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', required=True, help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
          parser.add_argument('--batch_size', type=int, default=1, help='input batch size')
          # Specify width and height for load and fine sizes
          parser.add_argument('--loadSizeW', type=int, default=220, help='scale images to this size (width)')
          parser.add_argument('--loadSizeH', type=int, default=220, help='scale images to this size (height)')
          parser.add_argument('--fineSizeW', type=int, default=200, help='then crop to this size (width)')
          parser.add_argument('--fineSizeH', type=int, default=200, help='then crop to this size (height)')
          # parser.add_argument('--loadSize', type=int, default=286, help='scale images to this size')
          # parser.add_argument('--fineSize', type=int, default=256, help='then crop to this size')
          parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML')
          parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels')
          parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels')
          parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in first conv layer')
          parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer')
          # Use set generator and set discriminator as default architectures
          parser.add_argument('--netD', type=str, default='set', help='selects model to use for netD')
          parser.add_argument('--netG', type=str, default='set', help='selects model to use for netG')
          parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers')
          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='experiment_name', help='name of the experiment. It decides where to store samples and models')
          # Use unaligned_seg as a default dataset_mode
          parser.add_argument('--dataset_mode', type=str, default='unaligned_seg', help='chooses how datasets are loaded. [unaligned | aligned | single]')
          # Use insta_gan as a default model
          parser.add_argument('--model', type=str, default='insta_gan', help='chooses which model to use. cycle_gan, pix2pix, test')
          parser.add_argument('--direction', type=str, default='AtoB', help='AtoB or BtoA')
          parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
          parser.add_argument('--load_iter', type=int, default='0', help='which iteration to load? if load_iter > 0, the code will load models by iter_[load_iter]; otherwise, the code will load models by [epoch]')
          parser.add_argument('--num_threads', default=4, 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('--no_dropout', action='store_true', help='no dropout for the generator')
          parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
          parser.add_argument('--resize_or_crop', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop|none]')
          parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation')
          parser.add_argument('--init_type', type=str, default='normal', 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('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{loadSize}')
          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_mode
          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
  
          # process opt.suffix
          if opt.suffix:
              suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else ''
              opt.name = opt.name + suffix
  
          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
  