Method __init__

models/template_model.py:41–70

Initialize this model class. Parameters: opt -- training/test options A few things can be done here. - (required) call the initialization function of BaseModel - define loss function, visualization images, model names, and optimizers

(self, opt)

        return parser

    def __init__(self, opt):
        """Initialize this model class.

        Parameters:
            opt -- training/test options

        A few things can be done here.
        - (required) call the initialization function of BaseModel
        - define loss function, visualization images, model names, and optimizers
        """
        BaseModel.__init__(self, opt)  # call the initialization method of BaseModel
        # specify the training losses you want to print out. The program will call base_model.get_current_losses to plot the losses to the console and save them to the disk.
        self.loss_names = ['loss_G']
        # specify the images you want to save and display. The program will call base_model.get_current_visuals to save and display these images.
        self.visual_names = ['data_A', 'data_B', 'output']
        # specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks to save and load networks.
        # you can use opt.isTrain to specify different behaviors for training and test. For example, some networks will not be used during test, and you don't need to load them.
        self.model_names = ['G']
        # define networks; you can use opt.isTrain to specify different behaviors for training and test.
        self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, gpu_ids=self.gpu_ids)
        if self.isTrain:  # only defined during training time
            # define your loss functions. You can use losses provided by torch.nn such as torch.nn.L1Loss.
            # We also provide a GANLoss class "networks.GANLoss". self.criterionGAN = networks.GANLoss().to(self.device)
            self.criterionLoss = torch.nn.L1Loss()
            # define and initialize optimizers. You can define one optimizer for each network.
            # If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example.
            self.optimizer = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
            self.optimizers = [self.optimizer]

        # Our program will automatically call <model.setup> to define schedulers, load networks, and print networks

    def set_input(self, input):
        """Unpack input data from the dataloader and perform necessary pre-processing steps.