class avalanche.training.plugins.CWRStarPlugin(model, cwr_layer_name=None, freeze_remaining_model=True)[source]

CWR* Strategy.

This plugin does not use task identities.

__init__(model, cwr_layer_name=None, freeze_remaining_model=True)[source]
  • model – the model.

  • cwr_layer_name – name of the last fully connected layer. Defaults to None, which means that the plugin will attempt an automatic detection.

  • freeze_remaining_model – If True, the plugin will freeze (set layers in eval mode and disable autograd for parameters) all the model except the cwr layer. Defaults to True.


__init__(model[, cwr_layer_name, ...])

param model

the model.

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.


Mean-shift for the target layer weights




reset weights


set trained weights