nnReconstructor.hpp

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/** \file nnReconstructor.hpp
 * \brief The MagAO-X generic ImageStreamIO stream integrator
 *
 * \ingroup app_files
 */
 
#ifndef nnReconstructor_hpp
#define nnReconstructor_hpp
 
#include <NvInfer.h>
#include <cuda_runtime_api.h>
#include <iostream>
#include <fstream>
#include <vector>
#include <limits>
 
#include <mx/improc/eigenCube.hpp>
#include <mx/improc/eigenImage.hpp>
 
#include "../../libMagAOX/libMagAOX.hpp" //Note this is included on command line to trigger pch
#include "../../magaox_git_version.h"
 
using namespace nvinfer1;
 
// Logger for TensorRT info/warning/errors
class Logger : public ILogger {
    void log(Severity severity, const char* msg) noexcept override {
        if (severity <= Severity::kWARNING) {
            std::cout << msg << std::endl;
        }
    }
};
 
// #define MAGAOX_CURRENT_SHA1 0
// #define MAGAOX_REPO_MODIFIED 0
 
namespace MagAOX
{
namespace app
{
 
class nnReconstructor : public MagAOXApp<true>, public dev::shmimMonitor<nnReconstructor>
{
    // Give the test harness access.
    friend class nnReconstructor_test;
 
    friend class dev::shmimMonitor<nnReconstructor>;
 
    // The base shmimMonitor type
    typedef dev::shmimMonitor<nnReconstructor> shmimMonitorT;
 
    /// Floating point type in which to do all calculations.
    typedef float realT;
 
  public:
    /** \name app::dev Configurations
     *@{
     */
 
    ///@}
 
  protected:
    /** \name Configurable Parameters
     *@{
     */
    std::string dataDirs;     // Location where the data (onnx file, engine, WFS reference) is stored
    std::string onnxFileName; // Name of the onnx files
    std::string engineName;   // Name of the engine
    std::string engineDirs;   // Name of the engine
    bool rebuildEngine;       // If true, it will rebuild the engine and save it at engineName
    
    Logger logger;           // The tensorRT logger
    std::vector<char> engineData; // for loading the engine file.
 
    IRuntime* runtime {nullptr};
    ICudaEngine* engine {nullptr};
    IExecutionContext* context {nullptr};
    int inputC {0};
    int inputH {0};
    int inputW {0};
    int inputSize {0};
    int outputSize {0};
 
    float* d_input {nullptr};
    float* d_output {nullptr};
 
    float imageNorm; // Normalization constant for the image intensities
    float modalNorm; // Normalization constant for the modal coefficients
 
    int m_pupPix;      // Number of pixels in the pupil used for the Neural Network
    int pup_offset1_x; // Horizontal offset to the first set of pupils
    int pup_offset1_y; // Vertical offset to the first set of pupils
    int pup_offset2_x; // Horizontal offset to the second set of pupils
    int pup_offset2_y; // Horizontal offset to the second set of pupils
    int pixels_per_quadrant;
    unsigned long frame_counter{ 0 };
 
    uint32_t Nmodes{ 0 }; // Number of modes to reconstruct
    int Npup{ 4 };        // Number of pupils
    float *modeval{ nullptr };
    float *pp_image{ nullptr };
 
    size_t m_pwfsWidth{ 0 };  ///< The width of the image
    size_t m_pwfsHeight{ 0 }; ///< The height of the image.
 
    uint8_t m_pwfsDataType{ 0 }; ///< The ImageStreamIO type code.
    size_t m_pwfsTypeSize{ 0 };  ///< The size of the type, in bytes.
 
    // variables for sending the output to aol_modevals
    std::string m_modevalChannel;
    IMAGE m_modevalStream;
    uint32_t m_modevalWidth {0}; ///< The width of the shmim
    uint32_t m_modevalHeight {0}; ///< The height of the shmim
    uint8_t m_modevalDataType {0}; ///< The ImageStreamIO type code.
    size_t m_modevalTypeSize {0};  ///< The size of the type, in bytes.
 
    bool m_modevalOpened {false};
    bool m_modevalRestart {false};
 
  public:
    /// Default c'tor.
    nnReconstructor();
 
    /// D'tor, declared and defined for noexcept.
    ~nnReconstructor() noexcept
    {
    }
 
    virtual void setupConfig();
 
    /// Implementation of loadConfig logic, separated for testing.
    /** This is called by loadConfig().
     */
    int loadConfigImpl(
        mx::app::appConfigurator &_config /**< [in] an application configuration from which to load values*/ );
 
    virtual void loadConfig();
 
    /// Startup function
    /**
     *
     */
    virtual int appStartup();
 
    /// Implementation of the FSM for nnReconstructor.
    /**
     * \returns 0 on no critical error
     * \returns -1 on an error requiring shutdown
     */
    virtual int appLogic();
 
    /// Shutdown the app.
    /**
     *
     */
    virtual int appShutdown();
 
    // Custom functions
    int send_to_shmim();
    
    void load_engine(const std::string filename);
    void create_engine_context();
    void prepare_engine_memory();
    void cleanup_engine_memory();
    void cleanup_engine_context();
 
    // void build_engine(){};
 
  protected:
    int allocate( const dev::shmimT &dummy /**< [in] tag to differentiate shmimMonitor parents.*/ );
 
    int processImage( void *curr_src,          ///< [in] pointer to start of current frame.
                      const dev::shmimT &dummy ///< [in] tag to differentiate shmimMonitor parents.
    );
};
 
void nnReconstructor::load_engine(const std::string filename) {
    
    std::ifstream file(filename, std::ios::binary);
    if (!file) {
        std::cout << "Error opening " << filename << std::endl;
    }
 
    file.seekg(0, std::ios::end);
    
    engineData = std::vector<char>(file.tellg());
    file.seekg(0, std::ios::beg);
    file.read(engineData.data(), engineData.size());
 
}
 
void nnReconstructor::create_engine_context(){
 
    // Create the runtime and deserialize the engine
    runtime = createInferRuntime(logger);
    if (!runtime) {
        std::cout << "Failed to createInferRuntime\n";
    }
    
    engine = runtime->deserializeCudaEngine(engineData.data(), engineData.size());
    if (!engine) {
        std::cout << "Failed to deserialize CUDA engine.\n";
    } else {
        std::cout << "Deserialized CUDA engine.\n";
    }
    
    context = engine->createExecutionContext();
 
 
    int numIOTensors = engine->getNbIOTensors();
    std::cout << "Number of IO Tensors: " << numIOTensors << std::endl;
 
    auto inputName = engine->getIOTensorName(0);
    auto outputName = engine->getIOTensorName(1);
    std::cout << "Tensor IO names: " << inputName << " " << outputName << std::endl;
 
    const auto inputDims = engine->getTensorShape(inputName);
    const auto outputDims = engine->getTensorShape(outputName);
    inputC = inputDims.d[1];
    inputH = inputDims.d[2];
    inputW = inputDims.d[3];
    inputSize = inputC * inputH * inputW;
 
    outputSize = outputDims.d[1];
    std::cout << "Tensor input dimensions: " << inputC << "x" << inputH << "x" << inputW << std::endl;
    std::cout << "Tensor output dimensions: " << outputSize << std::endl;
 
}
 
void nnReconstructor::prepare_engine_memory(){
 
    // Allocate device memory for input and output
    cudaMalloc((void**)&d_input, inputSize * sizeof(float));
    cudaMalloc((void**)&d_output, outputSize * sizeof(float));
 
}
 
void nnReconstructor::cleanup_engine_memory(){
    if(d_input)
        cudaFree(d_input);
    
    if(d_output)
        cudaFree(d_output);
}
 
void nnReconstructor::cleanup_engine_context(){
    if(context)
        delete context;
    if(engine)
        delete engine;
    if(runtime)
        delete runtime;
};
 
inline int nnReconstructor::send_to_shmim()
{
    // Check if processImage is running
    // while(m_dmStream.md[0].write == 1);
 
    m_modevalStream.md[0].write = 1;
    memcpy( m_modevalStream.array.raw, modeval, outputSize * m_modevalTypeSize );
    m_modevalStream.md[0].cnt0++;
    m_modevalStream.md[0].write = 0;
 
    ImageStreamIO_sempost( &m_modevalStream, -1 );
 
    return 0;
}
 
inline nnReconstructor::nnReconstructor() : MagAOXApp( MAGAOX_CURRENT_SHA1, MAGAOX_REPO_MODIFIED )
{
    return;
}
 
inline void nnReconstructor::setupConfig()
{
    std::cout << "setupConfig()" << std::endl;
    shmimMonitorT::setupConfig( config );
 
    config.add( "parameters.dataDirs",
                "",
                "parameters.dataDirs",
                argType::Required,
                "parameters",
                "dataDirs",
                false,
                "string",
                "The path to the directory with the onnx model." );
 
    config.add( "parameters.engineDirs",
                "",
                "parameters.engineDirs",
                argType::Required,
                "parameters",
                "engineDirs",
                false,
                "string",
                "The path to the directory with the TRT engine." );
 
    config.add( "parameters.onnxFileName",
                "",
                "parameters.onnxFileName",
                argType::Required,
                "parameters",
                "onnxFileName",
                false,
                "string",
                "Name of the Neural Net ONNX file" );
 
    config.add( "parameters.engineName",
                "",
                "parameters.engineName",
                argType::Required,
                "parameters",
                "engineName",
                false,
                "string",
                "Name of the TRT engine." );
    config.add( "parameters.rebuildEngine",
                "",
                "parameters.rebuildEngine",
                argType::Required,
                "parameters",
                "rebuildEngine",
                false,
                "bool",
                "If true the engine will be rebuild." );
 
    config.add( "parameters.imageNorm",
                "",
                "parameters.imageNorm",
                argType::Required,
                "parameters",
                "imageNorm",
                false,
                "float",
                "Normalization term for the preprocessed images." );
 
    config.add( "parameters.modalNorm",
                "",
                "parameters.modalNorm",
                argType::Required,
                "parameters",
                "modalNorm",
                false,
                "float",
                "Normalization term for the modal coefficients." );
 
    config.add( "parameters.channel",
                "",
                "parameters.channel",
                argType::Required,
                "parameters",
                "channel",
                false,
                "string",
                "The output channel." );
 
    config.add( "parameters.m_pupPix",
                "",
                "parameters.m_pupPix",
                argType::Required,
                "parameters",
                "m_pupPix",
                false,
                "int",
                "Number of pixels across a PWFS pupil." );
 
    config.add( "parameters.pup_offset1_x",
                "",
                "parameters.pup_offset1_x",
                argType::Required,
                "parameters",
                "pup_offset1_x",
                false,
                "int",
                "Horizontal offset to the top left of the closest set op PWFS pupils." );
 
    config.add( "parameters.pup_offset1_y",
                "",
                "parameters.pup_offset1_y",
                argType::Required,
                "parameters",
                "pup_offset1_y",
                false,
                "int",
                "Vertical offset to the top left of the closest set op PWFS pupils." );
 
    config.add( "parameters.pup_offset2_x",
                "",
                "parameters.pup_offset2_x",
                argType::Required,
                "parameters",
                "pup_offset2_x",
                false,
                "int",
                "Horizontal offset to the top left of the furthest set op PWFS pupils." );
 
    config.add( "parameters.pup_offset2_y",
                "",
                "parameters.pup_offset2_y",
                argType::Required,
                "parameters",
                "pup_offset2_y",
                false,
                "int",
                "Vertical offset to the top left of the furthest set op PWFS pupils." );
 
}
 
inline int nnReconstructor::loadConfigImpl( mx::app::appConfigurator &_config )
{
    std::cout << "loadConfigImpl()" << std::endl;
    shmimMonitorT::loadConfig( config );
 
    _config( dataDirs, "parameters.dataDirs" );
    _config( engineDirs, "parameters.engineDirs" );
    _config( onnxFileName, "parameters.onnxFileName" );
    _config( engineName, "parameters.engineName" );
    _config( rebuildEngine, "parameters.rebuildEngine" );
 
    _config( imageNorm, "parameters.imageNorm" );
    _config( modalNorm, "parameters.modalNorm" );
    _config( m_modevalChannel, "parameters.channel");
 
    _config( m_pupPix, "parameters.m_pupPix" );
    _config( pup_offset1_x, "parameters.pup_offset1_x" );
    _config( pup_offset1_y, "parameters.pup_offset1_y" );
    _config( pup_offset2_x, "parameters.pup_offset2_x" );
    _config( pup_offset2_y, "parameters.pup_offset2_y" );
 
    if( true )
    {
        std::cout << "Debug configuration loading: " << std::endl;
        std::cout << "dataDirs: " << dataDirs << std::endl;
        std::cout << "engineDirs: " << engineDirs << std::endl;
        std::cout << "onnxFileName: " << onnxFileName << std::endl;
        std::cout << "engineName: " << engineName << std::endl;
        std::cout << "rebuildEngine: " << rebuildEngine << std::endl;
        std::cout << "imageNorm: " << imageNorm << std::endl;
        std::cout << "modalNorm: " << modalNorm << std::endl;
        std::cout << "modeval Channel: " << m_modevalChannel << std::endl;
 
        std::cout << "m_pupPix: " << m_pupPix << std::endl;
        std::cout << "pup_offset1_x: " << pup_offset1_x << std::endl;
        std::cout << "pup_offset1_y: " << pup_offset1_y << std::endl;
        std::cout << "pup_offset2_x: " << pup_offset2_x << std::endl;
        std::cout << "pup_offset2_y: " << pup_offset2_y << std::endl;
    }
 
    return 0;
}
 
inline void nnReconstructor::loadConfig()
{
    loadConfigImpl( config );
}
 
inline int nnReconstructor::appStartup()
{
    if( shmimMonitorT::appStartup() < 0 )
    {
        return log<software_error, -1>( { __FILE__, __LINE__ } );
    }
  
    std::string full_filepath = engineDirs + "/" + engineName;
    std::cout << "file: " << full_filepath << std::endl;
 
    load_engine(full_filepath);
    create_engine_context();
    prepare_engine_memory();
 
    // state(stateCodes::READY);
    state( stateCodes::OPERATING );
    return 0;
}
 
inline int nnReconstructor::appLogic()
{
    if( shmimMonitorT::appLogic() < 0 )
    {
        return log<software_error, -1>( { __FILE__, __LINE__ } );
    }
 
    std::unique_lock<std::mutex> lock( m_indiMutex );
 
    if( shmimMonitorT::updateINDI() < 0 )
    {
        log<software_error>( { __FILE__, __LINE__ } );
    }
 
    return 0;
}
 
inline int nnReconstructor::appShutdown()
{
    shmimMonitorT::appShutdown();
 
    if( pp_image )
    {
        delete[] pp_image;
    }
 
    cleanup_engine_context();
    cleanup_engine_memory();
 
    return 0;
}
 
inline int nnReconstructor::allocate( const dev::shmimT &dummy )
{
    std::cout << "allocate()" << std::endl;
    static_cast<void>( dummy ); // be unused
 
    // Wavefront sensor setup
    m_pwfsWidth = shmimMonitorT::m_width;
    m_pwfsHeight = shmimMonitorT::m_height;
    std::cout << "Width: " << m_pwfsWidth << " Height: " << m_pwfsHeight << std::endl;
 
    pixels_per_quadrant = m_pupPix * m_pupPix;
    std::cout << "Pixels: " << pixels_per_quadrant << std::endl;
    pp_image = new float[Npup * pixels_per_quadrant];
    memset( pp_image, 0, sizeof( float ) * Npup * pixels_per_quadrant );
 
    std::cout << "Close shmims" << std::endl;
    // Allocate the DM shmim interface
    if(m_modevalOpened){
        ImageStreamIO_closeIm(&m_modevalStream);
    }
 
    std::cout << "Open shmims" << std::endl;
    m_modevalOpened = false;
    m_modevalRestart = false; //Set this up front, since we're about to restart.
 
    if( ImageStreamIO_openIm(&m_modevalStream, m_modevalChannel.c_str()) == 0){
        if(m_modevalStream.md[0].sem < 10){
            ImageStreamIO_closeIm(&m_modevalStream);
        }else{
            m_modevalOpened = true;
        }
    }
 
    std::cout << "Done!" << std::endl;
    if(!m_modevalOpened){
        log<text_log>( m_modevalChannel + " not opened.", logPrio::LOG_NOTICE);
        return -1;
    }else{
        m_modevalWidth = m_modevalStream.md->size[0];
        m_modevalHeight = m_modevalStream.md->size[1];
 
        m_modevalDataType = m_modevalStream.md->datatype;
        m_modevalTypeSize = sizeof(float);
 
        log<text_log>( "Opened " + m_modevalChannel + " " + std::to_string(m_modevalWidth) + " x " + std::to_string(m_modevalHeight) + " with data type: " + std::to_string(m_modevalDataType), logPrio::LOG_NOTICE);
    }
 
 
    return 0;
}
 
inline int nnReconstructor::processImage( void *curr_src, const dev::shmimT &dummy )
{
    static_cast<void>( dummy ); // be unused
 
    // aol_imwfs2 is reference and dark subtracted and is power normalized.
    Eigen::Map<eigenImage<unsigned short>> pwfsIm(static_cast<unsigned short *>( curr_src ), m_pwfsHeight, m_pwfsWidth );
 
    // Split up the four pupils for the Neural Network.
    int ki = 0;
 
    for( int col_i = 0; col_i < m_pupPix; ++col_i )
    {
        for( int row_i = 0; row_i < m_pupPix; ++row_i )
        {
            pp_image[ki] = imageNorm * (realT)pwfsIm(pup_offset1_y + row_i, pup_offset1_x + col_i );
            pp_image[ki + pixels_per_quadrant] = imageNorm * (realT)pwfsIm( pup_offset1_y + row_i, pup_offset2_x + col_i );
            pp_image[ki + 2 * pixels_per_quadrant] = imageNorm * (realT)pwfsIm( pup_offset2_y + row_i, pup_offset1_x + col_i );
            pp_image[ki + 3 * pixels_per_quadrant] = imageNorm * (realT)pwfsIm( pup_offset2_y + row_i, pup_offset2_x + col_i );
 
            ++ki;
        }
    }
 
    // Copy input data to device
    // cudaMemcpy(d_input, pp_image, inputSize * sizeof(float), cudaMemcpyHostToDevice);
 
    // Run inference
    // void* buffers[] = {d_input, d_output};
    // context->executeV2(buffers);
 
    // Copy output data back to host
    // cudaMemcpy(modeval, d_output, outputSize * sizeof(float), cudaMemcpyDeviceToHost);
 
    if(frame_counter % 2000 == 0)
        std::cout << "HOWDY" << std::endl;
 
    // Send modal coefficients to the correct stream
    // send_to_shmim();
 
    frame_counter++;
 
    return 0;
}
 
} // namespace app
} // namespace MagAOX
 
#endif // nnReconstructor_hpp