160 lines
4.8 KiB
Python
160 lines
4.8 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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 __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import sys
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import math
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from paddle.optimizer.lr import LinearWarmup
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from paddle.optimizer.lr import PiecewiseDecay
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from paddle.optimizer.lr import CosineAnnealingDecay
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from paddle.optimizer.lr import ExponentialDecay
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__all__ = ['LearningRateBuilder']
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class Cosine(CosineAnnealingDecay):
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"""
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Cosine learning rate decay
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lr = 0.05 * (math.cos(epoch * (math.pi / epochs)) + 1)
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Args:
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lr(float): initial learning rate
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step_each_epoch(int): steps each epoch
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epochs(int): total training epochs
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"""
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def __init__(self, lr, step_each_epoch, epochs, **kwargs):
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super(Cosine, self).__init__(
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learning_rate=lr,
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T_max=step_each_epoch * epochs, )
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self.update_specified = False
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class Piecewise(PiecewiseDecay):
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"""
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Piecewise learning rate decay
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Args:
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lr(float): initial learning rate
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step_each_epoch(int): steps each epoch
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decay_epochs(list): piecewise decay epochs
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gamma(float): decay factor
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"""
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def __init__(self, lr, step_each_epoch, decay_epochs, gamma=0.1, **kwargs):
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boundaries = [step_each_epoch * e for e in decay_epochs]
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lr_values = [lr * (gamma**i) for i in range(len(boundaries) + 1)]
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super(Piecewise, self).__init__(
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boundaries=boundaries, values=lr_values)
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self.update_specified = False
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class CosineWarmup(LinearWarmup):
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"""
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Cosine learning rate decay with warmup
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[0, warmup_epoch): linear warmup
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[warmup_epoch, epochs): cosine decay
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Args:
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lr(float): initial learning rate
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step_each_epoch(int): steps each epoch
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epochs(int): total training epochs
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warmup_epoch(int): epoch num of warmup
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"""
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def __init__(self, lr, step_each_epoch, epochs, warmup_epoch=5, **kwargs):
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assert epochs > warmup_epoch, "total epoch({}) should be larger than warmup_epoch({}) in CosineWarmup.".format(
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epochs, warmup_epoch)
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warmup_step = warmup_epoch * step_each_epoch
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start_lr = 0.0
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end_lr = lr
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lr_sch = Cosine(lr, step_each_epoch, epochs - warmup_epoch)
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super(CosineWarmup, self).__init__(
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learning_rate=lr_sch,
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warmup_steps=warmup_step,
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start_lr=start_lr,
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end_lr=end_lr)
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self.update_specified = False
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class ExponentialWarmup(LinearWarmup):
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"""
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Exponential learning rate decay with warmup
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[0, warmup_epoch): linear warmup
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[warmup_epoch, epochs): Exponential decay
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Args:
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lr(float): initial learning rate
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step_each_epoch(int): steps each epoch
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decay_epochs(float): decay epochs
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decay_rate(float): decay rate
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warmup_epoch(int): epoch num of warmup
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"""
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def __init__(self,
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lr,
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step_each_epoch,
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decay_epochs=2.4,
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decay_rate=0.97,
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warmup_epoch=5,
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**kwargs):
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warmup_step = warmup_epoch * step_each_epoch
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start_lr = 0.0
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end_lr = lr
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lr_sch = ExponentialDecay(lr, decay_rate)
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super(ExponentialWarmup, self).__init__(
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learning_rate=lr_sch,
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warmup_steps=warmup_step,
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start_lr=start_lr,
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end_lr=end_lr)
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# NOTE: hac method to update exponential lr scheduler
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self.update_specified = True
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self.update_start_step = warmup_step
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self.update_step_interval = int(decay_epochs * step_each_epoch)
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self.step_each_epoch = step_each_epoch
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class LearningRateBuilder():
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"""
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Build learning rate variable
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https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/layers_cn.html
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Args:
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function(str): class name of learning rate
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params(dict): parameters used for init the class
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"""
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def __init__(self,
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function='Linear',
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params={'lr': 0.1,
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'steps': 100,
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'end_lr': 0.0}):
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self.function = function
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self.params = params
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def __call__(self):
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mod = sys.modules[__name__]
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lr = getattr(mod, self.function)(**self.params)
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return lr
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