tf

def create_mtcnn(sess, model_path):
    if not model_path:
        model_path, _ = os.path.split(os.path.realpath(__file__))

    with tf.variable_scope('pnet'):
        data = tf.placeholder(tf.float32, (None,None,None,3), 'input')
        pnet = PNet({'data':data})
        pnet.load(os.path.join(model_path, 'det1.npy'), sess)
    with tf.variable_scope('rnet'):
        data = tf.placeholder(tf.float32, (None,24,24,3), 'input')
        rnet = RNet({'data':data})
        rnet.load(os.path.join(model_path, 'det2.npy'), sess)
    with tf.variable_scope('onet'):
        data = tf.placeholder(tf.float32, (None,48,48,3), 'input')
        onet = ONet({'data':data})
        onet.load(os.path.join(model_path, 'det3.npy'), sess)
        
    pnet_fun = lambda img : sess.run(('pnet/conv4-2/BiasAdd:0', 'pnet/prob1:0'), feed_dict={'pnet/input:0':img})
    rnet_fun = lambda img : sess.run(('rnet/conv5-2/conv5-2:0', 'rnet/prob1:0'), feed_dict={'rnet/input:0':img})
    onet_fun = lambda img : sess.run(('onet/conv6-2/conv6-2:0', 'onet/conv6-3/conv6-3:0', 'onet/prob1:0'), feed_dict={'onet/input:0':img})
    return pnet_fun, rnet_fun, onet_fun