birds_skip_thought_demo.py 文件源码

def build_model(sess, embedding_dim, batch_size):
    model = CondGAN(
        lr_imsize=cfg.TEST.LR_IMSIZE,
        hr_lr_ratio=int(cfg.TEST.HR_IMSIZE/cfg.TEST.LR_IMSIZE))

    embeddings = tf.placeholder(
        tf.float32, [batch_size, embedding_dim],
        name='conditional_embeddings')
    with pt.defaults_scope(phase=pt.Phase.test):
        with tf.variable_scope("g_net"):
            c = sample_encoded_context(embeddings, model)
            z = tf.random_normal([batch_size, cfg.Z_DIM])
            fake_images = model.get_generator(tf.concat(1, [c, z]))
        with tf.variable_scope("hr_g_net"):
            hr_c = sample_encoded_context(embeddings, model)
            hr_fake_images = model.hr_get_generator(fake_images, hr_c)

    ckt_path = cfg.TEST.PRETRAINED_MODEL
    if ckt_path.find('.ckpt') != -1:
        print("Reading model parameters from %s" % ckt_path)
        saver = tf.train.Saver(tf.all_variables())
        saver.restore(sess, ckt_path)
    else:
        print("Input a valid model path.")
    return embeddings, fake_images, hr_fake_images