forked from huawei/mindspore2022
371 lines
15 KiB
Python
371 lines
15 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""dynamic learning rate"""
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import math
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from mindspore._checkparam import Validator as validator
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from mindspore._checkparam import Rel
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def piecewise_constant_lr(milestone, learning_rates):
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r"""
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Get piecewise constant learning rate.
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Calculate learning rate by given `milestone` and `learning_rates`. Let the value of `milestone` be
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:math:`(M_1, M_2, ..., M_N)` and the value of `learning_rates` be :math:`(x_1, x_2, ..., x_N)`. N is the length of
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`milestone`. Let the output learning rate be `y`.
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.. math::
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y[i] = x_t,\ for\ i \in [M_{t-1}, M_t)
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Args:
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milestone (Union[list[int], tuple[int]]): A list of milestone. This list is a monotone increasing list.
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Every element is a milestone step, and must be greater than 0.
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learning_rates (Union[list[float], tuple[float]]): A list of learning rates.
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Returns:
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list[float]. The size of list is :math:`M_N`.
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Examples:
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>>> milestone = [2, 5, 10]
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>>> learning_rates = [0.1, 0.05, 0.01]
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>>> piecewise_constant_lr(milestone, learning_rates)
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[0.1, 0.1, 0.05, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01]
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"""
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validator.check_value_type('milestone', milestone, (tuple, list), None)
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validator.check_value_type('learning_rates', learning_rates, (tuple, list), None)
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if len(milestone) != len(learning_rates):
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raise ValueError('The size of `milestone` must be same with the size of `learning_rates`.')
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lr = []
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last_item = 0
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for i, item in enumerate(milestone):
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validator.check_integer(f'milestone[{i}]', item, 0, Rel.GT, None)
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validator.check_float_legal_value(f'learning_rates[{i}]', learning_rates[i], None)
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if item < last_item:
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raise ValueError(f'The value of milestone[{i}] must be greater than milestone[{i - 1}]')
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lr += [learning_rates[i]] * (item - last_item)
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last_item = item
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return lr
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def _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair):
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validator.check_integer('total_step', total_step, 0, Rel.GT, None)
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validator.check_integer('step_per_epoch', step_per_epoch, 0, Rel.GT, None)
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validator.check_integer('decay_epoch', decay_epoch, 0, Rel.GT, None)
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validator.check_float_positive('learning_rate', learning_rate, None)
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validator.check_float_legal_value('learning_rate', learning_rate, None)
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validator.check_float_positive('decay_rate', decay_rate, None)
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validator.check_float_legal_value('decay_rate', decay_rate, None)
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validator.check_value_type('is_stair', is_stair, [bool], None)
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def exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair=False):
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r"""
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Calculate learning rate base on exponential decay function.
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For the i-th step, the formula of computing decayed_learning_rate[i] is:
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.. math::
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decayed\_learning\_rate[i] = learning\_rate * decay\_rate^{\frac{current\_epoch}{decay\_epoch}}
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Where :math:`current\_epoch=floor(\frac{i}{step\_per\_epoch})`.
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Args:
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learning_rate (float): The initial value of learning rate.
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decay_rate (float): The decay rate.
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total_step (int): The total number of steps.
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step_per_epoch (int): The number of steps in per epoch.
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decay_epoch (int): A value used to calculate decayed learning rate.
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is_stair (bool): If true, learning rate decay once every `decay_epoch` times. Default: False.
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Returns:
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list[float]. The size of list is `total_step`.
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Examples:
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>>> learning_rate = 0.1
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>>> decay_rate = 0.9
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>>> total_step = 6
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>>> step_per_epoch = 2
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>>> decay_epoch = 1
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>>> exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch)
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[0.1, 0.1, 0.09000000000000001, 0.09000000000000001, 0.08100000000000002, 0.08100000000000002]
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"""
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_check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair)
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lr = []
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for i in range(total_step):
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if is_stair:
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lr.append(learning_rate * decay_rate ** math.floor(math.floor(i / step_per_epoch) / decay_epoch))
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else:
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lr.append(learning_rate * decay_rate ** (math.floor(i / step_per_epoch) / decay_epoch))
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return lr
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def natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair=False):
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r"""
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Calculate learning rate base on natural exponential decay function.
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For the i-th step, the formula of computing decayed_learning_rate[i] is:
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.. math::
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decayed\_learning\_rate[i] = learning\_rate * e^{-decay\_rate * current\_epoch}
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Where :math:`current\_epoch=floor(\frac{i}{step\_per\_epoch})`.
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Args:
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learning_rate (float): The initial value of learning rate.
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decay_rate (float): The decay rate.
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total_step (int): The total number of steps.
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step_per_epoch (int): The number of steps in per epoch.
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decay_epoch (int): A value used to calculate decayed learning rate.
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is_stair (bool): If true, learning rate decay once every `decay_epoch` times. Default: False.
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Returns:
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list[float]. The size of list is `total_step`.
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Examples:
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>>> learning_rate = 0.1
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>>> decay_rate = 0.9
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>>> total_step = 6
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>>> step_per_epoch = 2
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>>> decay_epoch = 2
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>>> natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True)
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[0.1, 0.1, 0.1, 0.1, 0.016529888822158657, 0.016529888822158657]
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"""
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_check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair)
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function = lambda x, y: x
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if is_stair:
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function = lambda x, y: math.floor(x / y) * y
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lr = []
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for i in range(total_step):
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lr.append(learning_rate * math.e ** (-decay_rate * function(math.floor(i / step_per_epoch), decay_epoch)))
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return lr
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def inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair=False):
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r"""
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Calculate learning rate base on inverse-time decay function.
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For the i-th step, the formula of computing decayed_learning_rate[i] is:
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.. math::
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decayed\_learning\_rate[i] = learning\_rate / (1 + decay\_rate * current\_epoch / decay\_epoch)
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Where :math:`current\_epoch=floor(\frac{i}{step\_per\_epoch})`.
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Args:
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learning_rate (float): The initial value of learning rate.
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decay_rate (float): The decay rate.
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total_step (int): The total number of steps.
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step_per_epoch (int): The number of steps in per epoch.
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decay_epoch (int): A value used to calculate decayed learning rate.
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is_stair (bool): If true, learning rate decay once every `decay_epoch` times. Default: False.
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Returns:
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list[float]. The size of list is `total_step`.
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Examples:
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>>> learning_rate = 0.1
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>>> decay_rate = 0.5
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>>> total_step = 6
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>>> step_per_epoch = 1
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>>> decay_epoch = 1
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>>> inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True)
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[0.1, 0.06666666666666667, 0.05, 0.04, 0.03333333333333333, 0.028571428571428574]
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"""
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_check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair)
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lr = []
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for i in range(total_step):
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if is_stair:
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lr.append(learning_rate / (1 + decay_rate * math.floor(math.floor(i / step_per_epoch) / decay_epoch)))
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else:
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lr.append(learning_rate / (1 + decay_rate * math.floor(i / step_per_epoch) / decay_epoch))
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return lr
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def cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch):
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r"""
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Calculate learning rate base on cosine decay function.
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For the i-th step, the formula of computing decayed_learning_rate[i] is:
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.. math::
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decayed\_learning\_rate[i] = min\_learning\_rate + 0.5 * (max\_learning\_rate - min\_learning\_rate) *
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(1 + cos(\frac{current\_epoch}{decay\_epoch}\pi))
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Where :math:`current\_epoch=floor(\frac{i}{step\_per\_epoch})`.
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Args:
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min_lr (float): The minimum value of learning rate.
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max_lr (float): The maximum value of learning rate.
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total_step (int): The total number of steps.
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step_per_epoch (int): The number of steps in per epoch.
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decay_epoch (int): A value used to calculate decayed learning rate.
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Returns:
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list[float]. The size of list is `total_step`.
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Examples:
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>>> min_lr = 0.01
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>>> max_lr = 0.1
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>>> total_step = 6
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>>> step_per_epoch = 2
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>>> decay_epoch = 2
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>>> cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch)
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[0.1, 0.1, 0.05500000000000001, 0.05500000000000001, 0.01, 0.01]
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"""
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if not isinstance(min_lr, float):
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raise TypeError("min_lr must be float.")
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validator.check_number_range("min_lr", min_lr, 0.0, float("inf"), Rel.INC_LEFT, None)
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validator.check_float_positive('max_lr', max_lr, None)
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validator.check_float_legal_value('max_lr', max_lr, None)
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validator.check_integer('total_step', total_step, 0, Rel.GT, None)
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validator.check_integer('step_per_epoch', step_per_epoch, 0, Rel.GT, None)
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validator.check_integer('decay_epoch', decay_epoch, 0, Rel.GT, None)
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if min_lr >= max_lr:
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raise ValueError('`max_lr` should be greater than `min_lr`.')
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delta = 0.5 * (max_lr - min_lr)
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lr = []
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for i in range(total_step):
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tmp_epoch = min(math.floor(i / step_per_epoch), decay_epoch)
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lr.append(min_lr + delta * (1 + math.cos(math.pi * tmp_epoch / decay_epoch)))
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return lr
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def polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power,
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update_decay_epoch=False):
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r"""
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Calculate learning rate base on polynomial decay function.
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For the i-th step, the formula of computing decayed_learning_rate[i] is:
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.. math::
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decayed\_learning\_rate[i] = (learning\_rate - end\_learning\_rate) *
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(1 - tmp\_epoch / tmp\_decay\_epoch)^{power} + end\_learning\_rate
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Where :math:`tmp\_epoch=min(current\_epoch, decay\_epoch),\ current\_epoch=floor(\frac{i}{step\_per\_epoch})`, and
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:math:`tmp\_decay\_epoch = decay\_epoch`. If `update_decay_epoch` is true, update the value of `tmp_decay_epoch`
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every epoch. The formula is :math:`tmp\_decay\_epoch = decay\_epoch * ceil(current\_epoch / decay\_epoch)`
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Args:
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learning_rate (float): The initial value of learning rate.
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end_learning_rate (float): The end value of learning rate.
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total_step (int): The total number of steps.
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step_per_epoch (int): The number of steps in per epoch.
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decay_epoch (int): A value used to calculate decayed learning rate.
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power (float): A value used to calculate decayed learning rate. This parameter should be greater than 0.
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update_decay_epoch (bool): If true, update `decay_epoch`. Default: False.
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Returns:
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list[float]. The size of list is `total_step`.
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Examples:
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>>> learning_rate = 0.1
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>>> end_learning_rate = 0.01
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>>> total_step = 6
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>>> step_per_epoch = 2
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>>> decay_epoch = 2
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>>> power = 0.5
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>>> polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power)
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[0.1, 0.1, 0.07363961030678928, 0.07363961030678928, 0.01, 0.01]
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"""
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validator.check_float_positive('learning_rate', learning_rate, None)
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validator.check_float_legal_value('learning_rate', learning_rate, None)
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if not isinstance(end_learning_rate, float):
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raise TypeError("end_learning_rate must be float.")
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validator.check_number_range("end_learning_rate", end_learning_rate, 0.0, float("inf"), Rel.INC_LEFT, None)
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validator.check_float_positive('power', power, None)
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validator.check_float_legal_value('power', power, None)
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validator.check_integer('total_step', total_step, 0, Rel.GT, None)
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validator.check_integer('step_per_epoch', step_per_epoch, 0, Rel.GT, None)
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validator.check_integer('decay_epoch', decay_epoch, 0, Rel.GT, None)
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validator.check_value_type('update_decay_epoch', update_decay_epoch, [bool], None)
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origin_decay_epoch = decay_epoch
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function = lambda x, y: (x, min(x, y))
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if update_decay_epoch:
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function = lambda x, y: (origin_decay_epoch * max(math.ceil(y / origin_decay_epoch), 1), y)
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lr = []
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delta = learning_rate - end_learning_rate
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for i in range(total_step):
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current_epoch = math.floor(i / step_per_epoch)
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decay_epoch, tmp_epoch = function(decay_epoch, current_epoch)
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lr.append(delta * (1 - tmp_epoch / decay_epoch) ** power + end_learning_rate)
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return lr
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def warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch):
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r"""
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Get learning rate warming up.
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For the i-th step, the formula of computing warmup_learning_rate[i] is:
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.. math::
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warmup\_learning\_rate[i] = learning\_rate * tmp\_epoch / tmp\_warmup\_epoch
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Where :math:`tmp\_epoch=min(current\_epoch, warmup\_epoch),\ current\_epoch=floor(\frac{i}{step\_per\_epoch})`
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Args:
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learning_rate (float): The initial value of learning rate.
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warmup_steps (int): The warm up steps of learning rate.
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Inputs:
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Tensor. The current step number.
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Returns:
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Tensor. The learning rate value for the current step.
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Examples:
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>>> learning_rate = 0.1
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>>> total_step = 6
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>>> step_per_epoch = 2
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>>> warmup_epoch = 2
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>>> warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch)
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[0.0, 0.0, 0.05, 0.05, 0.1, 0.1]
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"""
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if not isinstance(learning_rate, float):
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raise TypeError("learning_rate must be float.")
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validator.check_number_range("learning_rate", learning_rate, 0.0, float("inf"), Rel.INC_LEFT, None)
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validator.check_integer('warmup_epoch', warmup_epoch, 0, Rel.GT, None)
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validator.check_integer('total_step', total_step, 0, Rel.GT, None)
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validator.check_integer('step_per_epoch', step_per_epoch, 0, Rel.GT, None)
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function = lambda x, y: (x, min(x, y))
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lr = []
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for i in range(total_step):
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current_epoch = math.floor(i / step_per_epoch)
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warmup_epoch, tmp_epoch = function(warmup_epoch, current_epoch)
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lr.append(learning_rate * tmp_epoch/ warmup_epoch)
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return lr
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__all__ = [
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'piecewise_constant_lr',
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'exponential_decay_lr',
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'natural_exp_decay_lr',
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'inverse_decay_lr',
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'cosine_decay_lr',
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'polynomial_decay_lr',
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'warmup_lr'
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]
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