docs: Add BackboneLambdaFinetuningCallback (#5553)

* Add and fix the docs of BackboneLambdaFinetuningCallback

* Apply suggestions from code review

Co-authored-by: Carlos Mocholí <[email protected]>
Co-authored-by: Roger Shieh <[email protected]>
Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>

Author: 4 people

docs/source/callbacks.rst

@@ -94,6 +94,8 @@ Lightning has a few built-in callbacks.
     :nosignatures:
     :template: classtemplate.rst
 
+    BackboneLambdaFinetuningCallback
+    BaseFinetuningCallback
     Callback
     EarlyStopping
     GPUStatsMonitor

pytorch_lightning/callbacks/finetuning.py

@@ -78,6 +78,7 @@ def _recursive_freeze(module: Module,
     def filter_params(module: Module,
                       train_bn: bool = True) -> Generator:
         """Yields the trainable parameters of a given module.
+
         Args:
             module: A given module
             train_bn: If True, leave the BatchNorm layers in training mode
@@ -98,6 +99,7 @@ def filter_params(module: Module,
     @staticmethod
     def freeze(module: Module, train_bn: bool = True) -> None:
         """Freezes the layers up to index n (if n is not None).
+
         Args:
             module: The module to freeze (at least partially)
             train_bn: If True, leave the BatchNorm layers in training mode
@@ -148,6 +150,7 @@ class BackboneLambdaFinetuningCallback(BaseFinetuningCallback):
     Finetunne a backbone model based on a learning rate user-defined scheduling.
     When the backbone learning rate reaches the current model learning rate
     and ``should_align`` is set to True, it will align with it for the rest of the training.
+
     Args:
         unfreeze_backbone_at_epoch: Epoch at which the backbone will be unfreezed.
         lambda_func: Scheduling function for increasing backbone learning rate.
@@ -165,7 +168,9 @@ class BackboneLambdaFinetuningCallback(BaseFinetuningCallback):
             reaches it.
         verbose: Display current learning rate for model and backbone
         round: Precision for displaying learning rate
+
     Example::
+
         >>> from pytorch_lightning import Trainer
         >>> from pytorch_lightning.callbacks import BackboneLambdaFinetuningCallback
         >>> multiplicative = lambda epoch: 1.5