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113 lines
3.5 KiB
113 lines
3.5 KiB
""" TanH Scheduler
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TanH schedule with warmup, cycle/restarts, noise.
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Hacked together by / Copyright 2021 Ross Wightman
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"""
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import logging
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import math
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import numpy as np
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import torch
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from .scheduler import Scheduler
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_logger = logging.getLogger(__name__)
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class TanhLRScheduler(Scheduler):
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"""
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Hyberbolic-Tangent decay with restarts.
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This is described in the paper https://arxiv.org/abs/1806.01593
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"""
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def __init__(
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self,
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optimizer: torch.optim.Optimizer,
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t_initial: int,
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lb: float = -7.,
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ub: float = 3.,
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lr_min: float = 0.,
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cycle_mul: float = 1.,
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cycle_decay: float = 1.,
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cycle_limit: int = 1,
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warmup_t=0,
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warmup_lr_init=0,
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warmup_prefix=False,
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t_in_epochs=True,
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noise_range_t=None,
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noise_pct=0.67,
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noise_std=1.0,
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noise_seed=42,
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initialize=True,
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) -> None:
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super().__init__(
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optimizer,
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param_group_field="lr",
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t_in_epochs=t_in_epochs,
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noise_range_t=noise_range_t,
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noise_pct=noise_pct,
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noise_std=noise_std,
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noise_seed=noise_seed,
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initialize=initialize,
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)
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assert t_initial > 0
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assert lr_min >= 0
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assert lb < ub
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assert cycle_limit >= 0
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assert warmup_t >= 0
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assert warmup_lr_init >= 0
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self.lb = lb
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self.ub = ub
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self.t_initial = t_initial
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self.lr_min = lr_min
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self.cycle_mul = cycle_mul
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self.cycle_decay = cycle_decay
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self.cycle_limit = cycle_limit
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self.warmup_t = warmup_t
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self.warmup_lr_init = warmup_lr_init
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self.warmup_prefix = warmup_prefix
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if self.warmup_t:
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t_v = self.base_values if self.warmup_prefix else self._get_lr(self.warmup_t)
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self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in t_v]
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super().update_groups(self.warmup_lr_init)
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else:
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self.warmup_steps = [1 for _ in self.base_values]
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def _get_lr(self, t):
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if t < self.warmup_t:
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lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
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else:
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if self.warmup_prefix:
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t = t - self.warmup_t
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if self.cycle_mul != 1:
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i = math.floor(math.log(1 - t / self.t_initial * (1 - self.cycle_mul), self.cycle_mul))
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t_i = self.cycle_mul ** i * self.t_initial
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t_curr = t - (1 - self.cycle_mul ** i) / (1 - self.cycle_mul) * self.t_initial
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else:
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i = t // self.t_initial
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t_i = self.t_initial
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t_curr = t - (self.t_initial * i)
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if i < self.cycle_limit:
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gamma = self.cycle_decay ** i
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lr_max_values = [v * gamma for v in self.base_values]
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tr = t_curr / t_i
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lrs = [
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self.lr_min + 0.5 * (lr_max - self.lr_min) * (1 - math.tanh(self.lb * (1. - tr) + self.ub * tr))
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for lr_max in lr_max_values
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]
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else:
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lrs = [self.lr_min for _ in self.base_values]
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return lrs
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def get_cycle_length(self, cycles=0):
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cycles = max(1, cycles or self.cycle_limit)
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if self.cycle_mul == 1.0:
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return self.t_initial * cycles
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else:
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return int(math.floor(-self.t_initial * (self.cycle_mul ** cycles - 1) / (1 - self.cycle_mul)))
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