def adjust_learning_rate(optimizer,
epoch,
i_iter,
iters_per_epoch,
method='poly'):
if method == 'poly':
current_step = epoch * iters_per_epoch + i_iter
max_step = args.epochs * iters_per_epoch
# poly
lr = args.learning_rate * ((1 - current_step / max_step)**0.9)
# if epoch>args.epochs*0.9:
# lr = 0.1*args.learning_rate
elif method == 'cosine':
lr = cosine_decay(base_learning_rate=args.learning_rate,
global_step=epoch * iters_per_epoch + i_iter,
warm_step=10 * iters_per_epoch,
decay_steps=args.epochs * iters_per_epoch,
alpha=0.0001)
elif method == 'restart_cosine':
lr = restart_cosine_decay(base_learning_rate=args.learning_rate,
global_step=epoch * iters_per_epoch + i_iter,
warm_step=10 * iters_per_epoch,
decay_steps=args.epochs * iters_per_epoch,
alpha=0.0001)
else:
lr = args.learning_rate
optimizer.param_groups[0]['lr'] = lr
return lr
def adjust_learning_rate(optimizer, epoch, i_iter, iters_per_epoch, method='poly'):
if method == 'poly':
current_step = epoch * iters_per_epoch + i_iter
max_step = args.epochs * iters_per_epoch
lr = args.learning_rate * ((1 - current_step / max_step) ** 0.9)
elif method == 'cosine':
warm_step = 5 * iters_per_epoch
warm_lr = 0.001*args.learning_rate
decay_steps = 10 * iters_per_epoch
lr = cosine_decay(learning_rate=args.learning_rate, global_step=epoch * iters_per_epoch + i_iter,
warm_step=warm_step, warm_lr= warm_lr,
decay_steps=decay_steps, alpha=0.0001)
elif method == 'restart_cosine':
warm_step = 5 * iters_per_epoch
warm_lr = 0.001 * args.learning_rate
decay_steps = 10 * iters_per_epoch
lr = restart_cosine_decay(learning_rate=args.learning_rate, global_step=epoch * iters_per_epoch + i_iter,
warm_step=warm_step, warm_lr=warm_lr,
decay_steps=decay_steps, alpha=0.0001)
else:
lr = args.learning_rate
optimizer.param_groups[0]['lr'] = lr
return lr
