yolov10_det.cpp

#include <fstream>
#include <iostream>
#include <opencv2/opencv.hpp>
#include "cuda_utils.h"
#include "logging.h"
#include "model.h"
#include "postprocess.h"
#include "preprocess.h"
#include "utils.h"

Logger gLogger;
using namespace nvinfer1;
const int kOutputSize = kMaxNumOutputBbox * sizeof(Detection) / sizeof(float) + 1;

void serialize_engine(std::string& wts_name, std::string& engine_name, std::string& type, float& gd, float& gw,
                      int& max_channels) {
    IBuilder* builder = createInferBuilder(gLogger);
    IBuilderConfig* config = builder->createBuilderConfig();
    IHostMemory* serialized_engine = nullptr;

    if (type == "n") {
        serialized_engine = buildEngineYolov10DetN(builder, config, DataType::kFLOAT, wts_name, gd, gw, max_channels);
    } else if (type == "s") {
        serialized_engine = buildEngineYolov10DetS(builder, config, DataType::kFLOAT, wts_name, gd, gw, max_channels);
    } else if (type == "m") {
        serialized_engine = buildEngineYolov10DetM(builder, config, DataType::kFLOAT, wts_name, gd, gw, max_channels);
    } else if (type == "b" || type == "l") {
        serialized_engine = buildEngineYolov10DetBL(builder, config, DataType::kFLOAT, wts_name, gd, gw, max_channels);
    } else if (type == "x") {
        serialized_engine = buildEngineYolov10DetX(builder, config, DataType::kFLOAT, wts_name, gd, gw, max_channels);
    } else {
        std::cerr << "Unsupported type!" << std::endl;
        exit(0);
    }

    assert(serialized_engine);
    std::ofstream p(engine_name, std::ios::binary);
    if (!p) {
        std::cout << "could not open plan output file" << std::endl;
        assert(false);
    }
    p.write(reinterpret_cast<const char*>(serialized_engine->data()), serialized_engine->size());

    delete serialized_engine;
    delete config;
    delete builder;
}

void deserialize_engine(std::string& engine_name, IRuntime** runtime, ICudaEngine** engine,
                        IExecutionContext** context) {
    std::ifstream file(engine_name, std::ios::binary);
    if (!file.good()) {
        std::cerr << "read " << engine_name << " error!" << std::endl;
        assert(false);
    }
    size_t size = 0;
    file.seekg(0, file.end);
    size = file.tellg();
    file.seekg(0, file.beg);
    char* serialized_engine = new char[size];
    assert(serialized_engine);
    file.read(serialized_engine, size);
    file.close();

    *runtime = createInferRuntime(gLogger);
    assert(*runtime);
    *engine = (*runtime)->deserializeCudaEngine(serialized_engine, size);
    assert(*engine);
    *context = (*engine)->createExecutionContext();
    assert(*context);
    delete[] serialized_engine;
}

void prepare_buffer(ICudaEngine* engine, float** input_buffer_device, float** output_buffer_device,
                    float** output_buffer_host) {
    assert(engine->getNbBindings() == 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(kInputTensorName);
    const int outputIndex = engine->getBindingIndex(kOutputTensorName);
    assert(inputIndex == 0);
    assert(outputIndex == 1);
    // Create GPU buffers on device
    CUDA_CHECK(cudaMalloc((void**)input_buffer_device, kBatchSize * 3 * kInputH * kInputW * sizeof(float)));
    CUDA_CHECK(cudaMalloc((void**)output_buffer_device, kBatchSize * kOutputSize * sizeof(float)));

    *output_buffer_host = new float[kBatchSize * kOutputSize];
}

void infer(IExecutionContext& context, cudaStream_t& stream, void** buffers, float* output, int batchsize) {
    // infer on the batch asynchronously, and DMA output back to host
    auto start = std::chrono::system_clock::now();
    context.enqueueV2(buffers, stream, nullptr);

    CUDA_CHECK(cudaMemcpyAsync(output, buffers[1], batchsize * kOutputSize * sizeof(float), cudaMemcpyDeviceToHost,
                               stream));
    auto end = std::chrono::system_clock::now();
    std::cout << "inference time: " << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count()
              << "ms" << std::endl;

    CUDA_CHECK(cudaStreamSynchronize(stream));
}

bool parse_args(int argc, char** argv, std::string& wts, std::string& engine, std::string& img_dir, std::string& type,
                float& gd, float& gw, int& max_channels) {
    if (argc < 4)
        return false;
    if (std::string(argv[1]) == "-s" && (argc == 5 || argc == 7)) {
        wts = std::string(argv[2]);
        engine = std::string(argv[3]);
        auto sub_type = std::string(argv[4]);

        if (sub_type[0] == 'n') {
            gd = 0.33;
            gw = 0.25;
            max_channels = 1024;
            type = "n";
        } else if (sub_type[0] == 's') {
            gd = 0.33;
            gw = 0.50;
            max_channels = 1024;
            type = "s";
        } else if (sub_type[0] == 'm') {
            gd = 0.67;
            gw = 0.75;
            max_channels = 768;
            type = "m";
        } else if (sub_type[0] == 'b') {
            gd = 0.67;
            gw = 1.0;
            max_channels = 512;
            type = "b";
        } else if (sub_type[0] == 'l') {
            gd = 1.0;
            gw = 1.0;
            max_channels = 512;
            type = "l";
        } else if (sub_type[0] == 'x') {
            gd = 1.0;
            gw = 1.25;
            max_channels = 512;
            type = "x";
        } else {
            return false;
        }
    } else if (std::string(argv[1]) == "-d" && argc == 4) {
        engine = std::string(argv[2]);
        img_dir = std::string(argv[3]);
    } else {
        return false;
    }
    return true;
}

int main(int argc, char** argv) {
    // -s ../models/yolov10n.wts ../models/yolov10n.fp32.trt n
    // -d ../models/yolov10n.fp32.trt ../images
    cudaSetDevice(kGpuId);
    std::string wts_name = "";
    std::string engine_name = "";
    std::string img_dir;
    std::string type = "";
    float gd = 0.0f, gw = 0.0f;
    int max_channels = 0;

    if (!parse_args(argc, argv, wts_name, engine_name, img_dir, type, gd, gw, max_channels)) {
        std::cerr << "Arguments not right!" << std::endl;
        std::cerr << "./yolov10_det -s [.wts] [.engine] [n/s/m/b/l/x]  // serialize model to "
                     "plan file"
                  << std::endl;
        std::cerr << "./yolov10_det -d [.engine] ../samples  // deserialize plan file and run inference" << std::endl;
        return -1;
    }

    // Create a model using the API directly and serialize it to a file
    if (!wts_name.empty()) {
        serialize_engine(wts_name, engine_name, type, gd, gw, max_channels);
        return 0;
    }

    // Deserialize the engine from file
    IRuntime* runtime = nullptr;
    ICudaEngine* engine = nullptr;
    IExecutionContext* context = nullptr;
    deserialize_engine(engine_name, &runtime, &engine, &context);
    cudaStream_t stream;
    CUDA_CHECK(cudaStreamCreate(&stream));
    cuda_preprocess_init(kMaxInputImageSize);
    // Prepare cpu and gpu buffers
    float* device_buffers[2];
    float* output_buffer_host = nullptr;

    // Read images from directory
    std::vector<std::string> file_names;
    if (read_files_in_dir(img_dir.c_str(), file_names) < 0) {
        std::cerr << "read_files_in_dir failed." << std::endl;
        return -1;
    }

    prepare_buffer(engine, &device_buffers[0], &device_buffers[1], &output_buffer_host);

    // batch predict
    for (size_t i = 0; i < file_names.size(); i += kBatchSize) {
        // Get a batch of images
        std::vector<cv::Mat> img_batch;
        std::vector<std::string> img_name_batch;
        for (size_t j = i; j < i + kBatchSize && j < file_names.size(); j++) {
            cv::Mat img = cv::imread(img_dir + "/" + file_names[j]);
            if (img.empty()) {
                std::cerr << "Fatal error: image cannot open!" << std::endl;
                return -1;
            }
            img_batch.push_back(img);
            img_name_batch.push_back(file_names[j]);
        }
        // Preprocess
        cuda_batch_preprocess(img_batch, device_buffers[0], kInputW, kInputH, stream);
        // Run inference
        infer(*context, stream, (void**)device_buffers, output_buffer_host, kBatchSize);
        // output_buffer_host保存前100个值到文件
        //        std::ofstream out_file("../output.txt");
        //        for (int i = 0; i < 100; i++) {
        //            out_file << output_buffer_host[i] << std::endl;
        //        }
        //        out_file.close();

        std::vector<std::vector<Detection>> res_batch;
        batch_topk(res_batch, output_buffer_host, img_batch.size(), kOutputSize, kConfThresh);

        // print results
        for (size_t j = 0; j < res_batch.size(); j++) {
            for (size_t k = 0; k < res_batch[j].size(); k++) {
                std::cout << "image: " << img_name_batch[j] << ", bbox: " << res_batch[j][k].bbox[0] << ", "
                          << res_batch[j][k].bbox[1] << ", " << res_batch[j][k].bbox[2] << ", "
                          << res_batch[j][k].bbox[3] << ", conf: " << res_batch[j][k].conf
                          << ", class_id: " << res_batch[j][k].class_id << std::endl;
            }
        }

        // Draw bounding boxes
        draw_bbox(img_batch, res_batch);
        // Save images
        for (size_t j = 0; j < img_batch.size(); j++) {
            cv::imwrite("_" + img_name_batch[j], img_batch[j]);
        }
    }

    // Release stream and buffers
    cudaStreamDestroy(stream);
    CUDA_CHECK(cudaFree(device_buffers[0]));
    CUDA_CHECK(cudaFree(device_buffers[1]));
    cuda_preprocess_destroy();
    // Destroy the engine
    delete context;
    delete engine;
    delete runtime;

    return 0;
}