avalanche.training.plugins.ReplayPlugin

class avalanche.training.plugins.ReplayPlugin(mem_size: int = 200, batch_size: Optional[int] = None, batch_size_mem: Optional[int] = None, task_balanced_dataloader: bool = False, storage_policy: Optional[avalanche.training.storage_policy.ExemplarsBuffer] = None)[source]

Experience replay plugin.

Handles an external memory filled with randomly selected patterns and implementing before_training_exp and after_training_exp callbacks. The before_training_exp callback is implemented in order to use the dataloader that creates mini-batches with examples from both training data and external memory. The examples in the mini-batch is balanced such that there are the same number of examples for each experience.

The after_training_exp callback is implemented in order to add new patterns to the external memory.

The :mem_size: attribute controls the total number of patterns to be stored in the external memory.

Parameters
  • batch_size – the size of the data batch. If set to None, it will be set equal to the strategy’s batch size.

  • batch_size_mem – the size of the memory batch. If task_balanced_dataloader is set to True, it must be greater than or equal to the number of tasks. If its value is set to None (the default value), it will be automatically set equal to the data batch size.

  • task_balanced_dataloader – if True, buffer data loaders will be task-balanced, otherwise it will create a single dataloader for the buffer samples.

  • storage_policy – The policy that controls how to add new exemplars in memory

__init__(mem_size: int = 200, batch_size: Optional[int] = None, batch_size_mem: Optional[int] = None, task_balanced_dataloader: bool = False, storage_policy: Optional[avalanche.training.storage_policy.ExemplarsBuffer] = None)[source]

Methods

__init__([mem_size, batch_size, ...])

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[, num_workers, ...])

Dataloader to build batches containing examples from both memories and the training dataset

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

ext_mem