def adjust_learning_rate()

in aiops/Pathformer_ICLR2024/utils/tools.py [0:0]

• 31 lines of code
• 19 McCabe index (conditional complexity)

def adjust_learning_rate(optimizer,scheduler, epoch, args, printout=True):
    # lr = args.learning_rate * (0.2 ** (epoch // 2))
    if args.lradj == 'type1':
        lr_adjust = {epoch: args.learning_rate * (0.5 ** ((epoch - 1) // 1))}
    elif args.lradj == 'type2':
        lr_adjust = {epoch: args.learning_rate * (0.5 ** ((epoch - 1) // 2))}
    elif args.lradj == 'type3':
        lr_adjust = {epoch: args.learning_rate * (0.5 ** ((epoch - 1) // 3))}
    elif args.lradj == 'type4':
        lr_adjust = {epoch: args.learning_rate * (0.5 ** ((epoch - 1) // 4))}
    elif args.lradj == 'type5':
        lr_adjust = {
            2: 5e-5, 4: 1e-5, 6: 5e-6, 8: 1e-6,
            10: 5e-7, 15: 1e-7, 20: 5e-8
        }
    elif args.lradj == 'constant':
        lr_adjust = {epoch: args.learning_rate}
    elif args.lradj == '3':
        lr_adjust = {epoch: args.learning_rate if epoch < 10 else args.learning_rate*0.1}
    elif args.lradj == '4':
        lr_adjust = {epoch: args.learning_rate if epoch < 15 else args.learning_rate*0.1}
    elif args.lradj == '5':
        lr_adjust = {epoch: args.learning_rate if epoch < 25 else args.learning_rate*0.1}
    elif args.lradj == '6':
        lr_adjust = {epoch: args.learning_rate if epoch < 5 else args.learning_rate*0.1}
    elif args.lradj == 'TST':
        lr_adjust = {epoch: scheduler.get_last_lr()[0]}
    if epoch in lr_adjust.keys():
        lr = lr_adjust[epoch]
        for param_group in optimizer.param_groups:
            param_group['lr'] = lr
        if printout: print('Updating learning rate to {}'.format(lr))