# MIT License
#
# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# SOFTWARE.
from dataclasses import dataclass, field
from omegaconf import DictConfig
from torch.optim import Optimizer
from typing import Optional
from openspeech.dataclass.configurations import LearningRateSchedulerConfigs
from openspeech.optim.scheduler import register_scheduler
from openspeech.optim.scheduler.lr_scheduler import LearningRateScheduler
[docs]@dataclass
class ReduceLROnPlateauConfigs(LearningRateSchedulerConfigs):
scheduler_name: str = field(
default="reduce_lr_on_plateau", metadata={"help": "Name of learning rate scheduler."}
)
lr_patience: int = field(
default=1, metadata={"help": "Number of epochs with no improvement after which learning rate will be reduced."}
)
lr_factor: float = field(
default=0.3, metadata={"help": "Factor by which the learning rate will be reduced. new_lr = lr * factor."}
)
[docs]@register_scheduler("reduce_lr_on_plateau", dataclass=ReduceLROnPlateauConfigs)
class ReduceLROnPlateauScheduler(LearningRateScheduler):
r"""
Reduce learning rate when a metric has stopped improving. Models often benefit from reducing the learning rate by
a factor of 2-10 once learning stagnates. This scheduler reads a metrics quantity and if no improvement is seen
for a ‘patience’ number of epochs, the learning rate is reduced.
Args:
optimizer (Optimizer): wrapped optimizer.
configs (DictConfig): configuration set.
"""
def __init__(
self,
optimizer: Optimizer,
configs: DictConfig,
) -> None:
super(ReduceLROnPlateauScheduler, self).__init__(optimizer, configs.lr_scheduler.lr)
self.lr = configs.lr_scheduler.lr
self.lr_patience = configs.lr_scheduler.lr_patience
self.lr_factor = configs.lr_scheduler.lr_factor
self.val_loss = 100.0
self.count = 0
def step(self, val_loss: Optional[float] = None):
if val_loss is not None:
if self.val_loss < val_loss:
self.count += 1
self.val_loss = val_loss
else:
self.count = 0
self.val_loss = val_loss
if self.lr_patience == self.count:
self.count = 0
self.lr *= self.lr_factor
self.set_lr(self.optimizer, self.lr)
return self.lr