argparse是深度学习项目调参时常用的python标准库,使用argparse后,我们在命令行输入的参数就可以以这种形式python filename.py --lr 1e-4 --batch_size 32来完成对常见超参数的设置。,一般使用时可以归纳为以下三个步骤
import argparse parser = argparse.ArgumentParser() # 创建一个解析对象 parser.add_argument() # 向该对象中添加你要关注的命令行参数和选项 args = parser.parse_args() # 调用parse_args()方法进行解析
为了使代码更加简洁和模块化,可以将有关超参数的操作写在config.py,然后在train.py或者其他文件导入就可以。具体的config.py可以参考如下内容。
import argparse def get_options(parser=argparse.ArgumentParser()): parser.add_argument('--workers', type=int, default=0, help='number of data loading workers, you had better put it ' '4 times of your gpu') parser.add_argument('--batch_size', type=int, default=4, help='input batch size, default=64') parser.add_argument('--niter', type=int, default=10, help='number of epochs to train for, default=10') parser.add_argument('--lr', type=float, default=3e-5, help='select the learning rate, default=1e-3') parser.add_argument('--seed', type=int, default=118, help="random seed") parser.add_argument('--cuda', action='store_true', default=True, help='enables cuda') parser.add_argument('--checkpoint_path',type=str,default='', help='Path to load a previous trained model if not empty (default empty)') parser.add_argument('--output',action='store_true',default=True,help="shows output") opt = parser.parse_args() if opt.output: print(f'num_workers: {opt.workers}') print(f'batch_size: {opt.batch_size}') print(f'epochs (niters) : {opt.niter}') print(f'learning rate : {opt.lr}') print(f'manual_seed: {opt.seed}') print(f'cuda enable: {opt.cuda}') print(f'checkpoint_path: {opt.checkpoint_path}') return opt if __name__ == '__main__': opt = get_options()
$ python config.py num_workers: 0 batch_size: 4 epochs (niters) : 10 learning rate : 3e-05 manual_seed: 118 cuda enable: True checkpoint_path:
随后在train.py等其他文件,我们就可以使用下面的这样的结构来调用参数。
# 导入必要库 ... import config opt = config.get_options() manual_seed = opt.seed num_workers = opt.workers batch_size = opt.batch_size lr = opt.lr niters = opt.niters checkpoint_path = opt.checkpoint_path # 随机数的设置,保证复现结果 def set_seed(seed): torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) random.seed(seed) np.random.seed(seed) torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True ... if __name__ == '__main__': set_seed(manual_seed) for epoch in range(niters): train(model,lr,batch_size,num_workers,checkpoint_path) val(model,lr,batch_size,num_workers,checkpoint_path)
# test.py import argparse if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument("--test_action", action='store_true') args = parser.parse_args() action_val = args.test_action print(action_val)
以上面的代码为例,若触发 test_action,则为 True, 否则为 False:
若在上面的代码中加入default,设为 False 时:
parser.add_argument("--test_action", default='False', action='store_true')
default 设为 True 时:
parser.add_argument("--test_action", default='True', action='store_true')
参考:https: