843d68642bd15d5737e3eb39abd76c748d6b52e8,models/cycle_gan_model.py,CycleGANModel,initialize,#CycleGANModel#Any#,14

Before Change

        // load/define networks
        // The naming conversion is different from those used in the paper
        // Code (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.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids)
        self.netG_B = networks.define_G(opt.output_nc, opt.input_nc,
                                        opt.ngf, opt.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids)

        if self.isTrain:
            use_sigmoid = opt.no_lsgan
            self.netD_A = networks.define_D(opt.output_nc, opt.ndf,
                                            opt.which_model_netD,
                                            opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, self.gpu_ids)
            self.netD_B = networks.define_D(opt.input_nc, opt.ndf,
                                            opt.which_model_netD,
                                            opt.n_layers_D, opt.norm, use_sigmoid, opt.init_type, self.gpu_ids)
        if not self.isTrain or opt.continue_train:
            which_epoch = opt.which_epoch
            self.load_network(self.netG_A, "G_A", which_epoch)
            self.load_network(self.netG_B, "G_B", which_epoch)
            if self.isTrain:
                self.load_network(self.netD_A, "D_A", which_epoch)
                self.load_network(self.netD_B, "D_B", which_epoch)

        if self.isTrain:
            self.fake_A_pool = ImagePool(opt.pool_size)
            self.fake_B_pool = ImagePool(opt.pool_size)
            // define loss functions
            self.criterionGAN = networks.GANLoss(use_lsgan=not opt.no_lsgan, tensor=self.Tensor)
            self.criterionCycle = torch.nn.L1Loss()
            self.criterionIdt = torch.nn.L1Loss()
            // initialize optimizers
            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 = []
            self.schedulers = []
            self.optimizers.append(self.optimizer_G)
            self.optimizers.append(self.optimizer_D)
            for optimizer in self.optimizers:
                self.schedulers.append(networks.get_scheduler(optimizer, opt))

        print("---------- Networks initialized -------------")
        networks.print_network(self.netG_A, opt.verbose)
        networks.print_network(self.netG_B, opt.verbose)
        if self.isTrain:
            networks.print_network(self.netD_A, opt.verbose)
            networks.print_network(self.netD_B, opt.verbose)
        print("-----------------------------------------------")

    def set_input(self, input):
        AtoB = self.opt.which_direction == "AtoB"

After Change

    def name(self):
        return "CycleGANModel"

    def initialize(self, opt):
        BaseModel.initialize(self, opt)

        // specify the training losses you want to print out. The program will call base_model.get_current_errors
        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 program will call base_model.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:
            visual_names_A.append("idt_A")
            visual_names_B.append("idt_B")

        self.visual_names = visual_names_A + visual_names_B
        // specify the models you want to save to the disk. The program will call base_model.save
        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"]

        // load/define networks
        // The naming conversion is different from those used in the paper
        // Code (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.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids)
        self.netG_B = networks.define_G(opt.output_nc, opt.input_nc,
                                        opt.ngf, opt.which_model_netG, opt.norm, not opt.no_dropout, opt.init_type, self.gpu_ids)

Instances