python 關鍵字之super

class C(B):
  def method(self, arg):
    super(C, self).method(arg)

子類C重寫了父類B中同名方法method，在重寫的實現中通過super實例化的代理對象調用父類的同名方法。

舉例如下

class BaseSuperResolutionModel(object):

    def __init__(self, model_name, scale_factor):
        """
        Base model to provide a standard interface of adding Super Resolution models
        """
        self.model = None # type: Model
        self.model_name = model_name
        self.scale_factor = scale_factor
        self.weight_path = None

        self.type_scale_type = "norm" # Default = "norm" = 1. / 255
        self.type_requires_divisible_shape = False
        self.type_true_upscaling = False

        self.evaluation_func = None
        self.uses_learning_phase = False

    def create_model(self, height=32, width=32, channels=3, load_weights=False, batch_size=128) -> Model:
        """
        Subclass dependent implementation.
        """
        if self.type_requires_divisible_shape:
            assert height * img_utils._image_scale_multiplier % 4 == 0, "Height of the image must be divisible by 4"
            assert width * img_utils._image_scale_multiplier % 4 == 0, "Width of the image must be divisible by 4"

        if K.image_dim_ordering() == "th":
            shape = (channels, width * img_utils._image_scale_multiplier, height * img_utils._image_scale_multiplier)
        else:
            shape = (width * img_utils._image_scale_multiplier, height * img_utils._image_scale_multiplier, channels)

        init = Input(shape=shape)

        return init
class ImageSuperResolutionModel(BaseSuperResolutionModel):

    def __init__(self, scale_factor):
        super(ImageSuperResolutionModel, self).__init__("Image SR", scale_factor)

        self.f1 = 9
        self.f2 = 1
        self.f3 = 5

        self.n1 = 64
        self.n2 = 32

        self.weight_path = "weights/SR Weights %dX.h5" % (self.scale_factor)

    def create_model(self, height=32, width=32, channels=3, load_weights=False, batch_size=128):
        """
            Creates a model to be used to scale images of specific height and width.
        """
        init = super(ImageSuperResolutionModel, self).create_model(height, width, channels, load_weights, batch_size)

        x = Convolution2D(self.n1, self.f1, self.f1, activation='relu', border_mode='same', name='level1')(init)
        x = Convolution2D(self.n2, self.f2, self.f2, activation='relu', border_mode='same', name='level2')(x)

        out = Convolution2D(channels, self.f3, self.f3, border_mode='same', name='output')(x)

        model = Model(init, out)

        adam = optimizers.Adam(lr=1e-3)
        model.compile(optimizer=adam, loss='mse', metrics=[PSNRLoss])
        if load_weights: model.load_weights(self.weight_path)

        self.model = model
return model

super(ImageSuperResolutionModel, self).__init__("Image SR", scale_factor) 繼承了基類中的初始化方法