synthesize.py 文件源码

def get_texture_loss_for_layer(x, s, l):
    with tf.name_scope('get_style_loss_for_layer'):
        # Compute gram matrices using the activated filter maps of the art and generated images
        x_layer_maps = getattr(x, l)
        t_layer_maps = getattr(s, l)
        x_layer_gram = convert_to_gram(x_layer_maps)
        t_layer_gram = convert_to_gram(t_layer_maps)

        # Make sure the feature map dimensions are the same
        assert_equal_shapes = tf.assert_equal(x_layer_maps.get_shape(), t_layer_maps.get_shape())
        with tf.control_dependencies([assert_equal_shapes]):
            # Compute and return the normalized gram loss using the gram matrices
            shape = x_layer_maps.get_shape().as_list()
            size = reduce(lambda a, b: a * b, shape) ** 2
            gram_loss = get_l2_norm_loss(x_layer_gram - t_layer_gram)
            return gram_loss / size


# Compute total variation regularization loss term given a variable image (x) and its shape