RING BUFFER

This module contains a version of the reservoir buffer that uses a ring buffer strategy instead of reservoir.

Classes

class utils.ring_buffer.RingBuffer(buffer_size, n_tasks=1, device='cpu')

Bases: object

The memory buffer of rehearsal method.

add_data(examples, labels=None, logits=None, task_labels=None)

Adds the data to the memory buffer according to the reservoir strategy.

Parameters:

• examples – tensor containing the images

• labels – tensor containing the labels

• logits – tensor containing the outputs of the network

• task_labels – tensor containing the task labels

empty()

Set all the tensors to None.

get_all_data(transform=None, device=None)

Return all the items in the memory buffer.

Parameters:

• transform (<module 'torchvision.transforms' from '/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/torchvision/transforms/__init__.py'>) – the transformation to be applied (data augmentation)

• device – the device to be used

Returns:

a tuple with all the items in the memory buffer

Return type:

Tuple

get_data(size, transform=None, device=None)

Random samples a batch of size items.

Parameters:

• size (int) – the number of requested items

• transform (<module 'torchvision.transforms' from '/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/torchvision/transforms/__init__.py'>) – the transformation to be applied (data augmentation)

Returns:

a tuple with the requested items

Return type:

Tuple

init_tensors(examples, labels, logits, task_labels)

Initializes just the required tensors.

Parameters:

• examples (Tensor) – tensor containing the images

• labels (Tensor) – tensor containing the labels

• logits (Tensor) – tensor containing the outputs of the network

• task_labels (Tensor) – tensor containing the task labels

is_empty()

Returns true if the buffer is empty, false otherwise.

Return type:

bool

Functions

utils.ring_buffer.ring(num_seen_examples, buffer_portion_size, task)

Return type:

int