masked_convolution2d.py 文件源码

def build(self, input_shape):
        super().build(input_shape)

        self.mask = np.ones(self.W_shape)

        assert mask.shape[0] == mask.shape[1]
        filter_size = self.mask.shape[0]
        filter_center = filter_size / 2

        self.mask[math.ceil(filter_center):] = 0
        self.mask[math.floor(filter_center):, math.ceil(filter_center):] = 0

        if self.mono:
            if self.mask_type == 'A':
                self.mask[math.floor(filter_center), math.floor(filter_center)] = 0
        else:
            op = np.greater_equal if self.mask_type == 'A' else np.greater
            for i in range(self.n_channels):
                for j in range(self.n_channels):
                    if op(i, j):
                        self.mask[math.floor(filter_center), math.floor(filter_center), i::self.n_channels, j::self.n_channels] = 0

        self.mask = K.variable(self.mask)