| |
| |
| |
| |
| |
|
|
| from torch.optim import Optimizer |
| from torch.optim.lr_scheduler import _LRScheduler |
|
|
|
|
| class PolynomialDecayLRScheduler(_LRScheduler): |
| """Polynomial decay LR scheduler. |
| |
| Args: |
| optimizer (Optimizer): Torch optimizer. |
| warmup_steps (int): Number of warmup steps. |
| total_steps (int): Total number of steps. |
| end_lr (float): Final learning rate to achieve over total number of steps. |
| zero_lr_warmup_steps (int): Number of steps with a learning rate of value 0. |
| power (float): Decay exponent. |
| """ |
| def __init__(self, optimizer: Optimizer, warmup_steps: int, total_steps: int, |
| end_lr: float = 0., zero_lr_warmup_steps: int = 0, power: float = 1.): |
| self.warmup_steps = warmup_steps |
| self.total_steps = total_steps |
| self.end_lr = end_lr |
| self.zero_lr_warmup_steps = zero_lr_warmup_steps |
| self.power = power |
| super().__init__(optimizer) |
|
|
| def _get_sched_lr(self, lr: float, step: int): |
| if self.zero_lr_warmup_steps > 0 and step <= self.zero_lr_warmup_steps: |
| lr = 0 |
| elif self.warmup_steps > 0 and step <= self.warmup_steps + self.zero_lr_warmup_steps: |
| lr_ratio = (step - self.zero_lr_warmup_steps) / float(self.warmup_steps) |
| lr = lr_ratio * lr |
| elif step >= self.total_steps: |
| lr = self.end_lr |
| else: |
| total_warmup_steps = self.warmup_steps + self.zero_lr_warmup_steps |
| lr_range = lr - self.end_lr |
| pct_remaining = 1 - (step - total_warmup_steps) / (self.total_steps - total_warmup_steps) |
| lr = lr_range * pct_remaining ** self.power + self.end_lr |
| return lr |
|
|
| def get_lr(self): |
| return [self._get_sched_lr(base_lr, self.last_epoch) for base_lr in self.base_lrs] |
|
|
