schedulers.py

from torch.optim.lr_scheduler import _LRScheduler
from ..utils.misc import listify


class LinearLR(_LRScheduler):
    """Linearly increases the learning rate between two boundaries over a number of
    iterations.
    Arguments:
        optimizer (torch.optim.Optimizer): wrapped optimizer.
        end_lr (float, optional): the initial learning rate which is the lower
            boundary of the test. Default: 10.
        num_iter (int, optional): the number of iterations over which the test
            occurs. Default: 100.
        last_epoch (int): the index of last epoch. Default: -1.
    """

    def __init__(self, optimizer, end_lr, num_iter, last_epoch=-1):
        self.end_lr = end_lr
        self.num_iter = num_iter
        super(LinearLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        curr_iter = self.last_epoch + 1
        r = curr_iter / self.num_iter
        return [base_lr + r * (self.end_lr - base_lr) for base_lr in self.base_lrs]


class ExponentialLR(_LRScheduler):
    """Exponentially increases the learning rate between two boundaries over a number of
    iterations.
    Arguments:
        optimizer (torch.optim.Optimizer): wrapped optimizer.
        end_lr (float, optional): the initial learning rate which is the lower
            boundary of the test. Default: 10.
        num_iter (int, optional): the number of iterations over which the test
            occurs. Default: 100.
        last_epoch (int): the index of last epoch. Default: -1.
    """

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

    def get_lr(self):
        curr_iter = self.last_epoch + 1
        r = curr_iter / self.num_iter
        return [base_lr * (self.end_lr / base_lr) ** r for base_lr in self.base_lrs]