add to documentation

Author: SkafteNicki

docs/source-pytorch/advanced/transfer_learning.rst

@@ -126,3 +126,136 @@ Here's a model that uses `Huggingface transformers <https://github.com/huggingfa
             h_cls = h[:, 0]
             logits = self.W(h_cls)
             return logits, attn
+
+----
+
+***********************************
+Automated Finetuning with Callbacks
+***********************************
+
+PyTorch Lightning provides the :class:`~lightning.pytorch.callbacks.BackboneFinetuning` callback to automate
+the finetuning process. This callback gradually unfreezes your model's backbone during training. This is particularly
+useful when working with large pretrained models, as it allows you to start training with a frozen backbone and
+then progressively unfreeze layers to fine-tune the model.
+
+The :class:`~lightning.pytorch.callbacks.BackboneFinetuning` callback expects your model to have a specific structure:
+
+.. testcode::
+
+    class MyModel(LightningModule):
+        def __init__(self):
+            super().__init__()
+
+            # REQUIRED: Your model must have a 'backbone' attribute
+            # This should be the pretrained part you want to finetune
+            self.backbone = some_pretrained_model
+
+            # Your task-specific layers (head, classifier, etc.)
+            self.head = nn.Linear(backbone_features, num_classes)
+
+        def configure_optimizers(self):
+            # Only optimize the head initially - backbone will be added automatically
+            return torch.optim.Adam(self.head.parameters(), lr=1e-3)
+
+************************************
+Example: Computer Vision with ResNet
+************************************
+
+Here's a complete example showing how to use :class:`~lightning.pytorch.callbacks.BackboneFinetuning`
+for computer vision:
+
+.. testcode::
+    :skipif: not _TORCHVISION_AVAILABLE
+
+    import torch
+    import torch.nn as nn
+    import torchvision.models as models
+    from lightning.pytorch import LightningModule, Trainer
+    from lightning.pytorch.callbacks import BackboneFinetuning
+
+
+    class ResNetClassifier(LightningModule):
+        def __init__(self, num_classes=10, learning_rate=1e-3):
+            super().__init__()
+            self.save_hyperparameters()
+
+            # Create backbone from pretrained ResNet
+            resnet = models.resnet50(weights="DEFAULT")
+            # Remove the final classification layer
+            self.backbone = nn.Sequential(*list(resnet.children())[:-1])
+
+            # Add custom classification head
+            self.head = nn.Sequential(
+                nn.Flatten(),
+                nn.Linear(resnet.fc.in_features, 512),
+                nn.ReLU(),
+                nn.Dropout(0.2),
+                nn.Linear(512, num_classes)
+            )
+
+        def forward(self, x):
+            # Extract features with backbone
+            features = self.backbone(x)
+            # Classify with head
+            return self.head(features)
+
+        def training_step(self, batch, batch_idx):
+            x, y = batch
+            y_hat = self(x)
+            loss = nn.functional.cross_entropy(y_hat, y)
+            self.log('train_loss', loss)
+            return loss
+
+        def configure_optimizers(self):
+            # Initially only train the head - backbone will be added by callback
+            return torch.optim.Adam(self.head.parameters(), lr=self.hparams.learning_rate)
+
+
+    # Setup the finetuning callback
+    backbone_finetuning = BackboneFinetuning(
+        unfreeze_backbone_at_epoch=10,  # Start unfreezing backbone at epoch 10
+        lambda_func=lambda epoch: 1.5,  # Gradually increase backbone learning rate
+        backbone_initial_ratio_lr=0.1,  # Backbone starts at 10% of head learning rate
+        should_align=True,  # Align rates when backbone rate reaches head rate
+        verbose=True  # Print learning rates during training
+    )
+
+    model = ResNetClassifier()
+    trainer = Trainer(callbacks=[backbone_finetuning], max_epochs=20)
+
+****************************
+Custom Finetuning Strategies
+****************************
+
+For more control, you can create custom finetuning strategies by subclassing
+:class:`~lightning.pytorch.callbacks.BaseFinetuning`:
+
+.. testcode::
+
+    from lightning.pytorch.callbacks.finetuning import BaseFinetuning
+
+
+    class CustomFinetuning(BaseFinetuning):
+        def __init__(self, unfreeze_at_epoch=5, layers_per_epoch=2):
+            super().__init__()
+            self.unfreeze_at_epoch = unfreeze_at_epoch
+            self.layers_per_epoch = layers_per_epoch
+
+        def freeze_before_training(self, pl_module):
+            # Freeze the entire backbone initially
+            self.freeze(pl_module.backbone)
+
+        def finetune_function(self, pl_module, epoch, optimizer):
+            # Gradually unfreeze layers
+            if epoch >= self.unfreeze_at_epoch:
+                layers_to_unfreeze = min(
+                    self.layers_per_epoch,
+                    len(list(pl_module.backbone.children()))
+                )
+
+                # Unfreeze from the top layers down
+                backbone_children = list(pl_module.backbone.children())
+                for layer in backbone_children[-layers_to_unfreeze:]:
+                    self.unfreeze_and_add_param_group(
+                        layer, optimizer, lr=1e-4
+                    )