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# --------------------------------------------------------
# SiamMask
# Licensed under The MIT License
# Written by Qiang Wang (wangqiang2015 at ia.ac.cn)
# --------------------------------------------------------
from __future__ import division
import numpy as np
import math
from torch.optim.lr_scheduler import _LRScheduler
class LRScheduler(_LRScheduler):
def __init__(self, optimizer, last_epoch=-1):
if 'lr_spaces' not in self.__dict__:
raise Exception('lr_spaces must be set in "LRSchduler"')
super(LRScheduler, self).__init__(optimizer, last_epoch)
def get_cur_lr(self):
return self.lr_spaces[self.last_epoch]
def get_lr(self):
epoch = self.last_epoch
return [self.lr_spaces[epoch] * pg['initial_lr'] / self.start_lr for pg in self.optimizer.param_groups]
def __repr__(self):
return "({}) lr spaces: \n{}".format(self.__class__.__name__, self.lr_spaces)
class LogScheduler(LRScheduler):
def __init__(self, optimizer, start_lr=0.03, end_lr=5e-4, epochs=50, last_epoch=-1, **kwargs):
self.start_lr = start_lr
self.end_lr = end_lr
self.epochs = epochs
self.lr_spaces = np.logspace(math.log10(start_lr), math.log10(end_lr), epochs)
super(LogScheduler, self).__init__(optimizer, last_epoch)
class StepScheduler(LRScheduler):
def __init__(self, optimizer, start_lr=0.01, end_lr=None, step=10, mult=0.1, epochs=50, last_epoch=-1, **kwargs):
if end_lr is not None:
if start_lr is None:
start_lr = end_lr / (mult ** (epochs // step))
else: # for warm up policy
mult = math.pow(end_lr/start_lr, 1. / (epochs // step))
self.start_lr = start_lr
self.lr_spaces = self.start_lr * (mult**(np.arange(epochs) // step))
self.mult = mult
self._step = step
super(StepScheduler, self).__init__(optimizer, last_epoch)
class MultiStepScheduler(LRScheduler):
def __init__(self, optimizer, start_lr=0.01, end_lr=None, steps=[10,20,30,40], mult=0.5, epochs=50, last_epoch=-1, **kwargs):
if end_lr is not None:
if start_lr is None:
start_lr = end_lr / (mult ** (len(steps)))
else:
mult = math.pow(end_lr/start_lr, 1. / len(steps))
self.start_lr = start_lr
self.lr_spaces = self._build_lr(start_lr, steps, mult, epochs)
self.mult = mult
self.steps = steps
super(MultiStepScheduler, self).__init__(optimizer, last_epoch)
def _build_lr(self, start_lr, steps, mult, epochs):
lr = [0] * epochs
lr[0] = start_lr
for i in range(1, epochs):
lr[i] = lr[i-1]
if i in steps:
lr[i] *= mult
return np.array(lr, dtype=np.float32)
class LinearStepScheduler(LRScheduler):
def __init__(self, optimizer, start_lr=0.01, end_lr=0.005, epochs=50, last_epoch=-1, **kwargs):
self.start_lr = start_lr
self.end_lr = end_lr
self.lr_spaces = np.linspace(start_lr, end_lr, epochs)
super(LinearStepScheduler, self).__init__(optimizer, last_epoch)
class CosStepScheduler(LRScheduler):
def __init__(self, optimizer, start_lr=0.01, end_lr=0.005, epochs=50, last_epoch=-1, **kwargs):
self.start_lr = start_lr
self.end_lr = end_lr
self.lr_spaces = self._build_lr(start_lr, end_lr, epochs)
super(CosStepScheduler, self).__init__(optimizer, last_epoch)
def _build_lr(self, start_lr, end_lr, epochs):
index = np.arange(epochs).astype(np.float32)
lr = end_lr + (start_lr - end_lr) * (1. + np.cos(index * np.pi/ epochs)) * 0.5
return lr.astype(np.float32)
class WarmUPScheduler(LRScheduler):
def __init__(self, optimizer, warmup, normal, epochs=50, last_epoch=-1):
warmup = warmup.lr_spaces # [::-1]
normal = normal.lr_spaces
self.lr_spaces = np.concatenate([warmup, normal])
self.start_lr = normal[0]
super(WarmUPScheduler, self).__init__(optimizer, last_epoch)
LRs = {
'log': LogScheduler,
'step': StepScheduler,
'multi-step': MultiStepScheduler,
'linear': LinearStepScheduler,
'cos': CosStepScheduler}
def _build_lr_scheduler(optimizer, cfg, epochs=50, last_epoch=-1):
if 'type' not in cfg:
# return LogScheduler(optimizer, last_epoch=last_epoch, epochs=epochs)
cfg['type'] = 'log'
if cfg['type'] not in LRs:
raise Exception('Unknown type of LR Scheduler "%s"'%cfg['type'])
return LRs[cfg['type']](optimizer, last_epoch=last_epoch, epochs=epochs, **cfg)
def _build_warm_up_scheduler(optimizer, cfg, epochs=50, last_epoch=-1):
warmup_epoch = cfg['warmup']['epoch']
sc1 = _build_lr_scheduler(optimizer, cfg['warmup'], warmup_epoch, last_epoch)
sc2 = _build_lr_scheduler(optimizer, cfg, epochs - warmup_epoch, last_epoch)
return WarmUPScheduler(optimizer, sc1, sc2, epochs, last_epoch)
def build_lr_scheduler(optimizer, cfg, epochs=50, last_epoch=-1):
if 'warmup' in cfg:
return _build_warm_up_scheduler(optimizer, cfg, epochs, last_epoch)
else:
return _build_lr_scheduler(optimizer, cfg, epochs, last_epoch)
if __name__ == '__main__':
import torch.nn as nn
from torch.optim import SGD
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv = nn.Conv2d(10, 10, kernel_size=3)
net = Net().parameters()
optimizer = SGD(net, lr=0.01)
# test1
step = {
'type': 'step',
'start_lr': 0.01,
'step': 10,
'mult': 0.1
}
lr = build_lr_scheduler(optimizer, step)
print(lr)
log = {
'type': 'log',
'start_lr': 0.03,
'end_lr': 5e-4,
}
lr = build_lr_scheduler(optimizer, log)
print(lr)
log = {
'type': 'multi-step',
"start_lr": 0.01,
"mult": 0.1,
"steps": [10, 15, 20]
}
lr = build_lr_scheduler(optimizer, log)
print(lr)
cos = {
"type": 'cos',
'start_lr': 0.01,
'end_lr': 0.0005,
}
lr = build_lr_scheduler(optimizer, cos)
print(lr)
step = {
'type': 'step',
'start_lr': 0.001,
'end_lr': 0.03,
'step': 1,
}
warmup = log.copy()
warmup['warmup'] = step
warmup['warmup']['epoch'] = 5
lr = build_lr_scheduler(optimizer, warmup, epochs=55)
print(lr)
lr.step()
print(lr.last_epoch)
lr.step(5)
print(lr.last_epoch)
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