options.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6

import argparse

def args_parser():
    parser = argparse.ArgumentParser()
    # federated arguments
    parser.add_argument('--epochs', type=int, default=1, help="rounds of training")
    parser.add_argument('--num_users', type=int, default=100, help="number of users: K")
    parser.add_argument('--frac', type=float, default=0.1, help='the fraction of clients: C')
    parser.add_argument('--local_ep', type=int, default=5, help="the number of local epochs: E")
    parser.add_argument('--local_bs', type=int, default=10, help="local batch size: B")
    parser.add_argument('--lr', type=float, default=0.0001, help='learning rate')

    # model arguments
    parser.add_argument('--model', type=str, default='mlp', help='model name')
    parser.add_argument('--kernel_num', type=int, default=9, help='number of each kind of kernel')
    parser.add_argument('--kernel_sizes', type=str, default='3,4,5',
                        help='comma-separated kernel size to use for convolution')
    parser.add_argument('--norm', type=str, default='batch_norm', help="batch_norm, layer_norm, or None")
    parser.add_argument('--num_filters', type=int, default=32,
                        help="number of filters for conv nets -- 32 for miniimagenet, 64 for omiglot.")
    parser.add_argument('--max_pool', type=str, default='True',
                        help="Whether use max pooling rather than strided convolutions")

    # other arguments
    parser.add_argument('--dataset', type=str, default='mnist', help="name of dataset")
    parser.add_argument('--iid', type=int, default=1, help='whether i.i.d or not, 1 for iid, 0 for non-iid')
    parser.add_argument('--num_classes', type=int, default=10, help="number of classes")
    parser.add_argument('--verbose', action='store_true', default=False, help="watch metrics during training and testing")
    parser.add_argument('--gpu', type=int, default=1, help="GPU ID")
    parser.add_argument('--stopping_rounds', type=int, default=10, help='rounds of early stopping')

    args = parser.parse_args()
    return args