| """ 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., | |
| 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 | |