预测模型inference

运行过程

1. HDF5数据加载

    /* code */
    //打开文件和指定的数据集
    hsize_t i,j;
    H5File file("../data/label_test/54000/54094.hdf5", H5F_ACC_RDONLY);
    Group rg(file.getObjId("/"));
    DataSet dset(rg.getObjId("/data/axuv"));
    DataSet basic(rg.getObjId("/data/basic"));
   
    //获取指定数据集的文件空间，方便后续获取其维度
    DataSpace filespace = dset.getSpace();
    //获取其维度
    int rank = filespace.getSimpleExtentNdims();
    //获取每一维度的大小，并用数组存储
    hsize_t dims[rank];
    rank = filespace.getSimpleExtentDims(dims);
    DataSpace mspace1(rank, dims);
    // 输出各维度的大小
    for (int i = 0; i < rank; i++){
        cout << "Dimension_" << i + 1 << " = " << dims[i] << endl;
    }
    // delete[]dims;
    // dims = nullptr;
    double data_out [dims[0]] [dims[1]];
    std::vector<vector<float>> array(dims[0],vector<float>(dims[1]));
    dset.read(data_out,PredType::NATIVE_DOUBLE, mspace1, filespace);
   
    for (j = 0; j < dims[0]; j++) {
        for (i = 0; i < dims[1]; i++)
            array[j][i]=data_out[j][i];
    }
    
    cout << data_out[0][0] << endl;
    filespace.close();
    dset.close();
   
    //获取指定数据集的文件空间，方便后续获取其维度
    DataSpace filespace2 = basic.getSpace();
    //获取其维度
    int rank2 = filespace2.getSimpleExtentNdims();
    //获取每一维度的大小，并用数组存储
    hsize_t dims2[rank2];
    rank2 = filespace2.getSimpleExtentDims(dims2);
    DataSpace mspace2(rank2, dims2);
    // 输出各维度的大小
    for (int i = 0; i < rank2; i++){
        cout << "Dimension_" << i + 1 << " = " << dims2[i] << endl;
    }
    // delete[]dims;
    // dims = nullptr;
    double data_basic [dims2[0]] [dims2[1]];
    std::vector<vector<float>> array_basic(dims2[0],vector<float>(dims2[1]));
    basic.read(data_basic,PredType::NATIVE_DOUBLE, mspace2, filespace2);
   
    for (j = 0; j < dims2[0]; j++) {
        for (i = 0; i < dims2[1]; i++)
            array_basic[j][i]=data_basic[j][i];
    }
    cout << data_basic[0] << endl;
    filespace.close();
    basic.close();
    rg.close();
    file.close();
    cout << endl << endl;
   

2. 信息配置

    string model_path="./et1";
    string input_tensor_name1="serving_default_input_1";
    string input_tensor_name2="serving_default_input_2";
    string output_tensor_name="StatefulPartitionedCall:0";
    
    
    tensorflow::SessionOptions sess_options;
    tensorflow::RunOptions run_options;
    tensorflow::SavedModelBundle bundle;
   

3. 模型加载

   Status status_create=LoadSavedModel(sess_options,run_options,model_path, {tensorflow::kSavedModelTagServe}, &bundle);
    if(!status_create.ok()){
    	cout << "ERROR: Creating graph in session failed..." << status_create.ToString() << std::endl;
        return -1;
    }
    cout << "<----Successfully created session and load graph.------->"<< endl;
   

4. 推理

   cout<<endl<<"<------------loading data-------------->"<<endl;
   
    auto input_tensor1 = MakeTensor(array[0], {1, 16});
    auto input_tensor2 = MakeTensor(array_basic[0], {1, 9});
   
    cout << input_tensor1.DebugString()<<endl;
    cout << input_tensor2.DebugString()<<endl;
    std::vector<tensorflow::Tensor> out_tensors;
   
    TF_CHECK_OK(bundle.GetSession()->Run({{input_tensor_name1, input_tensor1},{input_tensor_name2, input_tensor2}},
    {output_tensor_name}, {}, &out_tensors)); 
    int class_id=GetLabelFromOutput(out_tensors);
    cout << class_id << endl;
   

5. 附录函数

   Tensor MakeTensor(std::vector<float> const& batch, BatchDef const& batch_def) {
    Tensor t(DT_FLOAT,
        TensorShape(batch_def));
    for (int i = 0; i < batch.size(); ++i) {
      t.flat<float>()(i) = batch[i];
    }
    return t;
    }
   
    int GetLabelFromOutput(vector<tensorflow::Tensor> outputs)
    {
    //把输出值给提取出来
    
    Tensor t = outputs[0];                   // Fetch the first 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
   
    // 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++)
    {
        if (tmap(0, j) >= output_prob) {
            output_class_id = j;
            output_prob = tmap(0, j);
        }
    }
   
    return output_class_id;
    }
   

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