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67 lines
2.3 KiB
67 lines
2.3 KiB
3 years ago
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Union
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from paddle.optimizer.lr import LRScheduler
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from typeguard import check_argument_types
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from deepspeech.utils.log import Log
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__all__ = ["WarmupLR"]
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logger = Log(__name__).getlog()
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class WarmupLR(LRScheduler):
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"""The WarmupLR scheduler
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This scheduler is almost same as NoamLR Scheduler except for following
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difference:
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NoamLR:
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lr = optimizer.lr * model_size ** -0.5
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* min(step ** -0.5, step * warmup_step ** -1.5)
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WarmupLR:
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lr = optimizer.lr * warmup_step ** 0.5
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* min(step ** -0.5, step * warmup_step ** -1.5)
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Note that the maximum lr equals to optimizer.lr in this scheduler.
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"""
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def __init__(self,
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warmup_steps: Union[int, float]=25000,
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learning_rate=1.0,
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last_epoch=-1,
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verbose=False):
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assert check_argument_types()
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self.warmup_steps = warmup_steps
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super().__init__(learning_rate, last_epoch, verbose)
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def __repr__(self):
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return f"{self.__class__.__name__}(warmup_steps={self.warmup_steps})"
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def get_lr(self):
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step_num = self.last_epoch + 1
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return self.base_lr * self.warmup_steps**0.5 * min(
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step_num**-0.5, step_num * self.warmup_steps**-1.5)
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def set_step(self, step: int=None):
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'''
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It will update the learning rate in optimizer according to current ``epoch`` .
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The new learning rate will take effect on next ``optimizer.step`` .
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Args:
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step (int, None): specify current epoch. Default: None. Auto-increment from last_epoch=-1.
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Returns:
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None
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'''
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self.step(epoch=step)
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