text: import torch
  import itertools
  from ..util.image_pool import ImagePool
  from .base_model import BaseModel
  from . import networks
  
  
  class CycleGANModel(BaseModel):
      """
      This class implements the CycleGAN model, for learning image-to-image translation without paired data.
  
      The model training requires '--dataset_mode unaligned' dataset.
      By default, it uses a '--netG resnet_9blocks' ResNet generator,
      a '--netD basic' discriminator (PatchGAN introduced by pix2pix),
      and a least-square GANs objective ('--gan_mode lsgan').
  
      CycleGAN paper: https://arxiv.org/pdf/1703.10593.pdf
      """
      @staticmethod
      def modify_commandline_options(parser, is_train=True):
          """Add new dataset-specific options, and rewrite default values for existing options.
  
          Parameters:
              parser          -- original option parser
              is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
  
          Returns:
              the modified parser.
  
          For CycleGAN, in addition to GAN losses, we introduce lambda_A, lambda_B, and lambda_identity for the following losses.
          A (source domain), B (target domain).
          Generators: G_A: A -> B; G_B: B -> A.
          Discriminators: D_A: G_A(A) vs. B; D_B: G_B(B) vs. A.
          Forward cycle loss:  lambda_A * ||G_B(G_A(A)) - A|| (Eqn. (2) in the paper)
          Backward cycle loss: lambda_B * ||G_A(G_B(B)) - B|| (Eqn. (2) in the paper)
          Identity loss (optional): lambda_identity * (||G_A(B) - B|| * lambda_B + ||G_B(A) - A|| * lambda_A) (Sec 5.2 "Photo generation from paintings" in the paper)
          Dropout is not used in the original CycleGAN paper.
          """
          parser.set_defaults(no_dropout=True)  # default CycleGAN did not use dropout
          if is_train:
              parser.add_argument('--lambda_A', type=float, default=10.0, help='weight for cycle loss (A -> B -> A)')
              parser.add_argument('--lambda_B', type=float, default=10.0, help='weight for cycle loss (B -> A -> B)')
              parser.add_argument('--lambda_identity', type=float, default=0.5, help='use identity mapping. Setting lambda_identity other than 0 has an effect of scaling the weight of the identity mapping loss. For example, if the weight of the identity loss should be 10 times smaller than the weight of the reconstruction loss, please set lambda_identity = 0.1')
  
          return parser
  
      def __init__(self, opt):
          """Initialize the CycleGAN class.
  
          Parameters:
              opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
          """
          BaseModel.__init__(self, opt)
          # specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
          self.loss_names = ['D_A', 'G_A', 'cycle_A', 'idt_A', 'D_B', 'G_B', 'cycle_B', 'idt_B']
          # specify the images you want to save/display. The training/test scripts will call <BaseModel.get_current_visuals>
          visual_names_A = ['real_A', 'fake_B', 'rec_A']
          visual_names_B = ['real_B', 'fake_A', 'rec_B']
          if self.isTrain and self.opt.lambda_identity > 0.0:  # if identity loss is used, we also visualize idt_B=G_A(B) ad idt_A=G_A(B)
              visual_names_A.append('idt_B')
              visual_names_B.append('idt_A')
  
          self.visual_names = visual_names_A + visual_names_B  # combine visualizations for A and B
          # specify the models you want to save to the disk. The training/test scripts will call <BaseModel.save_networks> and <BaseModel.load_networks>.
          if self.isTrain:
              self.model_names = ['G_A', 'G_B', 'D_A', 'D_B']
          else:  # during test time, only load Gs
              self.model_names = ['G_A', 'G_B']
  
          # define networks (both Generators and discriminators)
          # The naming is different from those used in the paper.
          # Code (vs. paper): G_A (G), G_B (F), D_A (D_Y), D_B (D_X)
          self.netG_A = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm,
                                          not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
          self.netG_B = networks.define_G(opt.output_nc, opt.input_nc, opt.ngf, opt.netG, opt.norm,
                                          not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
  
          if self.isTrain:  # define discriminators
              self.netD_A = networks.define_D(opt.output_nc, opt.ndf, opt.netD,
                                              opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids)
              self.netD_B = networks.define_D(opt.input_nc, opt.ndf, opt.netD,
                                              opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids)
  
          if self.isTrain:
              if opt.lambda_identity > 0.0:  # only works when input and output images have the same number of channels
                  assert(opt.input_nc == opt.output_nc)
              self.fake_A_pool = ImagePool(opt.pool_size)  # create image buffer to store previously generated images
              self.fake_B_pool = ImagePool(opt.pool_size)  # create image buffer to store previously generated images
              # define loss functions
              self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device)  # define GAN loss.
              self.criterionCycle = torch.nn.L1Loss()
              self.criterionIdt = torch.nn.L1Loss()
              # initialize optimizers; schedulers will be automatically created by function <BaseModel.setup>.
              self.optimizer_G = torch.optim.Adam(itertools.chain(self.netG_A.parameters(), self.netG_B.parameters()), lr=opt.lr, betas=(opt.beta1, 0.999))
              self.optimizer_D = torch.optim.Adam(itertools.chain(self.netD_A.parameters(), self.netD_B.parameters()), lr=opt.lr, betas=(opt.beta1, 0.999))
              self.optimizers.append(self.optimizer_G)
              self.optimizers.append(self.optimizer_D)
  
      def set_input(self, input):
          """Unpack input data from the dataloader and perform necessary pre-processing steps.
  
          Parameters:
              input (dict): include the data itself and its metadata information.
  
          The option 'direction' can be used to swap domain A and domain B.
          """
          AtoB = self.opt.direction == 'AtoB'
          self.real_A = input['A' if AtoB else 'B'].to(self.device)
          self.real_B = input['B' if AtoB else 'A'].to(self.device)
          self.image_paths = input['A_paths' if AtoB else 'B_paths']
  
      def forward(self):
          """Run forward pass; called by both functions <optimize_parameters> and <test>."""
          self.fake_B = self.netG_A(self.real_A)  # G_A(A)
          self.rec_A = self.netG_B(self.fake_B)   # G_B(G_A(A))
          self.fake_A = self.netG_B(self.real_B)  # G_B(B)
          self.rec_B = self.netG_A(self.fake_A)   # G_A(G_B(B))
  
      def backward_D_basic(self, netD, real, fake):
          """Calculate GAN loss for the discriminator
  
          Parameters:
              netD (network)      -- the discriminator D
              real (tensor array) -- real images
              fake (tensor array) -- images generated by a generator
  
          Return the discriminator loss.
          We also call loss_D.backward() to calculate the gradients.
          """
          # Real
          pred_real = netD(real)
          loss_D_real = self.criterionGAN(pred_real, True)
          # Fake
          pred_fake = netD(fake.detach())
          loss_D_fake = self.criterionGAN(pred_fake, False)
          # Combined loss and calculate gradients
          loss_D = (loss_D_real + loss_D_fake) * 0.5
          loss_D.backward()
          return loss_D
  
      def backward_D_A(self):
          """Calculate GAN loss for discriminator D_A"""
          fake_B = self.fake_B_pool.query(self.fake_B)
          self.loss_D_A = self.backward_D_basic(self.netD_A, self.real_B, fake_B)
  
      def backward_D_B(self):
          """Calculate GAN loss for discriminator D_B"""
          fake_A = self.fake_A_pool.query(self.fake_A)
          self.loss_D_B = self.backward_D_basic(self.netD_B, self.real_A, fake_A)
  
      def backward_G(self):
          """Calculate the loss for generators G_A and G_B"""
          lambda_idt = self.opt.lambda_identity
          lambda_A = self.opt.lambda_A
          lambda_B = self.opt.lambda_B
          # Identity loss
          if lambda_idt > 0:
              # G_A should be identity if real_B is fed: ||G_A(B) - B||
              self.idt_A = self.netG_A(self.real_B)
              self.loss_idt_A = self.criterionIdt(self.idt_A, self.real_B) * lambda_B * lambda_idt
              # G_B should be identity if real_A is fed: ||G_B(A) - A||
              self.idt_B = self.netG_B(self.real_A)
              self.loss_idt_B = self.criterionIdt(self.idt_B, self.real_A) * lambda_A * lambda_idt
          else:
              self.loss_idt_A = 0
              self.loss_idt_B = 0
  
          # GAN loss D_A(G_A(A))
          self.loss_G_A = self.criterionGAN(self.netD_A(self.fake_B), True)
          # GAN loss D_B(G_B(B))
          self.loss_G_B = self.criterionGAN(self.netD_B(self.fake_A), True)
          # Forward cycle loss || G_B(G_A(A)) - A||
          self.loss_cycle_A = self.criterionCycle(self.rec_A, self.real_A) * lambda_A
          # Backward cycle loss || G_A(G_B(B)) - B||
          self.loss_cycle_B = self.criterionCycle(self.rec_B, self.real_B) * lambda_B
          # combined loss and calculate gradients
          self.loss_G = self.loss_G_A + self.loss_G_B + self.loss_cycle_A + self.loss_cycle_B + self.loss_idt_A + self.loss_idt_B
          self.loss_G.backward()
  
      def optimize_parameters(self):
          """Calculate losses, gradients, and update network weights; called in every training iteration"""
          # forward
          self.forward()      # compute fake images and reconstruction images.
          # G_A and G_B
          self.set_requires_grad([self.netD_A, self.netD_B], False)  # Ds require no gradients when optimizing Gs
          self.optimizer_G.zero_grad()  # set G_A and G_B's gradients to zero
          self.backward_G()             # calculate gradients for G_A and G_B
          self.optimizer_G.step()       # update G_A and G_B's weights
          # D_A and D_B
          self.set_requires_grad([self.netD_A, self.netD_B], True)
          self.optimizer_D.zero_grad()   # set D_A and D_B's gradients to zero
          self.backward_D_A()      # calculate gradients for D_A
          self.backward_D_B()      # calculate graidents for D_B
          self.optimizer_D.step()  # update D_A and D_B's weights
  