text: import argparse
  import os
  from util import util
  import torch
  
  
  class BaseOptions():
      def __init__(self):
          self.parser = argparse.ArgumentParser(
                  formatter_class=argparse.ArgumentDefaultsHelpFormatter)
          self.initialized = False
  
      def initialize(self):
          self.parser.add_argument(
                  '--dataroot',
                  required=True,
                  help=
                  'path to images (should have subfolders trainA, trainB, valA, valB, etc)'
                  )
          self.parser.add_argument(
                  '--batchSize', type=int, default=1, help='input batch size')
          self.parser.add_argument(
                  '--loadSize',
                  type=int,
                  default=286,
                  help='scale images to this size')
          self.parser.add_argument(
                  '--fineSize', type=int, default=256, help='then crop to this size')
          self.parser.add_argument(
                  '--input_nc',
                  type=int,
                  default=3,
                  help='# of input image channels')
          self.parser.add_argument(
                  '--output_nc',
                  type=int,
                  default=3,
                  help='# of output image channels')
          self.parser.add_argument(
                  '--ngf',
                  type=int,
                  default=64,
                  help='# of gen filters in first conv layer')
          self.parser.add_argument(
                  '--ndf',
                  type=int,
                  default=64,
                  help='# of discrim filters in first conv layer')
          self.parser.add_argument(
                  '--which_model_netD',
                  type=str,
                  default='basic',
                  help='selects model to use for netD')
          self.parser.add_argument(
                  '--which_model_netG',
                  type=str,
                  default='resnet_9blocks',
                  help='selects model to use for netG')
          self.parser.add_argument(
                  '--n_layers_D',
                  type=int,
                  default=3,
                  help='only used if which_model_netD==n_layers')
          self.parser.add_argument(
                  '--gpu_ids',
                  type=str,
                  default='-1',
                  help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')
          self.parser.add_argument(
                  '--name',
                  type=str,
                  default='experiment_name',
                  help=
                  'name of the experiment. It decides where to store samples and models'
                  )
          self.parser.add_argument(
                  '--dataset_mode',
                  type=str,
                  default='unaligned',
                  help=
                  'chooses how datasets are loaded. [voxel | slice | aligned | unaligned | single]'
                  )
          self.parser.add_argument(
                  '--model',
                  type=str,
                  default='pix2pix',
                  help='chooses which model to use. cycle_gan, pix2pix,vox2vox, test'
                  )
          self.parser.add_argument(
                  '--which_direction', type=str, default='AtoB', help='AtoB or BtoA')
          self.parser.add_argument(
                  '--nThreads',
                  type=int,
                  default=2,
                  help='# threads for loading data')
          self.parser.add_argument(
                  '--checkpoints_dir',
                  type=str,
                  default='./checkpoints',
                  help='models are saved here')
          self.parser.add_argument(
                  '--norm',
                  type=str,
                  default='instance',
                  help=
                  'instance normalization or batch normalization [batch | instance | batch_3d | instance_3d]'
                  )
          self.parser.add_argument(
                  '--serial_batches',
                  action='store_true',
                  help=
                  'if true, takes images in order to make batches, otherwise takes them randomly'
                  )
          self.parser.add_argument(
                  '--display_winsize',
                  type=int,
                  default=256,
                  help='display window size')
          self.parser.add_argument(
                  '--display_id',
                  type=int,
                  default=1,
                  help='window id of the web display')
          self.parser.add_argument(
                  '--display_port',
                  type=int,
                  default=8097,
                  help='visdom port of the web display')
          self.parser.add_argument(
                  '--no_dropout',
                  action='store_true',
                  help='no dropout for the generator')
          self.parser.add_argument(
                  '--max_dataset_size',
                  type=int,
                  default=float("inf"),
                  help=
                  'maximum number of samples allowed per dataset. If the directory contains more than the max size, then only a subset is laoded.'
                  )
          self.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]'
                  )
          self.parser.add_argument(
                  '--no_flip',
                  action='store_true',
                  help='if specified, do not flip the images for data augmentation')
          self.parser.add_argument(
                  '--init_type',
                  type=str,
                  default='normal',
                  help='network initialization [normal|xavier|kaiming|orthogonal]')
          self.parser.add_argument(
                  '--no_img',
                  action='store_true',
                  help=
                  'if specified, do not convert 1 channel to 3 channel output.')
  
          self.initialized = True
  
      def parse(self):
          if not self.initialized:
              self.initialize()
          self.opt = self.parser.parse_args()
          self.opt.isTrain = self.isTrain  # train or test
          self.opt.isOptim = self.isOptim
  
          str_ids = self.opt.gpu_ids.split(',')
          self.opt.gpu_ids = []
          for str_id in str_ids:
              id = int(str_id)
              if id >= 0:
                  self.opt.gpu_ids.append(id)
  
          # set gpu ids
          if len(self.opt.gpu_ids) > 0:
              torch.cuda.set_device(self.opt.gpu_ids[0])
  
          args = vars(self.opt)
  
          print('------------ Options -------------')
          for k, v in sorted(args.items()):
              print('%s: %s' % (str(k), str(v)))
          print('-------------- End ----------------')
  
          expr_dir = os.path.join(self.opt.checkpoints_dir, self.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('------------ Options -------------\n')
              for k, v in sorted(args.items()):
                  opt_file.write('%s: %s\n' % (str(k), str(v)))
              opt_file.write('-------------- End ----------------\n')
          return self.opt
  