option.py

import argparse
def args_parser():
    parser = argparse.ArgumentParser()
    parser.add_argument('--private_classes',type=list,default=[10,11,12,13,14,15],help='private_classes_list')
    parser.add_argument('--public_classes',type=list,default=[0,1,2,3,4,5,6,7,8,9],help='public_classes_list')
    parser.add_argument('--N_parties',type=int,default=10,help='the number of parties')
    parser.add_argument('--N_samples_per_class',type=int,default=12,help='N_samples_per_class')
    parser.add_argument('--gpu', default='0', type=str, help='set CUDA_VISIBLE_DEVICES')
    parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'])
    parser.add_argument('--parameter-server', default=False)
    parser.add_argument('--run-distribute', action='store_true', help="if set true, this code will be run on distributed architecture with mindspore")
    parser.add_argument('--lr',type=float,default=0.01,help='learning rate')
    parser.add_argument('--output_classes',type=int,default=16,help='set the output points')
    parser.add_argument('--user_number',type=int,default=10,help='number of user join in Federated Learning')
    parser.add_argument('--collaborative_epoch',type=int,default=1,help='collaborative_epoch for train on public mnist')
    parser.add_argument('--Communicationepoch',type=int,default=1,help='Collaobrative epoch in Step3') #30
    parser.add_argument('--Communication_private_epoch',type=int,default=2 ,help='Local private training during colaboratiive time')
    parser.add_argument('--Communication_domain_identifier_epochs',type=int,default=4,help='gan domain identitfer growup')
    parser.add_argument('--Communication_gan_local_epochs',type=int,default=4,help='gan domain identitfer growup')
    args = parser.parse_args(args=[])
    return args