def adjust_learning_rate(optimizer, epoch, step_in_epoch, total_steps_in_epoch,
args):
"""decrease the learning rate at 100 and 150 epoch"""
lr = args.lr
epoch = epoch + step_in_epoch / total_steps_in_epoch
# LR warm-up to handle large minibatch sizes from https://arxiv.org/abs/1706.02677
#lr = ramps.linear_rampup(epoch, args.lr_rampup) * (args.lr - args.initial_lr) + args.initial_lr
# Cosine LR rampdown from https://arxiv.org/abs/1608.03983 (but one cycle only)
#"""
if not args.step_lr:
lr *= ramps.cosine_rampdown(epoch, args.epochs)
#"""
#MultiStep LR
else:
#if epoch >= 150:
# lr /= 10
#if epoch >= 250:
# lr /= 10
for milestone in args.schedule:
lr *= 0.1 if epoch >= milestone else 1.
#"""
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def adjust_learning_rate(self, optimizer, lr, ini_lr, epoch):
# LR warm-up to handle large minibatch sizes from https://arxiv.org/abs/1706.02677
lr = ramps.linear_rampup(
epoch, self.config.lr_rampup) * (lr - ini_lr) + ini_lr
# Cosine LR rampdown from https://arxiv.org/abs/1608.03983 (but one cycle only)
if self.config.lr_rampdn:
assert self.config.lr_rampdn >= self.config.max_epochs
lr *= ramps.cosine_rampdown(epoch, self.config.lr_rampdn)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def adjust_learning_rate(optimizer, epoch, step_in_epoch,
total_steps_in_epoch):
lr = args.lr
epoch = epoch + step_in_epoch / total_steps_in_epoch
lr = ramps.linear_rampup(
epoch, args.lr_rampup) * (args.lr - args.initial_lr) + args.initial_lr
if args.lr_rampdown_epochs:
assert args.lr_rampdown_epochs >= args.epochs
lr *= ramps.cosine_rampdown(epoch, args.lr_rampdown_epochs)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def adjust_learning_rate(optimizer, epoch, step_in_epoch,
total_steps_in_epoch):
lr = args.lr
epoch = epoch + step_in_epoch / total_steps_in_epoch
# LR warm-up to handle large minibatch sizes from https://arxiv.org/abs/1706.02677
lr = ramps.linear_rampup(
epoch, args.lr_rampup) * (args.lr - args.initial_lr) + args.initial_lr
# Cosine LR rampdown from https://arxiv.org/abs/1608.03983 (but one cycle only)
if args.lr_rampdown_epochs:
assert args.lr_rampdown_epochs >= args.epochs
lr *= ramps.cosine_rampdown(epoch, args.lr_rampdown_epochs)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return lr