CosineLR.py

from math import pi, cos, log, floor
from torch.optim.lr_scheduler import _LRScheduler


class CosineWarmupLR(_LRScheduler):
    '''
    Cosine lr decay function with warmup.
    Ref: https://github.com/PistonY/torch-toolbox/blob/master/torchtoolbox/optimizer/lr_scheduler.py
         https://github.com/Randl/MobileNetV3-pytorch/blob/master/cosine_with_warmup.py
    Lr warmup is proposed by 
        `Accurate, Large Minibatch SGD:Training ImageNet in 1 Hour`
        `https://arxiv.org/pdf/1706.02677.pdf`
    Cosine decay is proposed by 
        `Stochastic Gradient Descent with Warm Restarts`
        `https://arxiv.org/abs/1608.03983`
    Args:
        optimizer (Optimizer): optimizer of a model.
        iter_in_one_epoch (int): number of iterations in one epoch.
        epochs (int): number of epochs to train.
        lr_min (float): minimum(final) lr.
        warmup_epochs (int): warmup epochs before cosine decay.
        last_epoch (int): init iteration. In truth, this is last_iter
    Attributes:
        niters (int): number of iterations of all epochs.
        warmup_iters (int): number of iterations of all warmup epochs.
        cosine_iters (int): number of iterations of all cosine epochs.
    '''

    def __init__(self, optimizer, epochs, iter_in_one_epoch, lr_min=0, warmup_epochs=0, last_epoch=-1):
        self.lr_min = lr_min
        self.niters = epochs * iter_in_one_epoch
        self.warmup_iters = iter_in_one_epoch * warmup_epochs
        self.cosine_iters = iter_in_one_epoch * (epochs - warmup_epochs)
        super(CosineWarmupLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        if self.last_epoch < self.warmup_iters:
            return [(self.lr_min + (base_lr - self.lr_min) * self.last_epoch / self.warmup_iters) for base_lr in self.base_lrs]
        else:
            return [(self.lr_min + (base_lr - self.lr_min) * (1 + cos(pi * (self.last_epoch - self.warmup_iters) / self.cosine_iters)) / 2) for base_lr in self.base_lrs]

class CosineAnnealingWarmRestarts(_LRScheduler):
    '''
    copied from https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#CosineAnnealingWarmRestarts
    Set the learning rate of each parameter group using a cosine annealing
    schedule, where :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}`
    is the number of epochs since the last restart and :math:`T_{i}` is the number
    of epochs between two warm restarts in SGDR:

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

    When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`.
    When :math:`T_{cur}=0`(after restart), set :math:`\eta_t=\eta_{max}`.

    It has been proposed in
    `SGDR: Stochastic Gradient Descent with Warm Restarts`_.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        T_0 (int): Number of iterations for the first restart.
        T_mult (int, optional): A factor increases :math:`T_{i}` after a restart. Default: 1.
        eta_min (float, optional): Minimum learning rate. Default: 0.
        last_epoch (int, optional): 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_0, T_mult=1, eta_min=0, last_epoch=-1, warmup_epochs=0, decay_rate=0.5):
        if T_0 <= 0 or not isinstance(T_0, int):
            raise ValueError("Expected positive integer T_0, but got {}".format(T_0))
        if T_mult < 1 or not isinstance(T_mult, int):
            raise ValueError("Expected integer T_mult >= 1, but got {}".format(T_mult))
        if warmup_epochs < 0 or not isinstance(warmup_epochs, int):
            raise ValueError("Expected positive integer warmup_epochs, but got {}".format(warmup_epochs))
        self.T_0 = T_0
        self.T_i = T_0
        self.T_mult = T_mult
        self.eta_min = eta_min
        self.warmup_epochs = warmup_epochs
        self.decay_rate = decay_rate
        self.decay_power = 0
        super(CosineAnnealingWarmRestarts, self).__init__(optimizer, last_epoch)
        self.T_cur = self.last_epoch

    def get_lr(self):
        if self.last_epoch < self.warmup_epochs:
            return [(self.eta_min + (base_lr - self.eta_min) * self.T_cur / self.warmup_epochs) for base_lr in self.base_lrs]
        else:
            return [self.eta_min + (base_lr * (self.decay_rate**self.decay_power) - self.eta_min) * (1 + cos(pi * self.T_cur / self.T_i)) / 2
                for base_lr in self.base_lrs]

    def step(self, epoch=None):
        '''Step could be called after every batch update

        Example:
            >>> scheduler = CosineAnnealingWarmRestarts(optimizer, T_0, T_mult)
            >>> iters = len(dataloader)
            >>> for epoch in range(20):
            >>>     for i, sample in enumerate(dataloader):
            >>>         inputs, labels = sample['inputs'], sample['labels']
            >>>         scheduler.step(epoch + i / iters)
            >>>         optimizer.zero_grad()
            >>>         outputs = net(inputs)
            >>>         loss = criterion(outputs, labels)
            >>>         loss.backward()
            >>>         optimizer.step()

        This function can be called in an interleaved way.

        Example:
            >>> scheduler = CosineAnnealingWarmRestarts(optimizer, T_0, T_mult)
            >>> for epoch in range(20):
            >>>     scheduler.step()
            >>> scheduler.step(26)
            >>> scheduler.step() # scheduler.step(27), instead of scheduler(20)
        '''
        if epoch is None:
            epoch = self.last_epoch + 1
            self.T_cur = self.T_cur + 1
            if self.T_cur >= self.T_i:
                self.T_cur = self.T_cur - self.T_i
                self.T_i = self.T_i * self.T_mult
        else:
            if epoch < 0:
                raise ValueError("Expected non-negative epoch, but got {}".format(epoch))
            if epoch < self.warmup_epochs:
                self.T_cur = epoch
            else:
                epoch_cur = epoch - self.warmup_epochs
                if epoch_cur >= self.T_0:
                    if self.T_mult == 1:
                        self.T_cur = epoch_cur % self.T_0
                        self.decay_power = epoch_cur // self.T_0
                    else:
                        n = int(log((epoch_cur / self.T_0 * (self.T_mult - 1) + 1), self.T_mult))
                        self.T_cur = epoch_cur - self.T_0 * (self.T_mult ** n - 1) / (self.T_mult - 1)
                        self.T_i = self.T_0 * self.T_mult ** (n)
                        self.decay_power = n
                else:
                    self.T_i = self.T_0
                    self.T_cur = epoch_cur
        self.last_epoch = floor(epoch)
        for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
            param_group['lr'] = lr