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"""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,
)
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