lr_scheduler.py

import types
import math
import torch
from torch._six import inf
from collections import Counter
from functools import partial
from .optimizer import Optimizer


class _LRScheduler(object):
    def __init__(self, optimizer, last_epoch=-1):
        if not isinstance(optimizer, Optimizer):
            raise TypeError('{} is not an Optimizer'.format(
                type(optimizer).__name__))
        self.optimizer = optimizer
        if last_epoch == -1:
            for group in optimizer.param_groups:
                group.setdefault('initial_lr', group['lr'])
        else:
            for i, group in enumerate(optimizer.param_groups):
                if 'initial_lr' not in group:
                    raise KeyError("param 'initial_lr' is not specified "
                                   "in param_groups[{}] when resuming an optimizer".format(i))
        self.base_lrs = list(map(lambda group: group['initial_lr'], optimizer.param_groups))
        self.step(last_epoch + 1)
        self.last_epoch = last_epoch

    def state_dict(self):
        """Returns the state of the scheduler as a :class:`dict`.

        It contains an entry for every variable in self.__dict__ which
        is not the optimizer.
        """
        return {key: value for key, value in self.__dict__.items() if key != 'optimizer'}

    def load_state_dict(self, state_dict):
        """Loads the schedulers state.

        Arguments:
            state_dict (dict): scheduler state. Should be an object returned
                from a call to :meth:`state_dict`.
        """
        self.__dict__.update(state_dict)

    def get_lr(self):
        raise NotImplementedError

    def step(self, epoch=None):
        if epoch is None:
            epoch = self.last_epoch + 1
        self.last_epoch = epoch
        for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
            param_group['lr'] = lr


class LambdaLR(_LRScheduler):
    """Sets the learning rate of each parameter group to the initial lr
    times a given function. When last_epoch=-1, sets initial lr as lr.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        lr_lambda (function or list): A function which computes a multiplicative
            factor given an integer parameter epoch, or a list of such
            functions, one for each group in optimizer.param_groups.
        last_epoch (int): The index of last epoch. Default: -1.

    Example:
        >>> # Assuming optimizer has two groups.
        >>> lambda1 = lambda epoch: epoch // 30
        >>> lambda2 = lambda epoch: 0.95 ** epoch
        >>> scheduler = LambdaLR(optimizer, lr_lambda=[lambda1, lambda2])
        >>> for epoch in range(100):
        >>>     scheduler.step()
        >>>     train(...)
        >>>     validate(...)
    """

    def __init__(self, optimizer, lr_lambda, last_epoch=-1):
        self.optimizer = optimizer
        if not isinstance(lr_lambda, list) and not isinstance(lr_lambda, tuple):
            self.lr_lambdas = [lr_lambda] * len(optimizer.param_groups)
        else:
            if len(lr_lambda) != len(optimizer.param_groups):
                raise ValueError("Expected {} lr_lambdas, but got {}".format(
                    len(optimizer.param_groups), len(lr_lambda)))
            self.lr_lambdas = list(lr_lambda)
        self.last_epoch = last_epoch
        super(LambdaLR, self).__init__(optimizer, last_epoch)

    def state_dict(self):
        """Returns the state of the scheduler as a :class:`dict`.

        It contains an entry for every variable in self.__dict__ which
        is not the optimizer.
        The learning rate lambda functions will only be saved if they are callable objects
        and not if they are functions or lambdas.
        """
        state_dict = {key: value for key, value in self.__dict__.items() if key not in ('optimizer', 'lr_lambdas')}
        state_dict['lr_lambdas'] = [None] * len(self.lr_lambdas)

        for idx, fn in enumerate(self.lr_lambdas):
            if not isinstance(fn, types.FunctionType):
                state_dict['lr_lambdas'][idx] = fn.__dict__.copy()

        return state_dict

    def load_state_dict(self, state_dict):
        """Loads the schedulers state.

        Arguments:
            state_dict (dict): scheduler state. Should be an object returned
                from a call to :meth:`state_dict`.
        """
        lr_lambdas = state_dict.pop('lr_lambdas')
        self.__dict__.update(state_dict)

        for idx, fn in enumerate(lr_lambdas):
            if fn is not None:
                self.lr_lambdas[idx].__dict__.update(fn)

    def get_lr(self):
        return [base_lr * lmbda(self.last_epoch)
                for lmbda, base_lr in zip(self.lr_lambdas, self.base_lrs)]


class StepLR(_LRScheduler):
    """Decays the learning rate of each parameter group by gamma every
    step_size epochs. Notice that such decay can happen simultaneously with
    other changes to the learning rate from outside this scheduler. When
    last_epoch=-1, sets initial lr as lr.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        step_size (int): Period of learning rate decay.
        gamma (float): Multiplicative factor of learning rate decay.
            Default: 0.1.
        last_epoch (int): The index of last epoch. Default: -1.

    Example:
        >>> # Assuming optimizer uses lr = 0.05 for all groups
        >>> # lr = 0.05     if epoch < 30
        >>> # lr = 0.005    if 30 <= epoch < 60
        >>> # lr = 0.0005   if 60 <= epoch < 90
        >>> # ...
        >>> scheduler = StepLR(optimizer, step_size=30, gamma=0.1)
        >>> for epoch in range(100):
        >>>     scheduler.step()
        >>>     train(...)
        >>>     validate(...)
    """

    def __init__(self, optimizer, step_size, gamma=0.1, last_epoch=-1):
        self.step_size = step_size
        self.gamma = gamma
        super(StepLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        if (self.last_epoch == 0) or (self.last_epoch % self.step_size != 0):
            return [group['lr'] for group in self.optimizer.param_groups]
        return [group['lr'] * self.gamma
                for group in self.optimizer.param_groups]


class MultiStepLR(_LRScheduler):
    """Decays the learning rate of each parameter group by gamma once the
    number of epoch reaches one of the milestones. Notice that such decay can
    happen simultaneously with other changes to the learning rate from outside
    this scheduler. When last_epoch=-1, sets initial lr as lr.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        milestones (list): List of epoch indices. Must be increasing.
        gamma (float): Multiplicative factor of learning rate decay.
            Default: 0.1.
        last_epoch (int): The index of last epoch. Default: -1.

    Example:
        >>> # Assuming optimizer uses lr = 0.05 for all groups
        >>> # lr = 0.05     if epoch < 30
        >>> # lr = 0.005    if 30 <= epoch < 80
        >>> # lr = 0.0005   if epoch >= 80
        >>> scheduler = MultiStepLR(optimizer, milestones=[30,80], gamma=0.1)
        >>> for epoch in range(100):
        >>>     scheduler.step()
        >>>     train(...)
        >>>     validate(...)
    """

    def __init__(self, optimizer, milestones, gamma=0.1, last_epoch=-1):
        self.milestones = Counter(milestones)
        self.gamma = gamma
        super(MultiStepLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        if self.last_epoch not in self.milestones:
            return [group['lr'] for group in self.optimizer.param_groups]
        return [group['lr'] * self.gamma ** self.milestones[self.last_epoch]
                for group in self.optimizer.param_groups]


class ExponentialLR(_LRScheduler):
    """Decays the learning rate of each parameter group by gamma every epoch.
    When last_epoch=-1, sets initial lr as lr.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        gamma (float): Multiplicative factor of learning rate decay.
        last_epoch (int): The index of last epoch. Default: -1.
    """

    def __init__(self, optimizer, gamma, last_epoch=-1):
        self.gamma = gamma
        super(ExponentialLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        if self.last_epoch == 0:
            return self.base_lrs
        return [group['lr'] * self.gamma
                for group in self.optimizer.param_groups]


class CosineAnnealingLR(_LRScheduler):
    r"""Set the learning rate of each parameter group using a cosine annealing
    schedule, where :math:`\eta_{max}` is set to the initial lr and
    :math:`T_{cur}` is the number of epochs since the last restart in SGDR:

    .. math::
        \eta_{t+1} = \eta_{min} + (\eta_t - \eta_{min})\frac{1 +
        \cos(\frac{T_{cur+1}}{T_{max}}\pi)}{1 + \cos(\frac{T_{cur}}{T_{max}}\pi)}

    When last_epoch=-1, sets initial lr as lr. Notice that because the schedule
    is defined recursively, the learning rate can be simultaneously modified
    outside this scheduler by other operators. If the learning rate is set
    solely by this scheduler, the learning rate at each step becomes:

    .. math::
        \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})(1 +
        \cos(\frac{T_{cur}}{T_{max}}\pi))

    It has been proposed in
    `SGDR: Stochastic Gradient Descent with Warm Restarts`_. Note that this only
    implements the cosine annealing part of SGDR, and not the restarts.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        T_max (int): Maximum number of iterations.
        eta_min (float): Minimum learning rate. Default: 0.
        last_epoch (int): The index of last epoch. Default: -1.

    .. _SGDR\: Stochastic Gradient Descent with Warm Restarts:
        https://arxiv.org/abs/1608.03983
    """

    def __init__(self, optimizer, T_max, eta_min=0, last_epoch=-1):
        self.T_max = T_max
        self.eta_min = eta_min
        super(CosineAnnealingLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        if self.last_epoch == 0:
            return self.base_lrs
        return [(1 + math.cos(math.pi * self.last_epoch / self.T_max)) /
                (1 + math.cos(math.pi * (self.last_epoch - 1) / self.T_max)) *
                (group['lr'] - self.eta_min) + self.eta_min
                for group in self.optimizer.param_groups]


class ReduceLROnPlateau(object):
    """Reduce learning rate when a metric has stopped improving.
    Models often benefit from reducing the learning rate by a factor
    of 2-10 once learning stagnates. This scheduler reads a metrics
    quantity and if no improvement is seen for a 'patience' number
    of epochs, the learning rate is reduced.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        mode (str): One of `min`, `max`. In `min` mode, lr will
            be reduced when the quantity monitored has stopped
            decreasing; in `max` mode it will be reduced when the
            quantity monitored has stopped increasing. Default: 'min'.
        factor (float): Factor by which the learning rate will be
            reduced. new_lr = lr * factor. Default: 0.1.
        patience (int): Number of epochs with no improvement after
            which learning rate will be reduced. For example, if
            `patience = 2`, then we will ignore the first 2 epochs
            with no improvement, and will only decrease the LR after the
            3rd epoch if the loss still hasn't improved then.
            Default: 10.
        verbose (bool): If ``True``, prints a message to stdout for
            each update. Default: ``False``.
        threshold (float): Threshold for measuring the new optimum,
            to only focus on significant changes. Default: 1e-4.
        threshold_mode (str): One of `rel`, `abs`. In `rel` mode,
            dynamic_threshold = best * ( 1 + threshold ) in 'max'
            mode or best * ( 1 - threshold ) in `min` mode.
            In `abs` mode, dynamic_threshold = best + threshold in
            `max` mode or best - threshold in `min` mode. Default: 'rel'.
        cooldown (int): Number of epochs to wait before resuming
            normal operation after lr has been reduced. Default: 0.
        min_lr (float or list): A scalar or a list of scalars. A
            lower bound on the learning rate of all param groups
            or each group respectively. Default: 0.
        eps (float): Minimal decay applied to lr. If the difference
            between new and old lr is smaller than eps, the update is
            ignored. Default: 1e-8.

    Example:
        >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
        >>> scheduler = ReduceLROnPlateau(optimizer, 'min')
        >>> for epoch in range(10):
        >>>     train(...)
        >>>     val_loss = validate(...)
        >>>     # Note that step should be called after validate()
        >>>     scheduler.step(val_loss)
    """

    def __init__(self, optimizer, mode='min', factor=0.1, patience=10,
                 verbose=False, threshold=1e-4, threshold_mode='rel',
                 cooldown=0, min_lr=0, eps=1e-8):

        if factor >= 1.0:
            raise ValueError('Factor should be < 1.0.')
        self.factor = factor

        if not isinstance(optimizer, Optimizer):
            raise TypeError('{} is not an Optimizer'.format(
                type(optimizer).__name__))
        self.optimizer = optimizer

        if isinstance(min_lr, list) or isinstance(min_lr, tuple):
            if len(min_lr) != len(optimizer.param_groups):
                raise ValueError("expected {} min_lrs, got {}".format(
                    len(optimizer.param_groups), len(min_lr)))
            self.min_lrs = list(min_lr)
        else:
            self.min_lrs = [min_lr] * len(optimizer.param_groups)

        self.patience = patience
        self.verbose = verbose
        self.cooldown = cooldown
        self.cooldown_counter = 0
        self.mode = mode
        self.threshold = threshold
        self.threshold_mode = threshold_mode
        self.best = None
        self.num_bad_epochs = None
        self.mode_worse = None  # the worse value for the chosen mode
        self.is_better = None
        self.eps = eps
        self.last_epoch = -1
        self._init_is_better(mode=mode, threshold=threshold,
                             threshold_mode=threshold_mode)
        self._reset()

    def _reset(self):
        """Resets num_bad_epochs counter and cooldown counter."""
        self.best = self.mode_worse
        self.cooldown_counter = 0
        self.num_bad_epochs = 0

    def step(self, metrics, epoch=None):
        current = metrics
        if epoch is None:
            epoch = self.last_epoch = self.last_epoch + 1
        self.last_epoch = epoch

        if self.is_better(current, self.best):
            self.best = current
            self.num_bad_epochs = 0
        else:
            self.num_bad_epochs += 1

        if self.in_cooldown:
            self.cooldown_counter -= 1
            self.num_bad_epochs = 0  # ignore any bad epochs in cooldown

        if self.num_bad_epochs > self.patience:
            self._reduce_lr(epoch)
            self.cooldown_counter = self.cooldown
            self.num_bad_epochs = 0

    def _reduce_lr(self, epoch):
        for i, param_group in enumerate(self.optimizer.param_groups):
            old_lr = float(param_group['lr'])
            new_lr = max(old_lr * self.factor, self.min_lrs[i])
            if old_lr - new_lr > self.eps:
                param_group['lr'] = new_lr
                if self.verbose:
                    print('Epoch {:5d}: reducing learning rate'
                          ' of group {} to {:.4e}.'.format(epoch, i, new_lr))

    @property
    def in_cooldown(self):
        return self.cooldown_counter > 0

    def _cmp(self, mode, threshold_mode, threshold, a, best):
        if mode == 'min' and threshold_mode == 'rel':
            rel_epsilon = 1. - threshold
            return a < best * rel_epsilon

        elif mode == 'min' and threshold_mode == 'abs':
            return a < best - threshold

        elif mode == 'max' and threshold_mode == 'rel':
            rel_epsilon = threshold + 1.
            return a > best * rel_epsilon

        else:  # mode == 'max' and epsilon_mode == 'abs':
            return a > best + threshold

    def _init_is_better(self, mode, threshold, threshold_mode):
        if mode not in {'min', 'max'}:
            raise ValueError('mode ' + mode + ' is unknown!')
        if threshold_mode not in {'rel', 'abs'}:
            raise ValueError('threshold mode ' + threshold_mode + ' is unknown!')

        if mode == 'min':
            self.mode_worse = inf
        else:  # mode == 'max':
            self.mode_worse = -inf

        self.is_better = partial(self._cmp, mode, threshold_mode, threshold)

    def state_dict(self):
        return {key: value for key, value in self.__dict__.items() if key not in {'optimizer', 'is_better'}}

    def load_state_dict(self, state_dict):
        self.__dict__.update(state_dict)
        self._init_is_better(mode=self.mode, threshold=self.threshold, threshold_mode=self.threshold_mode)