def __init__(
self,
model: Module,
optimizer: Optimizer,
criterion,
patterns_per_exp: int,
memory_strength: float = 0.5,
train_mb_size: int = 1,
train_epochs: int = 1,
eval_mb_size: int = None,
device=None,
plugins: Optional[List[SupervisedPlugin]] = None,
evaluator: EvaluationPlugin = default_evaluator,
eval_every=-1,
**base_kwargs
):
"""Init.
:param model: The model.
:param optimizer: The optimizer to use.
:param criterion: The loss criterion to use.
:param patterns_per_exp: number of patterns per experience in the memory
:param memory_strength: offset to add to the projection direction
in order to favour backward transfer (gamma in original paper).
:param train_mb_size: The train minibatch size. Defaults to 1.
:param train_epochs: The number of training epochs. Defaults to 1.
:param eval_mb_size: The eval minibatch size. Defaults to 1.
:param device: The device to use. Defaults to None (cpu).
:param plugins: Plugins to be added. Defaults to None.
:param evaluator: (optional) instance of EvaluationPlugin for logging
and metric computations.
:param eval_every: the frequency of the calls to `eval` inside the
training loop. -1 disables the evaluation. 0 means `eval` is called
only at the end of the learning experience. Values >0 mean that
`eval` is called every `eval_every` epochs and at the end of the
learning experience.
:param **base_kwargs: any additional
:class:`~avalanche.training.BaseTemplate` constructor arguments.
"""
gem = GEMPlugin(patterns_per_exp, memory_strength)
if plugins is None:
plugins = [gem]
else:
plugins.append(gem)
super().__init__(
model,
optimizer,
criterion,
train_mb_size=train_mb_size,
train_epochs=train_epochs,
eval_mb_size=eval_mb_size,
device=device,
plugins=plugins,
evaluator=evaluator,
eval_every=eval_every,
**base_kwargs
)
def __init__(self,
model: Module,
optimizer: Optimizer,
criterion,
patterns_per_exp: int,
memory_strength: float = 0.5,
train_mb_size: int = 1,
train_epochs: int = 1,
eval_mb_size: int = None,
device=None,
plugins: Optional[List[StrategyPlugin]] = None,
evaluator: EvaluationPlugin = default_logger,
eval_every=-1):
""" Gradient Episodic Memory (GEM) strategy.
See GEM plugin for details.
This strategy does not use task identities.
:param model: The model.
:param optimizer: The optimizer to use.
:param criterion: The loss criterion to use.
:param patterns_per_exp: number of patterns per experience in the memory
:param memory_strength: offset to add to the projection direction
in order to favour backward transfer (gamma in original paper).
:param train_mb_size: The train minibatch size. Defaults to 1.
:param train_epochs: The number of training epochs. Defaults to 1.
:param eval_mb_size: The eval minibatch size. Defaults to 1.
:param device: The device to use. Defaults to None (cpu).
:param plugins: Plugins to be added. Defaults to None.
:param evaluator: (optional) instance of EvaluationPlugin for logging
and metric computations.
:param eval_every: the frequency of the calls to `eval` inside the
training loop.
if -1: no evaluation during training.
if 0: calls `eval` after the final epoch of each training
experience.
if >0: calls `eval` every `eval_every` epochs and at the end
of all the epochs for a single experience.
"""
gem = GEMPlugin(patterns_per_exp, memory_strength)
if plugins is None:
plugins = [gem]
else:
plugins.append(gem)
super().__init__(model,
optimizer,
criterion,
train_mb_size=train_mb_size,
train_epochs=train_epochs,
eval_mb_size=eval_mb_size,
device=device,
plugins=plugins,
evaluator=evaluator,
eval_every=eval_every)