OpenHarmony-v3.2-Release/s

# 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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# ============================================================================
"""Dynamic Learning Rate"""
import math

from mindspore._checkparam import Validator as validator


def piecewise_constant_lr(milestone, learning_rates):
    r"""
    Get piecewise constant learning rate.

    Calculate learning rate by given `milestone` and `learning_rates`. Let the value of `milestone` be
    :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
    `milestone`. Let the output learning rate be `y`.

    .. math::
        y[i] = x_t,\ for\ i \in [M_{t-1}, M_t)

    Args:
        milestone (Union[list[int], tuple[int]]): A list of milestone. This list is a monotone increasing list.
            Every element is a milestone step, and must be greater than 0.
        learning_rates (Union[list[float], tuple[float]]): A list of learning rates.

    Returns:
        list[float]. The size of list is :math:`M_N`.

    Examples:
        >>> milestone = [2, 5, 10]
        >>> learning_rates = [0.1, 0.05, 0.01]
        >>> output = piecewise_constant_lr(milestone, learning_rates)
        >>> print(output)
        [0.1, 0.1, 0.05, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01]
    """
    validator.check_value_type('milestone', milestone, (tuple, list))
    validator.check_value_type('learning_rates', learning_rates, (tuple, list))
    if len(milestone) != len(learning_rates):
        raise ValueError('The size of `milestone` must be same with the size of `learning_rates`.')

    lr = []
    last_item = 0
    for i, item in enumerate(milestone):
        validator.check_positive_int(item, f'milestone[{i}]')
        validator.check_is_float(learning_rates[i], f'learning_rates[{i}]')
        if item < last_item:
            raise ValueError(f'The value of milestone[{i}] must be greater than milestone[{i - 1}]')
        lr += [learning_rates[i]] * (item - last_item)
        last_item = item

    return lr


def _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair):
    validator.check_positive_int(total_step, 'total_step')
    validator.check_positive_int(step_per_epoch, 'step_per_epoch')
    validator.check_positive_int(decay_epoch, 'decay_epoch')
    validator.check_positive_float(learning_rate, 'learning_rate')
    validator.check_is_float(learning_rate, 'learning_rate')
    validator.check_positive_float(decay_rate, 'decay_rate')
    validator.check_is_float(decay_rate, 'decay_rate')
    validator.check_value_type('is_stair', is_stair, [bool])


def exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair=False):
    r"""
    Calculates learning rate base on exponential decay function.

    For the i-th step, the formula of computing decayed_learning_rate[i] is:

    .. math::
        decayed\_learning\_rate[i] = learning\_rate * decay\_rate^{\frac{current\_epoch}{decay\_epoch}}

    Where :math:`current\_epoch=floor(\frac{i}{step\_per\_epoch})`.

    Args:
        learning_rate (float): The initial value of learning rate.
        decay_rate (float): The decay rate.
        total_step (int): The total number of steps.
        step_per_epoch (int): The number of steps in per epoch.
        decay_epoch (int): A value used to calculate decayed learning rate.
        is_stair (bool): If true, learning rate is decayed once every `decay_epoch` times. Default: False.

    Returns:
        list[float]. The size of list is `total_step`.

    Examples:
        >>> learning_rate = 0.1
        >>> decay_rate = 0.9
        >>> total_step = 6
        >>> step_per_epoch = 2
        >>> decay_epoch = 1
        >>> output = exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch)
        >>> print(output)
        [0.1, 0.1, 0.09000000000000001, 0.09000000000000001, 0.08100000000000002, 0.08100000000000002]
    """
    _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair)

    lr = []
    for i in range(total_step):
        if is_stair:
            lr.append(learning_rate * decay_rate ** math.floor(math.floor(i / step_per_epoch) / decay_epoch))
        else:
            lr.append(learning_rate * decay_rate ** (math.floor(i / step_per_epoch) / decay_epoch))
    return lr


def natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair=False):
    r"""
    Calculates learning rate base on natural exponential decay function.

    For the i-th step, the formula of computing decayed_learning_rate[i] is:

    .. math::
        decayed\_learning\_rate[i] = learning\_rate * e^{-decay\_rate * current\_epoch}

    Where :math:`current\_epoch=floor(\frac{i}{step\_per\_epoch})`.

    Args:
        learning_rate (float): The initial value of learning rate.
        decay_rate (float): The decay rate.
        total_step (int): The total number of steps.
        step_per_epoch (int): The number of steps in per epoch.
        decay_epoch (int): A value used to calculate decayed learning rate.
        is_stair (bool): If true, learning rate is decayed once every `decay_epoch` times. Default: False.

    Returns:
        list[float]. The size of list is `total_step`.

    Examples:
        >>> learning_rate = 0.1
        >>> decay_rate = 0.9
        >>> total_step = 6
        >>> step_per_epoch = 2
        >>> decay_epoch = 2
        >>> output = natural_exp_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True)
        >>> print(output)
        [0.1, 0.1, 0.1, 0.1, 0.016529888822158657, 0.016529888822158657]
    """
    _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair)

    function = lambda x, y: x
    if is_stair:
        function = lambda x, y: math.floor(x / y) * y

    lr = []
    for i in range(total_step):
        lr.append(learning_rate * math.e ** (-decay_rate * function(math.floor(i / step_per_epoch), decay_epoch)))
    return lr


def inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair=False):
    r"""
    Calculates learning rate base on inverse-time decay function.

    For the i-th step, the formula of computing decayed_learning_rate[i] is:

    .. math::
        decayed\_learning\_rate[i] = learning\_rate / (1 + decay\_rate * current\_epoch / decay\_epoch)

    Where :math:`current\_epoch=floor(\frac{i}{step\_per\_epoch})`.

    Args:
        learning_rate (float): The initial value of learning rate.
        decay_rate (float): The decay rate.
        total_step (int): The total number of steps.
        step_per_epoch (int): The number of steps in per epoch.
        decay_epoch (int): A value used to calculate decayed learning rate.
        is_stair (bool): If true, learning rate is decayed once every `decay_epoch` times. Default: False.

    Returns:
        list[float]. The size of list is `total_step`.

    Examples:
        >>> learning_rate = 0.1
        >>> decay_rate = 0.5
        >>> total_step = 6
        >>> step_per_epoch = 1
        >>> decay_epoch = 1
        >>> output = inverse_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, True)
        >>> print(output)
        [0.1, 0.06666666666666667, 0.05, 0.04, 0.03333333333333333, 0.028571428571428574]
    """
    _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair)

    lr = []
    for i in range(total_step):
        if is_stair:
            lr.append(learning_rate / (1 + decay_rate * math.floor(math.floor(i / step_per_epoch) / decay_epoch)))
        else:
            lr.append(learning_rate / (1 + decay_rate * math.floor(i / step_per_epoch) / decay_epoch))
    return lr


def cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch):
    r"""
    Calculates learning rate base on cosine decay function.

    For the i-th step, the formula of computing decayed_learning_rate[i] is:

    .. math::
        decayed\_learning\_rate[i] = min\_learning\_rate + 0.5 * (max\_learning\_rate - min\_learning\_rate) *
        (1 + cos(\frac{current\_epoch}{decay\_epoch}\pi))

    Where :math:`current\_epoch=floor(\frac{i}{step\_per\_epoch})`.

    Args:
        min_lr (float): The minimum value of learning rate.
        max_lr (float): The maximum value of learning rate.
        total_step (int): The total number of steps.
        step_per_epoch (int): The number of steps in per epoch.
        decay_epoch (int): A value used to calculate decayed learning rate.

    Returns:
        list[float]. The size of list is `total_step`.

    Examples:
        >>> min_lr = 0.01
        >>> max_lr = 0.1
        >>> total_step = 6
        >>> step_per_epoch = 2
        >>> decay_epoch = 2
        >>> output = cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch)
        >>> print(output)
        [0.1, 0.1, 0.05500000000000001, 0.05500000000000001, 0.01, 0.01]
    """
    if not isinstance(min_lr, float):
        raise TypeError("min_lr must be float.")
    validator.check_non_negative_float(min_lr, "min_lr", None)
    validator.check_positive_float(max_lr, 'max_lr')
    validator.check_is_float(max_lr, 'max_lr')
    validator.check_positive_int(total_step, 'total_step')
    validator.check_positive_int(step_per_epoch, 'step_per_epoch')
    validator.check_positive_int(decay_epoch, 'decay_epoch')
    if min_lr >= max_lr:
        raise ValueError('The `max_lr` should be greater than the `min_lr`.')

    delta = 0.5 * (max_lr - min_lr)
    lr = []
    for i in range(total_step):
        tmp_epoch = min(math.floor(i / step_per_epoch), decay_epoch)
        lr.append(min_lr + delta * (1 + math.cos(math.pi * tmp_epoch / decay_epoch)))
    return lr


def polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power,
                        update_decay_epoch=False):
    r"""
    Calculates learning rate base on polynomial decay function.

    For the i-th step, the formula of computing decayed_learning_rate[i] is:

    .. math::
        decayed\_learning\_rate[i] = (learning\_rate - end\_learning\_rate) *
        (1 - tmp\_epoch / tmp\_decay\_epoch)^{power} + end\_learning\_rate

    Where:

    .. math::
        tmp\_epoch = min(current\_epoch, decay\_epoch)

    .. math::
        current\_epoch=floor(\frac{i}{step\_per\_epoch})

    .. math::
        tmp\_decay\_epoch = decay\_epoch

    If `update_decay_epoch` is true, update the value of `tmp_decay_epoch` every epoch. The formula is:

    .. math::
        tmp\_decay\_epoch = decay\_epoch * ceil(current\_epoch / decay\_epoch)

    Args:
        learning_rate (float): The initial value of learning rate.
        end_learning_rate (float): The end value of learning rate.
        total_step (int): The total number of steps.
        step_per_epoch (int): The number of steps in per epoch.
        decay_epoch (int): A value used to calculate decayed learning rate.
        power (float): A value used to calculate decayed learning rate. This parameter must be greater than 0.
        update_decay_epoch (bool): If true, update `decay_epoch`. Default: False.

    Returns:
        list[float]. The size of list is `total_step`.

    Examples:
        >>> learning_rate = 0.1
        >>> end_learning_rate = 0.01
        >>> total_step = 6
        >>> step_per_epoch = 2
        >>> decay_epoch = 2
        >>> power = 0.5
        >>> r = polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power)
        >>> print(r)
        [0.1, 0.1, 0.07363961030678928, 0.07363961030678928, 0.01, 0.01]
    """
    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("end_learning_rate must be float.")
    validator.check_non_negative_float(end_learning_rate, "end_learning_rate", None)
    validator.check_positive_float(power, 'power')
    validator.check_is_float(power, 'power')
    validator.check_positive_int(total_step, 'total_step')
    validator.check_positive_int(step_per_epoch, 'step_per_epoch')
    validator.check_positive_int(decay_epoch, 'decay_epoch')
    validator.check_value_type('update_decay_epoch', update_decay_epoch, [bool])

    origin_decay_epoch = decay_epoch
    function = lambda x, y: (x, min(x, y))
    if update_decay_epoch:
        function = lambda x, y: (origin_decay_epoch * max(math.ceil(y / origin_decay_epoch), 1), y)

    lr = []
    delta = learning_rate - end_learning_rate
    for i in range(total_step):
        current_epoch = math.floor(i / step_per_epoch)
        decay_epoch, tmp_epoch = function(decay_epoch, current_epoch)
        lr.append(delta * (1 - tmp_epoch / decay_epoch) ** power + end_learning_rate)
    return lr


def warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch):
    r"""
    Gets learning rate warming up.

    For the i-th step, the formula of computing warmup_learning_rate[i] is:

    .. math::
        warmup\_learning\_rate[i] = learning\_rate * tmp\_epoch / tmp\_warmup\_epoch

    Where :math:`tmp\_epoch=min(current\_epoch, warmup\_epoch),\ current\_epoch=floor(\frac{i}{step\_per\_epoch})`

    Args:
        learning_rate (float): The initial value of learning rate.
        total_step (int): The total number of steps.
        step_per_epoch (int): The number of steps in per epoch.
        warmup_epoch (int): A value that determines the epochs of the learning rate is warmed up.

    Returns:
        list[float]. The size of list is `total_step`.

    Examples:
        >>> learning_rate = 0.1
        >>> total_step = 6
        >>> step_per_epoch = 2
        >>> warmup_epoch = 2
        >>> output = warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch)
        >>> print(output)
        [0.0, 0.0, 0.05, 0.05, 0.1, 0.1]
    """
    if not isinstance(learning_rate, float):
        raise TypeError("learning_rate must be float.")
    validator.check_non_negative_float(learning_rate, "learning_rate", None)
    validator.check_positive_int(warmup_epoch, 'warmup_epoch')
    validator.check_positive_int(total_step, 'total_step')
    validator.check_positive_int(step_per_epoch, 'step_per_epoch')

    function = lambda x, y: (x, min(x, y))

    lr = []
    for i in range(total_step):
        current_epoch = math.floor(i / step_per_epoch)
        warmup_epoch, tmp_epoch = function(warmup_epoch, current_epoch)
        lr.append(learning_rate * tmp_epoch / warmup_epoch)
    return lr


__all__ = [
    'piecewise_constant_lr',
    'exponential_decay_lr',
    'natural_exp_decay_lr',
    'inverse_decay_lr',
    'cosine_decay_lr',
    'polynomial_decay_lr',
    'warmup_lr'
]