tensorflow随笔——C++工程化

本文主要描述如何基于Tensroflow从线下训练到线上预测的整个流程。主要包括以下几步：

使用Python接口搭建模型训练参数并固化成pb文件
使用Bazel编译Tensorflow源码得到tensorflow_cc.so的动态库
调用Tensorflow C++ API编写预测代码

使用Python搭建模型训练mnist手写字符

简单搭建2层CNN+2层FC的神经网络，保存网络图结构和参数文件。

# coding=UTF-8
'''
当神经网络比较复杂,训练数据比较多,模型训练耗时很长,训练过程中会出现某些不可预计的错误而导致训练意外终止,
为了避免以上问题,可以通过模型持久化,保存为CKPT格式来暂存训练过程中的临时数据
保存ckpt格式的模型流程：
1.定义运算过程
2.声明并得到一个Saver
3.通过Saver.save保存模型
'''import os
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("MNIST_data/", one_hot=False)def build_network(x):def weight_variable(shape, name="weights"):initial = tf.truncated_normal(shape, dtype=tf.float32, stddev=0.1) * 0.001return tf.Variable(initial, name=name)def bias_variable(shape, name="biases"):initial = tf.constant(0.1, dtype=tf.float32, shape=shape)return tf.Variable(initial, name=name)def conv2d(input, w):return tf.nn.conv2d(input, w, [1, 1, 1, 1], padding='SAME')def pool_max(input):return tf.nn.max_pool(input, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1')def fc(input, w, b):return tf.matmul(input, w) + b# conv1with tf.name_scope('conv1_1') as scope:kernel = weight_variable([3, 3, 1, 32])biases = bias_variable([32])output_conv1_1 = tf.nn.relu(conv2d(x, kernel) + biases, name=scope)pool1 = pool_max(output_conv1_1)  # [n,14,14,32]# conv2with tf.name_scope('conv1_2') as scope:kernel = weight_variable([3, 3, 32, 64])biases = bias_variable([64])output_conv1_2 = tf.nn.relu(conv2d(pool1, kernel) + biases, name=scope)pool2 = pool_max(output_conv1_2)  # [n,7,7,64]# fc1with tf.name_scope('fc1') as scope:shape = int(np.prod(pool2.get_shape()[1:]))kernel = weight_variable([shape, 512])biases = bias_variable([512])pool2_flat = tf.reshape(pool2, [-1, shape])output_fc1 = tf.nn.relu(fc(pool2_flat, kernel, biases), name=scope)# fc2with tf.name_scope('fc2') as scope:kernel = weight_variable([512, 10])biases = bias_variable([10])output_fc2 = fc(output_fc1, kernel, biases)output = tf.nn.softmax(output_fc2, name='softmax')return outputdef train_network(batch_size, num_epochs, checkpoint_dir, model):x = tf.placeholder(tf.float32, shape=[None, 28, 28, 1], name='input')y_ = tf.placeholder(tf.int64, shape=[None,], name='y_')# 创建目录if not tf.gfile.Exists(checkpoint_dir):tf.gfile.MakeDirs(checkpoint_dir)logits = build_network(x)cost = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y_))optimize = tf.train.AdamOptimizer(learning_rate=0.01).minimize(cost)prediction_labels = tf.argmax(logits, axis=1, name="output")correct_prediction = tf.equal(prediction_labels, y_)accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))# 初始化参数init = tf.global_variables_initializer()# 声明saver保存模型saver = tf.train.Saver()with tf.Session() as sess:sess.run(init)# 验证之前是否已经保存了检查点文件ckpt = tf.train.get_checkpoint_state(checkpoint_dir)if ckpt and ckpt.model_checkpoint_path:saver.restore(sess, ckpt.model_checkpoint_path)for epoch in range(num_epochs):for i in range(1000):batch_xs, batch_ys = mnist.train.next_batch(batch_size)op, acc, costt = sess.run([optimize, accuracy, cost], feed_dict={x: np.reshape(batch_xs, (-1, 28, 28, 1)),y_: batch_ys})if i % 100 == 0:print('step', i, 'acc', acc, 'loss', costt)saver.save(sess, os.path.join(checkpoint_dir, model), global_step=i)# 打印模型节点信息#for op in tf.get_default_graph().get_operations():#    print(op.name, op.values())if __name__ == '__main__':ckpt_dir = '/home/positec/CW/tf_dispose/frozen_model/ckpt'model = 'model.ckpt'train_network(128, 2, ckpt_dir, model)

程序运行后会在ckpt目录下生成4个文件

checkpoint:检查点文件，文件保存了目录下所有的模型文件列表

model.ckpt.meta:保存Tensorflow计算图的结构

model.ckpt-data:保存模型中每个变量的取值

model.ckpt.index:保存参数索引文件

将ckpt固化成pb文件

code snippet widget

通过freeze_graph函数将ckpt格式的模型文件固化成pb格式，用于后续预测时的前向传播调用。

源码编译Tensorflow

先贴一份不同平台上Tensorflow与cudnn、cuda、bazel的版本配套关系表：

我采用的Tensorflow版本是1.4，

1.下载源码:git clone https://github.com/tensorflow/tensorflow.git

2. cd到tensorflow目录，更新到1.4版本:git checkout r1.4

3. 执行./configure进行项目配置

4. 使用bazel工具进行编译:bazel build --config=opt //tensorflow:libtensorflow_cc.so

编译完成后，在tensorflow根目录下出现 bazel-bin, bazel-genfiles 等文件夹, 按顺序执行以下命令将对应的libtensorflow_cc.so文件和其他文件拷贝进入 /usr/local/lib/ 目录

mkdir /usr/local/include/tf
cp -r bazel-genfiles/ /usr/local/include/tf/
cp -r tensorflow /usr/local/include/tf/
cp -r third_party /usr/local/include/tf/
cp -r bazel-bin/tensorflow/libtensorflow_cc.so /usr/local/lib/
完成后我们就准备好了tensorflow_cc.so文件，后面在自己的C++编译环境和代码目录下编译时链接这些库即可。

使用Tensorflow C++ API编写预测代码

预测代码主要包括以下几个步骤：

创建Session
导入之前生成的模型
将模型设置到创建的Session里
设置模型输入输出，调用Session的Run做预测
关闭Session

第一步，创建Session

//创建新会话Session
Session* session;
Status status = NewSession(SessionOptions(), &session);
if (!status.ok()){std::cout << status.ToString() << std::endl;
} else {std::cout << "Session created successful" << std::endl;
}

第二步，导入模型

string model_path = "/home/positec/CW/tf_dispose/frozen_model/pb/model.pb";
GraphDef graphdef;
//从pb文件中读取图模型
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;
}

第三步，将模型设置到创建的Session里

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;
}
cout << "Session successfully created." << endl;

第四步，设置模型输入

string image_path = "/home/positec/CW/tf_dispose/frozen_model/test/test.jpg";
int input_height = 28;
int input_width = 28;
// 从csv文件中读取数据
std::vector<Tensor> resized_tensors;
Status read_tensor_status = ReadTensorFromImageFile(image_path, input_height, input_width, &resized_tensors);
if (!read_tensor_status.ok()){LOG(ERROR) << read_tensor_status;cout << "resing error" << endl;return -1;
}
const Tensor& resized_tensor = resized_tensors[0];
std::cout << resized_tensor.DebugString()<<endl;

在ReadTensorFromImageFile()函数，建立一个会话session，在会话中对读取到的tensor进行预处理，例如，对tensor进行解码( DecodeJpeg)，resize，归一化等

第五步，预测结果

vector<tensorflow::Tensor> outputs;
string output_node = "softmax";
Status status_run = session->Run({{"input_x", resized_tensor}}, {output_node}, {}, &outputs);
if (!status_run.ok()) {std::cout << "ERROR: RUN failed..."  << std::endl;std::cout << status_run.ToString() << "\n";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()<<endl;
}

模型预测时使用的函数为session->Run({{"inputs_x", resized_tensor}}, {output_node}, {}, &outputs)。"inputs_x"是图模型输入tensor的名字(name)，变量output_node保存的是图模型输出tensor的名字(name)。这两个名字(name)一定要与保存的图模型(.pb)文件中的名字一致，否则会报错。最后得到的输出tensor保存在容器outputs中。

第六步，关闭Session

session->Close()

完整代码如下：

#include <iostream>
#include <fstream>
#include <utility>
#include <vector>
#include <Eigen/Core>
#include <Eigen/Dense>#include "tensorflow/cc/ops/const_op.h"
#include "tensorflow/cc/ops/image_ops.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/graph/default_device.h"
#include "tensorflow/core/graph/graph_def_builder.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/core/stringpiece.h"
#include "tensorflow/core/lib/core/threadpool.h"
#include "tensorflow/core/lib/io/path.h"
#include "tensorflow/core/lib/strings/stringprintf.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/init_main.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/public/session.h"
#include "tensorflow/core/util/command_line_flags.h"using namespace std;
using namespace tensorflow;
using namespace tensorflow::ops;
using tensorflow::Flag;
using tensorflow::Tensor;
using tensorflow::Status;
using tensorflow::string;
using tensorflow::int32;static Status ReadEntireFile(tensorflow::Env* env, const string& filename,Tensor* output) {tensorflow::uint64 file_size = 0;TF_RETURN_IF_ERROR(env->GetFileSize(filename, &file_size));string contents;contents.resize(file_size);std::unique_ptr<tensorflow::RandomAccessFile> file;TF_RETURN_IF_ERROR(env->NewRandomAccessFile(filename, &file));tensorflow::StringPiece data;TF_RETURN_IF_ERROR(file->Read(0, file_size, &data, &(contents)[0]));if (data.size() != file_size) {return tensorflow::errors::DataLoss("Truncated read of '", filename,"' expected ", file_size, " got ",data.size());}output->scalar<string>()() = data.ToString();return Status::OK();
}Status ReadTensorFromImageFile(const string& file_name, const int input_height, const int input_width, std::vector<Tensor>* out_tensors){auto root = tensorflow::Scope::NewRootScope();using namespace ::tensorflow::ops;// read file_name into a tensor named inputTensor input(tensorflow::DT_STRING, tensorflow::TensorShape());TF_RETURN_IF_ERROR(ReadEntireFile(tensorflow::Env::Default(), file_name, &input));// use a placeholder to read input dataauto file_reader =Placeholder(root.WithOpName("input_x"), tensorflow::DataType::DT_STRING);std::vector<std::pair<string, tensorflow::Tensor>> inputs = {{"input_x", input},};// Now try to figure out what kind of file it is and decode it.const int wanted_channels = 1;tensorflow::Output image_reader;if (tensorflow::StringPiece(file_name).ends_with(".png")) {image_reader = DecodePng(root.WithOpName("png_reader"), file_reader,DecodePng::Channels(wanted_channels));} else if (tensorflow::StringPiece(file_name).ends_with(".gif")) {// gif decoder returns 4-D tensor, remove the first dimimage_reader =Squeeze(root.WithOpName("squeeze_first_dim"),DecodeGif(root.WithOpName("gif_reader"), file_reader));} else if (tensorflow::StringPiece(file_name).ends_with(".bmp")) {image_reader = DecodeBmp(root.WithOpName("bmp_reader"), file_reader);} else {// Assume if it's neither a PNG nor a GIF then it must be a JPEG.image_reader = DecodeJpeg(root.WithOpName("jpeg_reader"), file_reader,DecodeJpeg::Channels(wanted_channels));}// Now cast the image data to float so we can do normal math on it.auto float_caster =Cast(root.WithOpName("float_caster"), image_reader, tensorflow::DT_FLOAT);// The convention for image ops in TensorFlow is that all images are expected// to be in batches, so that they're four-dimensional arrays with indices of// [batch, height, width, channel]. Because we only have a single image, we// have to add a batch dimension of 1 to the start with ExpandDims().auto dims_expander = ExpandDims(root.WithOpName("expand"), float_caster, 0);// Bilinearly resize the image to fit the required dimensions.// auto resized = ResizeBilinear(//root, dims_expander,//Const(root.WithOpName("size"), {input_height, input_width}));// Subtract the mean and divide by the scale.//Div(root.WithOpName(output_name), Sub(root, resized, {input_mean}),//{input_std});float input_max = 255;Div(root.WithOpName("div"),dims_expander,input_max);// This runs the GraphDef network definition that we've just constructed, and// returns the results in the output tensor.tensorflow::GraphDef graph;TF_RETURN_IF_ERROR(root.ToGraphDef(&graph));std::unique_ptr<tensorflow::Session> session(tensorflow::NewSession(tensorflow::SessionOptions()));TF_RETURN_IF_ERROR(session->Create(graph));TF_RETURN_IF_ERROR(session->Run({inputs}, {"div"}, {}, out_tensors));return Status::OK();
}int main(int argc, char** argv )
{//*******************step1: 加载模型************************////创建新会话SessionSession* session;Status status = NewSession(SessionOptions(), &session);if (!status.ok()){std::cout << status.ToString() << std::endl;} else {std::cout << "Session created successful" << std::endl;}string model_path = "/home/positec/CW/tf_dispose/frozen_model/pb/model.pb";// Graph Definition for current modelGraphDef graphdef;//从pb文件中读取图模型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;}//将模型导入会话Session中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;}cout << "Session successfully created." << endl;//*******************step2: 读取图片************************//string image_path = "/home/positec/CW/tf_dispose/frozen_model/test/test.jpg";int input_height = 28;int input_width = 28;// 从csv文件中读取数据std::vector<Tensor> resized_tensors;Status read_tensor_status = ReadTensorFromImageFile(image_path, input_height, input_width, &resized_tensors);if (!read_tensor_status.ok()){LOG(ERROR) << read_tensor_status;cout << "resing error" << endl;return -1;}const Tensor& resized_tensor = resized_tensors[0];std::cout << resized_tensor.DebugString()<<endl;//*******************step3: 运行模型************************//vector<tensorflow::Tensor> outputs;string output_node = "softmax";Status status_run = session->Run({{"input_x", resized_tensor}}, {output_node}, {}, &outputs);if (!status_run.ok()) {std::cout << "ERROR: RUN failed..."  << std::endl;std::cout << status_run.ToString() << "\n";return -1;}//Fetch output valuestd::cout << "Output tensor size:" << outputs.size() << std::endl;for (std::size_t i = 0; i < outputs.size(); i++) {std::cout << outputs[i].DebugString()<<endl;}//*******************step4: 输出类别概率**********************//Tensor t = outputs[0];                   // Fetch the first tensorint ndim2 = t.shape().dims();             // Get the dimension of the tensorauto 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 dimensionstd::vector<double> tout;// Argmax: Get Final Prediction Label and Probabilityint 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);}}// Logstd::cout << "Final class id: " << output_class_id << std::endl;std::cout << "Final class prob: " << output_prob << std::endl;// 关闭会话session-> Close();return 0;}

最后进入build文件夹，对工程进行编译：

mkdir build
cd build
cmake ..
make
sudo make install

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