text: import argparse
  import os
  from util import util
  import torch
  
  class BaseOptions():
  	def __init__(self):
  		self.parser = argparse.ArgumentParser()
  		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('--loadSizeX', type=int, default=360, help='scale images to this size')
  		self.parser.add_argument('--loadSizeY', type=int, default=640, 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('--learn_residual', action='store_true', help='if specified, model would learn only the residual to the input')
  		self.parser.add_argument('--gan_type', type=str, default='wgan-gp', help='wgan-gp : Wasserstein GAN with Gradient Penalty, lsgan : Least Sqaures GAN, gan : Vanilla GAN')
  		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='0', 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='aligned', help='chooses how datasets are loaded. [unaligned | aligned | single]')
  		self.parser.add_argument('--model', type=str, default='content_gan', help='chooses which model to use. pix2pix, test, content_gan')
  		self.parser.add_argument('--which_direction', type=str, default='AtoB', help='AtoB or BtoA')
  		self.parser.add_argument('--nThreads', default=2, type=int, 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')
  		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('--display_single_pane_ncols', type=int, default=0, help='if positive, display all images in a single visdom web panel with certain number of images per row.')
  		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 dataset directory contains more than max_dataset_size, only a subset is loaded.')
  		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.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
  
  		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 ----------------')
  
  		# save to the disk
  		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
  