text: import argparse
  import os
  from util import util
  import torch
  import models
  import data
  
  
  class BaseOptions():
      def __init__(self):
          self.initialized = False
  
      def initialize(self, parser):
          parser.add_argument('--dataroot', type=str, default='examples', help='path to images, should have training, validation and testing')
          parser.add_argument('--batch_size', type=int, default=2, help='input batch size')
          parser.add_argument('--useWidth', type=int, default=1248, help='scale images to this width')
          parser.add_argument('--useHeight', type=int, default=384, help='scale images to this height')
          parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')
          parser.add_argument('--name', type=str, default='kitti', help='name of the experiment. It decides where to store samples and models')
          parser.add_argument('--use_sne', type=bool, default=True, help='chooses if using sne')
          parser.add_argument('--dataset', type=str, default='kitti', help='chooses which dataset to load.')
          parser.add_argument('--model', type=str, default='roadseg', help='chooses which model to use.')
          parser.add_argument('--epoch', type=str, default='kitti', help='chooses which epoch to load')
          parser.add_argument('--num_threads', default=8, type=int, help='# threads for loading data')
          parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
          parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization')
          parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
          parser.add_argument('--init_type', type=str, default='kaiming', help='network initialization [normal|xavier|kaiming|orthogonal]')
          parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.')
          parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information')
          parser.add_argument('--seed', type=int, default=0, help='seed for random generators')
          self.initialized = True
          return parser
  
      def gather_options(self):
          # initialize parser with basic options
          if not self.initialized:
              parser = argparse.ArgumentParser(
                  formatter_class=argparse.ArgumentDefaultsHelpFormatter)
              parser = self.initialize(parser)
  
          # get the basic options
          opt, _ = parser.parse_known_args()
  
          # modify model-related parser options
          model_name = opt.model
          model_option_setter = models.get_option_setter(model_name)
          parser = model_option_setter(parser, self.isTrain)
          opt, _ = parser.parse_known_args()  # parse again with the new defaults
  
          # modify dataset-related parser options
          dataset_name = opt.dataset
          dataset_option_setter = data.get_option_setter(dataset_name)
          parser = dataset_option_setter(parser, self.isTrain)
  
          self.parser = parser
  
          return parser.parse_args()
  
      def print_options(self, opt):
          message = ''
          message += '----------------- Options ---------------\n'
          for k, v in sorted(vars(opt).items()):
              comment = ''
              default = self.parser.get_default(k)
              if v != default:
                  comment = '\t[default: %s]' % str(default)
              message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
          message += '----------------- End -------------------'
          print(message)
  
          # save to the disk
          expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
          util.mkdirs(expr_dir)
          file_name = os.path.join(expr_dir, 'opt.txt')
          with open(file_name, 'wt') as opt_file:
              opt_file.write(message)
              opt_file.write('\n')
  
      def parse(self):
          opt = self.gather_options()
          opt.isTrain = self.isTrain   # train or test
  
          self.print_options(opt)
  
          # set gpu ids
          str_ids = opt.gpu_ids.split(',')
          opt.gpu_ids = []
          for str_id in str_ids:
              id = int(str_id)
              if id >= 0:
                  opt.gpu_ids.append(id)
          if len(opt.gpu_ids) > 0:
              torch.cuda.set_device(opt.gpu_ids[0])
  
          self.opt = opt
          return self.opt
  