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
  
  def norm(tensor):
      r = tensor.max() - tensor.min()
      tensor = (tensor - tensor.min())/r
      return tensor
  
  def mxAxis(tensor):
      _, indices = torch.max(tensor, 0)
      return indices
  
  
  if __name__ == '__main__':
      opt = TestOptions().parse()
      opt.nThreads = 0   # test code only supports nThreads = 1
      opt.batchSize = 1  # test code only supports batchSize = 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 website
      web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch))
      webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.which_epoch))
      # test
  
      for i, data in enumerate(dataset):
  
          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 %04d' % (i, len(dataset)))
          # import pdb; pdb.set_trace()
          # for i in range(opt.batchSize):
          #     vs2 = {}
          #     for k, v in visuals.items():
          #         vs2[k] = v
          #     save_images(webpage, vs2, img_path[i], aspect_ratio=opt.aspect_ratio, width=opt.display_winsize)
          save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize)
  
      webpage.save()
  