text: import os
  import argparse
  import importlib.util
  
  import torch
  from isegm.utils.exp import init_experiment
  
  
  def main():
      args = parse_args()
      if args.temp_model_path:
          model_script = load_module(args.temp_model_path)
      else:
          model_script = load_module(args.model_path)
  
      model_base_name = getattr(model_script, 'MODEL_NAME', None)
  
      args.distributed = 'WORLD_SIZE' in os.environ
      cfg = init_experiment(args, model_base_name)
  
      torch.backends.cudnn.benchmark = True
      torch.multiprocessing.set_sharing_strategy('file_system')
  
      model_script.main(cfg)
  
  
  def parse_args():
      parser = argparse.ArgumentParser()
  
      parser.add_argument('model_path', type=str,
                          help='Path to the model script.')
  
      parser.add_argument('--exp-name', type=str, default='',
                          help='Here you can specify the name of the experiment. '
                               'It will be added as a suffix to the experiment folder.')
  
      parser.add_argument('--workers', type=int, default=4,
                          metavar='N', help='Dataloader threads.')
  
      parser.add_argument('--batch-size', type=int, default=-1,
                          help='You can override model batch size by specify positive number.')
  
      parser.add_argument('--ngpus', type=int, default=1,
                          help='Number of GPUs. '
                               'If you only specify "--gpus" argument, the ngpus value will be calculated automatically. '
                               'You should use either this argument or "--gpus".')
  
      parser.add_argument('--gpus', type=str, default='', required=False,
                          help='Ids of used GPUs. You should use either this argument or "--ngpus".')
  
      parser.add_argument('--resume-exp', type=str, default=None,
                          help='The prefix of the name of the experiment to be continued. '
                               'If you use this field, you must specify the "--resume-prefix" argument.')
  
      parser.add_argument('--resume-prefix', type=str, default='latest',
                          help='The prefix of the name of the checkpoint to be loaded.')
  
      parser.add_argument('--start-epoch', type=int, default=0,
                          help='The number of the starting epoch from which training will continue. '
                               '(it is important for correct logging and learning rate)')
  
      parser.add_argument('--weights', type=str, default=None,
                          help='Model weights will be loaded from the specified path if you use this argument.')
  
      parser.add_argument('--temp-model-path', type=str, default='',
                          help='Do not use this argument (for internal purposes).')
  
      parser.add_argument("--local_rank", type=int, default=0)
  
      return parser.parse_args()
  
  
  def load_module(script_path):
      spec = importlib.util.spec_from_file_location("model_script", script_path)
      model_script = importlib.util.module_from_spec(spec)
      spec.loader.exec_module(model_script)
  
      return model_script
  
  
  if __name__ == '__main__':
      main()