[Error] Jupyter notebook에서 parser 사용 시 SystemExit 에러

나는 PyCharm 환경에서 Jupyter notebook을 사용하고 있다. parser 사용 시 다음과 같은 에러가 발생하였다.

 

 

문제 상황

나는 config.py 파일에 argparser를 사용하여 코드에서 사용할 파라미터들을 정의해두었다. 아래 코드는 중요한 것은 아니고 그저 "argparser를 사용했다"라고만 알아두면 된다.

# config.py
import os
import platform
from argparse import ArgumentParser
import torch


class ModelConfig(object):
    """Wrapper class for model hyperparameters."""
    def __init__(self):
        """
        Defaults
        """
        self.parser = self.setup_parser()
        self.args = vars(self.parser.parse_args())

        self.torch_version = torch.__version__
        self.cuda_version = torch.version.cuda
        self.hostname = platform.node()

        print('\n~~~~~~~~ Hyperparameters: ~~~~~~~~')
        self.__dict__.update(self.args)
        for x, y in self.__dict__.items():
            if x in ['args', 'parser']:
                continue
            print("{} : {}".format(x, y))

        self.steps = list(map(int, self.steps.split('_')))
        assert self.val_size >= 0, self.val_size
        assert self.num_gpus == 1, 'this code does not support multiple gpus.'

        if self.save_dir == '':
            self.save_dir = None
        if self.save_dir is not None and not os.path.exists(self.save_dir):
            os.makedirs(self.save_dir)

        if self.test_bias:
            assert self.use_bias, 'use_bias must be specified in this case.'

        if self.perturb:
            assert self.gan, ('GAN must be used in case of perturbations', self.gan)
            assert 0 <= self.L <= 1, ('perturbation intensity must be greater than 0 and less than 1', self.L)

        self.ganlosses = self.ganlosses.split('_')
        if self.gan:
            assert len(self.ganlosses) > 0, ('at least one GAN loss must be specified to train GAN', self.ganlosses)

        # Set seed everywhere
        # set_seed(self.seed)

        if not os.path.exists(self.data):
            if len(self.data) == 0:
                raise ValueError('data must be a valid path')
            os.mkdir(self.data)

        # Weights and Biases (WANDB) logging tool
        try:
            if self.wandb is None or len(self.wandb) == 0:
                raise ValueError('project name must be specified if you want to use wandb', self.wandb)
            import wandb
            stop_keys = ['args', 'parser']
            wandb.init(name=self.name, dir=self.wandb_dir, project=self.wandb,
                       config=dict(filter(lambda k: k[0] not in stop_keys, self.__dict__.items())), resume=False)

            def wandb_log(d, step, log_repeats=1, is_summary=False, prefix=''):
                for step__ in range(step, step + log_repeats):
                    try:
                        for key, value in d.items():
                            wandb.log({prefix + key: value}, step=step__)
                            if is_summary:
                                wandb.run.summary[prefix + key] = value
                    except Exception as e:
                        print('error logging with wandb:', e)
            self.wandb = wandb_log

        except Exception as e:
            print('\nwarning: Logging using Weights and Biases will not be used:', e)
            self.wandb_log = None

    def data_exists(self):
        return (os.path.exists(os.path.join(self.data, 'visual_genome', 'VG_100K')) and
                os.path.exists(os.path.join(self.data, 'visual_genome', 'standford_filtered')))

    def setup_parser(self):
        """
        Sets up an argument parser
        :return: the argument parser
        """
        parser = ArgumentParser(description='training code')

        parser.add_argument('-data', dest='data', help='path where Visual Genome is located',
                            type=str, default=r'.\data')
        parser.add_argument('-ckpt', dest='ckpt', help='Filename to load from', type=str, default='')
        parser.add_argument('-save_dir', dest='save_dir', help='Directory to save things to, such as checkpoints/save',
                            default=None, type=str)
        parser.add_argument('-notest', dest='notest', help='do not evaluate on the test set after training',
                            action='store_true')
        parser.add_argument('-save_scores', dest='save_scores', help='save test predictions', action='store_true')
        parser.add_argument('-ngpu', dest='num_gpus', help='number of GPUs', type=int, default=1)
        parser.add_argument('-nwork', dest='num_workers', help='num processes to use as workers', type=int, default=2)
        parser.add_argument('-seed', dest='seed', type=int, default=111,
                            help='random seed for model parameters and others')
        parser.add_argument('-device', dest='device', help='cpu/cuda device to use (cpu might be useful for debugging)',
                            type=str, default='cuda')

        # Main learning args
        parser.add_argument('-lr', dest='lr', help='learning rate', type=float, default=1e-3)
        parser.add_argument('-lr_decay', dest='lr_decay', help='learning rate decay factor', type=float, default=0.1)
        parser.add_argument('-steps', dest='steps', help='the epochs after which decay the learning rate', type=str,
                            default='15')
        parser.add_argument('-nepoch', dest='num_epochs', help='Number of epochs to train the model for', type=int,
                            default=20)
        parser.add_argument('-b', dest='batch_size', help='batch size per GPU', type=int, default=6)
        parser.add_argument('-val_size', dest='val_size', help='val size to use (if 0 we wont use val)', type=int,
                            default=5000)
        parser.add_argument('-l2', dest='l2', help='weight decay', type=float, default=1e-4)
        parser.add_argument('-clip', dest='clip', help='gradients will be clipped to have norm less than this',
                            type=float, default=5.0)
        parser.add_argument('-m', dest='mode', help='mode in {sgdet, sgcls, predcls}', type=str, default='sgcls',
                            choices=['sgdet', 'sgcls', 'predcls'])
        parser.add_argument('-use_bias', dest='use_bias', action='store_true', help='Use Frequency Bias')
        parser.add_argument('-test_bias', dest='test_bias', action='store_true', help='Use only Frequency Bias')
        parser.add_argument('-edge_model', dest='edge_model', type=str, default='motifs',
                            choices=['motifs', 'raw_boxes'], help='which box features use for edges')
        parser.add_argument('-pred_weight', dest='pred_weight',
                            help='weight for predicate softmax scores in the evaluation mode', type=float, default=0)

        # SGG losses
        parser.add_argument('-loss', dest='loss', type=str, default='baseline',
                            choices=['baseline', 'dnorm', 'dnorm-fgbg'], help='type of loss for SG prediction')
        parser.add_argument('-gamma', dest='gamma', type=float, default=1.0,
                            help='weight for the relationship loss')
        parser.add_argument('-alpha', dest='alpha', type=float, default=1.0, help='weight for the foreground edges')
        parser.add_argument('-beta', dest='beta', type=float, default=1.0, help='weight for the background edges')
        parser.add_argument('-rels_per_img', dest='rels_per_img', type=int, default=1024,
                            help='the maximum number of edges per image sampled during training')

        # Object detector
        parser.add_argument('-backbone', dest='backbone', type=str, default='vgg16', choices=['vgg16', 'resnet50'],
                            help='backbone used to detect objects and extract object/edge features')

        # Dataset
        parser.add_argument('-min_graph_size', dest='min_graph_size', type=int, default=-1,
                            help='min number of nodes used during training')
        parser.add_argument('-max_graph_size', dest='max_graph_size', type=int, default=-1,
                            help='max number of nodes used during training')
        parser.add_argument('-exclude_left_right', dest='exclude_left_right',
                            help='exclude left/right relationships (for GQA)', action='store_true')

        # Extra args
        parser.add_argument('-p', dest='print_interval', help='print during training', type=int, default=100)
        parser.add_argument('-wandb', dest='wandb', type=str, default=None,
                            help='the name of the weights and biases project (empty to avoid wandb)')
        parser.add_argument('-wandb_dir', dest='wandb_dir',
                            help='directory for wandb logging (can take a lot of space)', type=str, default='./')
        parser.add_argument('-name', dest='name', help='name of the experiment', type=str, default=None)
        parser.add_argument('-debug', dest='debug', action='store_true')

        # GAN args
        parser.add_argument('-gan', dest='gan', action='store_true', help='train with GAN')
        parser.add_argument('-ganlosses', dest='ganlosses', type=str, default='D_G_rec')
        parser.add_argument('-lrG', dest='lrG', type=float, default=0.0001, help='for GAN with Adam optimizer')
        parser.add_argument('-lrD', dest='lrD', type=float, default=0.0004, help='for GAN with Adam optimizer')
        parser.add_argument('-ganw', dest='ganw', type=float, default=5.0, help='weight for the GAN loss terms')
        parser.add_argument('-vis_cond', dest='vis_cond', type=str, default=None,
                            help='condition the GAN on precomputed visual features')
        parser.add_argument('-attachG', dest='attachG', action='store_true',
                            help='update G on the reconstruction error')
        parser.add_argument('-init_embed', dest='init_embed', action='store_true',
                            help='initialize conditioning embeddings with the GloVe ones')
        parser.add_argument('-largeD', dest='largeD', action='store_true', help='use a larger discriminator')
        parser.add_argument('-beta1', dest='beta1', type=float, default=0,
                            help='Adam hyperparameter for GAN, good values for GANs are 0 and 0.5')
        parser.add_argument('-beta2', dest='beta2', type=float, default=0.9,
                            help='Adam hyperparameter for GAN, good values for GANs are 0.9 and 0.999')

        # Perturbation args
        parser.add_argument('-perturb', dest='perturb', type=str, default=None, choices=['rand', 'neigh', 'graphn'],
                            help='perturbation method, None denotes no perturbations')
        parser.add_argument('-L', dest='L', type=float, default=0.2,
                            help='perturbation intensity, 0 denotes no perturbations')
        parser.add_argument('-topk', dest='topk', type=int, default=5,
                            help='number of semantic neighbors to sample from')
        parser.add_argument('-graphn_a', dest='graphn_a', type=int, default=2,
                            help='alpha parameter of GraphN controlling rare vs frequent compositions')
        parser.add_argument('-uniform', dest='uniform', action='store_true',
                            help='sample nodes uniformly instead of by node degree')
        parser.add_argument('-degree_smoothing', dest='degree_smoothing', type=float, default=1.0,
                            help='flatten the degree distribution '
                                 '(ignored if uniform=True)')

        return parser

 

해당 파일을 import해서 jupyter notebook에서 사용해주었다.

from config import ModelConfig
conf = ModelConfig()

 

하지만 아래와 같이 에러가 발생된다.

usage: ipykernel_launcher.py
ipykernel_launcher.py: error: unrecognized arguments: -f C:\Users\lime9\AppData\Roaming\jupyter\runtime\kernel-7725de1e-d565-4061-b69f-75813623fff6.json
SystemExit: 2
C:\Users\lime9\anaconda3\envs\SemCom\lib\site-packages\IPython\core\interactiveshell.py:3561: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.
  warn("To exit: use 'exit', 'quit', or Ctrl-D.", stacklevel=1)

 

 

해결 방법을 서치해도 나오지 않다가 간신히 해결 방법을 알아냈다.

 

 

해결 방법

해결 방법은 매우 간단했다. 함수 parse_args()를 사용하는 부분에 작은 따옴표를 인자로 넣어주면 된다.

args = parser.parse_args('')

 

나의 코드 같은 경우에는 다음과 같이 변경해주었다.

# class ModelConfig().__init__()
self.parser = self.setup_parser()
self.args = vars(self.parser.parse_args(''))

 

간단한 수정으로 문제가 해결되었다!

 

해결된 출력