IAM datasets implemented.

1 files changed, 52 insertions, 0 deletions

diff --git a/src/training/trainer/callbacks/lr_schedulers.py b/src/training/trainer/callbacks/lr_schedulers.py
index ba2226a..bb41d2d 100644
--- a/src/training/trainer/callbacks/lr_schedulers.py
+++ b/src/training/trainer/callbacks/lr_schedulers.py
@@ -1,6 +1,7 @@
 """Callbacks for learning rate schedulers."""
 from typing import Callable, Dict, List, Optional, Type
 
+from torch.optim.swa_utils import update_bn
 from training.trainer.callbacks import Callback
 
 from text_recognizer.models import Model
@@ -95,3 +96,54 @@ class OneCycleLR(Callback):
     def on_train_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None:
         """Takes a step at the end of every training batch."""
         self.lr_scheduler.step()
+
+
+class CosineAnnealingLR(Callback):
+    """Callback for Cosine Annealing."""
+
+    def __init__(self) -> None:
+        """Initializes the callback."""
+        super().__init__()
+        self.lr_scheduler = None
+
+    def set_model(self, model: Type[Model]) -> None:
+        """Sets the model and lr scheduler."""
+        self.model = model
+        self.lr_scheduler = self.model.lr_scheduler
+
+    def on_epoch_end(self, epoch: int, logs: Optional[Dict] = None) -> None:
+        """Takes a step at the end of every epoch."""
+        self.lr_scheduler.step()
+
+
+class SWA(Callback):
+    """Stochastic Weight Averaging callback."""
+
+    def __init__(self) -> None:
+        """Initializes the callback."""
+        super().__init__()
+        self.swa_scheduler = None
+
+    def set_model(self, model: Type[Model]) -> None:
+        """Sets the model and lr scheduler."""
+        self.model = model
+        self.swa_start = self.model.swa_start
+        self.swa_scheduler = self.model.lr_scheduler
+        self.lr_scheduler = self.model.lr_scheduler
+
+    def on_epoch_end(self, epoch: int, logs: Optional[Dict] = None) -> None:
+        """Takes a step at the end of every training batch."""
+        if epoch > self.swa_start:
+            self.model.swa_network.update_parameters(self.model.network)
+            self.swa_scheduler.step()
+        else:
+            self.lr_scheduler.step()
+
+    def on_fit_end(self) -> None:
+        """Update batch norm statistics for the swa model at the end of training."""
+        if self.model.swa_network:
+            update_bn(
+                self.model.val_dataloader(),
+                self.model.swa_network,
+                device=self.model.device,
+            )