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
  # from torchvision.utils import save_image
  import torch
  import numpy as np
  from PIL import Image
  
  def save_image(image_numpy, image_path, aspect_ratio=1.0):
      """Save a numpy image to the disk
  
      Parameters:
          image_numpy (numpy array) -- input numpy array
          image_path (str)          -- the path of the image
      """
  
      image_pil = Image.fromarray(image_numpy)
      h, w, _ = image_numpy.shape
  
      if aspect_ratio > 1.0:
          image_pil = image_pil.resize((h, int(w * aspect_ratio)), Image.BICUBIC)
      if aspect_ratio < 1.0:
          image_pil = image_pil.resize((int(h / aspect_ratio), w), Image.BICUBIC)
      image_pil.save(image_path)
  
  def tensor2im(input_image, imtype=np.uint8):
      """"Converts a Tensor array into a numpy image array.
  
      Parameters:
          input_image (tensor) --  the input image tensor array
          imtype (type)        --  the desired type of the converted numpy array
      """
      if not isinstance(input_image, np.ndarray):
          if isinstance(input_image, torch.Tensor):  # get the data from a variable
              image_tensor = input_image.data
          else:
              return input_image
          image_numpy = image_tensor[0].cpu().float().numpy()  # convert it into a numpy array
          if image_numpy.shape[0] == 1:  # grayscale to RGB
              image_numpy = np.tile(image_numpy, (3, 1, 1))
          image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0  # post-processing: tranpose and scaling
      else:  # if it is a numpy array, do nothing
          image_numpy = input_image
      return image_numpy.astype(imtype)
  
  
  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
      # create a website
      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.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()
      
      print('Dataset size: ', len(dataset))
      for i, data in enumerate(dataset):
          print(i, end='\r')
          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
          os.makedirs(opt.dataroot + 'cyclegan_images/', exist_ok=True)
          original_path = dataset.dataset[i]['B_paths']
          save_path = opt.dataroot + 'cyclegan_images/' + original_path.split('/')[-1]
          
          # import pdb;pdb.set_trace()
          im = tensor2im(visuals['fake_A'])
          # import pdb;pdb.set_trace()
          save_image(im, save_path)
          # 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, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize)
      # webpage.save()  # save the HTML
  