用C++调用tensorflow在python下训练好的模型(centos7)

本文主要参考博客https://blog.csdn.net/luoyexuge/article/details/80399265 [1]
bazel安装参考:https://blog.csdn.net/luoyi131420/article/details/78585989 [2]

首先介绍下自己的环境是centos7，tensorflow版本是1.7，python是3.6（anaconda3）。

要调用tensorflow c++接口，首先要编译tensorflow，要装bazel，要装protobuf，要装Eigen；然后是用python训练模型并保存，最后才是调用训练好的模型，整体过程还是比较麻烦，下面按步骤一步步说明。

1.安装bazel
以下是引用的[2]

首先安装bazel依赖的环境：
sudo add-apt-repository ppa:webupd8team/javasudo apt-get install openjdk-8-jdk openjdk-8-source sudo apt-get install pkg-config zip g++ zlib1g-dev unzip 注意：如果你没有安装add-apt-repository命令，需要执行sudo apt-get install software-properties-common命令。

实际上我自己只缺jdk工具，加上我没有sudo权限，我自己是在网上直接下的jdk-8，链接是
http://www.oracle.com/technetwork/java/javase/downloads/java-archive-javase8-2177648.html
然后解压，最后将其路径添加到环境变量中：
export JAVA_HOME=/home/guozitao001/tools/jdk1.8.0_171
export PATH=$JAVA_HOME/bin:$PATH

然后去git上下载bazel的安装文件https://github.com/bazelbuild/bazel/releases，具体是文件bazel-0.15.0-installer-linux-x86_64.sh。
（1）终端切换到.sh文件存放的路径，文件添加可执行权限：
$ chmod +x bazel-0.5.3-installer-linux-x86_64.sh
（2）然后执行该文件：
$ ./bazel-0.5.3-installer-linux-x86_64.sh –user
注意：–user选项表示bazel安装到HOME/bin目录下，并设置.bazelrc的路径为HOME/.bazelrc。
安装完成后执行bazel看是否安装成功，这里我并没有添加环境变量就可以直接运行，大家根据自己需要添加。

2.安装protobuf

下载地址：https://github.com/google/protobuf/releases ，我下载的是3.5.1版本，如果你是下载新版的tensorflow，请确保protobuf版本也是最新的，安装步骤：
cd /protobuf
./configure
make
sudo make install
安装之后查看protobuf版本：
protoc --version

根据[1]的作者采坑经历所说，protoc一定要注意版本要和tensorflow匹配，总之这里3.5.1的protoc和tensorflow1.7是能够匹配的。

3.安装Eigen

wget http://bitbucket.org/eigen/eigen/get/3.3.4.tar.bz2 下载之后解压放在重新命名为eigen3，我存放的路径是,/Users/zhoumeixu/Downloads/eigen3

这个没什么好多说的，如果wget失败就直接用浏览器或者迅雷下载就是了。

4.tensorflow下载以及编译：
1下载TensorFlow ，使用 git clone - –recursive https://github.com/tensorflow/tensorflow
2.下载bazel工具（mac下载installer-darwin、linux用installer-linux）
3. 进入tensorflow的根目录
3.1 执行./configure 根据提示配置一下环境变量，这个不大重要。
要GPU的话要下载nvidia驱动的尽量装最新版的驱动吧还有cudnn version为5以上的这些在官网都有提及的
3.2 有显卡的执行 ” bazel build –config=opt –config=cuda //tensorflow:libtensorflow_cc.so ”
没显卡的 ” –config=cuda ” 就不要加了
bazel build –config=opt //tensorflow:libtensorflow_cc.so。
编译成功后会有bazel成功的提示。
3.3这里编译完过后，最后调用tensorflow模型的时候的时候提示文件tensorflow/tensorflow/core/platform/default/mutex.h缺2个头文件:nsync_cv.h,nsync_mu.h，仔细查找后，发现这两个头文件在python的site-papackages里面，它只是没找到而已，所以我们在mutex.h中将这两个头文件的路径补充完整：
这里写图片描述
这样之后调用就不会提示缺少头文件了。

4.python训练tensorflow模型：
下面训练tensorflow模型的pb模型，[1]作者做了个简单的线性回归模型及生成pb格式模型代码：

# coding:utf-8
# python 3.6
import tensorflow as  tf
import numpy as np import os tf.app.flags.DEFINE_integer('training_iteration', 1000, 'number of training iterations.') tf.app.flags.DEFINE_integer('model_version', 1, 'version number of the model.') tf.app.flags.DEFINE_string('work_dir', 'model/', 'Working directory.') FLAGS = tf.app.flags.FLAGS sess = tf.InteractiveSession() x = tf.placeholder('float', shape=[None, 5],name="inputs") y_ = tf.placeholder('float', shape=[None, 1]) w = tf.get_variable('w', shape=[5, 1], initializer=tf.truncated_normal_initializer) b = tf.get_variable('b', shape=[1], initializer=tf.zeros_initializer) sess.run(tf.global_variables_initializer()) y = tf.add(tf.matmul(x, w) , b,name="outputs") ms_loss = tf.reduce_mean((y - y_) ** 2) train_step = tf.train.GradientDescentOptimizer(0.005).minimize(ms_loss) train_x = np.random.randn(1000, 5) # let the model learn the equation of y = x1 * 1 + x2 * 2 + x3 * 3 train_y = np.sum(train_x * np.array([1, 2, 3,4,5]) + np.random.randn(1000, 5) / 100, axis=1).reshape(-1, 1) for i in range(FLAGS.training_iteration): loss, _ = sess.run([ms_loss, train_step], feed_dict={x: train_x, y_: train_y}) if i%100==0: print("loss is:",loss) graph = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ["inputs", "outputs"]) tf.train.write_graph(graph, ".", FLAGS.work_dir + "liner.pb", as_text=False) print('Done exporting!')

注意这里一定要把需要输入和输出的变量要以string形式的name在tf.graph_util.convert_variables_to_constants中进行保存，比如说这里的inputs和outputs。得到一个后缀为pb的文件
然后加载该模型，验证是否成功保存模型：

import tensorflow as tf
import  numpy as np
logdir = '/Users/zhoumeixu/Documents/python/credit-nlp-ner/model/'
output_graph_path = logdir+'liner.pb'
with tf.Graph().as_default(): output_graph_def = tf.GraphDef() with open(output_graph_path, "rb") as f: output_graph_def.ParseFromString(f.read()) _ = tf.import_graph_def(output_graph_def,name="") with tf.Session() as sess: input = sess.graph.get_tensor_by_name("inputs:0") output = sess.graph.get_tensor_by_name("outputs:0") result = sess.run(output, feed_dict={input: np.reshape([1.0,1.0,1.0,1.0,1.0],[-1,5])}) print(result)

运行结果:[[14.998546]]，该结果完全符合预期。

5.C++项目代码，一共有4个文件

model_loader_base.h:

#ifndef CPPTENSORFLOW_MODEL_LOADER_BASE_H
#define CPPTENSORFLOW_MODEL_LOADER_BASE_H
#include <iostream>
#include <vector>
#include "tensorflow/core/public/session.h" #include "tensorflow/core/platform/env.h" using namespace tensorflow; namespace tf_model { /** * Base Class for feature adapter, common interface convert input format to tensors * */ class FeatureAdapterBase{ public: FeatureAdapterBase() {}; virtual ~FeatureAdapterBase() {}; virtual void assign(std::string, std::vector<double>*) = 0; // tensor_name, tensor_double_vector std::vector<std::pair<std::string, tensorflow::Tensor> > input; }; class ModelLoaderBase { public: ModelLoaderBase() {}; virtual ~ModelLoaderBase() {}; virtual int load(tensorflow::Session*, const std::string) = 0; //pure virutal function load method virtual int predict(tensorflow::Session*, const FeatureAdapterBase&, const std::string, double*) = 0; tensorflow::GraphDef graphdef; //Graph Definition for current model }; } #endif //CPPTENSORFLOW_MODEL_LOADER_BASE_H

ann_model_loader.h:

#ifndef CPPTENSORFLOW_ANN_MODEL_LOADER_H
#define CPPTENSORFLOW_ANN_MODEL_LOADER_H#include "model_loader_base.h"
#include "tensorflow/core/public/session.h"
#include "tensorflow/core/platform/env.h" using namespace tensorflow; namespace tf_model { /** * @brief: Model Loader for Feed Forward Neural Network * */ class ANNFeatureAdapter: public FeatureAdapterBase { public: ANNFeatureAdapter(); ~ANNFeatureAdapter(); void assign(std::string tname, std::vector<double>*) override; // (tensor_name, tensor) }; class ANNModelLoader: public ModelLoaderBase { public: ANNModelLoader(); ~ANNModelLoader(); int load(tensorflow::Session*, const std::string) override; //Load graph file and new session int predict(tensorflow::Session*, const FeatureAdapterBase&, const std::string, double*) override; }; } #endif //CPPTENSORFLOW_ANN_MODEL_LOADER_H

ann_model_loader.cpp:

#include <iostream>
#include <vector>
#include <map>
#include "ann_model_loader.h"
//#include <tensor_shape.h> using namespace tensorflow; namespace tf_model { /** * ANNFeatureAdapter Implementation * */ ANNFeatureAdapter::ANNFeatureAdapter() { } ANNFeatureAdapter::~ANNFeatureAdapter() { } /* * @brief: Feature Adapter: convert 1-D double vector to Tensor, shape [1, ndim] * @param: std::string tname, tensor name; * @parma: std::vector<double>*, input vector; * */ void ANNFeatureAdapter::assign(std::string tname, std::vector<double>* vec) { //Convert input 1-D double vector to Tensor int ndim = vec->size(); if (ndim == 0) { std::cout << "WARNING: Input Vec size is 0 ..." << std::endl; return; } // Create New tensor and set value Tensor x(tensorflow::DT_FLOAT, tensorflow::TensorShape({1, ndim})); // New Tensor shape [1, ndim] auto x_map = x.tensor<float, 2>(); for (int j = 0; j < ndim; j++) { x_map(0, j) = (*vec)[j]; } // Append <tname, Tensor> to input input.push_back(std::pair<std::string, tensorflow::Tensor>(tname, x)); } /** * ANN Model Loader Implementation * */ ANNModelLoader::ANNModelLoader() { } ANNModelLoader::~ANNModelLoader() { } /** * @brief: load the graph and add to Session * @param: Session* session, add the graph to the session * @param: model_path absolute path to exported protobuf file *.pb * */ int ANNModelLoader::load(tensorflow::Session* session, const std::string model_path) { //Read the pb file into the grapgdef member tensorflow::Status status_load = ReadBinaryProto(Env::Default(), model_path, &graphdef); if (!status_load.ok()) { std::cout << "ERROR: Loading model failed..." << model_path << std::endl; std::cout << status_load.ToString() << "\n"; return -1; } // Add the graph to the session tensorflow::Status status_create = session->Create(graphdef); if (!status_create.ok()) { std::cout << "ERROR: Creating graph in session failed..." << status_create.ToString() << std::endl; return -1; } return 0; } /** * @brief: Making new prediction * @param: Session* session * @param: FeatureAdapterBase, common interface of input feature * @param: std::string, output_node, tensorname of output node * @param: double, prediction values * */ int ANNModelLoader::predict(tensorflow::Session* session, const FeatureAdapterBase& input_feature, const std::string output_node, double* prediction) { // The session will initialize the outputs std::vector<tensorflow::Tensor> outputs; //shape [batch_size] // @input: vector<pair<string, tensor> >, feed_dict // @output_node: std::string, name of the output node op, defined in the protobuf file tensorflow::Status status = session->Run(input_feature.input, {output_node}, {}, &outputs); if (!status.ok()) { std::cout << "ERROR: prediction failed..." << status.ToString() << std::endl; return -1; } //Fetch output value std::cout << "Output tensor size:" << outputs.size() << std::endl; for (std::size_t i = 0; i < outputs.size(); i++) { std::cout << outputs[i].DebugString(); } std::cout << std::endl; Tensor t = outputs[0]; // Fetch the first tensor int ndim = t.shape().dims(); // Get the dimension of the tensor auto tmap = t.tensor<float, 2>(); // Tensor Shape: [batch_size, target_class_num] int output_dim = t.shape().dim_size(1); // Get the target_class_num from 1st dimension std::vector<double> tout; // Argmax: Get Final Prediction Label and Probability int output_class_id = -1; double output_prob = 0.0; for (int j = 0; j < output_dim; j++) { std::cout << "Class " << j << " prob:" << tmap(0, j) << "," << std::endl; if (tmap(0, j) >= output_prob) { output_class_id = j; output_prob = tmap(0, j); } } // Log std::cout << "Final class id: " << output_class_id << std::endl; std::cout << "Final value is: " << output_prob << std::endl; (*prediction) = output_prob; // Assign the probability to prediction return 0; } }

main.cpp：

#include <iostream>
#include "tensorflow/core/public/session.h"
#include "tensorflow/core/platform/env.h"
#include "ann_model_loader.h"using namespace tensorflow; int main(int argc, char* argv[]) { if (argc != 2) { std::cout << "WARNING: Input Args missing" << std::endl; return 0; } std::string model_path = argv[1]; // Model_path *.pb file // TensorName pre-defined in python file, Need to extract values from tensors std::string input_tensor_name = "inputs"; std::string output_tensor_name = "outputs"; // Create New Session Session* session; Status status = NewSession(SessionOptions(), &session); if (!status.ok()) { std::cout << status.ToString() << "\n"; return 0; } // Create prediction demo tf_model::ANNModelLoader model; //Create demo for prediction if (0 != model.load(session, model_path)) { std::cout << "Error: Model Loading failed..." << std::endl; return 0; } // Define Input tensor and Feature Adapter // Demo example: [1.0, 1.0, 1.0, 1.0, 1.0] for Iris Example, including bias int ndim = 5; std::vector<double> input; for (int i = 0; i < ndim; i++) { input.push_back(1.0); } // New Feature Adapter to convert vector to tensors dictionary tf_model::ANNFeatureAdapter input_feat; input_feat.assign(input_tensor_name, &input); //Assign vec<double> to tensor // Make New Prediction double prediction = 0.0; if (0 != model.predict(session, input_feat, output_tensor_name, &prediction)) { std::cout << "WARNING: Prediction failed..." << std::endl; } std::cout << "Output Prediction Value:" << prediction << std::endl; return 0; }

将这四个文件放在同一个路径下，然后还需要添加一个Cmake的txt文件：


cmake_minimum_required(VERSION 2.8) project(cpptensorflow) set(CMAKE_CXX_STANDARD 11) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=gnu++0x -g -fopenmp -fno-strict-aliasing") link_directories(/home/xxx/tensorflow/bazel-bin/tensorflow) include_directories( /home/xxx/tensorflow /home/xxx/tensorflow/bazel-genfiles /home/xxx/tensorflow/bazel-bin/tensorflow /home/xxx/tools/eigen3 ) add_executable(cpptensorflow main.cpp ann_model_loader.h model_loader_base.h ann_model_loader.cpp) target_link_libraries(cpptensorflow tensorflow_cc tensorflow_framework)

这里注意cmake_minimum_required(VERSION 2.8)要和自己系统的cmake最低版本相符合。

然后在当前目录下建立一个build的空文件夹：
这里写图片描述

mkdir  build
cd  build
cmake ..
make 
生成cpptensorflow执行文件，后接保存的模型pb文件路径：
./cpptensorflow /Users/zhoumeixu/Documents/python/credit-nlp-ner/model/liner.pb
Final value is: 14.9985
Output Prediction Value:14.9985

到此基本就结束了，最后感谢下作者[1]，我真是差点被搞疯了。。。

原文：https://blog.csdn.net/gzt940726/article/details/81053378

转载于:https://www.cnblogs.com/Ph-one/p/9516490.html