text: """General-purpose test script for image-to-image translation.
  
  Once you have trained your model with train.py, you can use this script to test the model.
  It will load a saved model from '--checkpoints_dir' and save the results to '--results_dir'.
  
  It first creates model and dataset given the option. It will hard-code some parameters.
  It then runs inference for '--num_test' images and save results to an HTML file.
  The results will be saved at ./results/.
  Use '--results_dir <directory_path_to_save_result>' to specify the results directory.
  
  Test a pix2pix model:
      python test.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix
  """
  import os
  from options.test_options import TestOptions
  from data import create_dataset
  from models import create_model
  from util.evaluation import ExcelEvaluate
  from util.visualizer import save_images, save_nifti_images, save_web_nifti
  from util import html_handler
  from util.util import postprocess_images, print_timestamped
  import time
  
  if __name__ == '__main__':
      opt = TestOptions().parse()  # get test options
      # hard-code some parameters for test
      opt.num_threads = 0  # test code only supports num_threads = 0
      opt.batch_size = 1  # test code only supports batch_size = 1
      opt.serial_batches = True  # disable data shuffling;
      # comment this line if results on randomly chosen images are needed.
      opt.no_flip = True  # no flip; comment this line if results on flipped images are needed.
      opt.display_id = -1  # no visdom display; the test code saves the results to a HTML file.
      dataset = create_dataset(opt)  # create a dataset given opt.dataset_mode and other options
      model = create_model(opt)  # create a model given opt.model and other options
      model.setup(opt)  # regular setup: load and print networks; create schedulers
      # Determine whether we are using nifti images
      nifti = True if opt.dataset_mode == "nifti" else False
      # create a website
      exc_eval = None
      if nifti:
          web_dir = os.path.join(opt.results_dir, opt.name,
                                 '{}_{}_{}'.format(opt.phase, opt.epoch, opt.postprocess))  # define the website directory
          excel_filename = os.path.join(web_dir, (opt.phase + "_" + str(opt.name) + ".csv"))
          exc_eval = ExcelEvaluate(excel_filename, opt.excel)
      else:
          web_dir = os.path.join(opt.results_dir, opt.name,
                                 '{}_{}'.format(opt.phase, opt.epoch))  # define the website directory
      if opt.load_iter > 0:  # load_iter is 0 by default
          web_dir = '{:s}_iter{:d}'.format(web_dir, opt.load_iter)
      print('creating web directory', web_dir)
      webpage = html_handler.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.
      # For [pix2pix]: we use batchnorm and dropout in the original pix2pix.
      # You can experiment it with and without eval() mode.
      # For [CycleGAN]: It should not affect CycleGAN as CycleGAN uses instancenorm without dropout.
      if opt.eval:
          model.eval()
      init_time = time.time()
      for i, data in enumerate(dataset):
          if i >= opt.num_test:  # only apply our model to opt.num_test images.
              break
          model.set_input(data)  # unpack data from data loader
          model.test()  # run inference
          visuals = model.get_current_visuals()  # get image results
          img_path = model.get_image_paths()  # get image paths
          if i % 5 == 0:  # save images to an HTML file
              print('processing (%04d)-th image... %s' % (i, img_path))
          if nifti:
              query_name = os.path.basename(img_path[0])
              # Postprocess the results to numpy arrays
              np_post = postprocess_images(visuals, opt, dataset.dataset.original_shape)
              query_name_noext = query_name.split(".")[0]
              if 'real_B' in np_post:
                  # Evaluate the results
                  exc_eval.evaluate(np_post, query_name_noext, opt.smoothing)
              save_nifti_images(np_post, query_name_noext, opt, web_dir, dataset.dataset.affine)
              save_web_nifti(webpage, np_post, img_path, opt.show_plots, width=opt.display_winsize)
          else:
              save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize)
      webpage.save()  # save the HTML
      end_time = round(time.time() - init_time, 3)
      print_timestamped("The testing process took " + str(end_time) + "s.")
      if nifti:
          exc_eval.close()
  