问当我输入单个句子时，ok运行时-c++推断时间的使用是浮动的，但是输入文本文件是可以的。

EN

class BertModel
{
    public:
        BertModel(){};
        BertModel(const char* path)
        {              
            // initial tokenizer
            string vocab_path = join(path, "vocab.txt");
            pTokenizer = new FullTokenizer(vocab_path);

            // onnxruntime setup
            Ort::Env env(ORT_LOGGING_LEVEL_WARNING, "small_bert_onnx"); //Set a  Env for this session,the Env holds the logging state used by all other objects.
            session_options.SetIntraOpNumThreads(1); //Sets the number of threads used to parallelize the execution within nodes. Default is 0 to let onnxruntime choose.
            session_options.SetInterOpNumThreads(1); //Sets the number of threads used to parallelize the execution of the graph (across nodes). Default is 0 to let onnxruntime choose.

            string model_path = join(path, "bert_model_quant.onnx");
            session = new Ort::Session(env, model_path.c_str(), session_options); //create a session,session is
            // session = new Ort::Session(env, model_path.c_str(), Ort::SessionOptions{ nullptr }); //don't do anyOptions

            size_t num_input_nodes = session->GetInputCount(); //  num_input_nodes size of model need,eg:(ids,mask,labels),your will get 3;
            char* input_name = session -> GetInputName(0, allocator);
            input_node_names = {input_name};
            output_node_names = {"logits"};
            // print input node types
            Ort::TypeInfo type_info = session -> GetInputTypeInfo(0);
            auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
            ONNXTensorElementDataType type = tensor_info.GetElementType();
            // print input shapes/dims
            input_node_dims = tensor_info.GetShape();
            cout << "session初始化成功" << endl;
        }

        string join(const char *a, const char *b);
        vector<long> textTokenizer(string text);
        int predicts(string text);

    private:
        FullTokenizer* pTokenizer;
        Ort::SessionOptions session_options;
        std::vector<int64_t> input_node_dims;
        std::vector<const char*> output_node_names;
        Ort::AllocatorWithDefaultOptions allocator; // allocator
        std::vector<const char*> input_node_names;
        Ort::Session* session;
};

int BertModel::predicts(string text)
{   
    vector<long> input_tensor_values = textTokenizer(text);

    auto memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);

    input_node_dims[0]=1;
    input_node_dims[1]=input_tensor_values.size();

    Ort::Value input_tensor = Ort::Value::CreateTensor<long>(memory_info,input_tensor_values.data(), 
                              input_tensor_values.size(), input_node_dims.data(), input_node_dims.size());
    assert(input_tensor.IsTensor());

    //outputs from session run is vector<Value>
    auto output_tensors = session -> Run(Ort::RunOptions{nullptr}, 
                                      input_node_names.data(), 
                                      &input_tensor,
                                      1,
                                      output_node_names.data(),
                                      1);

    // output_tensors 2, logitspreds
    // onnlogitsfloatfloat
    float* floatarr = output_tensors[0].GetTensorMutableData<float>();
    int res = max_element(floatarr, floatarr + 3) - floatarr;
    return res;
}

    string text;
    while(true)
    {
        cout << "enter your input" << endl;
        getline(cin, text);
        high_resolution_clock::time_point beginTime = high_resolution_clock::now();
        int res = model.predicts(text);
        high_resolution_clock::time_point endTime = high_resolution_clock::now();
        milliseconds timeInterval = std::chrono::duration_cast<milliseconds>(endTime - beginTime);
        cout << "predict result:" << res << endl;
        cout << "time spent:" << timeInterval.count() << "ms" << endl;
    }

你给我想没有包子铺的你也灯关的水都关了新的利润都被人骨的肌
predict result:1
time spent:16ms
enter your input
你给我想没有包子铺的你也灯关的水都关了新的利润都被人骨的肌
predict result:1
time spent:16ms
enter your input
你给我想没有包子铺的你也灯关的水都关了新的利润都被人骨的肌
predict result:1
time spent:14ms
enter your input
你给我想没有包子铺的你也灯关的水都关了新的利润都被人骨的肌
predict result:1
time spent:8ms
enter your input
你给我想没有包子铺的你也灯关的水都关了新的利润都被人骨的肌
predict result:1
time spent:15ms
enter your input
你给我想没有包子铺的你也灯关的水都关了新的利润都被人骨的肌
predict result:1
time spent:8ms
enter your input
你给我想没有包子铺的你也灯关的水都关了新的利润都被人骨的肌
predict result:1
time spent:13ms

    string input_path = "../../test0711.txt";
    string output_path = "../../test0711_result.txt";
    ifstream input_file(input_path);
    ofstream output_file(output_path);
    if (!input_file.is_open()) {
        cerr << "Could not open the file - '"
             << input_path << "'" << endl;
        return EXIT_FAILURE;
    }

    if (!output_file.is_open()) {
        cerr << "Could not open the file - '"
             << output_path << "'" << endl;
        return EXIT_FAILURE;
    }

    int time_spent = 0;
    int seq_nums = 0;

    string line;
    while (getline(input_file, line))
    {   
        high_resolution_clock::time_point beginTime = high_resolution_clock::now(); //start time
        int res = model.predicts(line); //predicts single sentence
        high_resolution_clock::time_point endTime = high_resolution_clock::now(); //end time
        milliseconds timeInterval = std::chrono::duration_cast<milliseconds>(endTime - beginTime); //spent time
        cout << "bytes length of this sentence:" << line.size()/3 << endl;
        cout << "predict result:" << res << endl;
        cout << "time spent:" << timeInterval.count() << "ms" << endl;
        output_file << line << '\t' << res << '\t' << timeInterval.count() << "ms" << endl;
        time_spent += timeInterval.count();
        seq_nums++;
    }
    input_file.close();
    output_file.close();

你知道什么是版权问题吗就是他们就是这个 1   6ms
北石店 2   3ms
我要去新街口  0   4ms
导航到向阳小区 0   4ms
只想守护你   0   3ms
将车道偏离预警开关打开 0   4ms
导航到南海意库 0   4ms
导航去1号公馆 0   4ms
1米制的恭喜发财    1   4ms
你给我想没有包子铺的你也灯关的水都关了新的利润都被人骨的肌   1   8ms
你吃不吃粑粑  1   4ms
导航去深圳湾创新科技中心    0   4ms
个性也没看就行了    1   4ms
三好听你就三个1390这个都是套餐5万双送给您的    1   6ms