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117 lines
3.9 KiB
117 lines
3.9 KiB
""" Polynomial Scheduler
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Polynomial LR schedule with warmup, noise.
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Hacked together by / Copyright 2021 Ross Wightman
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"""
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import math
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import logging
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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 PolyLRScheduler(Scheduler):
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""" Polynomial LR Scheduler w/ warmup, noise, and k-decay
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k-decay option based on `k-decay: A New Method For Learning Rate Schedule` - https://arxiv.org/abs/2004.05909
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"""
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def __init__(self,
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optimizer: torch.optim.Optimizer,
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t_initial: int,
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power: float = 0.5,
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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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k_decay=1.0,
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initialize=True) -> None:
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super().__init__(
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optimizer, param_group_field="lr",
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noise_range_t=noise_range_t, noise_pct=noise_pct, noise_std=noise_std, noise_seed=noise_seed,
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initialize=initialize)
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assert t_initial > 0
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assert lr_min >= 0
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if t_initial == 1 and cycle_mul == 1 and cycle_decay == 1:
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_logger.warning("Cosine annealing scheduler will have no effect on the learning "
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"rate since t_initial = t_mul = eta_mul = 1.")
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self.t_initial = t_initial
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self.power = power
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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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self.t_in_epochs = t_in_epochs
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self.k_decay = k_decay
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if self.warmup_t:
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self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values]
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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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gamma = self.cycle_decay ** i
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lr_max_values = [v * gamma for v in self.base_values]
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k = self.k_decay
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if i < self.cycle_limit:
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lrs = [
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self.lr_min + (lr_max - self.lr_min) * (1 - t_curr ** k / t_i ** k) ** self.power
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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_epoch_values(self, epoch: int):
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if self.t_in_epochs:
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return self._get_lr(epoch)
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else:
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return None
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def get_update_values(self, num_updates: int):
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if not self.t_in_epochs:
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return self._get_lr(num_updates)
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else:
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return None
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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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