import logging import math import numpy as np import torch from .scheduler import Scheduler logger = logging.getLogger(__name__) class TanhLRScheduler(Scheduler): """ Hyberbolic-Tangent decay with restarts. This is described in the paper https://arxiv.org/abs/1806.01593 """ def __init__(self, optimizer: torch.optim.Optimizer, t_initial: int, lb: float = -6., ub: float = 4., t_mul: float = 1., lr_min: float = 0., decay_rate: float = 1., warmup_t=0, warmup_lr_init=0, warmup_prefix=False, cycle_limit=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) assert t_initial > 0 assert lr_min >= 0 assert lb < ub assert cycle_limit >= 0 assert warmup_t >= 0 assert warmup_lr_init >= 0 self.lb = lb self.ub = ub self.t_initial = t_initial self.t_mul = t_mul self.lr_min = lr_min self.decay_rate = decay_rate 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 if self.warmup_t: t_v = self.base_values if self.warmup_prefix else self._get_lr(self.warmup_t) self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in t_v] 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.t_mul != 1: i = math.floor(math.log(1 - t / self.t_initial * (1 - self.t_mul), self.t_mul)) t_i = self.t_mul ** i * self.t_initial t_curr = t - (1 - self.t_mul ** i) / (1 - self.t_mul) * self.t_initial else: i = t // self.t_initial t_i = self.t_initial t_curr = t - (self.t_initial * i) if self.cycle_limit == 0 or (self.cycle_limit > 0 and i < self.cycle_limit): gamma = self.decay_rate ** i lr_min = self.lr_min * gamma lr_max_values = [v * gamma for v in self.base_values] tr = t_curr / t_i lrs = [ lr_min + 0.5 * (lr_max - lr_min) * (1 - math.tanh(self.lb * (1. - tr) + self.ub * tr)) for lr_max in lr_max_values ] else: lrs = [self.lr_min * (self.decay_rate ** self.cycle_limit) 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): if not cycles: cycles = self.cycle_limit assert cycles > 0 if self.t_mul == 1.0: return self.t_initial * cycles else: return int(math.floor(-self.t_initial * (self.t_mul ** cycles - 1) / (1 - self.t_mul)))