import os
from tensorflow.python import pywrap_tensorflow
from net2use import inception_resnet_v2_small#这里使用自己定义的模型函数即可
import tensorflow as tf
if __name__=='__main__':
  pb_file = "./model/output.pb"
  ckpt_file = "./model/model.ckpt-652900"
  '''
这里的节点名字可能跟设想的有出入，最直接的方法是直接输出ckpt中保存的节点名字，然后对应着找节点名字，具体的进入convert_variables_to_constants函数的实现中graph_util_impl.py，130行的函数：_assert_nodes_are_present 添加代码
  print('在图中的节点是：')
  for din in name_to_node:
    print('{},在图中'.format(din))
然后运行代码，若正确就会直接保存；若失败则会保存失败，找好输出节点的名字，在output_node_names 中添加就好
'''
  output_node_names = ["embedding"]

  with tf.name_scope('input'):
    image = tf.placeholder(tf.float32,shape=(None,79,199,1),name='input_image')


  net, endpoints=inception_resnet_v2_small(image, is_training=False)
  embedding = tf.nn.l2_normalize(net,1,1e-10,name='embedding')

  config=tf.ConfigProto(allow_soft_placement=True)
  config.gpu_options.per_process_gpu_memory_fraction = 0.45
  sess = tf.Session(config = config)
  saver = tf.train.Saver()
  saver.restore(sess, ckpt_file)
  print('read success')
  converted_graph_def = tf.graph_util.convert_variables_to_constants(sess,
                input_graph_def = sess.graph.as_graph_def(),
                output_node_names = output_node_names)

  with tf.gfile.GFile(pb_file, "wb") as f:
    f.write(converted_graph_def.SerializeToString())

  print('保存成功')