Source code for pypots.optim.lr_scheduler.lambda_lrs

"""
Lambda learning rate scheduler.
"""

# Created by Wenjie Du <wenjay.du@gmail.com>
# License: BSD-3-Clause

from typing import Callable, Union

from .base import LRScheduler, logger




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.

    Parameters
    ----------
    lr_lambda: Callable 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.

    verbose: bool,
        If ``True``, prints a message to stdout for each update. Default: ``False``.

    Notes
    -----
    This class works the same with ``torch.optim.lr_scheduler.LambdaLR``.
    The only difference that is also why we implement them is that you don't have to pass according optimizers
    into them immediately while initializing them.

    Example
    -------
    >>> lambda1 = lambda epoch: epoch // 30
    >>> scheduler = LambdaLR(lr_lambda=lambda1)
    >>> adam = pypots.optim.Adam(lr=1e-3, lr_scheduler=scheduler)

    """

    def __init__(
        self,
        lr_lambda: Union[Callable, list],
        last_epoch: int = -1,
        verbose: bool = False,
    ):
        super().__init__(last_epoch, verbose)
        self.lr_lambda = lr_lambda
        self.lr_lambdas = None



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

        super().init_scheduler(optimizer)




    def get_lr(self):
        if not self._get_lr_called_within_step:
            logger.warning("⚠️ To get the last learning rate computed by the scheduler, please use `get_last_lr()`.")

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