Source code for monai.handlers.lr_schedule_handler

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# Licensed under the Apache License, Version 2.0 (the "License");
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from __future__ import annotations

import logging
from import Callable
from typing import TYPE_CHECKING, Any

from torch.optim.lr_scheduler import ReduceLROnPlateau, _LRScheduler

from monai.config import IgniteInfo
from monai.utils import ensure_tuple, min_version, optional_import

Events, _ = optional_import("ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Events")
    from ignite.engine import Engine
    Engine, _ = optional_import(
        "ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Engine", as_type="decorator"

[docs] class LrScheduleHandler: """ Ignite handler to update the Learning Rate based on PyTorch LR scheduler. """
[docs] def __init__( self, lr_scheduler: _LRScheduler | ReduceLROnPlateau, print_lr: bool = True, name: str | None = 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
[docs] 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:"Current learning rate: {self.lr_scheduler._last_lr[0]}") # type: ignore[union-attr]