tensorflow c++ API加载.pb模型文件并预测图片

tensorflow  python创建模型，训练模型，得到.pb模型文件后，用c++ api进行预测

 #include <iostream>
 #include <map>

 #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/platform/logging.h"
 #include "tensorflow/core/platform/types.h"
 #include "tensorflow/core/public/session.h"

 using namespace std ;
 using namespace tensorflow;
 using tensorflow::Tensor;
 using tensorflow::Status;
 using tensorflow::string;
 using tensorflow::int32;

 //从文件名中读取数据
 Status ReadTensorFromImageFile(string file_name, const int input_height,
                                const int input_width,
                                vector<Tensor>* out_tensors) {
     auto root = Scope::NewRootScope();
     using namespace ops;

     auto file_reader = ops::ReadFile(root.WithOpName("file_reader"),file_name);
     const int wanted_channels = ;
     Output image_reader;
     std::size_t found = file_name.find(".png");
     //判断文件格式
     if (found!=std::string::npos) {
         image_reader = DecodePng(root.WithOpName("png_reader"), file_reader,DecodePng::Channels(wanted_channels));
     }
     else {
         image_reader = DecodeJpeg(root.WithOpName("jpeg_reader"), file_reader,DecodeJpeg::Channels(wanted_channels));
     }
     // 下面几步是读取图片并处理
     auto float_caster =Cast(root.WithOpName("float_caster"), image_reader, DT_FLOAT);
     auto dims_expander = ExpandDims(root, float_caster, );
     auto resized = ResizeBilinear(root, dims_expander,Const(root.WithOpName("resize"), {input_height, input_width}));
     // Div(root.WithOpName(output_name), Sub(root, resized, {input_mean}),{input_std});
     Transpose(root.WithOpName("transpose"),resized,{,,,});

     GraphDef graph;
     root.ToGraphDef(&graph);

     unique_ptr<Session> session(NewSession(SessionOptions()));
     session->Create(graph);
     session->Run({}, {"transpose"}, {}, out_tensors);//Run，获取图片数据保存到Tensor中

     return Status::OK();
 }

 int main(int argc, char* argv[]) {

     string graph_path = "aov_crnn.pb";
     GraphDef graph_def;
     //读取模型文件
     if (!ReadBinaryProto(Env::Default(), graph_path, &graph_def).ok()) {
         cout << "Read model .pb failed"<<endl;
         return -;
     }

     //新建session
     unique_ptr<Session> session;
     SessionOptions sess_opt;
     sess_opt.config.mutable_gpu_options()->set_allow_growth(true);
     (&session)->reset(NewSession(sess_opt));
     if (!session->Create(graph_def).ok()) {
         cout<<"Create graph failed"<<endl;
         return -;
     }

     //读取图像到inputs中
     int input_height = ;
     int input_width = ;
     vector<Tensor> inputs;
     // string image_path(argv[1]);
     string image_path("test.jpg");
     if (!ReadTensorFromImageFile(image_path, input_height, input_width,&inputs).ok()) {
         cout<<"Read image file failed"<<endl;
         return -;
     }

     vector<Tensor> outputs;
     string input = "inputs_sq";
     string output = "results_sq";//graph中的输入节点和输出节点，需要预先知道

     pair<string,Tensor>img(input,inputs[]);
     Status status = session->Run({img},{output}, {}, &outputs);//Run,得到运行结果，存到outputs中
     if (!status.ok()) {
         cout<<"Running model failed"<<endl;
         cout<<status.ToString()<<endl;
         return -;
     }

     //得到模型运行结果
     Tensor t = outputs[];
     auto tmap = t.tensor<int64, >();
     int output_dim = t.shape().dim_size(); 

     return ;
 }

g++ -g  tf_predict.cpp -o tf_predict -I /usr/include/eigen3 -I /usr/local/include/tf  -L/usr/local/lib/ `pkg-config --cflags --libs protobuf`  -ltensorflow_cc  -ltensorflow_framework

也可以用opencv c++库读取图片Mat复制到Tensor中

 tensorflow::Tensor readTensor(string filename){
     tensorflow::Tensor input_tensor(DT_FLOAT,TensorShape({,,,}));
     Mat src=imread(filename,);
     Mat dst;
     resize(src,dst,Size(,));//resize
     Mat dst_transpose=dst.t();//transpose

     auto tmap=input_tensor.tensor<float,>();

     for(int i=;i<;i++){//Mat复制到Tensor
         for(int j=;j<;j++){
             tmap(,i,j,)=dst_transpose.at<uchar>(i,j);
         }
     }

     return input_tensor;
 }

也可用指针引用的方式转换

             tensorflow::Tensor input_tensor(DT_FLOAT,TensorShape({,height,width,}));
         float *tensor_data_ptr = input_tensor.flat<float>().data();
         cv::Mat fake_mat(dst.rows, dst.cols, CV_32FC(src.channels()), tensor_data_ptr);
         dst.convertTo(fake_mat, CV_32FC3);