229 lines
8.2 KiB
Python
229 lines
8.2 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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from __future__ import unicode_literals
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from paddle.optimizer import lr
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from .lr_scheduler import CyclicalCosineDecay
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class Linear(object):
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"""
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Linear learning rate decay
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Args:
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lr (float): The initial learning rate. It is a python float number.
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epochs(int): The decay step size. It determines the decay cycle.
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end_lr(float, optional): The minimum final learning rate. Default: 0.0001.
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power(float, optional): Power of polynomial. Default: 1.0.
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last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
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"""
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def __init__(self,
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learning_rate,
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epochs,
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step_each_epoch,
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end_lr=0.0,
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power=1.0,
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warmup_epoch=0,
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last_epoch=-1,
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**kwargs):
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super(Linear, self).__init__()
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self.learning_rate = learning_rate
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self.epochs = epochs * step_each_epoch
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self.end_lr = end_lr
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self.power = power
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self.last_epoch = last_epoch
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self.warmup_epoch = round(warmup_epoch * step_each_epoch)
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def __call__(self):
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learning_rate = lr.PolynomialDecay(
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learning_rate=self.learning_rate,
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decay_steps=self.epochs,
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end_lr=self.end_lr,
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power=self.power,
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last_epoch=self.last_epoch)
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if self.warmup_epoch > 0:
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learning_rate = lr.LinearWarmup(
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learning_rate=learning_rate,
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warmup_steps=self.warmup_epoch,
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start_lr=0.0,
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end_lr=self.learning_rate,
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last_epoch=self.last_epoch)
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return learning_rate
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class Cosine(object):
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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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last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
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"""
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def __init__(self,
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learning_rate,
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step_each_epoch,
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epochs,
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warmup_epoch=0,
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last_epoch=-1,
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**kwargs):
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super(Cosine, self).__init__()
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self.learning_rate = learning_rate
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self.T_max = step_each_epoch * epochs
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self.last_epoch = last_epoch
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self.warmup_epoch = round(warmup_epoch * step_each_epoch)
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def __call__(self):
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learning_rate = lr.CosineAnnealingDecay(
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learning_rate=self.learning_rate,
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T_max=self.T_max,
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last_epoch=self.last_epoch)
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if self.warmup_epoch > 0:
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learning_rate = lr.LinearWarmup(
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learning_rate=learning_rate,
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warmup_steps=self.warmup_epoch,
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start_lr=0.0,
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end_lr=self.learning_rate,
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last_epoch=self.last_epoch)
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return learning_rate
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class Step(object):
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"""
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Piecewise learning rate decay
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Args:
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step_each_epoch(int): steps each epoch
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learning_rate (float): The initial learning rate. It is a python float number.
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step_size (int): the interval to update.
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gamma (float, optional): The Ratio that the learning rate will be reduced. ``new_lr = origin_lr * gamma`` .
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It should be less than 1.0. Default: 0.1.
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last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
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"""
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def __init__(self,
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learning_rate,
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step_size,
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step_each_epoch,
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gamma,
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warmup_epoch=0,
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last_epoch=-1,
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**kwargs):
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super(Step, self).__init__()
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self.step_size = step_each_epoch * step_size
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self.learning_rate = learning_rate
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self.gamma = gamma
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self.last_epoch = last_epoch
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self.warmup_epoch = round(warmup_epoch * step_each_epoch)
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def __call__(self):
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learning_rate = lr.StepDecay(
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learning_rate=self.learning_rate,
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step_size=self.step_size,
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gamma=self.gamma,
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last_epoch=self.last_epoch)
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if self.warmup_epoch > 0:
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learning_rate = lr.LinearWarmup(
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learning_rate=learning_rate,
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warmup_steps=self.warmup_epoch,
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start_lr=0.0,
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end_lr=self.learning_rate,
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last_epoch=self.last_epoch)
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return learning_rate
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class Piecewise(object):
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"""
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Piecewise learning rate decay
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Args:
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boundaries(list): A list of steps numbers. The type of element in the list is python int.
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values(list): A list of learning rate values that will be picked during different epoch boundaries.
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The type of element in the list is python float.
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last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
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"""
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def __init__(self,
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step_each_epoch,
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decay_epochs,
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values,
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warmup_epoch=0,
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last_epoch=-1,
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**kwargs):
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super(Piecewise, self).__init__()
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self.boundaries = [step_each_epoch * e for e in decay_epochs]
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self.values = values
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self.last_epoch = last_epoch
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self.warmup_epoch = round(warmup_epoch * step_each_epoch)
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def __call__(self):
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learning_rate = lr.PiecewiseDecay(
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boundaries=self.boundaries,
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values=self.values,
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last_epoch=self.last_epoch)
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if self.warmup_epoch > 0:
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learning_rate = lr.LinearWarmup(
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learning_rate=learning_rate,
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warmup_steps=self.warmup_epoch,
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start_lr=0.0,
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end_lr=self.values[0],
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last_epoch=self.last_epoch)
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return learning_rate
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class CyclicalCosine(object):
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"""
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Cyclical cosine learning rate decay
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Args:
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learning_rate(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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cycle(int): period of the cosine learning rate
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last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
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"""
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def __init__(self,
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learning_rate,
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step_each_epoch,
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epochs,
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cycle,
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warmup_epoch=0,
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last_epoch=-1,
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**kwargs):
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super(CyclicalCosine, self).__init__()
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self.learning_rate = learning_rate
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self.T_max = step_each_epoch * epochs
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self.last_epoch = last_epoch
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self.warmup_epoch = round(warmup_epoch * step_each_epoch)
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self.cycle = round(cycle * step_each_epoch)
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def __call__(self):
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learning_rate = CyclicalCosineDecay(
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learning_rate=self.learning_rate,
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T_max=self.T_max,
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cycle=self.cycle,
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last_epoch=self.last_epoch)
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if self.warmup_epoch > 0:
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learning_rate = lr.LinearWarmup(
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learning_rate=learning_rate,
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warmup_steps=self.warmup_epoch,
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start_lr=0.0,
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end_lr=self.learning_rate,
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last_epoch=self.last_epoch)
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return learning_rate
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