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| """ Polynomial Scheduler | |
| Polynomial LR schedule with warmup, noise. | |
| Hacked together by / Copyright 2021 Ross Wightman | |
| """ | |
| import math | |
| import logging | |
| import torch | |
| from .scheduler import Scheduler | |
| _logger = logging.getLogger(__name__) | |
| class PolyLRScheduler(Scheduler): | |
| """ Polynomial LR Scheduler w/ warmup, noise, and k-decay | |
| k-decay option based on `k-decay: A New Method For Learning Rate Schedule` - https://arxiv.org/abs/2004.05909 | |
| """ | |
| def __init__(self, | |
| optimizer: torch.optim.Optimizer, | |
| t_initial: int, | |
| power: float = 0.5, | |
| lr_min: float = 0., | |
| cycle_mul: float = 1., | |
| cycle_decay: float = 1., | |
| cycle_limit: int = 1, | |
| warmup_t=0, | |
| warmup_lr_init=0, | |
| warmup_prefix=False, | |
| t_in_epochs=True, | |
| noise_range_t=None, | |
| noise_pct=0.67, | |
| noise_std=1.0, | |
| noise_seed=42, | |
| k_decay=1.0, | |
| 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) | |
| assert t_initial > 0 | |
| assert lr_min >= 0 | |
| if t_initial == 1 and cycle_mul == 1 and cycle_decay == 1: | |
| _logger.warning("Cosine annealing scheduler will have no effect on the learning " | |
| "rate since t_initial = t_mul = eta_mul = 1.") | |
| self.t_initial = t_initial | |
| self.power = power | |
| self.lr_min = lr_min | |
| self.cycle_mul = cycle_mul | |
| self.cycle_decay = cycle_decay | |
| self.cycle_limit = cycle_limit | |
| self.warmup_t = warmup_t | |
| self.warmup_lr_init = warmup_lr_init | |
| self.warmup_prefix = warmup_prefix | |
| self.t_in_epochs = t_in_epochs | |
| self.k_decay = k_decay | |
| 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: | |
| if self.warmup_prefix: | |
| t = t - self.warmup_t | |
| if self.cycle_mul != 1: | |
| i = math.floor(math.log(1 - t / self.t_initial * (1 - self.cycle_mul), self.cycle_mul)) | |
| t_i = self.cycle_mul ** i * self.t_initial | |
| t_curr = t - (1 - self.cycle_mul ** i) / (1 - self.cycle_mul) * self.t_initial | |
| else: | |
| i = t // self.t_initial | |
| t_i = self.t_initial | |
| t_curr = t - (self.t_initial * i) | |
| gamma = self.cycle_decay ** i | |
| lr_max_values = [v * gamma for v in self.base_values] | |
| k = self.k_decay | |
| if i < self.cycle_limit: | |
| lrs = [ | |
| self.lr_min + (lr_max - self.lr_min) * (1 - t_curr ** k / t_i ** k) ** self.power | |
| for lr_max in lr_max_values | |
| ] | |
| else: | |
| lrs = [self.lr_min for _ 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 | |
| def get_cycle_length(self, cycles=0): | |
| cycles = max(1, cycles or self.cycle_limit) | |
| if self.cycle_mul == 1.0: | |
| return self.t_initial * cycles | |
| else: | |
| return int(math.floor(-self.t_initial * (self.cycle_mul ** cycles - 1) / (1 - self.cycle_mul))) | |