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
def adjust_learning_rate(optimizer, epoch, step_in_epoch, total_steps_in_epoch,
args):
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) * (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
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
if args.lr_rampdown_epochs:
if epoch < args.epochs:
# Cosine LR rampdown from https://arxiv.org/abs/1608.03983
assert args.lr_rampdown_epochs >= args.epochs
lr *= ramps.cosine_rampdown(epoch, args.lr_rampdown_epochs)
elif epoch >= args.epochs:
if args.constant_lr:
constant_lr = ramps.cosine_rampdown(args.constant_lr_epoch, args.lr_rampdown_epochs)
lr *= constant_lr
else:
lr_rampdown_epochs = args.lr_rampdown_epochs if args.cycle_rampdown_epochs == 0 else args.cycle_rampdown_epochs
lr *= ramps.cosine_rampdown((lr_rampdown_epochs - (args.lr_rampdown_epochs - args.epochs) - args.cycle_interval) + ((epoch - args.epochs) % args.cycle_interval),
lr_rampdown_epochs)
for param_group in optimizer.param_groups:
param_group['lr'] = lr