synthesize.py 文件源码

def get_total_variation(x, shape):
    with tf.name_scope('get_total_variation'):
        # Get the dimensions of the variable image
        height = shape[1]
        width = shape[2]
        size = reduce(lambda a, b: a * b, shape) ** 2

        # Disjoin the variable image and evaluate the total variation
        x_cropped = x[:, :height - 1, :width - 1, :]
        left_term = tf.square(x[:, 1:, :width - 1, :] - x_cropped)
        right_term = tf.square(x[:, :height - 1, 1:, :] - x_cropped)
        smoothed_terms = tf.pow(left_term + right_term, TOTAL_VARIATION_SMOOTHING / 2.)
        return tf.reduce_sum(smoothed_terms) / size


# Parse arguments and assign them to their respective global variables