avalanche.training.ExperienceBalancedBuffer

class avalanche.training.ExperienceBalancedBuffer(max_size: int, adaptive_size: bool = True, num_experiences=None)

Rehearsal buffer with samples balanced over experiences.

The number of experiences can be fixed up front or adaptive, based on the ‘adaptive_size’ attribute. When adaptive, the memory is equally divided over all the unique observed experiences so far.

__init__(max_size: int, adaptive_size: bool = True, num_experiences=None)

Parameters:

• max_size – max number of total input samples in the replay memory.

• adaptive_size – True if mem_size is divided equally over all observed experiences (keys in replay_mem).

• num_experiences – If adaptive size is False, the fixed number of experiences to divide capacity over.

Methods

__init__(max_size[, adaptive_size, ...])	param max_size: max number of total input samples in the replay
get_group_lengths(num_groups)	Compute groups lengths given the number of groups num_groups.
post_adapt(agent, exp)	Update self.buffer using the agent state and current experience.
resize(strategy, new_size)	Update the maximum size of the buffers.
update(strategy, **kwargs)	Update self.buffer using the strategy state.

Attributes

buffer	Buffer of samples.
buffer_datasets	Return group buffers as a list of `AvalancheDataset`s.
buffer_groups	Dictionary of buffers.
max_size	Maximum size of the buffer.