text: import torch
  import torch.nn as nn
  from torch.nn import init
  import functools
  from torch.optim import lr_scheduler
  import torch.nn.utils.spectral_norm as spectral_norm
  
  
  ###############################################################################
  # Helper Functions
  ###############################################################################
  
  
  class Identity(nn.Module):
      def forward(self, x):
          return x
  
  
  def get_norm_layer(norm_type='instance'):
      """Return a normalization layer
  
      Parameters:
          norm_type (str) -- the name of the normalization layer: batch | instance | none
  
      For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev).
      For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics.
      """
      if norm_type == 'batch':
          norm_layer = functools.partial(nn.BatchNorm2d, affine=True, track_running_stats=True)
      elif norm_type == 'instance':
          norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
      elif norm_type == 'none':
          def norm_layer(x): return Identity()
      else:
          raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
      return norm_layer
  
  
  def get_scheduler(optimizer, opt):
      """Return a learning rate scheduler
  
      Parameters:
          optimizer          -- the optimizer of the network
          opt (option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions．　
                                opt.lr_policy is the name of learning rate policy: linear | step | plateau | cosine
  
      For 'linear', we keep the same learning rate for the first <opt.n_epochs> epochs
      and linearly decay the rate to zero over the next <opt.n_epochs_decay> epochs.
      For other schedulers (step, plateau, and cosine), we use the default PyTorch schedulers.
      See https://pytorch.org/docs/stable/optim.html for more details.
      """
      if opt.lr_policy == 'linear':
          def lambda_rule(epoch):
              lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs_decay + 1)
              return lr_l
          scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)
      elif opt.lr_policy == 'step':
          scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1)
      elif opt.lr_policy == 'plateau':
          scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5)
      elif opt.lr_policy == 'cosine':
          scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.n_epochs, eta_min=0)
      else:
          return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy)
      return scheduler
  
  
  def init_weights(net, init_type='normal', init_gain=0.02):
      """Initialize network weights.
  
      Parameters:
          net (network)   -- network to be initialized
          init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
          init_gain (float)    -- scaling factor for normal, xavier and orthogonal.
  
      We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might
      work better for some applications. Feel free to try yourself.
      """
      def init_func(m):  # define the initialization function
          classname = m.__class__.__name__
          if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
              if init_type == 'normal':
                  init.normal_(m.weight.data, 0.0, init_gain)
              elif init_type == 'xavier':
                  init.xavier_normal_(m.weight.data, gain=init_gain)
              elif init_type == 'kaiming':
                  init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
              elif init_type == 'orthogonal':
                  init.orthogonal_(m.weight.data, gain=init_gain)
              else:
                  raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
              if hasattr(m, 'bias') and m.bias is not None:
                  init.constant_(m.bias.data, 0.0)
          elif classname.find('BatchNorm2d') != -1:  # BatchNorm Layer's weight is not a matrix; only normal distribution applies.
              init.normal_(m.weight.data, 1.0, init_gain)
              init.constant_(m.bias.data, 0.0)
  
      print('initialize network with %s' % init_type)
      net.apply(init_func)  # apply the initialization function <init_func>
  
  
  def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]):
      """Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights
      Parameters:
          net (network)      -- the network to be initialized
          init_type (str)    -- the name of an initialization method: normal | xavier | kaiming | orthogonal
          gain (float)       -- scaling factor for normal, xavier and orthogonal.
          gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
  
      Return an initialized network.
      """
      if len(gpu_ids) > 0:
          assert(torch.cuda.is_available())
          net.to(gpu_ids[0])
          net = torch.nn.DataParallel(net, gpu_ids)  # multi-GPUs
      init_weights(net, init_type, init_gain=init_gain)
      return net
  
  
  def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]):
      """Create a generator
  
      Parameters:
          input_nc (int) -- the number of channels in input images
          output_nc (int) -- the number of channels in output images
          ngf (int) -- the number of filters in the last conv layer
          netG (str) -- the architecture's name: resnet_9blocks | resnet_6blocks | unet_256 | unet_128
          norm (str) -- the name of normalization layers used in the network: batch | instance | none
          use_dropout (bool) -- if use dropout layers.
          init_type (str)    -- the name of our initialization method.
          init_gain (float)  -- scaling factor for normal, xavier and orthogonal.
          gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
  
      Returns a generator
  
      Our current implementation provides two types of generators:
          U-Net: [unet_128] (for 128x128 input images) and [unet_256] (for 256x256 input images)
          The original U-Net paper: https://arxiv.org/abs/1505.04597
  
          Resnet-based generator: [resnet_6blocks] (with 6 Resnet blocks) and [resnet_9blocks] (with 9 Resnet blocks)
          Resnet-based generator consists of several Resnet blocks between a few downsampling/upsampling operations.
          We adapt Torch code from Justin Johnson's neural style transfer project (https://github.com/jcjohnson/fast-neural-style).
  
  
      The generator has been initialized by <init_net>. It uses RELU for non-linearity.
      """
      net = None
      norm_layer = get_norm_layer(norm_type=norm)
  
      if netG == 'resnet_9blocks':
          net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9)
      elif netG == 'resnet_6blocks':
          net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6)
      elif netG == 'resnet_6blocks_sn':
          net = ResnetGenerator_SN(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6)
      elif netG == 'unet_128':
          net = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
      elif netG == 'unet_256':
          net = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
      else:
          raise NotImplementedError('Generator model name [%s] is not recognized' % netG)
      return init_net(net, init_type, init_gain, gpu_ids)
  
  
  def define_D(input_nc, ndf, netD, n_layers_D=3, norm='batch', init_type='normal', init_gain=0.02, gpu_ids=[]):
      """Create a discriminator
  
      Parameters:
          input_nc (int)     -- the number of channels in input images
          ndf (int)          -- the number of filters in the first conv layer
          netD (str)         -- the architecture's name: basic | n_layers | pixel
          n_layers_D (int)   -- the number of conv layers in the discriminator; effective when netD=='n_layers'
          norm (str)         -- the type of normalization layers used in the network.
          init_type (str)    -- the name of the initialization method.
          init_gain (float)  -- scaling factor for normal, xavier and orthogonal.
          gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
  
      Returns a discriminator
  
      Our current implementation provides three types of discriminators:
          [basic]: 'PatchGAN' classifier described in the original pix2pix paper.
          It can classify whether 70×70 overlapping patches are real or fake.
          Such a patch-level discriminator architecture has fewer parameters
          than a full-image discriminator and can work on arbitrarily-sized images
          in a fully convolutional fashion.
  
          [n_layers]: With this mode, you can specify the number of conv layers in the discriminator
          with the parameter <n_layers_D> (default=3 as used in [basic] (PatchGAN).)
  
          [pixel]: 1x1 PixelGAN discriminator can classify whether a pixel is real or not.
          It encourages greater color diversity but has no effect on spatial statistics.
  
      The discriminator has been initialized by <init_net>. It uses Leakly RELU for non-linearity.
      """
      net = None
      norm_layer = get_norm_layer(norm_type=norm)
  
      if netD == 'basic':  # default PatchGAN classifier
          net = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer)
      elif netD == 'n_layers':  # more options
          net = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer)
      elif netD == 'n_layers_SN':  # more options
          net = NLayerDiscriminator_SN(input_nc, ndf, n_layers_D)
      elif netD == 'pixel':     # classify if each pixel is real or fake
          net = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer)
      elif netD == 'contrastive': # contrastiv loss w/ Siamese discriminator
          net = ContrastiveDiscriminator(input_nc//2, ndf, n_layers_D, norm_layer=norm_layer)
      elif netD == 'full':    # not a PatchGAN
          net = FullDiscriminator_SN( input_nc )
      else:
          raise NotImplementedError('Discriminator model name [%s] is not recognized' % netD)
      return init_net(net, init_type, init_gain, gpu_ids)
  
  
  ##############################################################################
  # Classes
  ##############################################################################
  class GANLoss(nn.Module):
      """Define different GAN objectives.
  
      The GANLoss class abstracts away the need to create the target label tensor
      that has the same size as the input.
      """
  
      def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0):
          """ Initialize the GANLoss class.
  
          Parameters:
              gan_mode (str) - - the type of GAN objective. It currently supports vanilla, lsgan, and wgangp.
              target_real_label (bool) - - label for a real image
              target_fake_label (bool) - - label of a fake image
  
          Note: Do not use sigmoid as the last layer of Discriminator.
          LSGAN needs no sigmoid. vanilla GANs will handle it with BCEWithLogitsLoss.
          """
          super(GANLoss, self).__init__()
          self.register_buffer('real_label', torch.tensor(target_real_label))
          self.register_buffer('fake_label', torch.tensor(target_fake_label))
          self.gan_mode = gan_mode
          if gan_mode == 'lsgan':
              self.loss = nn.MSELoss()
          elif gan_mode == 'vanilla':
              self.loss = nn.BCEWithLogitsLoss()
          elif gan_mode in ['wgangp', 'wgansn']:
              self.loss = None
          elif gan_mode == 'contrastive':
              self.loss = nn.L1Loss()
              self.m = 1.0
          else:
              raise NotImplementedError('gan mode %s not implemented' % gan_mode)
  
      def get_target_tensor(self, prediction, target_is_real):
          """Create label tensors with the same size as the input.
  
          Parameters:
              prediction (tensor) - - tpyically the prediction from a discriminator
              target_is_real (bool) - - if the ground truth label is for real images or fake images
  
          Returns:
              A label tensor filled with ground truth label, and with the size of the input
          """
  
          if target_is_real:
              target_tensor = self.real_label
          else:
              target_tensor = self.fake_label
          return target_tensor.expand_as(prediction)
  
      def __call__(self, prediction, target_is_real):
          """Calculate loss given Discriminator's output and grount truth labels.
  
          Parameters:
              prediction (tensor) - - tpyically the prediction output from a discriminator
              target_is_real (bool) - - if the ground truth label is for real images or fake images
  
          Returns:
              the calculated loss.
          """
          if self.gan_mode in ['lsgan', 'vanilla']:
              target_tensor = self.get_target_tensor(prediction, target_is_real)
              loss = self.loss(prediction, target_tensor)
          elif self.gan_mode in ['wgangp', 'wgansn']:
              if target_is_real:
                  loss = -prediction.mean()
              else:
                  loss = prediction.mean()
          elif self.gan_mode == 'contrastive':
              if target_is_real:
                  loss = self.loss(prediction[:,0], prediction[:,1])
              else:
                  #loss = 1.0 - self.loss(prediction[:,0], prediction[:,1])
                  loss = torch.nn.functional.relu(self.m - self.loss(prediction[:,0], prediction[:,1]))
  
          return loss
  
  
  def cal_gradient_penalty(netD, real_data, fake_data, device, type='mixed', constant=1.0, lambda_gp=10.0):
      """Calculate the gradient penalty loss, used in WGAN-GP paper https://arxiv.org/abs/1704.00028
  
      Arguments:
          netD (network)              -- discriminator network
          real_data (tensor array)    -- real images
          fake_data (tensor array)    -- generated images from the generator
          device (str)                -- GPU / CPU: from torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu')
          type (str)                  -- if we mix real and fake data or not [real | fake | mixed].
          constant (float)            -- the constant used in formula ( ||gradient||_2 - constant)^2
          lambda_gp (float)           -- weight for this loss
  
      Returns the gradient penalty loss
      """
      if lambda_gp > 0.0:
          if type == 'real':   # either use real images, fake images, or a linear interpolation of two.
              interpolatesv = real_data
          elif type == 'fake':
              interpolatesv = fake_data
          elif type == 'mixed':
              alpha = torch.rand(real_data.shape[0], 1, device=device)
              alpha = alpha.expand(real_data.shape[0], real_data.nelement() // real_data.shape[0]).contiguous().view(*real_data.shape)
              interpolatesv = alpha * real_data + ((1 - alpha) * fake_data)
          else:
              raise NotImplementedError('{} not implemented'.format(type))
          interpolatesv.requires_grad_(True)
          disc_interpolates = netD(interpolatesv)
          gradients = torch.autograd.grad(outputs=disc_interpolates, inputs=interpolatesv,
                                          grad_outputs=torch.ones(disc_interpolates.size()).to(device),
                                          create_graph=True, retain_graph=True, only_inputs=True)
          gradients = gradients[0].view(real_data.size(0), -1)  # flat the data
          gradient_penalty = (((gradients + 1e-16).norm(2, dim=1) - constant) ** 2).mean() * lambda_gp        # added eps
          return gradient_penalty
      else:
          return 0.0
  
  
  class ResnetGenerator(nn.Module):
      """Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations.
  
      We adapt Torch code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style)
      """
  
      def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'):
          """Construct a Resnet-based generator
  
          Parameters:
              input_nc (int)      -- the number of channels in input images
              output_nc (int)     -- the number of channels in output images
              ngf (int)           -- the number of filters in the last conv layer
              norm_layer          -- normalization layer
              use_dropout (bool)  -- if use dropout layers
              n_blocks (int)      -- the number of ResNet blocks
              padding_type (str)  -- the name of padding layer in conv layers: reflect | replicate | zero
          """
          assert(n_blocks >= 0)
          super(ResnetGenerator, self).__init__()
          if type(norm_layer) == functools.partial:
              use_bias = norm_layer.func == nn.InstanceNorm2d
          else:
              use_bias = norm_layer == nn.InstanceNorm2d
  
          model = [nn.ReflectionPad2d(3),
                   nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias),
                   norm_layer(ngf),
                   nn.ReLU(True)]
  
          n_downsampling = 2
          for i in range(n_downsampling):  # add downsampling layers
              mult = 2 ** i
              model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias),
                        norm_layer(ngf * mult * 2),
                        nn.ReLU(True)]
  
          mult = 2 ** n_downsampling
          for i in range(n_blocks):       # add ResNet blocks
  
              model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)]
  
          for i in range(n_downsampling):  # add upsampling layers
              mult = 2 ** (n_downsampling - i)
              model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),
                                           kernel_size=3, stride=2,
                                           padding=1, output_padding=1,
                                           bias=use_bias),
                        norm_layer(int(ngf * mult / 2)),
                        nn.ReLU(True)]
          model += [nn.ReflectionPad2d(3)]
          model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
          model += [nn.Tanh()]
  
          self.model = nn.Sequential(*model)
  
      def forward(self, input):
          """Standard forward"""
          return self.model(input)
  
  
  class ResnetGenerator_SN(nn.Module):
      """Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations. This one adds spectral normalization.
  
      We adapt Torch code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style)
      """
  
      def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'):
          """Construct a Resnet-based generator
  
          Parameters:
              input_nc (int)      -- the number of channels in input images
              output_nc (int)     -- the number of channels in output images
              ngf (int)           -- the number of filters in the last conv layer
              norm_layer          -- normalization layer
              use_dropout (bool)  -- if use dropout layers
              n_blocks (int)      -- the number of ResNet blocks
              padding_type (str)  -- the name of padding layer in conv layers: reflect | replicate | zero
          """
          assert(n_blocks >= 0)
          super(ResnetGenerator_SN, self).__init__()
          if type(norm_layer) == functools.partial:
              use_bias = norm_layer.func == nn.InstanceNorm2d
          else:
              use_bias = norm_layer == nn.InstanceNorm2d
  
          model = [nn.ReflectionPad2d(3),
                   spectral_norm(nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias)),
                   norm_layer(ngf),
                   nn.ReLU(True)]
  
          n_downsampling = 2
          for i in range(n_downsampling):  # add downsampling layers
              mult = 2 ** i
              model += [spectral_norm(nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias)),
                        norm_layer(ngf * mult * 2),
                        nn.ReLU(True)]
  
          mult = 2 ** n_downsampling
          for i in range(n_blocks):       # add ResNet blocks
  
              model += [ResnetBlock_SN(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)]
  
          for i in range(n_downsampling):  # add upsampling layers
              mult = 2 ** (n_downsampling - i)
              model += [spectral_norm(nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),
                                           kernel_size=3, stride=2,
                                           padding=1, output_padding=1,
                                           bias=use_bias)),
                        norm_layer(int(ngf * mult / 2)),
                        nn.ReLU(True)]
          model += [nn.ReflectionPad2d(3)]
          model += [spectral_norm(nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0))]
          model += [nn.Tanh()]
  
          self.model = nn.Sequential(*model)
  
      def forward(self, input):
          """Standard forward"""
          return self.model(input)
  
  class ResnetBlock(nn.Module):
      """Define a Resnet block"""
  
      def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias):
          """Initialize the Resnet block
  
          A resnet block is a conv block with skip connections
          We construct a conv block with build_conv_block function,
          and implement skip connections in <forward> function.
          Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf
          """
          super(ResnetBlock, self).__init__()
          self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias)
  
      def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias):
          """Construct a convolutional block.
  
          Parameters:
              dim (int)           -- the number of channels in the conv layer.
              padding_type (str)  -- the name of padding layer: reflect | replicate | zero
              norm_layer          -- normalization layer
              use_dropout (bool)  -- if use dropout layers.
              use_bias (bool)     -- if the conv layer uses bias or not
  
          Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU))
          """
          conv_block = []
          p = 0
          if padding_type == 'reflect':
              conv_block += [nn.ReflectionPad2d(1)]
          elif padding_type == 'replicate':
              conv_block += [nn.ReplicationPad2d(1)]
          elif padding_type == 'zero':
              p = 1
          else:
              raise NotImplementedError('padding [%s] is not implemented' % padding_type)
  
          conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim), nn.ReLU(True)]
          if use_dropout:
              conv_block += [nn.Dropout(0.5)]
  
          p = 0
          if padding_type == 'reflect':
              conv_block += [nn.ReflectionPad2d(1)]
          elif padding_type == 'replicate':
              conv_block += [nn.ReplicationPad2d(1)]
          elif padding_type == 'zero':
              p = 1
          else:
              raise NotImplementedError('padding [%s] is not implemented' % padding_type)
          conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim)]
  
          return nn.Sequential(*conv_block)
  
      def forward(self, x):
          """Forward function (with skip connections)"""
          out = x + self.conv_block(x)  # add skip connections
          return out
  
  class ResnetBlock_SN(nn.Module):
      """Define a Resnet block with spectral normalization"""
  
      def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias):
          """Initialize the Resnet block
  
          A resnet block is a conv block with skip connections
          We construct a conv block with build_conv_block function,
          and implement skip connections in <forward> function.
          Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf
          """
          super(ResnetBlock_SN, self).__init__()
          self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias)
  
      def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias):
          """Construct a convolutional block.
  
          Parameters:
              dim (int)           -- the number of channels in the conv layer.
              padding_type (str)  -- the name of padding layer: reflect | replicate | zero
              norm_layer          -- normalization layer
              use_dropout (bool)  -- if use dropout layers.
              use_bias (bool)     -- if the conv layer uses bias or not
  
          Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU))
          """
          conv_block = []
          p = 0
          if padding_type == 'reflect':
              conv_block += [nn.ReflectionPad2d(1)]
          elif padding_type == 'replicate':
              conv_block += [nn.ReplicationPad2d(1)]
          elif padding_type == 'zero':
              p = 1
          else:
              raise NotImplementedError('padding [%s] is not implemented' % padding_type)
  
          conv_block += [spectral_norm(nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias)), norm_layer(dim), nn.ReLU(True)]
          if use_dropout:
              conv_block += [nn.Dropout(0.5)]
  
          p = 0
          if padding_type == 'reflect':
              conv_block += [nn.ReflectionPad2d(1)]
          elif padding_type == 'replicate':
              conv_block += [nn.ReplicationPad2d(1)]
          elif padding_type == 'zero':
              p = 1
          else:
              raise NotImplementedError('padding [%s] is not implemented' % padding_type)
          conv_block += [spectral_norm(nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias)), norm_layer(dim)]
  
          return nn.Sequential(*conv_block)
  
      def forward(self, x):
          """Forward function (with skip connections)"""
          out = x + self.conv_block(x)  # add skip connections
          return out
  
  
  class UnetGenerator(nn.Module):
      """Create a Unet-based generator"""
  
      def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False):
          """Construct a Unet generator
          Parameters:
              input_nc (int)  -- the number of channels in input images
              output_nc (int) -- the number of channels in output images
              num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7,
                                  image of size 128x128 will become of size 1x1 # at the bottleneck
              ngf (int)       -- the number of filters in the last conv layer
              norm_layer      -- normalization layer
  
          We construct the U-Net from the innermost layer to the outermost layer.
          It is a recursive process.
          """
          super(UnetGenerator, self).__init__()
          # construct unet structure
          unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True)  # add the innermost layer
          for i in range(num_downs - 5):          # add intermediate layers with ngf * 8 filters
              unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout)
          # gradually reduce the number of filters from ngf * 8 to ngf
          unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
          unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
          unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
          self.model = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer)  # add the outermost layer
  
      def forward(self, input):
          """Standard forward"""
          return self.model(input)
  
  
  class UnetSkipConnectionBlock(nn.Module):
      """Defines the Unet submodule with skip connection.
          X -------------------identity----------------------
          |-- downsampling -- |submodule| -- upsampling --|
      """
  
      def __init__(self, outer_nc, inner_nc, input_nc=None,
                   submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False):
          """Construct a Unet submodule with skip connections.
  
          Parameters:
              outer_nc (int) -- the number of filters in the outer conv layer
              inner_nc (int) -- the number of filters in the inner conv layer
              input_nc (int) -- the number of channels in input images/features
              submodule (UnetSkipConnectionBlock) -- previously defined submodules
              outermost (bool)    -- if this module is the outermost module
              innermost (bool)    -- if this module is the innermost module
              norm_layer          -- normalization layer
              use_dropout (bool)  -- if use dropout layers.
          """
          super(UnetSkipConnectionBlock, self).__init__()
          self.outermost = outermost
          if type(norm_layer) == functools.partial:
              use_bias = norm_layer.func == nn.InstanceNorm2d
          else:
              use_bias = norm_layer == nn.InstanceNorm2d
          if input_nc is None:
              input_nc = outer_nc
          downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4,
                               stride=2, padding=1, bias=use_bias)
          downrelu = nn.LeakyReLU(0.2, True)
          downnorm = norm_layer(inner_nc)
          uprelu = nn.ReLU(True)
          upnorm = norm_layer(outer_nc)
  
          if outermost:
              upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,
                                          kernel_size=4, stride=2,
                                          padding=1)
              down = [downconv]
              up = [uprelu, upconv, nn.Tanh()]
              model = down + [submodule] + up
          elif innermost:
              upconv = nn.ConvTranspose2d(inner_nc, outer_nc,
                                          kernel_size=4, stride=2,
                                          padding=1, bias=use_bias)
              down = [downrelu, downconv]
              up = [uprelu, upconv, upnorm]
              model = down + up
          else:
              upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,
                                          kernel_size=4, stride=2,
                                          padding=1, bias=use_bias)
              down = [downrelu, downconv, downnorm]
              up = [uprelu, upconv, upnorm]
  
              if use_dropout:
                  model = down + [submodule] + up + [nn.Dropout(0.5)]
              else:
                  model = down + [submodule] + up
  
          self.model = nn.Sequential(*model)
  
      def forward(self, x):
          if self.outermost:
              return self.model(x)
          else:   # add skip connections
              return torch.cat([x, self.model(x)], 1)
  
  
  class NLayerDiscriminator(nn.Module):
      """Defines a PatchGAN discriminator"""
  
      def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d):
          """Construct a PatchGAN discriminator
  
          Parameters:
              input_nc (int)  -- the number of channels in input images
              ndf (int)       -- the number of filters in the last conv layer
              n_layers (int)  -- the number of conv layers in the discriminator
              norm_layer      -- normalization layer
          """
          super(NLayerDiscriminator, self).__init__()
          if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm2d has affine parameters
              use_bias = norm_layer.func == nn.InstanceNorm2d
          else:
              use_bias = norm_layer == nn.InstanceNorm2d
  
          kw = 4
          padw = 1
          sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
          nf_mult = 1
          nf_mult_prev = 1
          for n in range(1, n_layers):  # gradually increase the number of filters
              nf_mult_prev = nf_mult
              nf_mult = min(2 ** n, 8)
              sequence += [
                  nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
                  norm_layer(ndf * nf_mult),
                  nn.LeakyReLU(0.2, True)
              ]
  
          nf_mult_prev = nf_mult
          nf_mult = min(2 ** n_layers, 8)
          sequence += [
              nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
              norm_layer(ndf * nf_mult),
              nn.LeakyReLU(0.2, True)
          ]
  
          sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]  # output 1 channel prediction map
          self.model = nn.Sequential(*sequence)
  
      def forward(self, input):
          """Standard forward."""
          return self.model(input)
  
  class NLayerDiscriminator_SN(nn.Module):
      """Defines a PatchGAN discriminator with spectral normalization"""
  
      def __init__(self, input_nc, ndf=64, n_layers=3):
          """Construct a PatchGAN discriminator
  
          Parameters:
              input_nc (int)  -- the number of channels in input images
              ndf (int)       -- the number of filters in the last conv layer
              n_layers (int)  -- the number of conv layers in the discriminator
              norm_layer      -- normalization layer
          """
          super(NLayerDiscriminator_SN, self).__init__()
          print("spectral norm discriminator")
  
          kw = 4
          padw = 1
          sequence = [spectral_norm(nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw)),
                      nn.LeakyReLU(0.2, True)]
          nf_mult = 1
          nf_mult_prev = 1
          for n in range(1, n_layers):  # gradually increase the number of filters
              nf_mult_prev = nf_mult
              nf_mult = min(2 ** n, 8)
              sequence += [
                  spectral_norm(nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw)),
                  nn.LeakyReLU(0.2, True)
              ]
  
          nf_mult_prev = nf_mult
          nf_mult = min(2 ** n_layers, 8)
          sequence += [
              spectral_norm(nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw)),
              nn.LeakyReLU(0.2, True)
          ]
  
          sequence += [spectral_norm(nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw))]  # output 1 channel prediction map
          self.model = nn.Sequential(*sequence)
  
      def forward(self, input):
          """Standard forward."""
          return self.model(input)
  
  class ContrastiveDiscriminator(nn.Module):
      """Defines a convolutional encoder"""
  
      def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d):
          """Construct a convolutional encoder
  
          Parameters:
              input_nc (int)  -- the number of channels in input images
              ndf (int)       -- the number of filters in the last conv layer
              n_layers (int)  -- the number of conv layers in the discriminator
              norm_layer      -- normalization layer
          """
          super(ContrastiveDiscriminator, self).__init__()
          if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm2d has affine parameters
              use_bias = norm_layer.func == nn.InstanceNorm2d
          else:
              use_bias = norm_layer == nn.InstanceNorm2d
  
          self.nc = input_nc
          self.enc = Encoder( input_nc, ndf, n_layers, norm_layer )
  
      def forward(self, input):
          """Standard forward."""
          z1 = self.enc(input[:,0:1,:,:])
          z2 = self.enc(input[:,1:2,:,:])
          z = torch.cat((z1,z2))
          return z
  
  
  class Encoder(nn.Module):
      """Defines a convolutional encoder"""
  
      def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d):
          """Construct a convolutional encoder
  
          Parameters:
              input_nc (int)  -- the number of channels in input images
              ndf (int)       -- the number of filters in the last conv layer
              n_layers (int)  -- the number of conv layers in the discriminator
              norm_layer      -- normalization layer
          """
          super(Encoder, self).__init__()
          if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm2d has affine parameters
              use_bias = norm_layer.func == nn.InstanceNorm2d
          else:
              use_bias = norm_layer == nn.InstanceNorm2d
  
  
          kw = 4
          padw = 1
          sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
          nf_mult = 1
          nf_mult_prev = 1
          for n in range(1, n_layers):  # gradually increase the number of filters
              nf_mult_prev = nf_mult
              nf_mult = min(2 ** n, 8)
              sequence += [
                  nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
                  norm_layer(ndf * nf_mult),
                  nn.LeakyReLU(0.2, True)
              ]
          sequence += [torch.nn.Flatten()]
          sequence += [nn.Linear(8192,2048), nn.LeakyReLU(0.2,True)]
          sequence += [nn.Linear(2048,2048)]
  
          self.model = nn.Sequential(*sequence)
  
      def forward(self, input):
          """Standard forward."""
          return self.model(input)
  
  class FullDiscriminator_SN(nn.Module):
      """Defines a full discriminator (not a PatchGAN), using spectral norm"""
  
      def __init__(self, input_nc, ndf=64, n_layers=5, norm_layer=nn.BatchNorm2d):
          """Construct a convolutional encoder
  
          Parameters:
              input_nc (int)  -- the number of channels in input images
              ndf (int)       -- the number of filters in the last conv layer
              n_layers (int)  -- the number of conv layers in the discriminator. For a 128x128 image, this should be 5
              norm_layer      -- normalization layer
          """
          super(FullDiscriminator_SN, self).__init__()
          if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm2d has affine parameters
              use_bias = norm_layer.func == nn.InstanceNorm2d
          else:
              use_bias = norm_layer == nn.InstanceNorm2d
  
          print("full discriminator")
  
          kw = 4
          padw = 1
          sequence = [spectral_norm(nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw)), nn.LeakyReLU(0.2, True)]
          nf_mult = 1
          nf_mult_prev = 1
          for n in range(1, n_layers):  # gradually increase the number of filters
              nf_mult_prev = nf_mult
              nf_mult = min(2 ** n, 8)
              sequence += [
                  spectral_norm(nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias)),
                  nn.LeakyReLU(0.2, True)
              ]
          sequence += [torch.nn.Flatten()]
          sequence += [spectral_norm(nn.Linear(8192,2048)), nn.LeakyReLU(0.2,True)]
          sequence += [spectral_norm(nn.Linear(2048,2048)), nn.LeakyReLU(0.2,True)]
          sequence += [spectral_norm(nn.Linear(2048,1))]
  
          self.model = nn.Sequential(*sequence)
  
      def forward(self, input):
          """Standard forward."""
          return self.model(input)
  
  class PixelDiscriminator(nn.Module):
      """Defines a 1x1 PatchGAN discriminator (pixelGAN)"""
  
      def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d):
          """Construct a 1x1 PatchGAN discriminator
  
          Parameters:
              input_nc (int)  -- the number of channels in input images
              ndf (int)       -- the number of filters in the last conv layer
              norm_layer      -- normalization layer
          """
          super(PixelDiscriminator, self).__init__()
          if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm2d has affine parameters
              use_bias = norm_layer.func == nn.InstanceNorm2d
          else:
              use_bias = norm_layer == nn.InstanceNorm2d
  
          self.net = [
              nn.Conv2d(input_nc, ndf, kernel_size=1, stride=1, padding=0),
              nn.LeakyReLU(0.2, True),
              nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias),
              norm_layer(ndf * 2),
              nn.LeakyReLU(0.2, True),
              nn.Conv2d(ndf * 2, 1, kernel_size=1, stride=1, padding=0, bias=use_bias)]
  
          self.net = nn.Sequential(*self.net)
  
      def forward(self, input):
          """Standard forward."""
          return self.net(input)
  