Ubuntu16.04下Tensorflow C++編譯並調用pb文件（一）

通常tensorflow訓練深度學習網絡都是在python語言中實現的，因爲在python環境中安裝tensorflow非常方便並且tensorflow針對python的接口也非常友好，但有些時候我們又必須在C++環境中進行開發。所以我們希望利用python去訓練網絡，訓練完後將網絡凍結生成pb文件，然後通過C++版的tensorflow進行調用。但編譯C++版的tensorflow相對python要麻煩些，所以本文主要是做個編譯記錄方便以後查看。當然網上也有很多參考文獻，但自己在編譯過程中總是會遇到一些問題，所以本文會記錄的更加細緻。

說下運行環境，Ubuntu16.04，python3.5，CUDA 9.0，cuDNN8.0， tensorflow1.9-GPU（python環境下的），準備編譯安裝Tensorflow1.9-GPU（C++）版。

列個主要步驟：1.安裝protobuf   2.安裝Bazel   3.clone tensorflow源碼並編譯  4.安裝Eigen庫  5.通過Cmake讀取訓練好的pb文件 6.通過qtcreator讀取訓練好的pb文件

1 安裝Protobuf

安裝Protobuf有一點很重要，要與所編譯的tensorflow版本相對應，否則後期會遇到很多問題。但具體哪個版本的Tensorflow對應哪個版本的Protobuf我也沒找到相關文獻，因爲在我安裝過程中遇到過很多問題，最後試了好幾個版本後在找到合適的。這裏我使用的Protobuf版本是3.5.0（對應tensorflow1.9）。首先貼出protobuf官方的鏈接。

 

如圖所示，點擊最下面的選項Source Code（tar.gz）進行下載，下載後解壓到個人文件夾（平時要養成文件歸類的好習慣啊，要不然打開文件夾一團糟頭都大）， 打開文件夾反擊鼠標——打開終端。在編譯安裝protobuf之前需要先安裝一些工具（automake libtool）。

 

在終端依次輸入指令：

sudo apt-get install automake libtool
./autogen.sh
./configure
make
sudo make install
sudo ldconfig
# sudo make uninstall 安裝錯版本後卸載指令
protoc --version  # 查看protobuf版本

 

2 安裝Bazel

同樣安裝Bazel也要與Tensorflow版本相對應，這裏我使用的Bazel版本是0.15.2.給出Bazel的下載鏈接。

下載二進制文件bazel-0.15.2-installer-linux-x86_64.sh.

接着打開終端運行Bazel安裝程序

chmod +x bazel-0.15.2-installer-linux-x86_64.sh
./bazel-0.15.2-installer-linux-x86_64.sh --user

安裝完後設置環境。打開~/.bashrc文件（sudo gedit ~/.bashrc）並在文件最後加入如下指令

export PATH="$PATH:$HOME/bin"

保存後使其生效，輸入指令（source ~/.bashrc）

 

3 Clone Tensorflow源碼並進行編譯

首先從github上clone Tensorflow的源碼，打開終端輸入指令如下

git clone --recursive https://github.com/tensorflow/tensorflow

文件下載完後，通過終端打開文件（由於現在Tensorflow已經更新到1.13版本，而我要的是1.9的，所以先切換分支）

cd ./tensorflow
git checkout r1.9
./configure

在configure過程中基本都可以選No，具體每項的含義大家可以百度，但需要注意的是，如果要用GPU在build Tensorflow with CUDA support ? 選項中一定要選擇Y，並輸入對應的CUDA以及cuDNN版本（Tensorflow1.9-GPU所需的CUDA是9.0，cuDNN是8.0）。

配置完成後用Bazel進行編譯：

bazel build --config=opt //tensorflow:libtensorflow_cc.so  # CPU版
bazel build --config=opt --config=cuda //tensorflow:libtensorflow_cc.so  # GPU版

注意： 若在C++環境中需要使用opencv環境，建議使用以下指令編譯：（若不使用該指令可能會遇到opencv imread圖像失效問題，問題詳情見鏈接）

bazel build --config=monolithic //tensorflow:libtensorflow_cc.so

 

一般都要編譯很長時間30-60min的樣子，編譯完後的大致信息如下所示：

Target //tensorflow:libtensorflow_cc.so up-to-date:
  bazel-bin/tensorflow/libtensorflow_cc.so

INFO: Elapsed time: 1233.631s, Critical Path: 48.36s
INFO: 2724 processes: 2724 local.
INFO: Build completed successfully, 2842 total actions

 注意：看下路徑 ./tensorflow/tensorflow/contrib/makefile下有沒有downloads文件夾。如果沒有的話需要在./tensorflow/tensorflow/contrib/makefile文件夾下打開終端執行一個sh腳本文件：

./download_dependencies.sh

執行腳本文件後，會開始下載一些依賴文件，下載完後就會有downloads文件夾了。

 

4 安裝Eigen庫

首先打開上一步的downloads文件夾，裏面會有個eigen文件夾，進入eigen文件夾打開終端依次輸入如下指令：

mkdir build
cd build
cmake ..
make
sudo make install

安裝完畢後，在usr/local/include目錄下會出現eigen3文件夾

 

5 Cmake讀取訓練好的pb文件

（這一步基本都是按照網上網友的流程走的）建立一個Python項目生成一個訓練模型，首先在項目文件夾中建立一個model文件夾，在創建Python文件執行以下代碼：

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!')
print('Done training!')

執行完後會在model文件下生成一個liner.pb文件。

在使用C++調用pb文件之前升級一下Cmake（這裏需要升級到3.10版本以上），給出下載鏈接。

我下載的是3.11版（cmake-3.11.0-Linux-x86_64.tar.gz）

下載完後進行解壓，解壓後將文件的./cmake-3.11.0-Linux-x86_64./bin目錄添加到~./bashrc文件中。

$ gedit ~/.bashrc  # 打開~/.bashrc

將bin目錄鏈接添加到~./bashrc文件最後：

export PATH=/home/wz/cmake-3.11.0/bin:$PATH

接着source一下：

source ~/.bashrc

查看cmake版本：

cmake --version

接下來建立C++項目，在文件夾中創建以下幾個文件：

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;
}

接着在文件夾中建立一個CMakeList.txt文件，在文件中寫入：

cmake_minimum_required(VERSION 3.10)
project(cpptensorflow)
set(CMAKE_CXX_STANDARD 11)
link_directories(/home/wz/cpptest/tensorflow/bazel-bin/tensorflow)
include_directories(
        /home/wz/cpptest/tensorflow
        /home/wz/cpptest/tensorflow/bazel-genfiles
        /home/wz/cpptest/tensorflow/bazel-bin/tensorflow
        /usr/local/include/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)

注意將所有的directories改成你自己相應文件所在的目錄。編輯好保存，文件夾中應該有這麼幾個文件：

接着在該文件夾中打開終端輸入指令，開始編譯項目：

mkdir  build
cd  build
cmake ..
make 

編譯完後會在新建的build文件夾中生成cpptensorflow可執行文件，接着在終端中調用該執行文件：

./cpptensorflow /home/wz/PycharmProjects/create_C++_TEST/model/liner.pb

注意，將後面的liner.pb文件所在目錄改成你自己pb文件所在的位置，最後可得到以下結果：

到這就算成功了！

 

6 使用qtcreator環境讀取pb文件

（1）將"tensorflow/bazel-genfiles/tensorflow/"中的cc和core文件夾中的內容copy到"tensorflow/tensorflow/"中，然後選擇合併覆蓋。

（2）進入tensorflow/bazel-bin/tensorflow文件夾下，會有一個編譯生成的libtensorflow_cc.so文件。若你是通過bazel build --config=opt //tensorflow:libtensorflow_cc.so指令進行編譯的，那麼將libtensorflow_cc.so和libtensorflow_framework.so兩個文件複製到/usr/local/lib/文件夾下：

sudo cp libtensorflow_cc.so /usr/local/lib/
sudo cp libtensorflow_framework.so /usr/local/lib/

若你是通過bazel build --config=monolithic //tensorflow:libtensorflow_cc.so指令進行編譯的，那麼你只需要將libtensorflow_cc.so文件複製到/usr/local/lib文件夾下：

sudo cp libtensorflow_cc.so /usr/local/lib

（3）接下來安裝qtcreator和opencv，具體安裝流程可參考該博文。

（4）打開qtcreator建立一個新的空項目，然後將剛使用的Cmake進行編譯的四個文件添加進項目（ann_model_loader.cpp，ann_model_loader.h，model_loader_base.h，main.cpp）。

（5）接着配置.pro文件，由於我需要使用opencv，所以我是通過bazel build --config=monolithic //tensorflow:libtensorflow_cc.so指令編譯的，現在需要將可能用到的頭文件和可能需要鏈接的文件寫入.pro文件中，以下是我的編寫的.pro文件：

TEMPLATE = app
CONFIG += console c++11
CONFIG -= app_bundle
CONFIG -= qt

SOURCES += \
    main.cpp

INCLUDEPATH += /usr/local/include \
/usr/local/include/opencv \
/usr/local/include/opencv2 \
/home/wz/tf_c_install/tensor_source/tensorflow \
/usr/local/include/eigen3



LIBS += /usr/local/lib/libtensorflow_cc.so \
/usr/local/lib/libopencv_calib3d.so \
/usr/local/lib/libopencv_shape.so.3.4.4 \
/usr/local/lib/libopencv_calib3d.so.3.4 \
/usr/local/lib/libopencv_stitching.so \
/usr/local/lib/libopencv_calib3d.so.3.4.4 \
/usr/local/lib/libopencv_stitching.so.3.4 \
/usr/local/lib/libopencv_core.so \
/usr/local/lib/libopencv_stitching.so.3.4.4 \
/usr/local/lib/libopencv_core.so.3.4 \
/usr/local/lib/libopencv_superres.so \
/usr/local/lib/libopencv_core.so.3.4.4 \
/usr/local/lib/libopencv_superres.so.3.4 \
/usr/local/lib/libopencv_dnn.so \
/usr/local/lib/libopencv_superres.so.3.4.4 \
/usr/local/lib/libopencv_dnn.so.3.4 \
/usr/local/lib/libopencv_videoio.so \
/usr/local/lib/libopencv_dnn.so.3.4.4 \
/usr/local/lib/libopencv_videoio.so.3.4 \
/usr/local/lib/libopencv_features2d.so \
/usr/local/lib/libopencv_videoio.so.3.4.4 \
/usr/local/lib/libopencv_features2d.so.3.4 \
/usr/local/lib/libopencv_video.so \
/usr/local/lib/libopencv_features2d.so.3.4.4 \
/usr/local/lib/libopencv_video.so.3.4 \
/usr/local/lib/libopencv_flann.so \
/usr/local/lib/libopencv_video.so.3.4.4 \
/usr/local/lib/libopencv_flann.so.3.4 \
/usr/local/lib/libopencv_videostab.so \
/usr/local/lib/libopencv_flann.so.3.4.4 \
/usr/local/lib/libopencv_videostab.so.3.4 \
/usr/local/lib/libopencv_highgui.so \
/usr/local/lib/libopencv_videostab.so.3.4.4 \
/usr/local/lib/libopencv_highgui.so.3.4 \
/usr/local/lib/libopencv_highgui.so.3.4.4 \
/usr/local/lib/libopencv_imgcodecs.so \
/usr/local/lib/libopencv_imgcodecs.so.3.4 \
/usr/local/lib/libopencv_imgcodecs.so.3.4.4 \
/usr/local/lib/libopencv_imgproc.so \
/usr/local/lib/libopencv_imgproc.so.3.4 \
/usr/local/lib/libopencv_imgproc.so.3.4.4 \
/usr/local/lib/libopencv_ml.so \
/usr/local/lib/libopencv_ml.so.3.4 \
/usr/local/lib/libopencv_ml.so.3.4.4 \
/usr/local/lib/libopencv_objdetect.so \
/usr/local/lib/libopencv_objdetect.so.3.4 \
/usr/local/lib/libopencv_objdetect.so.3.4.4 \
/usr/local/lib/libopencv_photo.so \
/usr/local/lib/libopencv_photo.so.3.4 \
/usr/local/lib/libopencv_photo.so.3.4.4 \
/usr/local/lib/libopencv_shape.so \
/usr/local/lib/libopencv_shape.so.3.4 \

使用時記得將路徑改成自己的路徑，接着就可以編譯了，若編譯過程中提示缺少什麼頭文件，不要慌，打開tensorflow/tensorflow/文件夾搜索提示中缺少的頭文件，一般都能找到，然後將這個頭文件所在目錄添加到.pro文件的INCLUDEPATH中。到這一步基本就沒什麼問題了，撒花！！！

（在我的下一篇博文中進一步結合代碼講解了如何調用Tensorflow提供的object detection模塊的pb文件，有興趣的同學可以看看）

最後給些重要參考鏈接：

tensorflow c++接口，python訓練模型，c++調用

tensorflow1.4 c++編譯以及API使用

安裝並使用tensorflow c++ api

tensorflow和opencv衝突問題的解決

Ubuntu16.04 安裝/更新/升級cmake到 cmake3.9.1的具體安裝過程