| """ TanH Scheduler |
| |
| TanH schedule with warmup, cycle/restarts, noise. |
| |
| Hacked together by / Copyright 2021 Ross Wightman |
| """ |
| import logging |
| import math |
| import numpy as np |
| import torch |
| from typing import List |
|
|
| 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 = -7., |
| ub: float = 3., |
| 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, |
| initialize=True, |
| ) -> None: |
| super().__init__( |
| optimizer, |
| param_group_field="lr", |
| t_in_epochs=t_in_epochs, |
| 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.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 |
| 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: int) -> List[float]: |
| 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) |
|
|
| if i < self.cycle_limit: |
| gamma = self.cycle_decay ** i |
| lr_max_values = [v * gamma for v in self.base_values] |
|
|
| tr = t_curr / t_i |
| lrs = [ |
| self.lr_min + 0.5 * (lr_max - self.lr_min) * (1 - math.tanh(self.lb * (1. - tr) + self.ub * tr)) |
| for lr_max in lr_max_values |
| ] |
| else: |
| lrs = [self.lr_min for _ in self.base_values] |
| return lrs |
|
|
| def get_cycle_length(self, cycles=0): |
| cycles = max(1, cycles or self.cycle_limit) |
| if self.cycle_mul == 1.0: |
| t = self.t_initial * cycles |
| else: |
| t = int(math.floor(-self.t_initial * (self.cycle_mul ** cycles - 1) / (1 - self.cycle_mul))) |
| return t + self.warmup_t if self.warmup_prefix else t |
|
|
