avalanche.training.ClassBalancedBuffer

class avalanche.training.ClassBalancedBuffer(max_size: int, adaptive_size: bool = True, total_num_classes: int | None = None)

Stores samples for replay, equally divided over classes.

There is a separate buffer updated by reservoir sampling for each class. It should be called in the ‘after_training_exp’ phase (see ExperienceBalancedStoragePolicy). The number of classes 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 classes so far.

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

Init.

Parameters:

• max_size – The max capacity of the replay memory.

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

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

Methods

__init__(max_size[, adaptive_size, ...])	Init.
get_group_lengths(num_groups)	Compute groups lengths given the number of groups num_groups.
post_adapt(agent, exp)	Update buffer.
resize(strategy, new_size)	Update the maximum size of the buffers.
update(strategy, **kwargs)	Update self.buffer using the strategy state.
update_from_dataset(new_data[, strategy])

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.