cyclegan.py 文件源码

def build_summaries(model):
    """
    """
    images_summary = []

    generations = [
        ('summary_x_gx', 'xx_real', 'gx_fake'),
        ('summary_y_fy', 'yy_real', 'fy_fake')]

    for g in generations:
        images = tf.concat([model[g[1]], model[g[2]]], axis=2)

        images = tf.reshape(images, [1, FLAGS.batch_size * 256, 512, 3])

        images = tf.saturate_cast(images * 127.5 + 127.5, tf.uint8)

        summary = tf.summary.image(g[0], images, max_outputs=4)

        images_summary.append(summary)

    #
    summary_loss_d = tf.summary.scalar('d', model['loss_d'])
    summary_loss_dx = tf.summary.scalar('dx', model['loss_dx'])
    summary_loss_dy = tf.summary.scalar('dy', model['loss_dy'])
    summary_d = \
        tf.summary.merge([summary_loss_d, summary_loss_dx, summary_loss_dy])

    summary_loss_g = tf.summary.scalar('g', model['loss_g'])
    summary_loss_gx = tf.summary.scalar('gx', model['loss_gx'])
    summary_loss_fy = tf.summary.scalar('fy', model['loss_fy'])
    summary_g = \
        tf.summary.merge([summary_loss_g, summary_loss_gx, summary_loss_fy])

    return {
        'images': tf.summary.merge(images_summary),
        'loss_d': summary_d,
        'loss_g': summary_g,
    }