code: import numpy as np
  import os
  import sys
  import ntpath
  import time
  from . import util, html
  from subprocess import Popen, PIPE
  from scipy.misc import imresize
  
  if sys.version_info[0] == 2:
      VisdomExceptionBase = Exception
  else:
      VisdomExceptionBase = ConnectionError
  
  
  def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256):
      """Save images to the disk.
  
      Parameters:
          webpage (the HTML class) -- the HTML webpage class that stores these imaegs (see html.py for more details)
          visuals (OrderedDict)    -- an ordered dictionary that stores (name, images (either tensor or numpy) ) pairs
          image_path (str)         -- the string is used to create image paths
          aspect_ratio (float)     -- the aspect ratio of saved images
          width (int)              -- the images will be resized to width x width
  
      This function will save images stored in 'visuals' to the HTML file specified by 'webpage'.
      """
      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():
      """This class includes several functions that can display/save images and print/save logging information.
  
      It uses a Python library 'visdom' for display, and a Python library 'dominate' (wrapped in 'HTML') for creating HTML files with images.
      """
  
      def __init__(self, opt):
          """Initialize the Visualizer class
  
          Parameters:
              opt -- stores all the experiment flags; needs to be a subclass of BaseOptions
          Step 1: Cache the training/test options
          Step 2: connect to a visdom server
          Step 3: create an HTML object for saveing HTML filters
          Step 4: create a logging file to store training losses
          """
          self.opt = opt  # cache the option
          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.port = opt.display_port
          self.saved = False
          if self.display_id > 0:  # connect to a visdom server given <display_port> and <display_server>
              import visdom
              self.ncols = opt.display_ncols
              self.vis = visdom.Visdom(server=opt.display_server, port=opt.display_port, env=opt.display_env,raise_exceptions=True,proxies={'http': '172.16.2.30:8080', 'https': '172.16.2.30:8080'})
  
  
          if self.use_html:  # create an HTML object at <checkpoints_dir>/web/; images will be saved under <checkpoints_dir>/web/images/
              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])
          # create a logging file to store training losses
          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):
          """Reset the self.saved status"""
          self.saved = False
  
      def create_visdom_connections(self):
          """If the program could not connect to Visdom server, this function will start a new server at port < self.port > """
          cmd = sys.executable + ' -m visdom.server -p %d &>/dev/null &' % self.port
          print('\n\nCould not connect to Visdom server. \n Trying to start a server....')
          print('Command: %s' % cmd)
          Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE)
  
      def display_current_results(self, visuals, epoch, save_result):
          """Display current results on visdom; save current results to an HTML file.
  
          Parameters:
              visuals (OrderedDict) - - dictionary of images to display or save
              epoch (int) - - the current epoch
              save_result (bool) - - if save the current results to an HTML file
          """
          if self.display_id > 0:  # show images in the browser using visdom
              ncols = self.ncols
              if ncols > 0:        # show all the images in one visdom panel
                  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)  # create a table css
                  # create a table of images.
                  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
                  try:
                      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'))
                  except VisdomExceptionBase:
                      self.create_visdom_connections()
  
              else:     # show each image in a separate visdom panel;
                  idx = 1
                  try:
                      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
                  except VisdomExceptionBase:
                      self.create_visdom_connections()
  
          if self.use_html and (save_result or not self.saved):  # save images to an HTML file if they haven't been saved.
              self.saved = True
              # save images to the disk
              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, refresh=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()
  
      def plot_current_losses(self, epoch, counter_ratio, losses):
          """display the current losses on visdom display: dictionary of error labels and values
  
          Parameters:
              epoch (int)           -- current epoch
              counter_ratio (float) -- progress (percentage) in the current epoch, between 0 to 1
              losses (OrderedDict)  -- training losses stored in the format of (name, float) pairs
          """
          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']])
          try:
              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)
          except VisdomExceptionBase:
              self.create_visdom_connections()
  
      # losses: same format as |losses| of plot_current_losses
      def print_current_losses(self, epoch, iters, losses, t_comp, t_data):
          """print current losses on console; also save the losses to the disk
  
          Parameters:
              epoch (int) -- current epoch
              iters (int) -- current training iteration during this epoch (reset to 0 at the end of every epoch)
              losses (OrderedDict) -- training losses stored in the format of (name, float) pairs
              t_comp (float) -- computational time per data point (normalized by batch_size)
              t_data (float) -- data loading time per data point (normalized by batch_size)
          """
          message = '(epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (epoch, iters, t_comp, t_data)
          for k, v in losses.items():
              message += '%s: %.3f ' % (k, v)
  
          print(message)  # print the message
          with open(self.log_name, "a") as log_file:
              log_file.write('%s\n' % message)  # save the message
  
apis: numpy.array