本文主要是介绍TensorRT——INT8推理，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！

原理

• 为什么要使用INT8推理：更高的吞吐量/处理的fps提高以及更低的内存占用(8-bit vs 32-bit)
• 将FP32模型转换成INT8模型存在的挑战：更低的动态范围和精度

Consider that 32-bit floating-point can represent roughly 4 billion numbers in the interval [-3.4e38, 3.40e38]. This interval of representable numbers is also known as the dynamic-range. The distance between two neighboring representable numbers is the precision of the representation. ——《Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT》

• 如何将FP32量化成INT8：最简单的一种方式是Symmetric Linear Quantization，每个Tensor都可以用一个和它关联的scalar factor乘以量化后的INT8值。那么如何确定这个scalar factor呢？

对于weights，TensorRT采用左图的方式进行映射，这样不会带来accuracy drop；对于activations，TensorRT采用上图右边这种方式进行INT8量化，这面临一个新的问题，如何为每个activation tensor选取最佳的|threshold|呢？（这个其实就是calibration的过程）
选取不同的threshold，相当于是不同的编码方式。从信息论的角度看，我们希望选取一种编码方式，使得编码前后的信息损失最小，我们可以用KL散度来衡量这个信息损失。

• 对activations的calibration
  

实践

为了使用TensorRT的INT8推理，我们需要编写一个自己的calibrator类，然后通过builder->setInt8Calibrator(calibrator)告诉builder使用这个calibrator来做数据标定，从而减小量化误差。
至于builder具体是怎么去做标定的，builder类实现了以下功能：

• builder首先调用calibrator类的getBatchSize()来获取input batch的大小
• 然后builder不断调用getBatch()来获取用于标定的输入数据，读入的batch data的大小必须和getBatchSize()得到的大小一致，如果没有input batch数据了，getBatch()返回false
• builder会先建立一个32-bit的Engine，对calibration set进行前向推理，并记录下每层activations的直方图
• 根据获得的直方图建立一个calibration table
• 基于得到的calibration table和network definition来创建8-bit的Engine

而calibration的过程是比较耗时的，通过对calibration table进行缓存，可以高效地对同一网络build多次。要实现对calibration table的缓存功能，需要实现calibrator类中的writeCalibrationCache()和readCalibrationCache()两个函数。
综上所述，要实现一个INT8的Engine，开发人员需要实现一个calibrator类，这个类需override下面几个函数：

• getBatchSize
• getBatch
• writeCalibrationCache(optional)
• readCalibrationCache(optional)

这个calibrator类是一个IInt8Calibrator，TensorRT提供了4个IInt8Calibrator的派生类(IInt8EntropyCalibrator、IInt8EntropyCalibrator2、IInt8MinMaxCalibrator、IInt8LegacyCalibrator，我们例子中的calibrator继承自IInt8EntropyCalibrator.

#include <algorithm>
#include <assert.h>
#include <cmath>
#include <cuda_runtime_api.h>
#include <fstream>
#include <iomanip>
#include <iostream>
#include <sstream>
#include <sys/stat.h>
#include <time.h>
#include <opencv2/opencv.hpp>
#include "NvInfer.h"
#include "NvOnnxParser.h"
#include "argsParser.h"
#include "logger.h"
#include "common.h"
#include "image.hpp"
#define DebugP(x) std::cout << "Line" << __LINE__ << "  " << #x << "=" << x << std::endl


using namespace nvinfer1;

Logger gLogger;
// LogStreamConsumer gLogError;

static const int INPUT_H = 224;
static const int INPUT_W = 224;
static const int INPUT_C = 3;
static const int OUTPUT_SIZE = 1000;

const char* INPUT_BLOB_NAME = "input";
const char* OUTPUT_BLOB_NAME = "output";

const std::string gSampleName = "TensorRT.sample_onnx_image";
const std::string onnxFile = "resnet50.onnx";
const std::string engineFile = "../data/resnet50_int8.trt"
const std::string calibFile = "../data/calibration_img.txt"


samplesCommon::Args gArgs;

std::vector<float> prepareImage(cv::Mat &img) {
    int c = 3;
    int h = INPUT_H;
    int w = INPUT_W;

    // 1 Resize the source Image to a specific size(这里保持原图长宽比进行resize)
    float scale = std::min(float(w) / img.cols, float(h) / img.rows);
    auto scaleSize = cv::Size(img.cols * scale, img.rows * scale);

    // Convert BGR to RGB
    cv::Mat rgb;
    cv::cvtColor(img, rgb, CV_BGR2RGB);

    cv::Mat resized;
    cv::resize(rgb, resized, scaleSize, 0, 0, cv::INTER_CUBIC);

    // 2 Crop Image(将resize后的图像放在(H, W, C)的中心, 周围用127做padding)
    cv::Mat cropped(h, w, CV_8UC3, 127)
    // Rect(left_top_x, left_top_y, width, height)
    cv::Rect rect((w - scaleSize.width) / 2, (h - scaleSize.height) / 2, scaleSize.width, scaleSize.height);
    resize.copyTo(cropped(rect));

    // 3 Type conversion, convert unsigned int 8 to float 32
    cv::Mat img_float;
    cropped.convertTo(img_float, CV_32FC3, 1.f / 255.0);

    // HWC to CHW, and convert Mat to std::vector<float>
    std::vector<cv::Mat> input_channels(c);
    cv::split(cropped, input_channels);

    std::vector<float> result(h * w * c);
    auto data = result.data();
    int channelLength = h * w;
    for (int i = 0; i < c; ++i) {
        memcpy(data, input_channels[i].data, channelLength * sizeof(float));
        data += channelLength;
    }
    return result;
}

// 实现自己的calibrator类
namespace nvinfer1 {
    class int8EntropyCalibrator: public nvinfer1::IInt8EntropyCalibrator {
        public:
            int8EntropyCalibrator(const int &batchSize,
                const std::string &imgPath,
                const std::string &calibTablePath);

            virtual ~int8EntropyCalibrator();

            int getBatchSize() const override { return batchSize; }

            bool getBatch(void *bindings[], const char *names[], int nbBindings) override;

            const void *readCalibationCache(std::size_t &length) override;

            void writeCalibrationCache(const void *ptr, std::size_t length) override;
       
        private:
            int batchSize;
            size_t inputCount;
            size_t imageIndex;
            std::string calibTablePath;
            std::vector<std::string> imgPaths;

            float *batchData { nullptr };
            void *deviceInput { nullptr };

            bool readCache;
            std::vector<char> calibrationCache;
    };

    int8EntropyCalibrator::int8EntropyCalibrator(const int &batchSize, const std::string &imgPath,
        const std::string &calibTablePath) : batchSize(batchSize), calibTablePath(calibTablePath), imageIndex(0) {
            int inputChannel = 3;
            int inputH = 256;
            int inputW = 256;
            inputCount = batchSize * inputChannel  * inputH * inputW;

            std::fstream f(imgPath);
            if (f.is_open()) {
                std::string temp;
                while( std::getline(f, temp) ) imgPaths.push_back(temp);
            }
            int len = imgPaths.size();
            for( int i = 0; i < len; i++) {
                std::cout << imgPaths[i] << std::endl;
            }

            // allocate memory for a batch of data, batchData is for CPU, deviceInput is for GPU
            batchData = new flowt[inputCount];
            CHECK(cudaMalloc(&deviceInput, inputCount * sizeof(float)));
        }

        IInt8EntropyCalibrator::~IInt8EntropyCalibrator() {
            CHECK(cudaFree(deviceInput));
            if (batchData) {
                delete[] batchData;
            }
        }

        bool int8EntropyCalibrator::getBatch(void **bindings, const char **names, int nbBindings) {
            std::cout << imageIndex << " " << batchSize << std::endl;
            std::cout << imgPaths.size() << std::endl;
            if (imageIndex + batchSize > ing(imgPaths.size()))
                return false;
            // load batch
            float *ptr = batchData;
            for (size_t j = imageIndex; j < imageIndex + batchSize; ++j) {
                cv::Mat img = cv::imread(imgPaths[j]);
                std::vector<float> inputData = prepareImage(img);
                if (inputData.size() != inputCount) {
                    std::cout << "InputSize Error" << std::endl;
                    return false;
                }
                assert(inputData.size() == inputCount);
                memcpy(ptr, inputData.data(), (int)(inputData.size()) * sizeof(float));
                ptr += inputData.size();
                std::cout << "load image " << imgPaths[j] << " " << (j + 1) * 100. / imgPaths.size() << "%" << std::endl;
            }
            imageIndex += batchSize;
            // copy bytes from Host to Device
            CHECK(cudaMemcpy(deviceInput, batchData, inputCount * sizeof(float), cudaMemcpyHostToDevice));
            bindings[0] = deviceInput;
            return true;
        }

        const void* int8Entropycalibrator::readCalibrationCache(std::size_t &length) {
            calibrationCache.clear();
            std::ifstream input(calibTablePath, std::ios::binary);
            input >> std::noskipws;
            if (readCache && input.good()) {
                std::copy(std::istream_iterator<char>(input), std::istream_iterator<char>(),
                    std::back_inserter(calibrationCache));
            }
            length = calibrationCache.size();
            return length ? &calibrationCache[0] : nullptr;
        }

        void int8EntropyCalibrator::writeCalibrationCache(const void *cache, std::size_t length) {
            std::ofstream output(calibTablePath, std::ios::binary);
            output.write(reinterpret_cast<const char*>(cache), length);
        }
}

bool onnxToTRTModel(const std::string& modelFile, // name of the onnx model
                    unsigned int maxBatchSize,    // batch size - NB must be at least as large as the batch we want to run with
                    IHostMemory*& trtModelStream, // output buffer for the TensorRT model
                    const std::string& engineFile)
    // create the builder
    IBuilder* builder = createInferBuilder(gLogger.getTRTLogger());
    assert(builder != nullptr);

    // create the config
    auto config = builder->createBuilderConfig();
    assert(config != nullptr);

    if (! builder->platformHasFastInt8()) {
        std::cout << "builder platform do not support Int8" << std::endl;
        return false;
    }

    const auto explicitBatch = 1U << static_cast<uint32_t>(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
    std::cout << "explicitBatch is: " << explicitBatch << std::endl;
    nvinfer1::INetworkDefinition* network = builder->createNetworkV2(explicitBatch);

    auto parser = nvonnxparser::createParser(*network, gLogger.getTRTLogger());

    //Optional - uncomment below lines to view network layer information
    //config->setPrintLayerInfo(true);
    //parser->reportParsingInfo();

    if ( !parser->parseFromFile( locateFile(modelFile, gArgs.dataDirs).c_str(), static_cast<int>(gLogger.getReportableSeverity()) ) )
    {
        gLogError << "Failure while parsing ONNX file" << std::endl;
        return false;
    }
 
    // config
    config->setAvgTimingIterations(1);
    config->setMinTimingIterations(1);
    config->setMaxWorkspaceSize(1_GiB);

    // Build the engine
    builder->setMaxBatchSize(maxBatchSize);
    //builder->setMaxWorkspaceSize(1 << 20);
    builder->setMaxWorkspaceSize(10 << 20);

    nvinfer1::int8EntropyCalibrator *calibrator = nullptr;
    if (calibFile.size() > 0 ) calibrator = new nvinfer1::int8EntropyCalibrator(maxBatchSize, calibFile, "");

    // builder->setFp16Mode(gArgs.runInFp16);
    // builder->setInt8Mode(gArgs.runInInt8);

    // 对builder进行设置, 告诉它使用Int8模式, 并利用编写好的calibrator类进行calibration
    builder->setInt8Mode(true);
    builder->setInt8Calibrator(calibrator);

    // if (gArgs.runInInt8)
    // {
    //     samplesCommon::setAllTensorScales(network, 127.0f, 127.0f);
    // }
    config->setFlag(BuiderFlag::kINT8);
    config->setInt8Calibrator(calibrator);

    // 如果使用了calibrator, 应该参考https://github.com/enazoe/yolo-tensorrt/blob/dd4cb522625947bfe6bfbdfbb6890c3f7558864a/modules/yolo.cpp, 把下面这行注释掉，使用数据集校准得到dynamic range；否则使用下面这行手动设置dynamic range。
    // setAllTensorScales函数在官方TensorRT开源代码里有
    samplesCommon::setAllTensorScales(network, 127.0f, 127.0f);
    // samplesCommon::enableDLA(builder, gArgs.useDLACore);
   
    ICudaEngine* engine = builder->buildCudaEngine(*network);
    assert(engine);

    if (calibrator) {
        delete calibrator;
        calibrator = nullptr;
    }

    // we can destroy the parser
    parser->destroy();

    // serialize the engine, then close everything down
    trtModelStream = engine->serialize();
    std::ofstream file;
    file.open(engineFile, std::ios::binary | std::ios::out);
    file.write((const char*)data->data(), data->size());
    file.close();

    engine->destroy();
	config->destroy();
    network->destroy();
    builder->destroy();

    return true;
}

void doInference(IExecutionContext& context, float* input, float* output, int batchSize)
{
    const ICudaEngine& engine = context.getEngine();
    // input and output buffer pointers that we pass to the engine - the engine requires exactly IEngine::getNbBindings(),
    // of these, but in this case we know that there is exactly one input and one output.
    assert(engine.getNbBindings() == 2);
    void* buffers[2];

    // In order to bind the buffers, we need to know the names of the input and output tensors.
    // note that indices are guaranteed to be less than IEngine::getNbBindings()
   
    const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
    const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
   
    DebugP(inputIndex); DebugP(outputIndex);
    // create GPU buffers and a stream
    CHECK(cudaMalloc(&buffers[inputIndex], batchSize * INPUT_C * INPUT_H * INPUT_W * sizeof(float)));
    CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));

    cudaStream_t stream;
    CHECK(cudaStreamCreate(&stream));

    // DMA the input to the GPU,  execute the batch asynchronously, and DMA it back:
    CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * INPUT_C * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
    context.enqueue(batchSize, buffers, stream, nullptr);
    CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
    cudaStreamSynchronize(stream);

    // release the stream and the buffers
    cudaStreamDestroy(stream);
    CHECK(cudaFree(buffers[inputIndex]));
    CHECK(cudaFree(buffers[outputIndex]));
}

//!
//! \brief This function prints the help information for running this sample
//!
void printHelpInfo()
{
    std::cout << "Usage: ./sample_onnx_mnist [-h or --help] [-d or --datadir=<path to data directory>] [--useDLACore=<int>]\n";
    std::cout << "--help          Display help information\n";
    std::cout << "--datadir       Specify path to a data directory, overriding the default. This option can be used multiple times to add multiple directories. If no data directories are given, the default is to use (data/samples/mnist/, data/mnist/)" << std::endl;
    std::cout << "--useDLACore=N  Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, where n is the number of DLA engines on the platform." << std::endl;
    std::cout << "--int8          Run in Int8 mode.\n";
    std::cout << "--fp16          Run in FP16 mode." << std::endl;
}

int main(int argc, char** argv)
{
    bool argsOK = samplesCommon::parseArgs(gArgs, argc, argv);
    if (gArgs.help)
    {
        printHelpInfo();
        return EXIT_SUCCESS;
    }
    if (!argsOK)
    {
        std::cout << "Invalid arguments" << std::endl;
        // gLogError << "Invalid arguments" << std::endl;
        printHelpInfo();
        return EXIT_FAILURE;
    }
    if (gArgs.dataDirs.empty())
    {
        gArgs.dataDirs = std::vector<std::string>{"data/"};
    }

    auto sampleTest = gLogger.defineTest(gSampleName, argc, const_cast<const char**>(argv));

    gLogger.reportTestStart(sampleTest);

    // create a TensorRT model from the onnx model and serialize it to a stream
    nvinfer1::IHostMemory* trtModelStream{nullptr};

    if (!onnxToTRTModel(onnxFile, 1, trtModelStream))
        gLogger.reportFail(sampleTest);

    assert(trtModelStream != nullptr);
    std::cout << "Successfully parsed ONNX file!!!!" << std::endl;
   
   
    std::cout << "Start reading the input image!!!!" << std::endl;
   
    cv::Mat image = cv::imread(locateFile("test.jpg", gArgs.dataDirs), cv::IMREAD_COLOR);
    if (image.empty()) {
        std::cout << "The input image is empty!!! Please check....."<<std::endl;
    }
    DebugP(image.size());
    cv::cvtColor(image, image, cv::COLOR_BGR2RGB);

    cv::Mat dst = cv::Mat::zeros(INPUT_H, INPUT_W, CV_32FC3);
    cv::resize(image, dst, dst.size());
    DebugP(dst.size());

    float* data = normal(dst);

    // deserialize the engine
    IRuntime* runtime = createInferRuntime(gLogger);
    assert(runtime != nullptr);
    if (gArgs.useDLACore >= 0)
    {
        runtime->setDLACore(gArgs.useDLACore);
    }

    ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream->data(), trtModelStream->size(), nullptr);
    assert(engine != nullptr);
    trtModelStream->destroy();
    IExecutionContext* context = engine->createExecutionContext();
    assert(context != nullptr);
   
    float prob[OUTPUT_SIZE];
    typedef std::chrono::high_resolution_clock Time;
    typedef std::chrono::duration<double, std::ratio<1, 1000>> ms;
    typedef std::chrono::duration<float> fsec;
    double total = 0.0;

    // run inference and cout time
    auto t0 = Time::now();
    doInference(*context, data, prob, 1);
    auto t1 = Time::now();
    fsec fs = t1 - t0;
    ms d = std::chrono::duration_cast<ms>(fs);
    total += d.count();
    // destroy the engine
    context->destroy();
    engine->destroy();
    runtime->destroy();
   
    std::cout << std::endl << "Running time of one image is:" << total << "ms" << std::endl;
  
    std::cout << "Output:\n";
    for (int i = 0; i < OUTPUT_SIZE; i++)
    {
        gLogInfo << prob[i] << " ";
    }
    std::cout << std::endl;

    return gLogger.reportTest(sampleTest, true);
}

除了上面这个实现外，官方的sampleINT8.cpp也非常值得参考。

参考资料：

1. PPT《8-bit inference with TensorRT》
2. Video 《8-bit inference with TensorRT》

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