Inital commit for refactoring to lightning

1 files changed, 0 insertions, 77 deletions

diff --git a/src/training/trainer/callbacks/lr_schedulers.py b/src/training/trainer/callbacks/lr_schedulers.py
deleted file mode 100644
index 630c434..0000000
--- a/src/training/trainer/callbacks/lr_schedulers.py
+++ /dev/null
@@ -1,77 +0,0 @@
-"""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,
-            )