text: """General-purpose training script for image-to-image translation.
  
  This script works for various models (with option '--model': e.g., pix2pix, cyclegan, colorization) and
  different datasets (with option '--dataset_mode': e.g., aligned, unaligned, single, colorization).
  You need to specify the dataset ('--dataroot'), experiment name ('--name'), and model ('--model').
  
  It first creates model, dataset, and visualizer given the option.
  It then does standard network training. During the training, it also visualize/save the images, print/save the loss plot, and save models.
  The script supports continue/resume training. Use '--continue_train' to resume your previous training.
  
  Example:
      Train a CycleGAN model:
          python train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan
      Train a pix2pix model:
          python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA
  
  See options/base_options.py and options/train_options.py for more training 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 time
  from options.train_options import TrainOptions
  from data import create_dataset
  from models import create_model
  from util.visualizer import Visualizer
  
  if __name__ == '__main__':
      opt = TrainOptions().parse()   # get training options
      dataset = create_dataset(opt)  # create a dataset given opt.dataset_mode and other options
      dataset_size = len(dataset)    # get the number of images in the dataset.
      print('The number of training images = %d' % dataset_size)
  
      model = create_model(opt)      # create a model given opt.model and other options
      model.setup(opt)               # regular setup: load and print networks; create schedulers
      visualizer = Visualizer(opt)   # create a visualizer that display/save images and plots
      total_iters = 0                # the total number of training iterations
  
      for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
          # outer loop for different epochs; we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>
          epoch_start_time = time.time()  # timer for entire epoch
          iter_data_time = time.time()    # timer for data loading per iteration
          epoch_iter = 0                  # the number of training iterations in current epoch, reset to 0 every epoch
  
          for i, data in enumerate(dataset):  # inner loop within one epoch
              iter_start_time = time.time()  # timer for computation per iteration
              if total_iters % opt.print_freq == 0:
                  t_data = iter_start_time - iter_data_time
              visualizer.reset()
              total_iters += opt.batch_size
              epoch_iter += opt.batch_size
              model.set_input(data)         # unpack data from dataset and apply preprocessing
              model.optimize_parameters()   # calculate loss functions, get gradients, update network weights
  
              if total_iters % opt.display_freq == 0:   # display images on visdom and save images to a HTML file
                  save_result = total_iters % opt.update_html_freq == 0
                  model.compute_visuals()
                  visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
  
              if total_iters % opt.print_freq == 0:    # print training losses and save logging information to the disk
                  losses = model.get_current_losses()
                  t_comp = (time.time() - iter_start_time) / opt.batch_size
                  visualizer.print_current_losses(epoch, epoch_iter, losses, t_comp, t_data)
                  if opt.display_id > 0:
                      visualizer.plot_current_losses(epoch, float(epoch_iter) / dataset_size, losses)
  
              if total_iters % opt.save_latest_freq == 0:   # cache our latest model every <save_latest_freq> iterations
                  print('saving the latest model (epoch %d, total_iters %d)' % (epoch, total_iters))
                  save_suffix = 'iter_%d' % total_iters if opt.save_by_iter else 'latest'
                  model.save_networks(save_suffix)
  
              iter_data_time = time.time()
          if epoch % opt.save_epoch_freq == 0:              # cache our model every <save_epoch_freq> epochs
              print('saving the model at the end of epoch %d, iters %d' % (epoch, total_iters))
              model.save_networks('latest')
              model.save_networks(epoch)
  
          print('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
          model.update_learning_rate()                     # update learning rates at the end of every epoch.
  