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pytorch-image-models/scheduler/tanh_lr.py

100 lines
3.3 KiB

import logging
import math
import numpy as np
import torch
from .scheduler import Scheduler
logger = logging.getLogger(__name__)
class TanhLRScheduler(Scheduler):
"""
Cosine annealing with restarts.
This is described in the paper https://arxiv.org/abs/1608.03983.
"""
def __init__(self,
optimizer: torch.optim.Optimizer,
t_initial: int,
lb: float = -6.,
ub: float = 4.,
t_mul: float = 1.,
lr_min: float = 0.,
decay_rate: float = 1.,
warmup_t=0,
warmup_lr_init=0,
warmup_prefix=False,
cycle_limit=0,
t_in_epochs=False,
initialize=True) -> None:
super().__init__(optimizer, param_group_field="lr", 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.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:
t_v = self.base_values if self.warmup_prefix else self._get_lr(self.warmup_t)
print(t_v)
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.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)
if self.cycle_limit == 0 or (self.cycle_limit > 0 and i < self.cycle_limit):
gamma = self.decay_rate ** i
lr_min = self.lr_min * gamma
lr_max_values = [v * gamma for v in self.base_values]
tr = t_curr / t_i
lrs = [
lr_min + 0.5 * (lr_max - lr_min) * (1 - math.tanh(self.lb * (1. - tr) + self.ub * tr))
for lr_max in lr_max_values
]
else:
lrs = [self.lr_min * (self.decay_rate ** self.cycle_limit) 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