一、argparse模块用法

1、argparse是一个python模块，用途是：命令行选项、参数和子命令的解释。

2、argparse库下载：pip install argparse

3、使用步骤：
导入argparse模块，并创建解释器
添加所需参数
解析参数

二、代码

import argparse


def add_common_arguments(parser):
    """Add common arguments for training and inference."""
    parser.add_argument('--save_best_weights',
                        default='model_data/best.pth',
                        help="save best weights name.")
    parser.add_argument('--phi', type=str, default='s')
    parser.add_argument('--num_classes', type=int, default=10)

def get_parser_for_training():
    """Return argument parser for training."""
    # -------------------------------------------#
    #   Step 1. 构造解析器 argparse.ArgumentParser()
    # -------------------------------------------#
    parser = argparse.ArgumentParser("Training args")
    # -------------------------------------------#
    #   Step 2. 添加参数 .add_argument()
    # -------------------------------------------#
    parser.add_argument('--train_path',default='/data/train',help="The location of dataset.")
    parser.add_argument('--sync_bn', type=bool,default=False,help='use SyncBatchNorm, only available in DDP mode')
    parser.add_argument('--Cuda', type=bool,default=True)
    parser.add_argument('--fp16', type=bool,default=False)
    parser.add_argument('--num_workers', type=int, default=8,help="Number of workers for data loading.")
    parser.add_argument('--Total_epoch', type=int, default=300,help='Total Epoch')
    parser.add_argument('--Batch_size', type=int, default=64,help='Batch_size')
    # -------------------------------------------#
    #   Step 2. 添加参数 .add_argument()
    # -------------------------------------------#
    add_common_arguments(parser)
    return parser


if __name__=='__main__':
    # -------------------------------------------#
    #   Step 3. 解析参数 .parse_args()
    # -------------------------------------------#
    train_parser = get_parser_for_training()
    train_args = train_parser.parse_args()
    print(train_args)
    # -------------------------------------------#
    #   training args
    # -------------------------------------------#
    print("training data path:",train_args.train_path)
    print("training batch size:",train_args.Batch_size)
    print("Cuda:",train_args.Cuda)
    # -------------------------------------------#
    #   common args
    # -------------------------------------------#
    print("num classes:",train_args.num_classes)
    print("phi:",train_args.phi)
    print("save model path:",train_args.save_best_weights)

运行结果

用命令行查看parser的所有参数选项

用命令行修改parser的特定参数