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| import logging |
| from typing import TYPE_CHECKING, Any, Callable, Optional, Union |
|
|
| from torch.optim.lr_scheduler import ReduceLROnPlateau, _LRScheduler |
|
|
| from monai.utils import ensure_tuple, exact_version, optional_import |
|
|
| Events, _ = optional_import("ignite.engine", "0.3.0", exact_version, "Events") |
| if TYPE_CHECKING: |
| from ignite.engine import Engine |
| else: |
| Engine, _ = optional_import("ignite.engine", "0.3.0", exact_version, "Engine") |
|
|
|
|
| class LrScheduleHandler: |
| """ |
| Ignite handler to update the Learning Rate based on PyTorch LR scheduler. |
| """ |
|
|
| def __init__( |
| self, |
| lr_scheduler: Union[_LRScheduler, ReduceLROnPlateau], |
| print_lr: bool = True, |
| name: Optional[str] = None, |
| epoch_level: bool = True, |
| step_transform: Callable[[Engine], Any] = lambda engine: (), |
| ) -> None: |
| """ |
| Args: |
| lr_scheduler: typically, lr_scheduler should be PyTorch |
| lr_scheduler object. If customized version, must have `step` and `get_last_lr` methods. |
| print_lr: whether to print out the latest learning rate with logging. |
| name: identifier of logging.logger to use, if None, defaulting to ``engine.logger``. |
| epoch_level: execute lr_scheduler.step() after every epoch or every iteration. |
| `True` is epoch level, `False` is iteration level. |
| step_transform: a callable that is used to transform the information from `engine` |
| to expected input data of lr_scheduler.step() function if necessary. |
| |
| Raises: |
| TypeError: When ``step_transform`` is not ``callable``. |
| |
| """ |
| self.lr_scheduler = lr_scheduler |
| self.print_lr = print_lr |
| self.logger = logging.getLogger(name) |
| self.epoch_level = epoch_level |
| if not callable(step_transform): |
| raise TypeError(f"step_transform must be callable but is {type(step_transform).__name__}.") |
| self.step_transform = step_transform |
|
|
| self._name = name |
|
|
| def attach(self, engine: Engine) -> None: |
| """ |
| Args: |
| engine: Ignite Engine, it can be a trainer, validator or evaluator. |
| """ |
| if self._name is None: |
| self.logger = engine.logger |
| if self.epoch_level: |
| engine.add_event_handler(Events.EPOCH_COMPLETED, self) |
| else: |
| engine.add_event_handler(Events.ITERATION_COMPLETED, self) |
|
|
| def __call__(self, engine: Engine) -> None: |
| """ |
| Args: |
| engine: Ignite Engine, it can be a trainer, validator or evaluator. |
| """ |
| args = ensure_tuple(self.step_transform(engine)) |
| self.lr_scheduler.step(*args) |
| if self.print_lr: |
| self.logger.info(f"Current learning rate: {self.lr_scheduler._last_lr[0]}") |
|
|
