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.
  
  Example (You need to train models first or download pre-trained models from our website):
      Test a CycleGAN model (both sides):
          python test.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan
  
      Test a CycleGAN model (one side only):
          python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout
  
      The option '--model test' is used for generating CycleGAN results only for one side.
      This option will automatically set '--dataset_mode single', which only loads the images from one set.
      On the contrary, using '--model cycle_gan' requires loading and generating results in both directions,
      which is sometimes unnecessary. 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 --direction BtoA
  
  See options/base_options.py and options/test_options.py for more test options.
  See training and test tips at: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/tips.md
  See frequently asked questions at: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/qa.md
  """
  import os
  from options.test_options import TestOptions
  from data import create_dataset
  from models import create_model
  from util.visualizer import save_images
  from util import html
  import util.util as util
  
  
  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 = 1
      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
      train_dataset = create_dataset(util.copyconf(opt, phase="test"))
      model = create_model(opt)      # create a model given opt.model and other options
      # create a webpage for viewing the results
      web_dir = os.path.join(opt.results_dir, '{}_{}'.format(opt.phase, opt.epoch))  # define the website directory
      print('creating web directory', web_dir)
      webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.results_dir, opt.phase, opt.epoch))
  
      for i, data in enumerate(dataset):
          if i == 0:
              model.data_dependent_initialize(data)
              model.setup(opt)               # regular setup: load and print networks; create schedulers
              model.parallelize()
              if opt.eval:
                  model.eval()
          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))
          save_images(webpage, visuals, img_path, width=opt.display_winsize)
      webpage.save()  # save the HTML
  