You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
67 lines
2.3 KiB
67 lines
2.3 KiB
4 years ago
|
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||
|
|
#
|
||
|
|
# 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 typing import Union
|
||
|
|
|
||
|
|
from paddle.optimizer.lr import LRScheduler
|
||
|
|
from typeguard import check_argument_types
|
||
|
|
|
||
|
|
from deepspeech.utils.log import Log
|
||
|
|
|
||
|
|
__all__ = ["WarmupLR"]
|
||
|
|
|
||
|
|
logger = Log(__name__).getlog()
|
||
|
|
|
||
|
|
|
||
|
|
class WarmupLR(LRScheduler):
|
||
|
|
"""The WarmupLR scheduler
|
||
|
|
This scheduler is almost same as NoamLR Scheduler except for following
|
||
|
|
difference:
|
||
|
|
NoamLR:
|
||
|
|
lr = optimizer.lr * model_size ** -0.5
|
||
|
|
* min(step ** -0.5, step * warmup_step ** -1.5)
|
||
|
|
WarmupLR:
|
||
|
|
lr = optimizer.lr * warmup_step ** 0.5
|
||
|
|
* min(step ** -0.5, step * warmup_step ** -1.5)
|
||
|
|
Note that the maximum lr equals to optimizer.lr in this scheduler.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self,
|
||
|
|
warmup_steps: Union[int, float]=25000,
|
||
|
|
learning_rate=1.0,
|
||
|
|
last_epoch=-1,
|
||
|
|
verbose=False):
|
||
|
|
assert check_argument_types()
|
||
|
|
self.warmup_steps = warmup_steps
|
||
|
|
super().__init__(learning_rate, last_epoch, verbose)
|
||
|
|
|
||
|
|
def __repr__(self):
|
||
|
|
return f"{self.__class__.__name__}(warmup_steps={self.warmup_steps})"
|
||
|
|
|
||
|
|
def get_lr(self):
|
||
|
|
step_num = self.last_epoch + 1
|
||
|
|
return self.base_lr * self.warmup_steps**0.5 * min(
|
||
|
|
step_num**-0.5, step_num * self.warmup_steps**-1.5)
|
||
|
|
|
||
|
|
def set_step(self, step: int=None):
|
||
|
|
'''
|
||
|
|
It will update the learning rate in optimizer according to current ``epoch`` .
|
||
|
|
The new learning rate will take effect on next ``optimizer.step`` .
|
||
|
|
|
||
|
|
Args:
|
||
|
|
step (int, None): specify current epoch. Default: None. Auto-increment from last_epoch=-1.
|
||
|
|
Returns:
|
||
|
|
None
|
||
|
|
'''
|
||
|
|
self.step(epoch=step)
|