3dgan_mit_biasfree.py 文件源码

def testGAN(trained_model_path=None, n_batches=40):

    weights = initialiseWeights()

    z_vector = tf.placeholder(shape=[batch_size,z_size],dtype=tf.float32) 
    net_g_test = generator(z_vector, phase_train=True, reuse=True)

    vis = visdom.Visdom()

    sess = tf.Session()
    saver = tf.train.Saver()

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        saver.restore(sess, trained_model_path) 

        # output generated chairs
        for i in range(n_batches):
            next_sigma = float(raw_input())
            z_sample = np.random.normal(0, next_sigma, size=[batch_size, z_size]).astype(np.float32)
            g_objects = sess.run(net_g_test,feed_dict={z_vector:z_sample})
            id_ch = np.random.randint(0, batch_size, 4)
            for i in range(4):
                print g_objects[id_ch[i]].max(), g_objects[id_ch[i]].min(), g_objects[id_ch[i]].shape
                if g_objects[id_ch[i]].max() > 0.5:
                    d.plotVoxelVisdom(np.squeeze(g_objects[id_ch[i]]>0.5), vis, '_'.join(map(str,[i])))