layers.py 文件源码

def _masked_conv(self, x, filter_size, stack_name, layer_idx, mask_type='B'):
        if stack_name == 'vertical':
            res = ZeroPadding2D(padding=(filter_size[0]//2, 0, filter_size[1]//2, filter_size[1]//2), name='v_pad_'+str(layer_idx))(x)
            res = Convolution2D(2*self.nb_filters, filter_size[0]//2+1, filter_size[1], border_mode='valid', name='v_conv_'+str(layer_idx))(res)
        elif stack_name == 'horizontal':
            res = ZeroPadding2D(padding=(0, 0, filter_size[1]//2, 0), name='h_pad_'+str(layer_idx))(x)
            if mask_type == 'A':
                res = Convolution2D(2*self.nb_filters, 1, filter_size[1]//2, border_mode='valid', name='h_conv_'+str(layer_idx))(res)
            else:
                res = Convolution2D(2*self.nb_filters, 1, filter_size[1]//2+1, border_mode='valid', name='h_conv_'+str(layer_idx))(res)

        return res