| """ Step Scheduler |
| |
| Basic step LR schedule with warmup, noise. |
| |
| Hacked together by / Copyright 2020 Ross Wightman |
| """ |
| import math |
| import torch |
|
|
| from .scheduler import Scheduler |
|
|
|
|
| class StepLRScheduler(Scheduler): |
| """ |
| """ |
|
|
| def __init__( |
| self, |
| optimizer: torch.optim.Optimizer, |
| decay_t: float, |
| decay_rate: float = 1.0, |
| warmup_t=0, |
| warmup_lr_init=0, |
| t_in_epochs=True, |
| noise_range_t=None, |
| noise_pct=0.67, |
| noise_std=1.0, |
| noise_seed=42, |
| initialize=True, |
| ) -> None: |
| super().__init__( |
| optimizer, |
| param_group_field="lr", |
| noise_range_t=noise_range_t, |
| noise_pct=noise_pct, |
| noise_std=noise_std, |
| noise_seed=noise_seed, |
| initialize=initialize, |
| ) |
|
|
| self.decay_t = decay_t |
| self.decay_rate = decay_rate |
| self.warmup_t = warmup_t |
| self.warmup_lr_init = warmup_lr_init |
| self.t_in_epochs = t_in_epochs |
| if self.warmup_t: |
| self.warmup_steps = [ |
| (v - warmup_lr_init) / self.warmup_t for v in self.base_values |
| ] |
| super().update_groups(self.warmup_lr_init) |
| else: |
| self.warmup_steps = [1 for _ in self.base_values] |
|
|
| def _get_lr(self, t): |
| if t < self.warmup_t: |
| lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps] |
| else: |
| lrs = [ |
| v * (self.decay_rate ** (t // self.decay_t)) for v in self.base_values |
| ] |
| return lrs |
|
|
| def get_epoch_values(self, epoch: int): |
| if self.t_in_epochs: |
| return self._get_lr(epoch) |
| else: |
| return None |
|
|
| def get_update_values(self, num_updates: int): |
| if not self.t_in_epochs: |
| return self._get_lr(num_updates) |
| else: |
| return None |
|
|
