Source code for pypots.optim.lr_scheduler.constant_lrs

"""
Constant learning rate scheduler.
"""

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

from .base import LRScheduler, logger


[docs] class ConstantLR(LRScheduler): """Decays the learning rate of each parameter group by a small constant factor until the number of epoch reaches a pre-defined milestone: total_iters. 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. Parameters ---------- factor: float, default=1./3. The number we multiply learning rate until the milestone. total_iters: int, default=5, The number of steps that the scheduler decays the learning rate. 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.ConstantLR``. 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 ------- >>> # Assuming optimizer uses lr = 0.05 for all groups >>> # lr = 0.025 if epoch == 0 >>> # lr = 0.025 if epoch == 1 >>> # lr = 0.025 if epoch == 2 >>> # lr = 0.025 if epoch == 3 >>> # lr = 0.05 if epoch >= 4 >>> # xdoctest: +SKIP >>> scheduler = ConstantLR(factor=0.5, total_iters=4) >>> adam = pypots.optim.Adam(lr=1e-3, lr_scheduler=scheduler) """ def __init__(self, factor=1.0 / 3, total_iters=5, last_epoch=-1, verbose=False): super().__init__(last_epoch, verbose) if factor > 1.0 or factor < 0: raise ValueError( "Constant multiplicative factor expected to be between 0 and 1." ) self.factor = factor self.total_iters = total_iters
[docs] 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()`.", ) if self.last_epoch == 0: return [group["lr"] * self.factor for group in self.optimizer.param_groups] if self.last_epoch > self.total_iters or (self.last_epoch != self.total_iters): return [group["lr"] for group in self.optimizer.param_groups] if self.last_epoch == self.total_iters: return [ group["lr"] * (1.0 / self.factor) for group in self.optimizer.param_groups ]
def _get_closed_form_lr(self): return [ base_lr * (self.factor + (self.last_epoch >= self.total_iters) * (1 - self.factor)) for base_lr in self.base_lrs ]