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# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import logging
from collections.abc 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")
if TYPE_CHECKING:
from ignite.engine import Engine
else:
Engine, _ = optional_import(
"ignite.engine", IgniteInfo.OPT_IMPORT_VERSION, min_version, "Engine", as_type="decorator"
)
class LrScheduleHandler:
"""
Ignite handler to update the Learning Rate based on PyTorch LR scheduler.
"""
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
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]}") # type: ignore[union-attr]
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