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##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang
## ECE Department, Rutgers University
## Email: zhang.hang@rutgers.edu
## Copyright (c) 2017
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
import math
class LR_Scheduler(object):
"""Learning Rate Scheduler
Step mode: ``lr = baselr * 0.1 ^ {floor(epoch-1 / lr_step)}``
Cosine mode: ``lr = baselr * 0.5 * (1 + cos(iter/maxiter))``
Poly mode: ``lr = baselr * (1 - iter/maxiter) ^ 0.9``
Args:
args: :attr:`args.lr_scheduler` lr scheduler mode (`cos`, `poly`),
:attr:`args.lr` base learning rate, :attr:`args.epochs` number of epochs,
:attr:`args.lr_step`
iters_per_epoch: number of iterations per epoch
"""
def __init__(self, mode, base_lr, num_epochs, iters_per_epoch=0,
lr_step=0, warmup_epochs=0):
self.mode = mode
print('Using {} LR Scheduler!'.format(self.mode))
self.lr = base_lr
if mode == 'step':
assert lr_step
self.lr_step = lr_step
self.iters_per_epoch = iters_per_epoch
self.N = num_epochs * iters_per_epoch
self.epoch = -1
self.warmup_iters = warmup_epochs * iters_per_epoch
def __call__(self, optimizer, i, epoch, best_pred):
T = epoch * self.iters_per_epoch + i
if self.mode == 'cos':
lr = 0.5 * self.lr * (1 + math.cos(1.0 * T / self.N * math.pi))
elif self.mode == 'poly':
lr = self.lr * pow((1 - 1.0 * T / self.N), 0.9)
elif self.mode == 'step':
lr = self.lr * (0.1 ** (epoch // self.lr_step))
else:
raise NotImplemented
# warm up lr schedule
if self.warmup_iters > 0 and T < self.warmup_iters:
lr = lr * 1.0 * T / self.warmup_iters
if epoch > self.epoch:
print('\n=>Epoches %i, learning rate = %.7f, \
previous best = %.4f' % (epoch+1, lr, best_pred))
self.epoch = epoch
assert lr >= 0
self._adjust_learning_rate(optimizer, lr)
def _adjust_learning_rate(self, optimizer, lr):
if len(optimizer.param_groups) == 1:
optimizer.param_groups[0]['lr'] = lr
else:
# enlarge the lr at the head
for i in range(len(optimizer.param_groups)):
if optimizer.param_groups[i]['lr'] > 0: optimizer.param_groups[i]['lr'] = lr
# optimizer.param_groups[0]['lr'] = lr
# for i in range(1, len(optimizer.param_groups)):
# optimizer.param_groups[i]['lr'] = lr * 10
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