import os
from tensorflow.python import pywrap_tensorflow
from net2use import inception_resnet_v2_small#這里使用自己定義的模型函數(shù)即可
import tensorflow as tf
if __name__=='__main__':
  pb_file = "./model/output.pb"
  ckpt_file = "./model/model.ckpt-652900"
  '''
這里的節(jié)點(diǎn)名字可能跟設(shè)想的有出入，最直接的方法是直接輸出ckpt中保存的節(jié)點(diǎn)名字，然后對(duì)應(yīng)著找節(jié)點(diǎn)名字，具體的進(jìn)入convert_variables_to_constants函數(shù)的實(shí)現(xiàn)中g(shù)raph_util_impl.py，130行的函數(shù)：_assert_nodes_are_present 添加代碼
  print('在圖中的節(jié)點(diǎn)是：')
  for din in name_to_node:
    print('{},在圖中'.format(din))
然后運(yùn)行代碼，若正確就會(huì)直接保存；若失敗則會(huì)保存失敗，找好輸出節(jié)點(diǎn)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')