tensorflow实现将ckpt转pb文件的方法

import tensorflow as tf
# 声明两个变量
v1 = tf.Variable(tf.random_normal([1, 2]), name="v1")
v2 = tf.Variable(tf.random_normal([2, 3]), name="v2")
init_op = tf.global_variables_initializer() # 初始化全部变量
saver = tf.train.Saver() # 声明tf.train.Saver类用于保存模型
with tf.Session() as sess:
 sess.run(init_op)
 print("v1:", sess.run(v1)) # 打印v1、v2的值一会读取之后对比
 print("v2:", sess.run(v2))
 saver_path = saver.save(sess, "save/model.ckpt") # 将模型保存到save/model.ckpt文件
 print("Model saved in file:", saver_path)

def freeze_graph(input_checkpoint,output_graph):
 '''
 :param input_checkpoint:
 :param output_graph: PB模型保存路径
 :return:
 '''
 # checkpoint = tf.train.get_checkpoint_state(model_folder) #检查目录下ckpt文件状态是否可用
 # input_checkpoint = checkpoint.model_checkpoint_path #得ckpt文件路径
 
 # 指定输出的节点名称,该节点名称必须是原模型中存在的节点
 output_node_names = "InceptionV3/Logits/SpatialSqueeze"
 saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=True)
 graph = tf.get_default_graph() # 获得默认的图
 input_graph_def = graph.as_graph_def() # 返回一个序列化的图代表当前的图
 
 with tf.Session() as sess:
 saver.restore(sess, input_checkpoint) #恢复图并得到数据
 output_graph_def = graph_util.convert_variables_to_constants( # 模型持久化，将变量值固定
 sess=sess,
 input_graph_def=input_graph_def,# 等于:sess.graph_def
 output_node_names=output_node_names.split(","))# 如果有多个输出节点，以逗号隔开
 
 with tf.gfile.GFile(output_graph, "wb") as f: #保存模型
 f.write(output_graph_def.SerializeToString()) #序列化输出
 print("%d ops in the final graph." % len(output_graph_def.node)) #得到当前图有几个操作节点
 
 # for op in graph.get_operations():
 # print(op.name, op.values())

def freeze_graph(input_checkpoint,output_graph):
 '''
 :param input_checkpoint:
 :param output_graph: PB模型保存路径
 :return:
 '''
 # checkpoint = tf.train.get_checkpoint_state(model_folder) #检查目录下ckpt文件状态是否可用
 # input_checkpoint = checkpoint.model_checkpoint_path #得ckpt文件路径
 
 # 指定输出的节点名称,该节点名称必须是原模型中存在的节点
 output_node_names = "InceptionV3/Logits/SpatialSqueeze"
 saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=True)
 
 with tf.Session() as sess:
 saver.restore(sess, input_checkpoint) #恢复图并得到数据
 output_graph_def = graph_util.convert_variables_to_constants( # 模型持久化，将变量值固定
 sess=sess,
 input_graph_def=sess.graph_def,# 等于:sess.graph_def
 output_node_names=output_node_names.split(","))# 如果有多个输出节点，以逗号隔开
 
 with tf.gfile.GFile(output_graph, "wb") as f: #保存模型
 f.write(output_graph_def.SerializeToString()) #序列化输出
 print("%d ops in the final graph." % len(output_graph_def.node)) #得到当前图有几个操作节点

def freeze_graph_test(pb_path, image_path):
 '''
 :param pb_path:pb文件的路径
 :param image_path:测试图片的路径
 :return:
 '''
 with tf.Graph().as_default():
 output_graph_def = tf.GraphDef()
 with open(pb_path, "rb") as f:
 output_graph_def.ParseFromString(f.read())
 tf.import_graph_def(output_graph_def, name="")
 with tf.Session() as sess:
 sess.run(tf.global_variables_initializer())
 
 # 定义输入的张量名称,对应网络结构的输入张量
 # input:0作为输入图像,keep_prob:0作为dropout的参数,测试时值为1,is_training:0训练参数
 input_image_tensor = sess.graph.get_tensor_by_name("input:0")
 input_keep_prob_tensor = sess.graph.get_tensor_by_name("keep_prob:0")
 input_is_training_tensor = sess.graph.get_tensor_by_name("is_training:0")
 
 # 定义输出的张量名称
 output_tensor_name = sess.graph.get_tensor_by_name("InceptionV3/Logits/SpatialSqueeze:0")
 
 # 读取测试图片
 im=read_image(image_path,resize_height,resize_width,normalization=True)
 im=im[np.newaxis,:]
 # 测试读出来的模型是否正确，注意这里传入的是输出和输入节点的tensor的名字，不是操作节点的名字
 # out=sess.run("InceptionV3/Logits/SpatialSqueeze:0", feed_dict={'input:0': im,'keep_prob:0':1.0,'is_training:0':False})
 out=sess.run(output_tensor_name, feed_dict={input_image_tensor: im,
 input_keep_prob_tensor:1.0,
 input_is_training_tensor:False})
 print("out:{}".format(out))
 score = tf.nn.softmax(out, name='pre')
 class_id = tf.argmax(score, 1)
 print "pre class_id:{}".format(sess.run(class_id))

# -*-coding: utf-8 -*-
"""
 @Project: tensorflow_models_nets
 @File : convert_pb.py
 @Author : panjq
 @E-mail : pan_jinquan@163.com
 @Date : 2018-08-29 17:46:50
 @info :
 -通过传入 CKPT 模型的路径得到模型的图和变量数据
 -通过 import_meta_graph 导入模型中的图
 -通过 saver.restore 从模型中恢复图中各个变量的数据
 -通过 graph_util.convert_variables_to_constants 将模型持久化
"""
 
import tensorflow as tf
from create_tf_record import *
from tensorflow.python.framework import graph_util
 
resize_height = 299 # 指定图片高度
resize_width = 299 # 指定图片宽度
depths = 3
 
def freeze_graph_test(pb_path, image_path):
 '''
 :param pb_path:pb文件的路径
 :param image_path:测试图片的路径
 :return:
 '''
 with tf.Graph().as_default():
 output_graph_def = tf.GraphDef()
 with open(pb_path, "rb") as f:
 output_graph_def.ParseFromString(f.read())
 tf.import_graph_def(output_graph_def, name="")
 with tf.Session() as sess:
 sess.run(tf.global_variables_initializer())
 
 # 定义输入的张量名称,对应网络结构的输入张量
 # input:0作为输入图像,keep_prob:0作为dropout的参数,测试时值为1,is_training:0训练参数
 input_image_tensor = sess.graph.get_tensor_by_name("input:0")
 input_keep_prob_tensor = sess.graph.get_tensor_by_name("keep_prob:0")
 input_is_training_tensor = sess.graph.get_tensor_by_name("is_training:0")
 
 # 定义输出的张量名称
 output_tensor_name = sess.graph.get_tensor_by_name("InceptionV3/Logits/SpatialSqueeze:0")
 
 # 读取测试图片
 im=read_image(image_path,resize_height,resize_width,normalization=True)
 im=im[np.newaxis,:]
 # 测试读出来的模型是否正确，注意这里传入的是输出和输入节点的tensor的名字，不是操作节点的名字
 # out=sess.run("InceptionV3/Logits/SpatialSqueeze:0", feed_dict={'input:0': im,'keep_prob:0':1.0,'is_training:0':False})
 out=sess.run(output_tensor_name, feed_dict={input_image_tensor: im,
 input_keep_prob_tensor:1.0,
 input_is_training_tensor:False})
 print("out:{}".format(out))
 score = tf.nn.softmax(out, name='pre')
 class_id = tf.argmax(score, 1)
 print "pre class_id:{}".format(sess.run(class_id))
 
 
def freeze_graph(input_checkpoint,output_graph):
 '''
 :param input_checkpoint:
 :param output_graph: PB模型保存路径
 :return:
 '''
 # checkpoint = tf.train.get_checkpoint_state(model_folder) #检查目录下ckpt文件状态是否可用
 # input_checkpoint = checkpoint.model_checkpoint_path #得ckpt文件路径
 
 # 指定输出的节点名称,该节点名称必须是原模型中存在的节点
 output_node_names = "InceptionV3/Logits/SpatialSqueeze"
 saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=True)
 
 with tf.Session() as sess:
 saver.restore(sess, input_checkpoint) #恢复图并得到数据
 output_graph_def = graph_util.convert_variables_to_constants( # 模型持久化，将变量值固定
 sess=sess,
 input_graph_def=sess.graph_def,# 等于:sess.graph_def
 output_node_names=output_node_names.split(","))# 如果有多个输出节点，以逗号隔开
 
 with tf.gfile.GFile(output_graph, "wb") as f: #保存模型
 f.write(output_graph_def.SerializeToString()) #序列化输出
 print("%d ops in the final graph." % len(output_graph_def.node)) #得到当前图有几个操作节点
 
 # for op in sess.graph.get_operations():
 # print(op.name, op.values())
 
def freeze_graph2(input_checkpoint,output_graph):
 '''
 :param input_checkpoint:
 :param output_graph: PB模型保存路径
 :return:
 '''
 # checkpoint = tf.train.get_checkpoint_state(model_folder) #检查目录下ckpt文件状态是否可用
 # input_checkpoint = checkpoint.model_checkpoint_path #得ckpt文件路径
 
 # 指定输出的节点名称,该节点名称必须是原模型中存在的节点
 output_node_names = "InceptionV3/Logits/SpatialSqueeze"
 saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=True)
 graph = tf.get_default_graph() # 获得默认的图
 input_graph_def = graph.as_graph_def() # 返回一个序列化的图代表当前的图
 
 with tf.Session() as sess:
 saver.restore(sess, input_checkpoint) #恢复图并得到数据
 output_graph_def = graph_util.convert_variables_to_constants( # 模型持久化，将变量值固定
 sess=sess,
 input_graph_def=input_graph_def,# 等于:sess.graph_def
 output_node_names=output_node_names.split(","))# 如果有多个输出节点，以逗号隔开
 
 with tf.gfile.GFile(output_graph, "wb") as f: #保存模型
 f.write(output_graph_def.SerializeToString()) #序列化输出
 print("%d ops in the final graph." % len(output_graph_def.node)) #得到当前图有几个操作节点
 
 # for op in graph.get_operations():
 # print(op.name, op.values())
 
 
if __name__ == '__main__':
 # 输入ckpt模型路径
 input_checkpoint='models/model.ckpt-10000'
 # 输出pb模型的路径
 out_pb_path="models/pb/frozen_model.pb"
 # 调用freeze_graph将ckpt转为pb
 freeze_graph(input_checkpoint,out_pb_path)
 
 # 测试pb模型
 image_path = 'test_image/animal.jpg'
 freeze_graph_test(pb_path=out_pb_path, image_path=image_path)