"""
Modified from https://github.com/rwightman/pytorch-image-models/blob/master/timm/scheduler/cosine_lr.py
"""
import logging
import math
import numpy as np
import oneflow as flow
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
[docs]class CosineLRScheduler(Scheduler):
"""
Cosine decay with restarts borrowed from timm.
This is described in the paper https://arxiv.org/abs/1608.03983.
Inspiration from
https://github.com/allenai/allennlp/blob/master/allennlp/training/learning_rate_schedulers/cosine.py
Args:
optimizer: The optimizer will be used for the training process
t_initial: The initial number of epochs. Example, 50, 100 etc.
t_mul: updates the SGDR schedule annealing.
lr_min: Defaults to 1e-5. The minimum learning rate to use
during the scheduling. The learning rate does not ever go below this value.
decay_rate: When decay rate > 0 and < 1., at every restart
the learning rate is decayed by new learning rate which equals ``lr * decay_rate``.
If decay_rate=0.5, then in that case, the new learning rate becomes half the initial lr.
warmup_t: Defines the number of warmup epochs.
warmup_lr_init: The initial learning rate during warmup.
"""
def __init__(
self,
optimizer: flow.optim.Optimizer,
t_initial: int,
t_mul: float = 1.0,
lr_min: float = 0.0,
decay_rate: float = 1.0,
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,
) -> 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,
)
assert t_initial > 0
assert lr_min >= 0
if t_initial == 1 and t_mul == 1 and decay_rate == 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.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:
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.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)
gamma = self.decay_rate ** i
lr_min = self.lr_min * gamma
lr_max_values = [v * gamma for v in self.base_values]
if self.cycle_limit == 0 or (self.cycle_limit > 0 and i < self.cycle_limit):
lrs = [
lr_min
+ 0.5 * (lr_max - lr_min) * (1 + math.cos(math.pi * t_curr / t_i))
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):
if not cycles:
cycles = self.cycle_limit
cycles = max(1, cycles)
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)
)
)