Series

Code Example

include headers

#include <assert.h>
#include <sys/stat.h>
#include <time.h>

#include <iostream>
#include <fstream>
#include <sstream>
#include <iomanip>
#include <cmath>
#include <algorithm>

#include <cuda_runtime_api.h>

#include "NvCaffeParser.h"
#include "NvOnnxConfig.h"
#include "NvOnnxParser.h"
#include "NvInfer.h"
#include "common.h"

using namespace nvinfer1;
using namespace nvcaffeparser1;

static Logger gLogger;

// Attributes of MNIST Caffe model
static const int INPUT_H = 28;
static const int INPUT_W = 28;
static const int OUTPUT_SIZE = 10;
//const char* INPUT_BLOB_NAME = "data";
const char* OUTPUT_BLOB_NAME = "prob";
const std::string mnist_data_dir = "data/mnist/";


// Simple PGM (portable greyscale map) reader
void readPGMFile(const std::string& fileName, uint8_t buffer[INPUT_H * INPUT_W])
{
    readPGMFile(fileName, buffer, INPUT_H, INPUT_W);
}

caffe model to tensorrt

void caffeToTRTModel(const std::string& deployFilepath,       // Path of Caffe prototxt file
                     const std::string& modelFilepath,        // Path of Caffe model file
                     const std::vector<std::string>& outputs, // Names of network outputs
                     unsigned int maxBatchSize,               // Note: Must be at least as large as the batch we want to run with
                     IHostMemory*& trtModelStream)            // Output buffer for the TRT model
{
    // Create builder
    IBuilder* builder = createInferBuilder(gLogger);

    // Parse caffe model to populate network, then set the outputs
    std::cout << "Reading Caffe prototxt: " << deployFilepath << "\n";
    std::cout << "Reading Caffe model: " << modelFilepath << "\n";
    INetworkDefinition* network = builder->createNetwork();
    ICaffeParser* parser = createCaffeParser();

    bool useFp16 = builder->platformHasFastFp16();
    std::cout << "platformHasFastFp16: " << useFp16 << "\n";

    bool useInt8 = builder->platformHasFastInt8();
    std::cout << "platformHasFastInt8: " << useInt8 << "\n";

    // create a 16-bit model if it's natively supported
    DataType modelDataType = useFp16 ? DataType::kHALF : DataType::kFLOAT; 
    
    const IBlobNameToTensor* blobNameToTensor = parser->parse(deployFilepath.c_str(),
                                                              modelFilepath.c_str(),
                                                              *network,
                                                              modelDataType);
    // Specify output tensors of network
    // ERROR: Network must have at least one output
    for (auto& s : outputs){
        std::cout<<"output = "<< s.c_str() << std::endl;
        network->markOutput(*blobNameToTensor->find(s.c_str())); // prob
    } 

    builder->setMaxBatchSize(maxBatchSize);
    builder->setMaxWorkspaceSize(1 << 20);

    // set up the network for paired-fp16 format if available
    if(useFp16)
        builder->setFp16Mode(true);

    // Build engine
    ICudaEngine* engine = builder->buildCudaEngine(*network);
    assert(engine);

    // Destroy parser and network
    network->destroy();
    parser->destroy();

    // Serialize engine and destroy it
    trtModelStream = engine->serialize();
    engine->destroy();
    builder->destroy();

    //shutdownProtobufLibrary();
}

pytorch onnx to tensorrt

void onnxToTRTModel( const std::string& modelFilepath,        // 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
{
    // create the builder
    IBuilder* builder = createInferBuilder(gLogger);

    nvonnxparser::IOnnxConfig* config = nvonnxparser::createONNXConfig();
    config->setModelFileName(modelFilepath.c_str());
    
    nvonnxparser::IONNXParser* parser = nvonnxparser::createONNXParser(*config);
    
    //Optional - uncomment below lines to view network layer information
    //config->setPrintLayerInfo(true);
    //parser->reportParsingInfo();
    
    if (!parser->parse(modelFilepath.c_str(), DataType::kFLOAT))
    {
        string msg("failed to parse onnx file");
        gLogger.log(nvinfer1::ILogger::Severity::kERROR, msg.c_str());
        exit(EXIT_FAILURE);
    }
    
    if (!parser->convertToTRTNetwork()) {
        string msg("ERROR, failed to convert onnx network into TRT network");
        gLogger.log(nvinfer1::ILogger::Severity::kERROR, msg.c_str());
        exit(EXIT_FAILURE);
    }
    nvinfer1::INetworkDefinition* network = parser->getTRTNetwork();
    
    // Build the engine
    builder->setMaxBatchSize(maxBatchSize);
    builder->setMaxWorkspaceSize(1 << 20);

    ICudaEngine* engine = builder->buildCudaEngine(*network);
    assert(engine);

    // we don't need the network any more, and we can destroy the parser
    network->destroy();
    parser->destroy();

    // serialize the engine, then close everything down
    trtModelStream = engine->serialize();
    engine->destroy();
    builder->destroy();

    //shutdownProtobufLibrary();
}

do inference

void doInference(IExecutionContext& context, float* input, float* output, int batchSize)
{
    const ICudaEngine& engine = context.getEngine();
    // Pointers to input and output device buffers to pass to engine.
    // Engine requires exactly IEngine::getNbBindings() number of buffers.
    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()
    int inputIndex, outputIndex;

    printf("Bindings after deserializing:\n");
    for (int bi = 0; bi < engine.getNbBindings(); bi++) 
    {
        if (engine.bindingIsInput(bi) == true) 
        {
            inputIndex = bi;
            printf("Binding %d (%s): Input.\n",  bi, engine.getBindingName(bi));
        } else 
        {
            outputIndex = bi;
            printf("Binding %d (%s): Output.\n", bi, engine.getBindingName(bi));
        }
    }

    //const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
    //const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);

    std::cout<<"inputIndex = "<< inputIndex << std::endl; // 0   data
    std::cout<<"outputIndex = "<< outputIndex << std::endl; // 1  prob

    // Create GPU buffers on device
    CHECK(cudaMalloc(&buffers[inputIndex], batchSize * INPUT_H * INPUT_W * sizeof(float)));
    CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));

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

    // DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
    CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 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 stream and buffers
    cudaStreamDestroy(stream);
    CHECK(cudaFree(buffers[inputIndex]));
    CHECK(cudaFree(buffers[outputIndex]));
}

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save and load engine

void SaveEngine(const nvinfer1::IHostMemory& trtModelStream, const std::string& engine_filepath)
{
    std::ofstream file;
    file.open(engine_filepath, std::ios::binary | std::ios::out);
    if(!file.is_open())
    {
        std::cout << "read create engine file" << engine_filepath <<" failed" << std::endl;
        return;
    }
    file.write((const char*)trtModelStream.data(), trtModelStream.size());
    file.close();
};


ICudaEngine* LoadEngine(IRuntime& runtime, const std::string& engine_filepath)
{
    ifstream file;
    file.open(engine_filepath, ios::binary | ios::in);
    file.seekg(0, ios::end); 
    int length = file.tellg();         
    file.seekg(0, ios::beg); 

    std::shared_ptr<char> data(new char[length], std::default_delete<char[]>());
    file.read(data.get(), length);
    file.close();

    // runtime->deserializeCudaEngine(trtModelStream->data(), trtModelStream->size(), nullptr);
    ICudaEngine* engine = runtime.deserializeCudaEngine(data.get(), length, nullptr);
    assert(engine != nullptr);
    return engine;
}

example

void demo_save_caffe_to_trt(const std::string& engine_filepath)
{
    std::string deploy_filepath = mnist_data_dir + "mnist.prototxt";
    std::string model_filepath = mnist_data_dir + "mnist.caffemodel";
    
     // Create TRT model from caffe model and serialize it to a stream
    IHostMemory* trtModelStream{nullptr};
    caffeToTRTModel(deploy_filepath, model_filepath, std::vector<std::string>{OUTPUT_BLOB_NAME}, 1, trtModelStream);
    assert(trtModelStream != nullptr);

    SaveEngine(*trtModelStream, engine_filepath);

    // destroy stream
    trtModelStream->destroy();
}


void demo_save_onnx_to_trt(const std::string& engine_filepath)
{
    std::string onnx_filepath = mnist_data_dir + "mnist.onnx";
    
     // Create TRT model from caffe model and serialize it to a stream
    IHostMemory* trtModelStream{nullptr};
    onnxToTRTModel(onnx_filepath, 1, trtModelStream);
    assert(trtModelStream != nullptr);

    SaveEngine(*trtModelStream, engine_filepath);

    // destroy stream
    trtModelStream->destroy();
}


int mnist_demo()
{
    bool use_caffe = false; 
    std::string engine_filepath;
    if (use_caffe){
        engine_filepath = "cfg/mnist/caffe_minist_fp32.trt";
        demo_save_caffe_to_trt(engine_filepath);
    } else {
        engine_filepath = "cfg/mnist/onnx_minist_fp32.trt";
        demo_save_onnx_to_trt(engine_filepath);
    }
    std::cout<<"[API] Save engine to "<< engine_filepath <<std::endl;

    //if (watrix::algorithm::FilesystemUtil::not_exists(engine_filepath)){
    
    const int num = 6;
    std::string digit_filepath = mnist_data_dir + std::to_string(num) + ".pgm";

     // Read a digit file
    uint8_t fileData[INPUT_H * INPUT_W];
    readPGMFile(digit_filepath, fileData);
    float data[INPUT_H * INPUT_W];

    if (use_caffe){

        std::string mean_filepath = mnist_data_dir + "mnist_mean.binaryproto";
        // Parse mean file
        ICaffeParser* parser = createCaffeParser();
        IBinaryProtoBlob* meanBlob = parser->parseBinaryProto(mean_filepath.c_str());
        parser->destroy();

        // Subtract mean from image
        const float* meanData = reinterpret_cast<const float*>(meanBlob->getData()); // size 786

        for (int i = 0; i < INPUT_H * INPUT_W; i++)
            data[i] = float(fileData[i]) - meanData[i];
        
        meanBlob->destroy();
    } else {

        for (int i = 0; i < INPUT_H * INPUT_W; i++)
            data[i] = 1.0 - float(fileData[i]/255.0);
    }
    

    // Deserialize engine we serialized earlier
    IRuntime* runtime = createInferRuntime(gLogger);
    assert(runtime != nullptr);

    std::cout<<"[API] Load engine from "<< engine_filepath <<std::endl;
    ICudaEngine* engine = LoadEngine(*runtime, engine_filepath);
    assert(engine != nullptr);
    
    IExecutionContext* context = engine->createExecutionContext();
    assert(context != nullptr);

    // Run inference on input data
    float prob[OUTPUT_SIZE];
    doInference(*context, data, prob, 1);

    // Destroy the engine
    context->destroy();
    engine->destroy();
    runtime->destroy();

    // Print histogram of the output distribution
    std::cout << "\nOutput:\n\n";

    // for onnx,we get z as output, we need to use softmax to get probs
    if ( !use_caffe){

        //Calculate Softmax
        float sum{0.0f};
        for(int i = 0; i < OUTPUT_SIZE; i++)
        {
            prob[i] = exp(prob[i]);
            sum += prob[i];
        }
        for(int i = 0; i < OUTPUT_SIZE; i++)
        {
            prob[i] /= sum;
        }
    }
    
    // find max probs
    float val{0.0f};
    int idx{0};
    for (unsigned int i = 0; i < 10; i++)
    {
        val = std::max(val, prob[i]);
        if (val == prob[i]) {
            idx = i;
        }
        cout << " Prob " << i << "  "<< std::fixed << std::setw(5) << std::setprecision(4) << prob[i];
        std::cout << i << ": " << std::string(int(std::floor(prob[i] * 10 + 0.5f)), '*') << "\n";
    }
    std::cout << std::endl;

    return (idx == num && val > 0.9f) ? EXIT_SUCCESS : EXIT_FAILURE;
}


int main(int argc, char** argv)
{
    mnist_demo();
    return 0;
}

results

./bin/sample_mnist 
[API] Save engine to cfg/mnist/onnx_minist_fp32.trt
[API] Load engine from cfg/mnist/onnx_minist_fp32.trt
Bindings after deserializing:
Binding 0 (Input3): Input.
Binding 1 (Plus214_Output_0): Output.
inputIndex = 0
outputIndex = 1

Output:

 Prob 0  0.00000: 
 Prob 1  0.00001: 
 Prob 2  0.00002: 
 Prob 3  0.00003: 
 Prob 4  0.00004: 
 Prob 5  0.00005: 
 Prob 6  1.00006: **********
 Prob 7  0.00007: 
 Prob 8  0.00008: 
 Prob 9  0.00009:

Reference

tensorrt-api

History

20190422 created.

Copyright

Post author: kezunlin
Post link: https://kezunlin.me/post/bcdfb73c/
Copyright Notice: All articles in this blog are licensed under CC BY-NC-SA 3.0 unless stating additionally.

使用TensorRT对caffe和pytorch onnx版本的mnist模型进行fp32和fp16 推理

Series

Code Example

include headers

caffe model to tensorrt

pytorch onnx to tensorrt

do inference

save and load engine

example

results

Reference

History

Copyright

推荐阅读

kezunlin

目录