def __init__(self,
model: Module,
optimizer: Optimizer,
criterion,
alpha: Union[float, Sequence[float]],
temperature: float,
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):
""" Learning without Forgetting strategy.
See LwF 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 alpha: distillation hyperparameter. It can be either a float
number or a list containing alpha for each experience.
:param temperature: softmax temperature for distillation
: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.
"""
lwf = LwFPlugin(alpha, temperature)
if plugins is None:
plugins = [lwf]
else:
plugins.append(lwf)
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)
def __init__(self,
model: Module,
optimizer: Optimizer,
criterion,
alpha: Union[float, Sequence[float]],
temperature: float,
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 alpha: distillation hyperparameter. It can be either a float
number or a list containing alpha for each experience.
:param temperature: softmax temperature for distillation
: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.
"""
lwf = LwFPlugin(alpha, temperature)
if plugins is None:
plugins = [lwf]
else:
plugins.append(lwf)
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 test_warning_slda_lwf(self):
model, _, criterion, my_nc_benchmark = self.init_sit()
with self.assertWarns(Warning) as cm:
StreamingLDA(
model,
criterion,
input_size=10,
output_layer_name="features",
num_classes=10,
plugins=[LwFPlugin(), ReplayPlugin()],
)
def test_warning_slda_lwf(self):
model, _, criterion, my_nc_benchmark = self.init_sit()
with self.assertLogs('avalanche.training.strategies', "WARNING") as cm:
StreamingLDA(model, criterion, input_size=10,
output_layer_name='features', num_classes=10,
plugins=[LwFPlugin(), ReplayPlugin()])
self.assertEqual(1, len(cm.output))
self.assertIn(
"LwFPlugin seems to use the callback before_backward"
" which is disabled by StreamingLDA",
cm.output[0]
)
