icarl_lider#

Classes

class models.icarl_lider.ICarlLider(backbone, loss, args, transform)[source]#

Bases: LiderOptimizer

COMPATIBILITY: List[str] = ['class-il', 'task-il']#
NAME: str = 'icarl_lider'#
begin_task(dataset)[source]#
static binary_cross_entropy(pred, y)[source]#
compute_class_means()[source]#

Computes a vector representing mean features for each class.

end_task(dataset)[source]#
forward(x)[source]#
get_loss(inputs, labels, task_idx, logits)[source]#

Computes the loss tensor.

Parameters:

  • inputs (Tensor) – the images to be fed to the network

  • labels (Tensor) – the ground-truth labels

  • task_idx (int) – the task index

  • logits (Tensor) – the logits of the old network

Returns:

the loss tensor List[torch.Tensor]: the output features

Return type:

torch.Tensor

static get_parser()[source]#
Return type:

ArgumentParser

observe(inputs, labels, not_aug_inputs, logits=None, epoch=None)[source]#
to(device)[source]#