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"""Scheduling learning rate."""
import logging
from math import cos, pi

[docs]class LRScheduler(object):
    """Base class of a learning rate scheduler.

    A scheduler returns a new learning rate based on the number of updates that have
    been performed.

    Parameters
    ----------
    base_lr : float, optional
        The initial learning rate.
    warmup_steps: int
        number of warmup steps used before this scheduler starts decay
    warmup_begin_lr: float
        if using warmup, the learning rate from which it starts warming up
    warmup_mode: string
        warmup can be done in two modes.
        'linear' mode gradually increases lr with each step in equal increments
        'constant' mode keeps lr at warmup_begin_lr for warmup_steps
    """
    def __init__(self, base_lr=0.01,
                 warmup_steps=0, warmup_begin_lr=0, warmup_mode='linear'):
        self.base_lr = base_lr
        assert isinstance(warmup_steps, int)
        self.warmup_steps = warmup_steps

        self.warmup_final_lr = base_lr
        self.warmup_begin_lr = warmup_begin_lr
        if self.warmup_begin_lr > self.warmup_final_lr:
            raise ValueError("Base lr has to be higher than warmup_begin_lr")
        if self.warmup_steps < 0:
            raise ValueError("Warmup steps has to be positive or 0")
        if warmup_mode not in ['linear', 'constant']:
            raise ValueError("Supports only linear and constant modes of warmup")
        self.warmup_mode = warmup_mode

    def get_warmup_lr(self, num_update):
        assert num_update < self.warmup_steps
        if self.warmup_mode == 'linear':
            increase = (self.warmup_final_lr - self.warmup_begin_lr) \
                       * float(num_update) / float(self.warmup_steps)
            return self.warmup_begin_lr + increase
        elif self.warmup_mode == 'constant':
            return self.warmup_begin_lr
        else:
            raise ValueError("Invalid warmup mode %s"%self.warmup_mode)

    def __call__(self, num_update):
        """Return a new learning rate.

        The ``num_update`` is the upper bound of the number of updates applied to
        every weight.

        Assume the optimizer has updated *i*-th weight by *k_i* times, namely
        ``optimizer.update(i, weight_i)`` is called by *k_i* times. Then::

            num_update = max([k_i for all i])

        Parameters
        ----------
        num_update: int
            the maximal number of updates applied to a weight.
        """
        raise NotImplementedError("must override this")

[docs]class FactorScheduler(LRScheduler):
    """Reduce the learning rate by a factor for every *n* steps.

    It returns a new learning rate by::

        base_lr * pow(factor, floor(num_update/step))

    Parameters
    ----------
    step : int
        Changes the learning rate for every n updates.
    factor : float, optional
        The factor to change the learning rate.
    stop_factor_lr : float, optional
        Stop updating the learning rate if it is less than this value.
    """
    def __init__(self, step, factor=1, stop_factor_lr=1e-8, base_lr=0.01,
                 warmup_steps=0, warmup_begin_lr=0, warmup_mode='linear'):
        super(FactorScheduler, self).__init__(base_lr, warmup_steps, warmup_begin_lr, warmup_mode)
        if step < 1:
            raise ValueError("Schedule step must be greater or equal than 1 round")
        if factor > 1.0:
            raise ValueError("Factor must be no more than 1 to make lr reduce")
        self.step = step
        self.factor = factor
        self.stop_factor_lr = stop_factor_lr
        self.count = 0

    def __call__(self, num_update):
        if num_update < self.warmup_steps:
            return self.get_warmup_lr(num_update)

        # NOTE: use while rather than if  (for continuing training via load_epoch)
        while num_update > self.count + self.step:
            self.count += self.step
            self.base_lr *= self.factor
            if self.base_lr < self.stop_factor_lr:
                self.base_lr = self.stop_factor_lr
                logging.info("Update[%d]: now learning rate arrived at %0.5e, will not "
                             "change in the future", num_update, self.base_lr)
            else:
                logging.info("Update[%d]: Change learning rate to %0.5e",
                             num_update, self.base_lr)
        return self.base_lr

[docs]class MultiFactorScheduler(LRScheduler):
    """Reduce the learning rate by given a list of steps.

    Assume there exists *k* such that::

       step[k] <= num_update and num_update < step[k+1]

    Then calculate the new learning rate by::

       base_lr * pow(factor, k+1)

    Parameters
    ----------
    step: list of int
        The list of steps to schedule a change
    factor: float
        The factor to change the learning rate.
    warmup_steps: int
        number of warmup steps used before this scheduler starts decay
    warmup_begin_lr: float
        if using warmup, the learning rate from which it starts warming up
    warmup_mode: string
        warmup can be done in two modes.
        'linear' mode gradually increases lr with each step in equal increments
        'constant' mode keeps lr at warmup_begin_lr for warmup_steps
    """
    def __init__(self, step, factor=1, base_lr=0.01, warmup_steps=0, warmup_begin_lr=0,
                 warmup_mode='linear'):
        super(MultiFactorScheduler, self).__init__(base_lr, warmup_steps,
                                                   warmup_begin_lr, warmup_mode)
        assert isinstance(step, list) and len(step) >= 1
        for i, _step in enumerate(step):
            if i != 0 and step[i] <= step[i-1]:
                raise ValueError("Schedule step must be an increasing integer list")
            if _step < 1:
                raise ValueError("Schedule step must be greater or equal than 1 round")
        if factor > 1.0:
            raise ValueError("Factor must be no more than 1 to make lr reduce")
        self.step = step
        self.cur_step_ind = 0
        self.factor = factor
        self.count = 0

    def __call__(self, num_update):
        if num_update < self.warmup_steps:
            return self.get_warmup_lr(num_update)

        # NOTE: use while rather than if  (for continuing training via load_epoch)
        while self.cur_step_ind <= len(self.step)-1:
            if num_update > self.step[self.cur_step_ind]:
                self.count = self.step[self.cur_step_ind]
                self.cur_step_ind += 1
                self.base_lr *= self.factor
                logging.info("Update[%d]: Change learning rate to %0.5e",
                             num_update, self.base_lr)
            else:
                return self.base_lr
        return self.base_lr

[docs]class PolyScheduler(LRScheduler):
    """ Reduce the learning rate according to a polynomial of given power.

    Calculate the new learning rate, after warmup if any, by::

       final_lr + (start_lr - final_lr) * (1-nup/max_nup)^pwr
       if nup < max_nup, 0 otherwise.

    Parameters
    ----------
        max_update: int
            maximum number of updates before the decay reaches final learning rate.
        base_lr: float
            base learning rate to start from
        pwr:   int
            power of the decay term as a function of the current number of updates.
        final_lr:   float
            final learning rate after all steps
        warmup_steps: int
            number of warmup steps used before this scheduler starts decay
        warmup_begin_lr: float
            if using warmup, the learning rate from which it starts warming up
        warmup_mode: string
            warmup can be done in two modes.
            'linear' mode gradually increases lr with each step in equal increments
            'constant' mode keeps lr at warmup_begin_lr for warmup_steps
    """

    def __init__(self, max_update, base_lr=0.01, pwr=2, final_lr=0,
                 warmup_steps=0, warmup_begin_lr=0, warmup_mode='linear'):
        super(PolyScheduler, self).__init__(base_lr, warmup_steps, warmup_begin_lr, warmup_mode)
        assert isinstance(max_update, int)
        if max_update < 1:
            raise ValueError("maximum number of updates must be strictly positive")
        self.power = pwr
        self.base_lr_orig = self.base_lr
        self.max_update = max_update
        self.final_lr = final_lr
        self.max_steps = self.max_update - self.warmup_steps

    def __call__(self, num_update):
        if num_update < self.warmup_steps:
            return self.get_warmup_lr(num_update)
        if num_update <= self.max_update:
            self.base_lr = self.final_lr + (self.base_lr_orig - self.final_lr) * \
                pow(1 - float(num_update - self.warmup_steps) / float(self.max_steps), self.power)
        return self.base_lr

[docs]class CosineScheduler(LRScheduler):
    """ Reduce the learning rate according to a cosine function

    Calculate the new learning rate by::

       final_lr + (start_lr - final_lr) * (1+cos(pi * nup/max_nup))/2
       if nup < max_nup, 0 otherwise.

    Parameters
    ----------
        max_update: int
            maximum number of updates before the decay reaches 0
        base_lr: float
            base learning rate
        final_lr: float
            final learning rate after all steps
        warmup_steps: int
            number of warmup steps used before this scheduler starts decay
        warmup_begin_lr: float
            if using warmup, the learning rate from which it starts warming up
        warmup_mode: string
            warmup can be done in two modes.
            'linear' mode gradually increases lr with each step in equal increments
            'constant' mode keeps lr at warmup_begin_lr for warmup_steps
    """

    def __init__(self, max_update, base_lr=0.01, final_lr=0,
                 warmup_steps=0, warmup_begin_lr=0, warmup_mode='linear'):
        super(CosineScheduler, self).__init__(base_lr, warmup_steps, warmup_begin_lr, warmup_mode)
        assert isinstance(max_update, int)
        if max_update < 1:
            raise ValueError("maximum number of updates must be strictly positive")
        self.base_lr_orig = base_lr
        self.max_update = max_update
        self.final_lr = final_lr
        self.max_steps = self.max_update - self.warmup_steps

    def __call__(self, num_update):
        if num_update < self.warmup_steps:
            return self.get_warmup_lr(num_update)
        if num_update <= self.max_update:
            self.base_lr = self.final_lr + (self.base_lr_orig - self.final_lr) * \
                (1 + cos(pi * (num_update - self.warmup_steps) / self.max_steps)) / 2
        return self.base_lr