BaseFinetuning¶
-
class
pytorch_lightning.callbacks.
BaseFinetuning
[source]¶ Bases:
pytorch_lightning.callbacks.base.Callback
This class implements the base logic for writing your own Finetuning Callback.
Override
freeze_before_training
andfinetune_function
methods with your own logic.freeze_before_training
: This method is called beforeconfigure_optimizers
and should be used to freeze any modules parameters.
finetune_function
: This method is called on every train epoch start and should be used tounfreeze
any parameters. Those parameters needs to be added in a newparam_group
within the optimizer.
Note
Make sure to filter the parameters based on
requires_grad
.Example:
class MyModel(LightningModule) ... def configure_optimizer(self): # Make sure to filter the parameters based on `requires_grad` return Adam(filter(lambda p: p.requires_grad, self.parameters)) class FeatureExtractorFreezeUnfreeze(BaseFinetuning): def __init__(self, unfreeze_at_epoch=10) self._unfreeze_at_epoch = unfreeze_at_epoch def freeze_before_training(self, pl_module): # freeze any module you want # Here, we are freezing ``feature_extractor`` self.freeze(pl_module.feature_extractor) def finetune_function(self, pl_module, current_epoch, optimizer, optimizer_idx): # When `current_epoch` is 10, feature_extractor will start training. if current_epoch == self._unfreeze_at_epoch: self.unfreeze_and_add_param_group( modules=pl_module.feature_extractor, optimizer=optimizer, train_bn=True, )
-
static
filter_on_optimizer
(optimizer, params)[source]¶ This function is used to exclude any parameter which already exists in this optimizer
-
static
filter_params
(modules, train_bn=True, requires_grad=True)[source]¶ Yields the requires_grad parameters of a given module or list of modules.
- Parameters
- Return type
- Returns
Generator
-
finetune_function
(pl_module, epoch, optimizer, opt_idx)[source]¶ Override to add your unfreeze logic
-
static
flatten_modules
(modules)[source]¶ This function is used to flatten a module or an iterable of modules into a list of its modules.
-
on_before_accelerator_backend_setup
(trainer, pl_module)[source]¶ Called before accelerator is being setup
-
static
unfreeze_and_add_param_group
(modules, optimizer, lr=None, initial_denom_lr=10.0, train_bn=True)[source]¶ Unfreezes a module and adds its parameters to an optimizer.
- Parameters
modules¶ (
Union
[Module
,Iterable
[Union
[Module
,Iterable
]]]) – A module or iterable of modules to unfreeze. Their parameters will be added to an optimizer as a new param group.optimizer¶ (
Optimizer
) – The provided optimizer will receive new parameters and will add them to add_param_grouplr¶ (
Optional
[float
]) – Learning rate for the new param group.initial_denom_lr¶ (
float
) – If no lr is provided, the learning from the first param group will be used and divided by initial_denom_lr.train_bn¶ (
bool
) – Whether to train the BatchNormalization layers.
- Return type
- Returns
None