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64 lines
2.4 KiB
64 lines
2.4 KiB
from torch.optim.lr_scheduler import _LRScheduler
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from .delayed import WarmupScheduler
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class PolynomialLR(_LRScheduler):
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"""Polynomial learning rate scheduler.
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Args:
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optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
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total_steps (int): Number of total training steps.
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end_lr (float, optional): Minimum learning rate, defaults to 0.0001.
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power (float, optional): The power of polynomial, defaults to 1.0.
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last_epoch (int, optional): The index of last epoch, defaults to -1. When last_epoch=-1,
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the schedule is started from the beginning or When last_epoch=-1, sets initial lr as lr.
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"""
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def __init__(
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self, optimizer, total_steps: int, end_lr: float = 0.0001, power: float = 1.0, last_epoch: int = -1, **kwargs
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):
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if end_lr < 0:
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raise ValueError(f"end_lr must >= 0, got {end_lr}")
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self.total_steps = total_steps
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self.end_lr = end_lr
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self.power = power
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super().__init__(optimizer, last_epoch=last_epoch)
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def get_lr(self):
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return self._get_closed_form_lr()
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def _get_closed_form_lr(self):
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return [
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(base_lr - self.end_lr) * ((1 - min(self.last_epoch, self.total_steps) / self.total_steps) ** self.power)
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+ self.end_lr
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for base_lr in self.base_lrs
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]
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class PolynomialWarmupLR(WarmupScheduler):
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"""Polynomial learning rate scheduler with warmup.
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Args:
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optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
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total_steps (int): Number of total training steps.
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warmup_steps (int, optional): Number of warmup steps, defaults to 0.
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end_lr (float, optional): Minimum learning rate, defaults to 0.0001.
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power (float, optional): The power of polynomial, defaults to 1.0.
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last_epoch (int, optional): The index of last epoch, defaults to -1. When last_epoch=-1,
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the schedule is started from the beginning or When last_epoch=-1, sets initial lr as lr.
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"""
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def __init__(
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self,
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optimizer,
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total_steps: int,
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warmup_steps: int = 0,
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end_lr: float = 0.0001,
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power: float = 1.0,
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last_epoch: int = -1,
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**kwargs,
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):
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base_scheduler = PolynomialLR(optimizer, total_steps - warmup_steps, end_lr=end_lr, power=power)
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super().__init__(optimizer, warmup_steps, base_scheduler, last_epoch=last_epoch)
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