avalanche.training.ExperienceBalancedBuffer

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

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)[source]
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.

resize(strategy, new_size)

Update the maximum size of the buffers.

update(strategy, **kwargs)

Update self.buffer_groups using the strategy state.

Attributes

buffer

Buffer of samples.

buffer_datasets

Return group buffers as a list of `AvalancheDataset`s.