# Copyright 2020 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
#
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# ============================================================================
"""Learning rate schedule."""
from __future__ import absolute_import
from __future__ import division
import math
from mindspore.common import dtype as mstype
from mindspore.ops import operations as P
from mindspore.nn.cell import Cell
from mindspore import _checkparam as validator
class LearningRateSchedule(Cell):
"""Basic class of learning rate schedule."""
def __init__(self):
super(LearningRateSchedule, self).__init__()
def construct(self, global_step):
"""
Defines the computation to get the current learning rate.
This method must be overridden by all subclasses.
Note:
The output must be a Tensor of scalar.
Inputs:
- **global_step** (Tensor) - The current step number.
Inputs:
Tensor. Learning rate at current step with shape :math:`()`.
"""
raise NotImplementedError
def _check_inputs(learning_rate, decay_rate, decay_steps, is_stair, cls_name):
validator.check_positive_int(decay_steps, 'decay_steps', cls_name)
validator.check_positive_float(learning_rate, 'learning_rate', cls_name)
validator.check_is_float(learning_rate, 'learning_rate', cls_name)
validator.check_positive_float(decay_rate, 'decay_rate', cls_name)
validator.check_is_float(decay_rate, 'decay_rate', cls_name)
validator.check_value_type('is_stair', is_stair, [bool], cls_name)
[文档]class ExponentialDecayLR(LearningRateSchedule):
r"""
Calculates learning rate based on exponential decay function.
For current step, the formula of computing decayed learning rate is:
.. math::
decayed\_learning\_rate = learning\_rate * decay\_rate^{p}
Where :
.. math::
p = \frac{current\_step}{decay\_steps}
If `is_stair` is True, the formula is :
.. math::
p = floor(\frac{current\_step}{decay\_steps})
Args:
learning_rate (float): The initial value of learning rate.
decay_rate (float): The decay rate.
decay_steps (int): Number of steps to decay over.
is_stair (bool): If true, learning rate is decayed once every `decay_steps` time. Default: ``False`` .
Inputs:
- **global_step** (Tensor) - The current step number.
Outputs:
Tensor. The learning rate value for the current step with shape :math:`()`.
Raises:
TypeError: If `learning_rate` or `decay_rate` is not a float.
TypeError: If `decay_steps` is not an int or `is_stair` is not a bool.
ValueError: If `decay_steps` is less than 1.
ValueError: If `learning_rate` or `decay_rate` is less than or equal to 0.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, nn
>>>
>>> learning_rate = 0.1
>>> decay_rate = 0.9
>>> decay_steps = 4
>>> global_step = Tensor(2, mindspore.int32)
>>> exponential_decay_lr = nn.ExponentialDecayLR(learning_rate, decay_rate, decay_steps)
>>> lr = exponential_decay_lr(global_step)
>>> net = nn.Dense(2, 3)
>>> optim = nn.SGD(net.trainable_params(), learning_rate=lr)
"""
def __init__(self, learning_rate, decay_rate, decay_steps, is_stair=False):
super(ExponentialDecayLR, self).__init__()
_check_inputs(learning_rate, decay_rate, decay_steps, is_stair, self.cls_name)
self.learning_rate = learning_rate
self.decay_rate = decay_rate
self.decay_steps = decay_steps
self.is_stair = is_stair
self.pow = P.Pow()
self.cast = P.Cast()
def construct(self, global_step):
p = self.cast(global_step, mstype.float32) / self.decay_steps
if self.is_stair:
p = P.Floor()(p)
return self.learning_rate * self.pow(self.decay_rate, p)
[文档]class NaturalExpDecayLR(LearningRateSchedule):
r"""
Calculates learning rate base on natural exponential decay function.
For current step, the formula of computing decayed learning rate is:
.. math::
decayed\_learning\_rate = learning\_rate * e^{-decay\_rate * p}
Where :
.. math::
p = \frac{current\_step}{decay\_steps}
If `is_stair` is True, the formula is :
.. math::
p = floor(\frac{current\_step}{decay\_steps})
Args:
learning_rate (float): The initial value of learning rate.
decay_rate (float): The decay rate.
decay_steps (int): Number of steps to decay over.
is_stair (bool): If ``true`` , learning rate is decayed once every `decay_steps` time. Default: ``False`` .
Inputs:
- **global_step** (Tensor) - The current step number.
Outputs:
Tensor. The learning rate value for the current step with shape :math:`()`.
Raises:
TypeError: If `learning_rate` or `decay_rate` is not a float.
TypeError: If `decay_steps` is not an int or `is_stair` is not a bool.
ValueError: If `decay_steps` is less than 1.
ValueError: If `learning_rate` or `decay_rate` is less than or equal to 0.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, nn
>>>
>>> learning_rate = 0.1
>>> decay_rate = 0.9
>>> decay_steps = 4
>>> global_step = Tensor(2, mindspore.int32)
>>> natural_exp_decay_lr = nn.NaturalExpDecayLR(learning_rate, decay_rate, decay_steps, True)
>>> lr = natural_exp_decay_lr(global_step)
>>> net = nn.Dense(2, 3)
>>> optim = nn.SGD(net.trainable_params(), learning_rate=lr)
"""
def __init__(self, learning_rate, decay_rate, decay_steps, is_stair=False):
super(NaturalExpDecayLR, self).__init__()
_check_inputs(learning_rate, decay_rate, decay_steps, is_stair, self.cls_name)
self.learning_rate = learning_rate
self.decay_rate = decay_rate
self.decay_steps = decay_steps
self.is_stair = is_stair
self.math_e = math.e
self.pow = P.Pow()
self.cast = P.Cast()
def construct(self, global_step):
p = self.cast(global_step, mstype.float32)
if self.is_stair:
p = P.FloorDiv()(p, self.decay_steps) * self.decay_steps
return self.learning_rate * self.pow(self.math_e, -self.decay_rate * p)
[文档]class InverseDecayLR(LearningRateSchedule):
r"""
Calculates learning rate base on inverse-time decay function.
For current step, the formula of computing decayed learning rate is:
.. math::
decayed\_learning\_rate = learning\_rate / (1 + decay\_rate * p)
Where :
.. math::
p = \frac{current\_step}{decay\_steps}
If `is_stair` is True, The formula is :
.. math::
p = floor(\frac{current\_step}{decay\_steps})
Args:
learning_rate (float): The initial value of learning rate.
decay_rate (float): The decay rate.
decay_steps (int): Number of steps to decay over.
is_stair (bool): If true, learning rate decay once every `decay_steps` times. Default: ``False`` .
Inputs:
- **global_step** (Tensor) - The current step number.
Outputs:
Tensor. The learning rate value for the current step with shape :math:`()`.
Raises:
TypeError: If `learning_rate` or `decay_rate` is not a float.
TypeError: If `decay_steps` is not an int or `is_stair` is not a bool.
ValueError: If `decay_steps` is less than 1.
ValueError: If `learning_rate` or `decay_rate` is less than or equal to 0.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, nn
>>>
>>> learning_rate = 0.1
>>> decay_rate = 0.9
>>> decay_steps = 4
>>> global_step = Tensor(2, mindspore.int32)
>>> inverse_decay_lr = nn.InverseDecayLR(learning_rate, decay_rate, decay_steps, True)
>>> lr = inverse_decay_lr(global_step)
>>> net = nn.Dense(2, 3)
>>> optim = nn.SGD(net.trainable_params(), learning_rate=lr)
"""
def __init__(self, learning_rate, decay_rate, decay_steps, is_stair=False):
super(InverseDecayLR, self).__init__()
_check_inputs(learning_rate, decay_rate, decay_steps, is_stair, self.cls_name)
self.learning_rate = learning_rate
self.decay_rate = decay_rate
self.decay_steps = decay_steps
self.is_stair = is_stair
self.cast = P.Cast()
def construct(self, global_step):
p = self.cast(global_step, mstype.float32) / self.decay_steps
if self.is_stair:
p = P.Floor()(p)
return self.learning_rate / (1 + self.decay_rate * p)
[文档]class CosineDecayLR(LearningRateSchedule):
r"""
Calculates learning rate based on cosine decay function.
For current step, the formula of computing decayed learning rate is:
.. math::
decayed\_learning\_rate = &min\_lr + 0.5 * (max\_lr - min\_lr) *\\
&(1 + cos(\frac{current\_step}{decay\_steps}\pi))
Args:
min_lr (float): The minimum value of learning rate.
max_lr (float): The maximum value of learning rate.
decay_steps (int): Number of steps to decay over.
Inputs:
- **global_step** (Tensor) - The current step number.
Outputs:
Tensor. The learning rate value for the current step with shape :math:`()`.
Raises:
TypeError: If `min_lr` or `max_lr` is not a float.
TypeError: If `decay_steps` is not an int.
ValueError: If `min_lr` is less than 0 or `decay_steps` is less than 1.
ValueError: If `max_lr` is less than or equal to 0.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, nn
>>>
>>> min_lr = 0.01
>>> max_lr = 0.1
>>> decay_steps = 4
>>> global_steps = Tensor(2, mindspore.int32)
>>> cosine_decay_lr = nn.CosineDecayLR(min_lr, max_lr, decay_steps)
>>> lr = cosine_decay_lr(global_steps)
>>> net = nn.Dense(2, 3)
>>> optim = nn.SGD(net.trainable_params(), learning_rate=lr)
"""
def __init__(self, min_lr, max_lr, decay_steps):
super(CosineDecayLR, self).__init__()
if not isinstance(min_lr, float):
raise TypeError("For 'CosineDecayLR', the argument 'min_lr' must be type of float, "
"but got 'min_lr' type: {}.".format(type(min_lr)))
validator.check_non_negative_float(min_lr, "min_lr", self.cls_name)
validator.check_positive_float(max_lr, 'max_lr', self.cls_name)
validator.check_is_float(max_lr, 'max_lr', self.cls_name)
validator.check_positive_int(decay_steps, "decay_steps", self.cls_name)
if min_lr >= max_lr:
raise ValueError("For 'CosineDecayLR', the 'max_lr' must be greater than the 'min_lr', "
"but got 'max_lr' value: {}, 'min_lr' value: {}.".format(max_lr, min_lr))
self.min_lr = min_lr
self.max_lr = max_lr
self.decay_steps = decay_steps
self.math_pi = math.pi
self.delta = 0.5 * (max_lr - min_lr)
self.cos = P.Cos()
self.min = P.Minimum()
self.cast = P.Cast()
def construct(self, global_step):
p = self.cast(self.min(global_step, self.decay_steps), mstype.float32)
return self.min_lr + self.delta * (1.0 + self.cos(self.math_pi * p / self.decay_steps))
[文档]class PolynomialDecayLR(LearningRateSchedule):
r"""
Calculates learning rate base on polynomial decay function.
For current step, the formula of computing decayed learning rate is:
.. math::
decayed\_learning\_rate = &(learning\_rate - end\_learning\_rate) *\\
&(1 - tmp\_step / tmp\_decay\_steps)^{power}\\
&+ end\_learning\_rate
Where :
.. math::
tmp\_step= \min(current\_step, decay\_steps)
If `update_decay_steps` is true, update the value of `tmp_decay_step` every `decay_steps`. The formula is :
.. math::
tmp\_decay\_steps = decay\_steps * ceil(current\_step / decay\_steps)
Args:
learning_rate (float): The initial value of learning rate.
end_learning_rate (float): The end value of learning rate.
decay_steps (int): Number of steps to decay over.
power (float): The power of polynomial. It must be greater than 0.
update_decay_steps (bool): If ``true`` , learning rate is decayed once every `decay_steps` time.
Default: ``False`` .
Inputs:
- **global_step** (Tensor) - The current step number.
Outputs:
Tensor. The learning rate value for the current step with shape :math:`()`.
Raises:
TypeError: If `learning_rate`, `end_learning_rate` or `power` is not a float.
TypeError: If `decay_steps` is not an int or `update_decay_steps` is not a bool.
ValueError: If `end_learning_rate` is less than 0 or `decay_steps` is less than 1.
ValueError: If `learning_rate` or `power` is less than or equal to 0.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, nn
>>>
>>> learning_rate = 0.1
>>> end_learning_rate = 0.01
>>> decay_steps = 4
>>> power = 0.5
>>> global_step = Tensor(2, mindspore.int32)
>>> polynomial_decay_lr = nn.PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
>>> lr = polynomial_decay_lr(global_step)
>>> net = nn.Dense(2, 3)
>>> optim = nn.SGD(net.trainable_params(), learning_rate=lr)
"""
def __init__(self, learning_rate, end_learning_rate, decay_steps, power, update_decay_steps=False):
super(PolynomialDecayLR, self).__init__()
validator.check_positive_float(learning_rate, 'learning_rate')
validator.check_is_float(learning_rate, 'learning_rate')
if not isinstance(end_learning_rate, float):
raise TypeError("For 'PolynomialDecayLR', the argument 'end_learning_rate' "
"must be type of float, but got 'end_learning_rate' type: {}."
.format(type(end_learning_rate)))
validator.check_non_negative_float(end_learning_rate, "end_learning_rate", self.cls_name)
validator.check_positive_int(decay_steps, 'decay_steps', self.cls_name)
validator.check_value_type('update_decay_steps', update_decay_steps, [bool], self.cls_name)
validator.check_positive_float(power, 'power', self.cls_name)
validator.check_is_float(power, 'power', self.cls_name)
self.decay_steps = decay_steps
self.start_learning_rate = learning_rate
self.end_learning_rate = end_learning_rate
self.diff_learning_rate = learning_rate - end_learning_rate
self.power = power
self.update_decay_steps = update_decay_steps
self.pow = P.Pow()
self.ceil = P.Ceil()
self.min = P.Minimum()
self.max = P.Maximum()
def construct(self, global_step):
tmp_global_step = P.Cast()(global_step, mstype.float32)
tmp_decay_step = self.decay_steps
if self.update_decay_steps:
tmp_decay_step = tmp_decay_step * self.max(self.ceil(tmp_global_step / tmp_decay_step), 1)
else:
tmp_global_step = self.min(tmp_global_step, tmp_decay_step)
p = tmp_global_step / tmp_decay_step
lr = self.diff_learning_rate * self.pow(1.0 - p, self.power) + self.end_learning_rate
return lr
[文档]class WarmUpLR(LearningRateSchedule):
r"""
Gets learning rate warming up.
For current step, the formula of computing warmup learning rate is:
.. math::
warmup\_learning\_rate = learning\_rate * tmp\_step / warmup\_steps
Where
.. math::
tmp\_step= \min(current\_step, warmup\_steps)
Args:
learning_rate (float): The initial value of learning rate.
warmup_steps (int): The warm up steps of learning rate.
Inputs:
- **global_step** (Tensor) - The current step number.
Outputs:
Tensor. The learning rate value for the current step with shape :math:`()`.
Raises:
TypeError: If `learning_rate` is not a float.
TypeError: If `warmup_steps` is not an int.
ValueError: If `warmup_steps` is less than 1.
ValueError: If `learning_rate` is less than or equal to 0.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, nn
>>>
>>> learning_rate = 0.1
>>> warmup_steps = 2
>>> global_step = Tensor(2, mindspore.int32)
>>> warmup_lr = nn.WarmUpLR(learning_rate, warmup_steps)
>>> lr = warmup_lr(global_step)
>>> net = nn.Dense(2, 3)
>>> optim = nn.SGD(net.trainable_params(), learning_rate=lr)
"""
def __init__(self, learning_rate, warmup_steps):
super(WarmUpLR, self).__init__()
if not isinstance(learning_rate, float):
raise TypeError("For 'WarmUpLR', the argument 'learning_rate' must be type of float, "
"but got 'learning_rate' type: {}.".format(type(learning_rate)))
validator.check_non_negative_float(learning_rate, "learning_rate", self.cls_name)
validator.check_positive_int(warmup_steps, 'warmup_steps', self.cls_name)
self.warmup_steps = warmup_steps
self.learning_rate = learning_rate
self.min = P.Minimum()
self.cast = P.Cast()
def construct(self, global_step):
warmup_percent = self.cast(self.min(global_step, self.warmup_steps), mstype.float32) / self.warmup_steps
return self.learning_rate * warmup_percent
__all__ = [
'ExponentialDecayLR',
'NaturalExpDecayLR',
'InverseDecayLR',
'CosineDecayLR',
'PolynomialDecayLR',
'WarmUpLR'
]