yolov5_det.cpp

#include "cuda_utils.h"
#include "logging.h"
#include "model.h"
#include "postprocess.h"
#include "preprocess.h"
#include "utils.h"

#include <chrono>
#include <cmath>
#include <iostream>

using namespace nvinfer1;

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

bool parse_args(int argc, char** argv, std::string& wts, std::string& engine, bool& is_p6, float& gd, float& gw,
                std::string& img_dir) {
    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 net = std::string(argv[4]);
        if (net[0] == 'n') {
            gd = 0.33;
            gw = 0.25;
        } else if (net[0] == 's') {
            gd = 0.33;
            gw = 0.50;
        } else if (net[0] == 'm') {
            gd = 0.67;
            gw = 0.75;
        } else if (net[0] == 'l') {
            gd = 1.0;
            gw = 1.0;
        } else if (net[0] == 'x') {
            gd = 1.33;
            gw = 1.25;
        } else if (net[0] == 'c' && argc == 7) {
            gd = atof(argv[5]);
            gw = atof(argv[6]);
        } else {
            return false;
        }
        if (net.size() == 2 && net[1] == '6') {
            is_p6 = true;
        }
    } 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;
}

void prepare_buffers(ICudaEngine* engine, float** gpu_input_buffer, float** gpu_output_buffer,
                     float** cpu_output_buffer) {
    assert(engine->getNbIOTensors() == 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()
    TensorIOMode input_mode = engine->getTensorIOMode(kInputTensorName);
    if (input_mode != TensorIOMode::kINPUT) {
        std::cerr << kInputTensorName << " should be input tensor" << std::endl;
        assert(false);
    }
    TensorIOMode output_mode = engine->getTensorIOMode(kOutputTensorName);
    if (output_mode != TensorIOMode::kOUTPUT) {
        std::cerr << kOutputTensorName << " should be output tensor" << std::endl;
        assert(false);
    }
    // Create GPU buffers on device
    CUDA_CHECK(cudaMalloc((void**)gpu_input_buffer, kBatchSize * 3 * kInputH * kInputW * sizeof(float)));
    CUDA_CHECK(cudaMalloc((void**)gpu_output_buffer, kBatchSize * kOutputSize * sizeof(float)));

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

void infer(IExecutionContext& context, cudaStream_t& stream, void** gpu_buffers, float* output, int batchsize) {
    context.setInputTensorAddress(kInputTensorName, gpu_buffers[0]);
    context.setOutputTensorAddress(kOutputTensorName, gpu_buffers[1]);
    context.enqueueV3(stream);
    CUDA_CHECK(cudaMemcpyAsync(output, gpu_buffers[1], batchsize * kOutputSize * sizeof(float), cudaMemcpyDeviceToHost,
                               stream));
    cudaStreamSynchronize(stream);
}

void serialize_engine(unsigned int max_batchsize, bool& is_p6, float& gd, float& gw, std::string& wts_name,
                      std::string& engine_name) {
    // Create builder
    IBuilder* builder = createInferBuilder(gLogger);
    IBuilderConfig* config = builder->createBuilderConfig();

    // Create model to populate the network, then set the outputs and create an engine
    IHostMemory* serialized_engine = nullptr;
    if (is_p6) {
        serialized_engine = build_det_p6_engine(max_batchsize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
    } else {
        serialized_engine = build_det_engine(max_batchsize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
    }
    assert(serialized_engine != nullptr);

    // Serialize the engine
    assert(serialized_engine != nullptr);

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

    // Close everything down
    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;
}

int main(int argc, char** argv) {
    // -s ../models/yolov5n.wts ../models/yolov5n.fp32.trt n
    // -d ../models/yolov5n.fp32.trt ../images
    cudaSetDevice(kGpuId);

    std::string wts_name = "";
    std::string engine_name = "";
    bool is_p6 = false;
    float gd = 0.0f, gw = 0.0f;
    std::string img_dir;

    if (!parse_args(argc, argv, wts_name, engine_name, is_p6, gd, gw, img_dir)) {
        std::cerr << "arguments not right!" << std::endl;
        std::cerr << "./yolov5_det -s [.wts] [.engine] [n/s/m/l/x/n6/s6/m6/l6/x6 or c/c6 gd gw]  // serialize model to "
                     "plan file"
                  << std::endl;
        std::cerr << "./yolov5_det -d [.engine] ../images  // 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(kBatchSize, is_p6, gd, gw, wts_name, engine_name);
        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));

    // Init CUDA preprocessing
    cuda_preprocess_init(kMaxInputImageSize);

    // Prepare cpu and gpu buffers
    float* gpu_buffers[2];
    float* cpu_output_buffer = nullptr;
    prepare_buffers(engine, &gpu_buffers[0], &gpu_buffers[1], &cpu_output_buffer);

    // 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;
    }

    // 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]);
            img_batch.push_back(img);
            img_name_batch.push_back(file_names[j]);
        }

        // Preprocess
        cuda_batch_preprocess(img_batch, gpu_buffers[0], kInputW, kInputH, stream);

        // Run inference
        auto start = std::chrono::system_clock::now();
        infer(*context, stream, (void**)gpu_buffers, cpu_output_buffer, kBatchSize);
        auto end = std::chrono::system_clock::now();
        std::cout << "inference time: " << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count()
                  << "ms" << std::endl;

        // NMS
        std::vector<std::vector<Detection>> res_batch;
        batch_nms(res_batch, cpu_output_buffer, img_batch.size(), kOutputSize, kConfThresh, kNmsThresh);

        // 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(gpu_buffers[0]));
    CUDA_CHECK(cudaFree(gpu_buffers[1]));
    delete[] cpu_output_buffer;
    cuda_preprocess_destroy();
    // Destroy the engine
    delete context;
    delete engine;
    delete runtime;

    // Print histogram of the output distribution
    // std::cout << "\nOutput:\n\n";
    // for (unsigned int i = 0; i < kOutputSize; i++) {
    //   std::cout << prob[i] << ", ";
    //   if (i % 10 == 0) std::cout << std::endl;
    // }
    // std::cout << std::endl;

    return 0;
}