avalanche.training.plugins.FeatureDistillationPlugin(alpha=1, mode='cosine')

__init__(alpha=1, mode='cosine')

Adds a Distillation loss term on the features of the model, trying to maximize the cosine similarity between current and old features

Parameters:

alpha – distillation hyperparameter. It can be either a float number or a list containing alpha for each experience.

Methods

__init__([alpha, mode])	Adds a Distillation loss term on the features of the model, trying to maximize the cosine similarity between current and old features
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.

Attributes

supports_distributed

A flag describing whether this plugin supports distributed training.