text: import os
  from options.test_options import TestOptions
  from data import CreateDataLoader
  from models import create_model
  from util.visualizer import save_images
  from util import html
  
  
  if __name__ == '__main__':
      opt = TestOptions().parse()
      # hard-code some parameters for test
      opt.num_threads = 1   # test code only supports num_threads = 1
      opt.batch_size = 1    # test code only supports batch_size = 1
      opt.serial_batches = True  # no shuffle
      opt.no_flip = True    # no flip
      opt.display_id = -1   # no visdom display
      data_loader = CreateDataLoader(opt)
      dataset = data_loader.load_data()
      model = create_model(opt)
      model.setup(opt)
      # create a website
      web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.epoch))
      webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.epoch))
      # test with eval mode. This only affects layers like batchnorm and dropout.
      # pix2pix: we use batchnorm and dropout in the original pix2pix. You can experiment it with and without eval() mode.
      # CycleGAN: It should not affect CycleGAN as CycleGAN uses instancenorm without dropout.
      if opt.eval:
          model.eval()
      for i, data in enumerate(dataset):
          if i >= opt.num_test:
              break
          model.set_input(data)
          model.test()
          visuals = model.get_current_visuals()
          img_path = model.get_image_paths()
          if i % 5 == 0:
              print('processing (%04d)-th image... %s' % (i, img_path))
          save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize)
      # save the website
      webpage.save()
  