avalanche.training.plugins.CWRStarPlugin

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

CWR* Strategy.

This plugin does not use task identities.

__init__(model, cwr_layer_name=None, freeze_remaining_model=True)

Parameters:

• 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.

Methods

__init__(model[, cwr_layer_name, ...])	param model: the model.
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)	Called after train_exp by the BaseTemplate.
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, *args, **kwargs)	Called before criterion.backward() by the BaseTemplate.
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, **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.
consolidate_weights()	Mean-shift for the target layer weights
freeze_other_layers()
get_cwr_layer()
reset_weights(cur_clas)	reset weights
set_consolidate_weights()	set trained weights

Attributes

supports_distributed

A flag describing whether this plugin supports distributed training.