def _before_task(self, train_loader, val_loader):
if self._task != 0:
self._network = self._first_model.copy()
self._nb_inc_classes += self._task_size
utils.add_new_weights(self._network, "basic", self._n_classes,
self._nb_inc_classes, self.inc_dataset)
else:
utils.add_new_weights(self._network, "basic", self._n_classes,
self._task_size, self.inc_dataset)
self._network.classifier.reset_weights()
self._n_classes += self._task_size
logger.info("Now {} examplars per class.".format(
self._memory_per_class))
logger.info(
f"Nb classes in classifier {len(self._network.classifier.weights)}"
)
if self._groupwise_factors and isinstance(self._groupwise_factors,
dict):
if self._groupwise_factors_bis and self._task > 0:
logger.info("Using second set of groupwise lr.")
groupwise_factor = self._groupwise_factors_bis
else:
groupwise_factor = self._groupwise_factors
params = []
for group_name, group_params in self._network.get_group_parameters(
).items():
if group_params is None or group_name == "last_block":
continue
factor = groupwise_factor.get(group_name, 1.0)
if factor == 0.:
continue
params.append({
"params": group_params,
"lr": self._lr * factor
})
print(f"Group: {group_name}, lr: {self._lr * factor}.")
else:
params = self._network.parameters()
self._optimizer = factory.get_optimizer(params, self._opt_name,
self._lr, self.weight_decay)
self._scheduler = factory.get_lr_scheduler(self._scheduling,
self._optimizer,
nb_epochs=self._n_epochs,
lr_decay=self._lr_decay,
task=self._task)
def _before_task(self, train_loader, val_loader):
utils.add_new_weights(self._network, {"type": "basic"},
self._n_classes, self._task_size,
self.inc_dataset)
self._n_classes += self._task_size
self._optimizer = factory.get_optimizer(
[{
"params": self._network.convnet.parameters(),
}], self.unsupervised_training["optimizer"],
self.unsupervised_training["lr"], self._weight_decay)
self._scheduler = factory.get_lr_scheduler(
self.unsupervised_training["scheduling"],
self._optimizer,
nb_epochs=self.unsupervised_training["epochs"],
lr_decay=self.unsupervised_training.get("lr_decay", 0.1),
task=self._task)
def _gen_weights(self):
if self._weight_generation:
utils.add_new_weights(
self._network,
self._weight_generation if self._task != 0 else "basic",
self._n_classes, self._task_size, self.inc_dataset)
