Method __init__

models/base_model.py:18–44

Initialize the BaseModel class. Parameters: opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions When creating your custom class, you need to implement your own initialization. In this fucntion, you should first call <BaseM

(self, opt)

    """

    def __init__(self, opt):
        """Initialize the BaseModel class.

        Parameters:
            opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions

        When creating your custom class, you need to implement your own initialization.
        In this fucntion, you should first call <BaseModel.__init__(self, opt)>
        Then, you need to define four lists:
            -- self.loss_names (str list):          specify the training losses that you want to plot and save.
            -- self.model_names (str list):         specify the images that you want to display and save.
            -- self.visual_names (str list):        define networks used in our training.
            -- self.optimizers (optimizer list):    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.opt = opt
        self.gpu_ids = opt.gpu_ids
        self.isTrain = opt.isTrain
        self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu')  # get device name: CPU or GPU
        self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)  # save all the checkpoints to save_dir
        if opt.preprocess != 'scale_width':  # with [scale_width], input images might have different sizes, which hurts the performance of cudnn.benchmark.
            torch.backends.cudnn.benchmark = True
        self.loss_names = []
        self.model_names = []
        self.visual_names = []
        self.optimizers = []
        self.image_paths = []
        self.metric = 0  # used for learning rate policy 'plateau'

    @staticmethod
    def dict_grad_hook_factory(add_func=lambda x: x):