code: import numpy as np
  import os
  import ntpath
  import time
  from . import util
  from . import html
  from PIL import Image
  from torchvision import transforms
  
  
  def imresize(image, size, interp=Image.BICUBIC):
      return transforms.Resize(size=size, interpolation=interp)(image)
  
  # save image to the disk
  
  
  def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256):
      image_dir = webpage.get_image_dir()
      short_path = ntpath.basename(image_path[0])
      name = os.path.splitext(short_path)[0]
  
      webpage.add_header(name)
      ims, txts, links = [], [], []
  
      for label, im_data in visuals.items():
          im = util.tensor2im(im_data)
          image_name = '%s_%s.png' % (name, label)
          save_path = os.path.join(image_dir, image_name)
          h, w, _ = im.shape
          if aspect_ratio > 1.0:
              im = imresize(im, (h, int(w * aspect_ratio)), interp='bicubic')
          if aspect_ratio < 1.0:
              im = imresize(im, (int(h / aspect_ratio), w), interp='bicubic')
          util.save_image(im, save_path)
  
          ims.append(image_name)
          txts.append(label)
          links.append(image_name)
      webpage.add_images(ims, txts, links, width=width)
  
  
  class Visualizer():
      def __init__(self, opt):
          self.display_id = opt.display_id
          self.use_html = opt.isTrain and not opt.no_html
          self.win_size = opt.display_winsize
          self.name = opt.name
          self.opt = opt
          self.saved = False
          if self.display_id > 0:
              import visdom
              self.ncols = opt.display_ncols
              self.vis = visdom.Visdom(
                  server=opt.display_server, port=opt.display_port)
  
          if self.use_html:
              self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web')
              self.img_dir = os.path.join(self.web_dir, 'images')
              print('create web directory %s...' % self.web_dir)
              util.mkdirs([self.web_dir, self.img_dir])
          self.log_name = os.path.join(
              opt.checkpoints_dir, opt.name, 'loss_log.txt')
          with open(self.log_name, "a") as log_file:
              now = time.strftime("%c")
              log_file.write(
                  '================ Training Loss (%s) ================\n' % now)
  
      def reset(self):
          self.saved = False
  
      # |visuals|: dictionary of images to display or save
      def display_current_results(self, visuals, epoch, save_result):
          if self.display_id > 0:  # show images in the browser
              ncols = self.ncols
              if ncols > 0:
                  ncols = min(ncols, len(visuals))
                  h, w = next(iter(visuals.values())).shape[:2]
                  table_css = """<style>
                          table {border-collapse: separate; border-spacing:4px; white-space:nowrap; text-align:center}
                          table td {width: %dpx; height: %dpx; padding: 4px; outline: 4px solid black}
                          </style>""" % (w, h)
                  title = self.name
                  label_html = ''
                  label_html_row = ''
                  images = []
                  idx = 0
                  for label, image in visuals.items():
                      image_numpy = util.tensor2im(image)
                      label_html_row += '<td>%s</td>' % label
                      images.append(image_numpy.transpose([2, 0, 1]))
                      idx += 1
                      if idx % ncols == 0:
                          label_html += '<tr>%s</tr>' % label_html_row
                          label_html_row = ''
                  white_image = np.ones_like(
                      image_numpy.transpose([2, 0, 1])) * 255
                  while idx % ncols != 0:
                      images.append(white_image)
                      label_html_row += '<td></td>'
                      idx += 1
                  if label_html_row != '':
                      label_html += '<tr>%s</tr>' % label_html_row
                  # pane col = image row
                  self.vis.images(images, nrow=ncols, win=self.display_id + 1,
                                  padding=2, opts=dict(title=title + ' images'))
                  label_html = '<table>%s</table>' % label_html
                  self.vis.text(table_css + label_html, win=self.display_id + 2,
                                opts=dict(title=title + ' labels'))
              else:
                  idx = 1
                  for label, image in visuals.items():
                      image_numpy = util.tensor2im(image)
                      self.vis.image(image_numpy.transpose([2, 0, 1]), opts=dict(title=label),
                                     win=self.display_id + idx)
                      idx += 1
  
          # save images to a html file
          if self.use_html and (save_result or not self.saved):
              self.saved = True
              for label, image in visuals.items():
                  image_numpy = util.tensor2im(image)
                  img_path = os.path.join(
                      self.img_dir, 'epoch%.3d_%s.png' % (epoch, label))
                  util.save_image(image_numpy, img_path)
              # update website
              webpage = html.HTML(
                  self.web_dir, 'Experiment name = %s' % self.name, reflesh=1)
              for n in range(epoch, 0, -1):
                  webpage.add_header('epoch [%d]' % n)
                  ims, txts, links = [], [], []
  
                  for label, image_numpy in visuals.items():
                      image_numpy = util.tensor2im(image)
                      img_path = 'epoch%.3d_%s.png' % (n, label)
                      ims.append(img_path)
                      txts.append(label)
                      links.append(img_path)
                  webpage.add_images(ims, txts, links, width=self.win_size)
              webpage.save()
  
      # losses: dictionary of error labels and values
      def plot_current_losses(self, epoch, counter_ratio, opt, losses):
          if not hasattr(self, 'plot_data'):
              self.plot_data = {'X': [], 'Y': [], 'legend': list(losses.keys())}
          self.plot_data['X'].append(epoch + counter_ratio)
          self.plot_data['Y'].append([losses[k]
                                      for k in self.plot_data['legend']])
          self.vis.line(
              X=np.stack([np.array(self.plot_data['X'])] *
                         len(self.plot_data['legend']), 1),
              Y=np.array(self.plot_data['Y']),
              opts={
                  'title': self.name + ' loss over time',
                  'legend': self.plot_data['legend'],
                  'xlabel': 'epoch',
                  'ylabel': 'loss'},
              win=self.display_id)
  
      # losses: same format as |losses| of plot_current_losses
      def print_current_losses(self, epoch, i, losses, t, t_data):
          message = '(epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (
              epoch, i, t, t_data)
          for k, v in losses.items():
              message += '%s: %.3f, ' % (k, v)
  
          print(message)
          with open(self.log_name, "a") as log_file:
              log_file.write('%s\n' % message)
  
apis: numpy.array