pypots/optim/lr_scheduler/step_lrs.py
"""
Step learning rate scheduler.
"""
# Created by Wenjie Du <wenjay.du@gmail.com>
# License: BSD-3-Clause
from .base import LRScheduler, logger
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.
Parameters
----------
step_size: int,
Period of learning rate decay.
gamma: float, default=0.1,
Multiplicative factor of learning rate decay.
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.StepLR``.
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 = 0.05 if epoch < 30
>>> # lr = 0.005 if 30 <= epoch < 60
>>> # lr = 0.0005 if 60 <= epoch < 90
>>> # ...
>>> # xdoctest: +SKIP
>>> scheduler = StepLR(step_size=30, gamma=0.1)
>>> adam = pypots.optim.Adam(lr=1e-3, lr_scheduler=scheduler)
"""
def __init__(self, step_size, gamma=0.1, last_epoch=-1, verbose=False):
super().__init__(last_epoch, verbose)
self.step_size = step_size
self.gamma = gamma
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) 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]
def _get_closed_form_lr(self):
return [base_lr * self.gamma ** (self.last_epoch // self.step_size) for base_lr in self.base_lrs]