class avalanche.training.plugins.GDumbPlugin(mem_size: int = 200)[source]

GDumb plugin.

At each experience the model is trained from scratch using a buffer of samples collected from all the previous learning experiences. The buffer is updated at the start of each experience to add new classes or new examples of already encountered classes. In multitask scenarios, mem_size is the memory size for each task. This plugin can be combined with a Naive strategy to obtain the standard GDumb strategy. https://www.robots.ox.ac.uk/~tvg/publications/2020/gdumb.pdf

__init__(mem_size: int = 200)[source]



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, ...)

Reset model.

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.