"""
Modified from https://github.com/rwightman/pytorch-image-models/blob/master/timm/scheduler/tanh_lr.py
"""
import math
import logging
import numpy as np
import oneflow as flow
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
[docs]class TanhLRScheduler(Scheduler):
"""
Hyberbolic-Tangent decay with restarts.
This is described in the paper https://arxiv.org/abs/1806.01593
"""
def __init__(
self,
optimizer: flow.optim.Optimizer,
t_initial: int,
lb: float = -7.0,
ub: float = 3.0,
lr_min: float = 0.0,
cycle_mul: float = 1.0,
cycle_decay: float = 1.0,
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",
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
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.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 * (1.0 - 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_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):
cycles = max(1, cycles or self.cycle_limit)
if self.cycle_mul == 1.0:
return self.t_initial * cycles
else:
return int(
math.floor(
-self.t_initial
* (self.cycle_mul ** cycles - 1)
/ (1 - self.cycle_mul)
)
)