text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from torch.nn import init
  import functools
  from torch.optim import lr_scheduler
  import numpy as np
  from .stylegan_networks import StyleGAN2Discriminator, StyleGAN2Generator, TileStyleGAN2Discriminator
  
  ###############################################################################
  # Helper Functions
  ###############################################################################
  
  
  def get_filter(filt_size=3):
      if(filt_size == 1):
          a = np.array([1., ])
      elif(filt_size == 2):
          a = np.array([1., 1.])
      elif(filt_size == 3):
          a = np.array([1., 2., 1.])
      elif(filt_size == 4):
          a = np.array([1., 3., 3., 1.])
      elif(filt_size == 5):
          a = np.array([1., 4., 6., 4., 1.])
      elif(filt_size == 6):
          a = np.array([1., 5., 10., 10., 5., 1.])
      elif(filt_size == 7):
          a = np.array([1., 6., 15., 20., 15., 6., 1.])
  
      filt = torch.Tensor(a[:, None] * a[None, :])
      filt = filt / torch.sum(filt)
  
      return filt
  
  
  class Downsample(nn.Module):
      def __init__(self, channels, pad_type='reflect', filt_size=3, stride=2, pad_off=0):
          super(Downsample, self).__init__()
          self.filt_size = filt_size
          self.pad_off = pad_off
          self.pad_sizes = [int(1. * (filt_size - 1) / 2), int(np.ceil(1. * (filt_size - 1) / 2)), int(1. * (filt_size - 1) / 2), int(np.ceil(1. * (filt_size - 1) / 2))]
          self.pad_sizes = [pad_size + pad_off for pad_size in self.pad_sizes]
          self.stride = stride
          self.off = int((self.stride - 1) / 2.)
          self.channels = channels
  
          filt = get_filter(filt_size=self.filt_size)
          self.register_buffer('filt', filt[None, None, :, :].repeat((self.channels, 1, 1, 1)))
  
          self.pad = get_pad_layer(pad_type)(self.pad_sizes)
  
      def forward(self, inp):
          if(self.filt_size == 1):
              if(self.pad_off == 0):
                  return inp[:, :, ::self.stride, ::self.stride]
              else:
                  return self.pad(inp)[:, :, ::self.stride, ::self.stride]
          else:
              return F.conv2d(self.pad(inp), self.filt, stride=self.stride, groups=inp.shape[1])
  
  
  class Upsample2(nn.Module):
      def __init__(self, scale_factor, mode='nearest'):
          super().__init__()
          self.factor = scale_factor
          self.mode = mode
  
      def forward(self, x):
          return torch.nn.functional.interpolate(x, scale_factor=self.factor, mode=self.mode)
  
  
  class Upsample(nn.Module):
      def __init__(self, channels, pad_type='repl', filt_size=4, stride=2):
          super(Upsample, self).__init__()
          self.filt_size = filt_size
          self.filt_odd = np.mod(filt_size, 2) == 1
          self.pad_size = int((filt_size - 1) / 2)
          self.stride = stride
          self.off = int((self.stride - 1) / 2.)
          self.channels = channels
  
          filt = get_filter(filt_size=self.filt_size) * (stride**2)
          self.register_buffer('filt', filt[None, None, :, :].repeat((self.channels, 1, 1, 1)))
  
          self.pad = get_pad_layer(pad_type)([1, 1, 1, 1])
  
      def forward(self, inp):
          ret_val = F.conv_transpose2d(self.pad(inp), self.filt, stride=self.stride, padding=1 + self.pad_size, groups=inp.shape[1])[:, :, 1:, 1:]
          if(self.filt_odd):
              return ret_val
          else:
              return ret_val[:, :, :-1, :-1]
  
  
  def get_pad_layer(pad_type):
      if(pad_type in ['refl', 'reflect']):
          PadLayer = nn.ReflectionPad2d
      elif(pad_type in ['repl', 'replicate']):
          PadLayer = nn.ReplicationPad2d
      elif(pad_type == 'zero'):
          PadLayer = nn.ZeroPad2d
      else:
          print('Pad type [%s] not recognized' % pad_type)
      return PadLayer
  
  
  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, debug=False):
      """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 debug:
                  print(classname)
              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)
  
      net.apply(init_func)  # apply the initialization function <init_func>
  
  
  def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[], debug=False, initialize_weights=True):
      """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])
          # if not amp:
          # net = torch.nn.DataParallel(net, gpu_ids)  # multi-GPUs for non-AMP training
      if initialize_weights:
          init_weights(net, init_type, init_gain=init_gain, debug=debug)
      return net
  
  
  def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, init_type='normal',
               init_gain=0.02, no_antialias=False, no_antialias_up=False, gpu_ids=[], opt=None):
      """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, no_antialias=no_antialias, no_antialias_up=no_antialias_up, n_blocks=9, opt=opt)
      elif netG == 'resnet_6blocks':
          net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, no_antialias=no_antialias, no_antialias_up=no_antialias_up, n_blocks=6, opt=opt)
      elif netG == 'resnet_4blocks':
          net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, no_antialias=no_antialias, no_antialias_up=no_antialias_up, n_blocks=4, opt=opt)
      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)
      elif netG == 'stylegan2':
          net = StyleGAN2Generator(input_nc, output_nc, ngf, use_dropout=use_dropout, opt=opt)
      elif netG == 'smallstylegan2':
          net = StyleGAN2Generator(input_nc, output_nc, ngf, use_dropout=use_dropout, n_blocks=2, opt=opt)
      elif netG == 'resnet_cat':
          n_blocks = 8
          net = G_Resnet(input_nc, output_nc, opt.nz, num_downs=2, n_res=n_blocks - 4, ngf=ngf, norm='inst', nl_layer='relu')
      else:
          raise NotImplementedError('Generator model name [%s] is not recognized' % netG)
      return init_net(net, init_type, init_gain, gpu_ids, initialize_weights=('stylegan2' not in netG))
  
  
  def define_F(input_nc, netF, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, no_antialias=False, gpu_ids=[], opt=None):
      if netF == 'global_pool':
          net = PoolingF()
      elif netF == 'reshape':
          net = ReshapeF()
      elif netF == 'sample':
          net = PatchSampleF(use_mlp=False, init_type=init_type, init_gain=init_gain, gpu_ids=gpu_ids, nc=opt.netF_nc)
      elif netF == 'mlp_sample':
          net = PatchSampleF(use_mlp=True, init_type=init_type, init_gain=init_gain, gpu_ids=gpu_ids, nc=opt.netF_nc)
      elif netF == 'strided_conv':
          net = StridedConvF(init_type=init_type, init_gain=init_gain, gpu_ids=gpu_ids)
      else:
          raise NotImplementedError('projection model name [%s] is not recognized' % netF)
      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, no_antialias=False, gpu_ids=[], opt=None):
      """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 cna 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 Leaky 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, no_antialias=no_antialias,)
      elif netD == 'n_layers':  # more options
          net = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer, no_antialias=no_antialias,)
      elif netD == 'pixel':     # classify if each pixel is real or fake
          net = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer)
      elif 'stylegan2' in netD:
          net = StyleGAN2Discriminator(input_nc, ndf, n_layers_D, no_antialias=no_antialias, opt=opt)
      else:
          raise NotImplementedError('Discriminator model name [%s] is not recognized' % netD)
      return init_net(net, init_type, init_gain, gpu_ids,
                      initialize_weights=('stylegan2' not in netD))
  
  
  ##############################################################################
  # 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', 'nonsaturating']:
              self.loss = None
          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.
          """
          bs = prediction.size(0)
          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 == 'wgangp':
              if target_is_real:
                  loss = -prediction.mean()
              else:
                  loss = prediction.mean()
          elif self.gan_mode == 'nonsaturating':
              if target_is_real:
                  loss = F.softplus(-prediction).view(bs, -1).mean(dim=1)
              else:
                  loss = F.softplus(prediction).view(bs, -1).mean(dim=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, gradients
      else:
          return 0.0, None
  
  
  class Normalize(nn.Module):
  
      def __init__(self, power=2):
          super(Normalize, self).__init__()
          self.power = power
  
      def forward(self, x):
          norm = x.pow(self.power).sum(1, keepdim=True).pow(1. / self.power)
          out = x.div(norm + 1e-7)
          return out
  
  
  class PoolingF(nn.Module):
      def __init__(self):
          super(PoolingF, self).__init__()
          model = [nn.AdaptiveMaxPool2d(1)]
          self.model = nn.Sequential(*model)
          self.l2norm = Normalize(2)
  
      def forward(self, x):
          return self.l2norm(self.model(x))
  
  
  class ReshapeF(nn.Module):
      def __init__(self):
          super(ReshapeF, self).__init__()
          model = [nn.AdaptiveAvgPool2d(4)]
          self.model = nn.Sequential(*model)
          self.l2norm = Normalize(2)
  
      def forward(self, x):
          x = self.model(x)
          x_reshape = x.permute(0, 2, 3, 1).flatten(0, 2)
          return self.l2norm(x_reshape)
  
  
  class StridedConvF(nn.Module):
      def __init__(self, init_type='normal', init_gain=0.02, gpu_ids=[]):
          super().__init__()
          # self.conv1 = nn.Conv2d(256, 128, 3, stride=2)
          # self.conv2 = nn.Conv2d(128, 64, 3, stride=1)
          self.l2_norm = Normalize(2)
          self.mlps = {}
          self.moving_averages = {}
          self.init_type = init_type
          self.init_gain = init_gain
          self.gpu_ids = gpu_ids
  
      def create_mlp(self, x):
          C, H = x.shape[1], x.shape[2]
          n_down = int(np.rint(np.log2(H / 32)))
          mlp = []
          for i in range(n_down):
              mlp.append(nn.Conv2d(C, max(C // 2, 64), 3, stride=2))
              mlp.append(nn.ReLU())
              C = max(C // 2, 64)
          mlp.append(nn.Conv2d(C, 64, 3))
          mlp = nn.Sequential(*mlp)
          init_net(mlp, self.init_type, self.init_gain, self.gpu_ids)
          return mlp
  
      def update_moving_average(self, key, x):
          if key not in self.moving_averages:
              self.moving_averages[key] = x.detach()
  
          self.moving_averages[key] = self.moving_averages[key] * 0.999 + x.detach() * 0.001
  
      def forward(self, x, use_instance_norm=False):
          C, H = x.shape[1], x.shape[2]
          key = '%d_%d' % (C, H)
          if key not in self.mlps:
              self.mlps[key] = self.create_mlp(x)
              self.add_module("child_%s" % key, self.mlps[key])
          mlp = self.mlps[key]
          x = mlp(x)
          self.update_moving_average(key, x)
          x = x - self.moving_averages[key]
          if use_instance_norm:
              x = F.instance_norm(x)
          return self.l2_norm(x)
  
  
  class PatchSampleF(nn.Module):
      def __init__(self, use_mlp=False, init_type='normal', init_gain=0.02, nc=256, gpu_ids=[]):
          # potential issues: currently, we use the same patch_ids for multiple images in the batch
          super(PatchSampleF, self).__init__()
          self.l2norm = Normalize(2)
          self.use_mlp = use_mlp
          self.nc = nc  # hard-coded
          self.mlp_init = False
          self.init_type = init_type
          self.init_gain = init_gain
          self.gpu_ids = gpu_ids
  
      def create_mlp(self, feats):
          for mlp_id, feat in enumerate(feats):
              input_nc = feat.shape[1]
              mlp = nn.Sequential(*[nn.Linear(input_nc, self.nc), nn.ReLU(), nn.Linear(self.nc, self.nc)])
              if len(self.gpu_ids) > 0:
                  mlp.cuda()
              setattr(self, 'mlp_%d' % mlp_id, mlp)
          init_net(self, self.init_type, self.init_gain, self.gpu_ids)
          self.mlp_init = True
  
      def forward(self, feats, num_patches=64, patch_ids=None):
          return_ids = []
          return_feats = []
          if self.use_mlp and not self.mlp_init:
              self.create_mlp(feats)
          for feat_id, feat in enumerate(feats):
              B, H, W = feat.shape[0], feat.shape[2], feat.shape[3]
              feat_reshape = feat.permute(0, 2, 3, 1).flatten(1, 2)
              if num_patches > 0:
                  if patch_ids is not None:
                      patch_id = patch_ids[feat_id]
                  else:
                      patch_id = torch.randperm(feat_reshape.shape[1], device=feats[0].device)
                      patch_id = patch_id[:int(min(num_patches, patch_id.shape[0]))]  # .to(patch_ids.device)
                  x_sample = feat_reshape[:, patch_id, :].flatten(0, 1)  # reshape(-1, x.shape[1])
              else:
                  x_sample = feat_reshape
                  patch_id = []
              if self.use_mlp:
                  mlp = getattr(self, 'mlp_%d' % feat_id)
                  x_sample = mlp(x_sample)
              return_ids.append(patch_id)
              x_sample = self.l2norm(x_sample)
  
              if num_patches == 0:
                  x_sample = x_sample.permute(0, 2, 1).reshape([B, x_sample.shape[-1], H, W])
              return_feats.append(x_sample)
          return return_feats, return_ids
  
  
  class G_Resnet(nn.Module):
      def __init__(self, input_nc, output_nc, nz, num_downs, n_res, ngf=64,
                   norm=None, nl_layer=None):
          super(G_Resnet, self).__init__()
          n_downsample = num_downs
          pad_type = 'reflect'
          self.enc_content = ContentEncoder(n_downsample, n_res, input_nc, ngf, norm, nl_layer, pad_type=pad_type)
          if nz == 0:
              self.dec = Decoder(n_downsample, n_res, self.enc_content.output_dim, output_nc, norm=norm, activ=nl_layer, pad_type=pad_type, nz=nz)
          else:
              self.dec = Decoder_all(n_downsample, n_res, self.enc_content.output_dim, output_nc, norm=norm, activ=nl_layer, pad_type=pad_type, nz=nz)
  
      def decode(self, content, style=None):
          return self.dec(content, style)
  
      def forward(self, image, style=None, nce_layers=[], encode_only=False):
          content, feats = self.enc_content(image, nce_layers=nce_layers, encode_only=encode_only)
          if encode_only:
              return feats
          else:
              images_recon = self.decode(content, style)
              if len(nce_layers) > 0:
                  return images_recon, feats
              else:
                  return images_recon
  
  ##################################################################################
  # Encoder and Decoders
  ##################################################################################
  
  
  class E_adaIN(nn.Module):
      def __init__(self, input_nc, output_nc=1, nef=64, n_layers=4,
                   norm=None, nl_layer=None, vae=False):
          # style encoder
          super(E_adaIN, self).__init__()
          self.enc_style = StyleEncoder(n_layers, input_nc, nef, output_nc, norm='none', activ='relu', vae=vae)
  
      def forward(self, image):
          style = self.enc_style(image)
          return style
  
  
  class StyleEncoder(nn.Module):
      def __init__(self, n_downsample, input_dim, dim, style_dim, norm, activ, vae=False):
          super(StyleEncoder, self).__init__()
          self.vae = vae
          self.model = []
          self.model += [Conv2dBlock(input_dim, dim, 7, 1, 3, norm=norm, activation=activ, pad_type='reflect')]
          for i in range(2):
              self.model += [Conv2dBlock(dim, 2 * dim, 4, 2, 1, norm=norm, activation=activ, pad_type='reflect')]
              dim *= 2
          for i in range(n_downsample - 2):
              self.model += [Conv2dBlock(dim, dim, 4, 2, 1, norm=norm, activation=activ, pad_type='reflect')]
          self.model += [nn.AdaptiveAvgPool2d(1)]  # global average pooling
          if self.vae:
              self.fc_mean = nn.Linear(dim, style_dim)  # , 1, 1, 0)
              self.fc_var = nn.Linear(dim, style_dim)  # , 1, 1, 0)
          else:
              self.model += [nn.Conv2d(dim, style_dim, 1, 1, 0)]
  
          self.model = nn.Sequential(*self.model)
          self.output_dim = dim
  
      def forward(self, x):
          if self.vae:
              output = self.model(x)
              output = output.view(x.size(0), -1)
              output_mean = self.fc_mean(output)
              output_var = self.fc_var(output)
              return output_mean, output_var
          else:
              return self.model(x).view(x.size(0), -1)
  
  
  class ContentEncoder(nn.Module):
      def __init__(self, n_downsample, n_res, input_dim, dim, norm, activ, pad_type='zero'):
          super(ContentEncoder, self).__init__()
          self.model = []
          self.model += [Conv2dBlock(input_dim, dim, 7, 1, 3, norm=norm, activation=activ, pad_type='reflect')]
          # downsampling blocks
          for i in range(n_downsample):
              self.model += [Conv2dBlock(dim, 2 * dim, 4, 2, 1, norm=norm, activation=activ, pad_type='reflect')]
              dim *= 2
          # residual blocks
          self.model += [ResBlocks(n_res, dim, norm=norm, activation=activ, pad_type=pad_type)]
          self.model = nn.Sequential(*self.model)
          self.output_dim = dim
  
      def forward(self, x, nce_layers=[], encode_only=False):
          if len(nce_layers) > 0:
              feat = x
              feats = []
              for layer_id, layer in enumerate(self.model):
                  feat = layer(feat)
                  if layer_id in nce_layers:
                      feats.append(feat)
                  if layer_id == nce_layers[-1] and encode_only:
                      return None, feats
              return feat, feats
          else:
              return self.model(x), None
  
          for layer_id, layer in enumerate(self.model):
              print(layer_id, layer)
  
  
  class Decoder_all(nn.Module):
      def __init__(self, n_upsample, n_res, dim, output_dim, norm='batch', activ='relu', pad_type='zero', nz=0):
          super(Decoder_all, self).__init__()
          # AdaIN residual blocks
          self.resnet_block = ResBlocks(n_res, dim, norm, activ, pad_type=pad_type, nz=nz)
          self.n_blocks = 0
          # upsampling blocks
          for i in range(n_upsample):
              block = [Upsample2(scale_factor=2), Conv2dBlock(dim + nz, dim // 2, 5, 1, 2, norm='ln', activation=activ, pad_type='reflect')]
              setattr(self, 'block_{:d}'.format(self.n_blocks), nn.Sequential(*block))
              self.n_blocks += 1
              dim //= 2
          # use reflection padding in the last conv layer
          setattr(self, 'block_{:d}'.format(self.n_blocks), Conv2dBlock(dim + nz, output_dim, 7, 1, 3, norm='none', activation='tanh', pad_type='reflect'))
          self.n_blocks += 1
  
      def forward(self, x, y=None):
          if y is not None:
              output = self.resnet_block(cat_feature(x, y))
              for n in range(self.n_blocks):
                  block = getattr(self, 'block_{:d}'.format(n))
                  if n > 0:
                      output = block(cat_feature(output, y))
                  else:
                      output = block(output)
              return output
  
  
  class Decoder(nn.Module):
      def __init__(self, n_upsample, n_res, dim, output_dim, norm='batch', activ='relu', pad_type='zero', nz=0):
          super(Decoder, self).__init__()
  
          self.model = []
          # AdaIN residual blocks
          self.model += [ResBlocks(n_res, dim, norm, activ, pad_type=pad_type, nz=nz)]
          # upsampling blocks
          for i in range(n_upsample):
              if i == 0:
                  input_dim = dim + nz
              else:
                  input_dim = dim
              self.model += [Upsample2(scale_factor=2), Conv2dBlock(input_dim, dim // 2, 5, 1, 2, norm='ln', activation=activ, pad_type='reflect')]
              dim //= 2
          # use reflection padding in the last conv layer
          self.model += [Conv2dBlock(dim, output_dim, 7, 1, 3, norm='none', activation='tanh', pad_type='reflect')]
          self.model = nn.Sequential(*self.model)
  
      def forward(self, x, y=None):
          if y is not None:
              return self.model(cat_feature(x, y))
          else:
              return self.model(x)
  
  ##################################################################################
  # Sequential Models
  ##################################################################################
  
  
  class ResBlocks(nn.Module):
      def __init__(self, num_blocks, dim, norm='inst', activation='relu', pad_type='zero', nz=0):
          super(ResBlocks, self).__init__()
          self.model = []
          for i in range(num_blocks):
              self.model += [ResBlock(dim, norm=norm, activation=activation, pad_type=pad_type, nz=nz)]
          self.model = nn.Sequential(*self.model)
  
      def forward(self, x):
          return self.model(x)
  
  
  ##################################################################################
  # Basic Blocks
  ##################################################################################
  def cat_feature(x, y):
      y_expand = y.view(y.size(0), y.size(1), 1, 1).expand(
          y.size(0), y.size(1), x.size(2), x.size(3))
      x_cat = torch.cat([x, y_expand], 1)
      return x_cat
  
  
  class ResBlock(nn.Module):
      def __init__(self, dim, norm='inst', activation='relu', pad_type='zero', nz=0):
          super(ResBlock, self).__init__()
  
          model = []
          model += [Conv2dBlock(dim + nz, dim, 3, 1, 1, norm=norm, activation=activation, pad_type=pad_type)]
          model += [Conv2dBlock(dim, dim + nz, 3, 1, 1, norm=norm, activation='none', pad_type=pad_type)]
          self.model = nn.Sequential(*model)
  
      def forward(self, x):
          residual = x
          out = self.model(x)
          out += residual
          return out
  
  
  class Conv2dBlock(nn.Module):
      def __init__(self, input_dim, output_dim, kernel_size, stride,
                   padding=0, norm='none', activation='relu', pad_type='zero'):
          super(Conv2dBlock, self).__init__()
          self.use_bias = True
          # initialize padding
          if pad_type == 'reflect':
              self.pad = nn.ReflectionPad2d(padding)
          elif pad_type == 'zero':
              self.pad = nn.ZeroPad2d(padding)
          else:
              assert 0, "Unsupported padding type: {}".format(pad_type)
  
          # initialize normalization
          norm_dim = output_dim
          if norm == 'batch':
              self.norm = nn.BatchNorm2d(norm_dim)
          elif norm == 'inst':
              self.norm = nn.InstanceNorm2d(norm_dim, track_running_stats=False)
          elif norm == 'ln':
              self.norm = LayerNorm(norm_dim)
          elif norm == 'none':
              self.norm = None
          else:
              assert 0, "Unsupported normalization: {}".format(norm)
  
          # initialize activation
          if activation == 'relu':
              self.activation = nn.ReLU(inplace=True)
          elif activation == 'lrelu':
              self.activation = nn.LeakyReLU(0.2, inplace=True)
          elif activation == 'prelu':
              self.activation = nn.PReLU()
          elif activation == 'selu':
              self.activation = nn.SELU(inplace=True)
          elif activation == 'tanh':
              self.activation = nn.Tanh()
          elif activation == 'none':
              self.activation = None
          else:
              assert 0, "Unsupported activation: {}".format(activation)
  
          # initialize convolution
          self.conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride, bias=self.use_bias)
  
      def forward(self, x):
          x = self.conv(self.pad(x))
          if self.norm:
              x = self.norm(x)
          if self.activation:
              x = self.activation(x)
          return x
  
  
  class LinearBlock(nn.Module):
      def __init__(self, input_dim, output_dim, norm='none', activation='relu'):
          super(LinearBlock, self).__init__()
          use_bias = True
          # initialize fully connected layer
          self.fc = nn.Linear(input_dim, output_dim, bias=use_bias)
  
          # initialize normalization
          norm_dim = output_dim
          if norm == 'batch':
              self.norm = nn.BatchNorm1d(norm_dim)
          elif norm == 'inst':
              self.norm = nn.InstanceNorm1d(norm_dim)
          elif norm == 'ln':
              self.norm = LayerNorm(norm_dim)
          elif norm == 'none':
              self.norm = None
          else:
              assert 0, "Unsupported normalization: {}".format(norm)
  
          # initialize activation
          if activation == 'relu':
              self.activation = nn.ReLU(inplace=True)
          elif activation == 'lrelu':
              self.activation = nn.LeakyReLU(0.2, inplace=True)
          elif activation == 'prelu':
              self.activation = nn.PReLU()
          elif activation == 'selu':
              self.activation = nn.SELU(inplace=True)
          elif activation == 'tanh':
              self.activation = nn.Tanh()
          elif activation == 'none':
              self.activation = None
          else:
              assert 0, "Unsupported activation: {}".format(activation)
  
      def forward(self, x):
          out = self.fc(x)
          if self.norm:
              out = self.norm(out)
          if self.activation:
              out = self.activation(out)
          return out
  
  ##################################################################################
  # Normalization layers
  ##################################################################################
  
  
  class LayerNorm(nn.Module):
      def __init__(self, num_features, eps=1e-5, affine=True):
          super(LayerNorm, self).__init__()
          self.num_features = num_features
          self.affine = affine
          self.eps = eps
  
          if self.affine:
              self.gamma = nn.Parameter(torch.Tensor(num_features).uniform_())
              self.beta = nn.Parameter(torch.zeros(num_features))
  
      def forward(self, x):
          shape = [-1] + [1] * (x.dim() - 1)
          mean = x.view(x.size(0), -1).mean(1).view(*shape)
          std = x.view(x.size(0), -1).std(1).view(*shape)
          x = (x - mean) / (std + self.eps)
  
          if self.affine:
              shape = [1, -1] + [1] * (x.dim() - 2)
              x = x * self.gamma.view(*shape) + self.beta.view(*shape)
          return x
  
  class SpatialAttention(nn.Module):
      def __init__(self, kernel_size=1):
          super(SpatialAttention, self).__init__()
          kernel_size = 1
          padding = 3 if kernel_size == 7 else 0
          
          self.conv1 = nn.Conv2d(64, 4, kernel_size, padding=padding, bias=False)
          self.conv2 = nn.Conv2d(4, 4, kernel_size , padding=padding, bias=False)
          self.conv3 = nn.Conv2d(4, 64, kernel_size, padding=padding, bias=False)
          self.maxPooling = nn.MaxPool2d(4,stride=4)
          self.relu = nn.ReLU()
          self.softmax = nn.Softmax(dim=1)
          self.upsample = nn.Upsample(scale_factor=4)
  
      def forward(self, x):
  
          x1 = self.conv1(x)
          x2 = self.maxPooling(x1)
          reshaped1 = torch.reshape(x2,(x2.shape[0],x2.shape[1],-1,x2.shape[2]))
          y = torch.matmul(reshaped1,x2)
          z = self.relu(y)
          z = self.conv2(z)
          t = self.softmax(z) 
          out1 = torch.matmul(t,reshaped1)
          conv3_out = self.conv3(out1)
          upsample_out = self.upsample(conv3_out)
          k = torch.reshape(upsample_out,(upsample_out.shape[0],upsample_out.shape[1],-1,upsample_out.shape[2]))
          output = k + x        
                  
          return output
  
  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', no_antialias=False, no_antialias_up=False, opt=None):
          """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__()
          self.opt = opt
          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)]
      
          self.SAB = SpatialAttention()
          model += [self.SAB]
          n_downsampling = 2
          for i in range(n_downsampling):  # add downsampling layers
              mult = 2 ** i
              if(no_antialias):
                  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)]
              else:
                  model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=1, padding=1, bias=use_bias),
                            norm_layer(ngf * mult * 2),
                            nn.ReLU(True),
                            Downsample(ngf * mult * 2)]
  
          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)
              if no_antialias_up:
                  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)]
              else:
                  model += [Upsample(ngf * mult),
                            nn.Conv2d(ngf * mult, int(ngf * mult / 2),
                                      kernel_size=3, stride=1,
                                      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, layers=[], encode_only=False):
          if -1 in layers:
              layers.append(len(self.model))
              # print("-1 layerdaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa")
          if len(layers) > 0:
              # print("iffffffffteyimmmmmmmmmmmmmmmmmmmmmmmmmmmm")
              feat = input
              feats = []
              for layer_id, layer in enumerate(self.model):
                  #print("hereeeeeeeeeeeeeeee",layer_id, layer)
                  feat = layer(feat)
       
                  if layer_id in layers:
                      # print("%d: adding the output of %s %d" % (layer_id, layer.__class__.__name__, feat.size(1)))
                      feats.append(feat)
                  else:
                      # print("%d: skipping %s %d" % (layer_id, layer.__class__.__name__, feat.size(1)))
                      pass
                  if layer_id == layers[-1] and encode_only:
                      #print('encoder only return featuresssssssssssssssssssss')
                      return feats  # return intermediate features alone; stop in the last layers
  
              return feat, feats  # return both output and intermediate features
          else:
              """Standard forward"""
              fake = self.model(input)
              # print("elseteyimmmmmmmmmmmmmmmmmmmmmmmmmmmmmm")
              return fake
  
  
  class ResnetDecoder(nn.Module):
      """Resnet-based decoder that consists of a few Resnet blocks + a few upsampling operations.
      """
  
      def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect', no_antialias=False):
          """Construct a Resnet-based decoder
  
          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(ResnetDecoder, self).__init__()
          if type(norm_layer) == functools.partial:
              use_bias = norm_layer.func == nn.InstanceNorm2d
          else:
              use_bias = norm_layer == nn.InstanceNorm2d
          model = []
          n_downsampling = 2
          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)
              if(no_antialias):
                  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)]
              else:
                  model += [Upsample(ngf * mult),
                            nn.Conv2d(ngf * mult, int(ngf * mult / 2),
                                      kernel_size=3, stride=1,
                                      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 ResnetEncoder(nn.Module):
      """Resnet-based encoder that consists of a few downsampling + several Resnet blocks
      """
  
      def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect', no_antialias=False):
          """Construct a Resnet-based encoder
  
          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(ResnetEncoder, 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
              if(no_antialias):
                  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)]
              else:
                  model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=1, padding=1, bias=use_bias),
                            norm_layer(ngf * mult * 2),
                            nn.ReLU(True),
                            Downsample(ngf * mult * 2)]
  
          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)]
  
          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 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, no_antialias=False):
          """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
          if(no_antialias):
              sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
          else:
              sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=1, padding=padw), nn.LeakyReLU(0.2, True), Downsample(ndf)]
          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)
              if(no_antialias):
                  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)
                  ]
              else:
                  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),
                      Downsample(ndf * nf_mult)]
  
          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 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)
  
  
  class PatchDiscriminator(NLayerDiscriminator):
      """Defines a PatchGAN discriminator"""
  
      def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, no_antialias=False):
          super().__init__(input_nc, ndf, 2, norm_layer, no_antialias)
  
      def forward(self, input):
          B, C, H, W = input.size(0), input.size(1), input.size(2), input.size(3)
          size = 16
          Y = H // size
          X = W // size
          input = input.view(B, C, Y, size, X, size)
          input = input.permute(0, 2, 4, 1, 3, 5).contiguous().view(B * Y * X, C, size, size)
          return super().forward(input)
  
  
  class GroupedChannelNorm(nn.Module):
      def __init__(self, num_groups):
          super().__init__()
          self.num_groups = num_groups
  
      def forward(self, x):
          shape = list(x.shape)
          new_shape = [shape[0], self.num_groups, shape[1] // self.num_groups] + shape[2:]
          x = x.view(*new_shape)
          mean = x.mean(dim=2, keepdim=True)
          std = x.std(dim=2, keepdim=True)
          x_norm = (x - mean) / (std + 1e-7)
          return x_norm.view(*shape)
  