OnnxRuntimeBase.h

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#include <vector>
#include <map>
#include <Eigen/Dense>
#include <onnxruntime_cxx_api.h>
#include <TString.h>
 
using NetworkBatchInput =
    Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
 
// General class that sets up the ONNX runtime framework for loading a ML model
// and using it for inference.
class OnnxRuntimeBase {
    public:        
        // int m_totalInputs;
 
        TString m_fileName;
        // int n_hits;
        // std::string m_trackType;
        // std::string m_order;
        // bool m_scaled;
 
        OnnxRuntimeBase(TString fileName);
        OnnxRuntimeBase();
        ~OnnxRuntimeBase(){}
        void initialize(TString);
 
        std::vector<float> runONNXInference(std::vector<float>& inputTensorValues) const;
        std::vector<std::vector<float>> runONNXInference(std::vector<std::vector<float> >& inputTensorValues) const;
        std::vector<std::vector<float>> runONNXInference(NetworkBatchInput& inputTensorValues) const;
        std::map<int, Eigen::MatrixXf> runONNXInferenceMultilayerOutput(NetworkBatchInput& inputTensorValues) const;
 
        const std::vector<int64_t>& getInputNodesDims(){return m_inputNodeDims;};
        const std::vector<int64_t>& getOutputNodesDims(){return m_outputNodeDims;};
 
    private:
        std::unique_ptr<Ort::Session> m_session;
 
        std::vector<const char*> m_inputNodeNames;
        std::vector<int64_t> m_inputNodeDims;
        std::vector<const char*> m_outputNodeNames;
        std::vector<int64_t> m_outputNodeDims;
 
        std::unique_ptr< Ort::Env > m_env;
 
};