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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import math
from functools import partial
class LRScheduler:
def __init__(self, name, lr, iters_per_epoch, total_epochs, **kwargs):
"""
Supported lr schedulers: [cos, warmcos, multistep]
Args:
lr (float): learning rate.
iters_per_peoch (int): number of iterations in one epoch.
total_epochs (int): number of epochs in training.
kwargs (dict):
- cos: None
- warmcos: [warmup_epochs, warmup_lr_start (default 1e-6)]
- multistep: [milestones (epochs), gamma (default 0.1)]
"""
self.lr = lr
self.iters_per_epoch = iters_per_epoch
self.total_epochs = total_epochs
self.total_iters = iters_per_epoch * total_epochs
self.__dict__.update(kwargs)
self.lr_func = self._get_lr_func(name)
def update_lr(self, iters):
return self.lr_func(iters)
def _get_lr_func(self, name):
if name == "cos": # cosine lr schedule
lr_func = partial(cos_lr, self.lr, self.total_iters)
elif name == "warmcos":
warmup_total_iters = self.iters_per_epoch * self.warmup_epochs
warmup_lr_start = getattr(self, "warmup_lr_start", 1e-6)
lr_func = partial(
warm_cos_lr,
self.lr,
self.total_iters,
warmup_total_iters,
warmup_lr_start,
)
elif name == "yoloxwarmcos":
warmup_total_iters = self.iters_per_epoch * self.warmup_epochs
no_aug_iters = self.iters_per_epoch * self.no_aug_epochs
warmup_lr_start = getattr(self, "warmup_lr_start", 0)
min_lr_ratio = getattr(self, "min_lr_ratio", 0.2)
lr_func = partial(
yolox_warm_cos_lr,
self.lr,
min_lr_ratio,
self.total_iters,
warmup_total_iters,
warmup_lr_start,
no_aug_iters,
)
elif name == "yoloxsemiwarmcos":
warmup_lr_start = getattr(self, "warmup_lr_start", 0)
min_lr_ratio = getattr(self, "min_lr_ratio", 0.2)
warmup_total_iters = self.iters_per_epoch * self.warmup_epochs
no_aug_iters = self.iters_per_epoch * self.no_aug_epochs
normal_iters = self.iters_per_epoch * self.semi_epoch
semi_iters = self.iters_per_epoch_semi * (
self.total_epochs - self.semi_epoch - self.no_aug_epochs
)
lr_func = partial(
yolox_semi_warm_cos_lr,
self.lr,
min_lr_ratio,
warmup_lr_start,
self.total_iters,
normal_iters,
no_aug_iters,
warmup_total_iters,
semi_iters,
self.iters_per_epoch,
self.iters_per_epoch_semi,
)
elif name == "multistep": # stepwise lr schedule
milestones = [
int(self.total_iters * milestone / self.total_epochs)
for milestone in self.milestones
]
gamma = getattr(self, "gamma", 0.1)
lr_func = partial(multistep_lr, self.lr, milestones, gamma)
else:
raise ValueError("Scheduler version {} not supported.".format(name))
return lr_func
def cos_lr(lr, total_iters, iters):
"""Cosine learning rate"""
lr *= 0.5 * (1.0 + math.cos(math.pi * iters / total_iters))
return lr
def warm_cos_lr(lr, total_iters, warmup_total_iters, warmup_lr_start, iters):
"""Cosine learning rate with warm up."""
if iters <= warmup_total_iters:
lr = (lr - warmup_lr_start) * iters / float(
warmup_total_iters
) + warmup_lr_start
else:
lr *= 0.5 * (
1.0
+ math.cos(
math.pi
* (iters - warmup_total_iters)
/ (total_iters - warmup_total_iters)
)
)
return lr
def yolox_warm_cos_lr(
lr,
min_lr_ratio,
total_iters,
warmup_total_iters,
warmup_lr_start,
no_aug_iter,
iters,
):
"""Cosine learning rate with warm up."""
min_lr = lr * min_lr_ratio
if iters <= warmup_total_iters:
# lr = (lr - warmup_lr_start) * iters / float(warmup_total_iters) + warmup_lr_start
lr = (lr - warmup_lr_start) * pow(
iters / float(warmup_total_iters), 2
) + warmup_lr_start
elif iters >= total_iters - no_aug_iter:
lr = min_lr
else:
lr = min_lr + 0.5 * (lr - min_lr) * (
1.0
+ math.cos(
math.pi
* (iters - warmup_total_iters)
/ (total_iters - warmup_total_iters - no_aug_iter)
)
)
return lr
def yolox_semi_warm_cos_lr(
lr,
min_lr_ratio,
warmup_lr_start,
total_iters,
normal_iters,
no_aug_iters,
warmup_total_iters,
semi_iters,
iters_per_epoch,
iters_per_epoch_semi,
iters,
):
"""Cosine learning rate with warm up."""
min_lr = lr * min_lr_ratio
if iters <= warmup_total_iters:
# lr = (lr - warmup_lr_start) * iters / float(warmup_total_iters) + warmup_lr_start
lr = (lr - warmup_lr_start) * pow(
iters / float(warmup_total_iters), 2
) + warmup_lr_start
elif iters >= normal_iters + semi_iters:
lr = min_lr
elif iters <= normal_iters:
lr = min_lr + 0.5 * (lr - min_lr) * (
1.0
+ math.cos(
math.pi
* (iters - warmup_total_iters)
/ (total_iters - warmup_total_iters - no_aug_iters)
)
)
else:
lr = min_lr + 0.5 * (lr - min_lr) * (
1.0
+ math.cos(
math.pi
* (
normal_iters
- warmup_total_iters
+ (iters - normal_iters)
* iters_per_epoch
* 1.0
/ iters_per_epoch_semi
)
/ (total_iters - warmup_total_iters - no_aug_iters)
)
)
return lr
def multistep_lr(lr, milestones, gamma, iters):
"""MultiStep learning rate"""
for milestone in milestones:
lr *= gamma if iters >= milestone else 1.0
return lr
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