avalanche.training.plugins.GSS_greedyPlugin

class avalanche.training.plugins.GSS_greedyPlugin(mem_size=200, mem_strength=5, input_size=[])

GSSPlugin replay plugin.

Code adapted from the repository: https://github.com/RaptorMai/online-continual-learning Handles an external memory fulled with samples selected using the Greedy approach of GSS algorithm. before_forward callback is used to process the current sample and estimate a score.

__init__(mem_size=200, mem_strength=5, input_size=[])

Parameters:

• mem_size – total number of patterns to be stored in the external memory.

• mem_strength –

• input_size –

Methods

__init__([mem_size, mem_strength, input_size])	param mem_size: total number of patterns to be stored
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[, num_workers, shuffle])	After every forward this function select sample to fill the memory buffer based on cosine similarity
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, *args, **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, **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.
cosine_similarity(x1[, x2, eps])
get_batch_sim(strategy, grad_dims, batch_x, ...)	Args:
get_each_batch_sample_sim(strategy, ...)	Args:
get_grad_vector(pp, grad_dims)	gather the gradients in one vector
get_rand_mem_grads(strategy, grad_dims, ...)	Args:

Attributes

supports_distributed

A flag describing whether this plugin supports distributed training.