Inital commit for refactoring to lightning

1 files changed, 77 insertions, 0 deletions

diff --git a/training/trainer/callbacks/lr_schedulers.py b/training/trainer/callbacks/lr_schedulers.py
new file mode 100644
index 0000000..630c434
--- /dev/null
+++ b/training/trainer/callbacks/lr_schedulers.py
@@ -0,0 +1,77 @@
+"""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
+
+
+class LRScheduler(Callback):
+    """Generic learning rate scheduler callback."""
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def set_model(self, model: Type[Model]) -> None:
+        """Sets the model and lr scheduler."""
+        self.model = model
+        self.lr_scheduler = self.model.lr_scheduler["lr_scheduler"]
+        self.interval = self.model.lr_scheduler["interval"]
+
+    def on_epoch_end(self, epoch: int, logs: Optional[Dict] = None) -> None:
+        """Takes a step at the end of every epoch."""
+        if self.interval == "epoch":
+            if "ReduceLROnPlateau" in self.lr_scheduler.__class__.__name__:
+                self.lr_scheduler.step(logs["val_loss"])
+            else:
+                self.lr_scheduler.step()
+
+    def on_train_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None:
+        """Takes a step at the end of every training batch."""
+        if self.interval == "step":
+            self.lr_scheduler.step()
+
+
+class SWA(Callback):
+    """Stochastic Weight Averaging callback."""
+
+    def __init__(self) -> None:
+        """Initializes the callback."""
+        super().__init__()
+        self.lr_scheduler = None
+        self.interval = None
+        self.swa_scheduler = None
+        self.swa_start = None
+        self.current_epoch = 1
+
+    def set_model(self, model: Type[Model]) -> None:
+        """Sets the model and lr scheduler."""
+        self.model = model
+        self.lr_scheduler = self.model.lr_scheduler["lr_scheduler"]
+        self.interval = self.model.lr_scheduler["interval"]
+        self.swa_scheduler = self.model.swa_scheduler["swa_scheduler"]
+        self.swa_start = self.model.swa_scheduler["swa_start"]
+
+    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()
+        elif self.interval == "epoch":
+            self.lr_scheduler.step()
+        self.current_epoch = epoch
+
+    def on_train_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None:
+        """Takes a step at the end of every training batch."""
+        if self.current_epoch < self.swa_start and self.interval == "step":
+            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,
+            )