text: #include <iostream>
  #include <chrono>
  #include <cmath>
  #include "cuda_utils.h"
  #include "logging.h"
  #include "common.hpp"
  #include "utils.h"
  #include "calibrator.h"
  #include "preprocess.h"
  
  #define USE_FP16  // set USE_INT8 or USE_FP16 or USE_FP32
  #define DEVICE 0  // GPU id
  #define NMS_THRESH 0.4
  #define CONF_THRESH 0.5
  #define BATCH_SIZE 1
  #define MAX_IMAGE_INPUT_SIZE_THRESH 3000 * 3000 // ensure it exceed the maximum size in the input images !
  
  // stuff we know about the network and the input/output blobs
  static const int INPUT_H = Yolo::INPUT_H;
  static const int INPUT_W = Yolo::INPUT_W;
  static const int CLASS_NUM = Yolo::CLASS_NUM;
  static const int OUTPUT_SIZE = Yolo::MAX_OUTPUT_BBOX_COUNT * sizeof(Yolo::Detection) / sizeof(float) + 1;  // we assume the yololayer outputs no more than MAX_OUTPUT_BBOX_COUNT boxes that conf >= 0.1
  const char* INPUT_BLOB_NAME = "data";
  const char* OUTPUT_BLOB_NAME = "prob";
  static Logger gLogger;
  
  static int get_width(int x, float gw, int divisor = 8) {
      return int(ceil((x * gw) / divisor)) * divisor;
  }
  
  static int get_depth(int x, float gd) {
      if (x == 1) return 1;
      int r = round(x * gd);
      if (x * gd - int(x * gd) == 0.5 && (int(x * gd) % 2) == 0) {
          --r;
      }
      return std::max<int>(r, 1);
  }
  
  ICudaEngine* build_engine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, float& gd, float& gw, std::string& wts_name) {
      INetworkDefinition* network = builder->createNetworkV2(0U);
  
      // Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
      ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });
      assert(data);
      std::map<std::string, Weights> weightMap = loadWeights(wts_name);
      /* ------ yolov5 backbone------ */
      auto conv0 = convBlock(network, weightMap, *data,  get_width(64, gw), 6, 2, 1,  "model.0");
      assert(conv0);
      auto conv1 = convBlock(network, weightMap, *conv0->getOutput(0), get_width(128, gw), 3, 2, 1, "model.1");
      auto bottleneck_CSP2 = C3(network, weightMap, *conv1->getOutput(0), get_width(128, gw), get_width(128, gw), get_depth(3, gd), true, 1, 0.5, "model.2");
      auto conv3 = convBlock(network, weightMap, *bottleneck_CSP2->getOutput(0), get_width(256, gw), 3, 2, 1, "model.3");
      auto bottleneck_csp4 = C3(network, weightMap, *conv3->getOutput(0), get_width(256, gw), get_width(256, gw), get_depth(6, gd), true, 1, 0.5, "model.4");
      auto conv5 = convBlock(network, weightMap, *bottleneck_csp4->getOutput(0), get_width(512, gw), 3, 2, 1, "model.5");
      auto bottleneck_csp6 = C3(network, weightMap, *conv5->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(9, gd), true, 1, 0.5, "model.6");
      auto conv7 = convBlock(network, weightMap, *bottleneck_csp6->getOutput(0), get_width(1024, gw), 3, 2, 1, "model.7");
      auto bottleneck_csp8 = C3(network, weightMap, *conv7->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), true, 1, 0.5, "model.8");
      auto spp9 = SPPF(network, weightMap, *bottleneck_csp8->getOutput(0), get_width(1024, gw), get_width(1024, gw), 5, "model.9");
      /* ------ yolov5 head ------ */
      auto conv10 = convBlock(network, weightMap, *spp9->getOutput(0), get_width(512, gw), 1, 1, 1, "model.10");
  
      auto upsample11 = network->addResize(*conv10->getOutput(0));
      assert(upsample11);
      upsample11->setResizeMode(ResizeMode::kNEAREST);
      upsample11->setOutputDimensions(bottleneck_csp6->getOutput(0)->getDimensions());
  
      ITensor* inputTensors12[] = { upsample11->getOutput(0), bottleneck_csp6->getOutput(0) };
      auto cat12 = network->addConcatenation(inputTensors12, 2);
      auto bottleneck_csp13 = C3(network, weightMap, *cat12->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.13");
      auto conv14 = convBlock(network, weightMap, *bottleneck_csp13->getOutput(0), get_width(256, gw), 1, 1, 1, "model.14");
  
      auto upsample15 = network->addResize(*conv14->getOutput(0));
      assert(upsample15);
      upsample15->setResizeMode(ResizeMode::kNEAREST);
      upsample15->setOutputDimensions(bottleneck_csp4->getOutput(0)->getDimensions());
  
      ITensor* inputTensors16[] = { upsample15->getOutput(0), bottleneck_csp4->getOutput(0) };
      auto cat16 = network->addConcatenation(inputTensors16, 2);
  
      auto bottleneck_csp17 = C3(network, weightMap, *cat16->getOutput(0), get_width(512, gw), get_width(256, gw), get_depth(3, gd), false, 1, 0.5, "model.17");
  
      /* ------ detect ------ */
      IConvolutionLayer* det0 = network->addConvolutionNd(*bottleneck_csp17->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.0.weight"], weightMap["model.24.m.0.bias"]);
      auto conv18 = convBlock(network, weightMap, *bottleneck_csp17->getOutput(0), get_width(256, gw), 3, 2, 1, "model.18");
      ITensor* inputTensors19[] = { conv18->getOutput(0), conv14->getOutput(0) };
      auto cat19 = network->addConcatenation(inputTensors19, 2);
      auto bottleneck_csp20 = C3(network, weightMap, *cat19->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.20");
      IConvolutionLayer* det1 = network->addConvolutionNd(*bottleneck_csp20->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.1.weight"], weightMap["model.24.m.1.bias"]);
      auto conv21 = convBlock(network, weightMap, *bottleneck_csp20->getOutput(0), get_width(512, gw), 3, 2, 1, "model.21");
      ITensor* inputTensors22[] = { conv21->getOutput(0), conv10->getOutput(0) };
      auto cat22 = network->addConcatenation(inputTensors22, 2);
      auto bottleneck_csp23 = C3(network, weightMap, *cat22->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.23");
      IConvolutionLayer* det2 = network->addConvolutionNd(*bottleneck_csp23->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.2.weight"], weightMap["model.24.m.2.bias"]);
  
      auto yolo = addYoLoLayer(network, weightMap, "model.24", std::vector<IConvolutionLayer*>{det0, det1, det2});
      yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);
      network->markOutput(*yolo->getOutput(0));
      // Build engine
      builder->setMaxBatchSize(maxBatchSize);
      config->setMaxWorkspaceSize(16 * (1 << 20));  // 16MB
  #if defined(USE_FP16)
      config->setFlag(BuilderFlag::kFP16);
  #elif defined(USE_INT8)
      std::cout << "Your platform support int8: " << (builder->platformHasFastInt8() ? "true" : "false") << std::endl;
      assert(builder->platformHasFastInt8());
      config->setFlag(BuilderFlag::kINT8);
      Int8EntropyCalibrator2* calibrator = new Int8EntropyCalibrator2(1, INPUT_W, INPUT_H, "./coco_calib/", "int8calib.table", INPUT_BLOB_NAME);
      config->setInt8Calibrator(calibrator);
  #endif
  
      std::cout << "Building engine, please wait for a while..." << std::endl;
      ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
      std::cout << "Build engine successfully!" << std::endl;
  
      // Don't need the network any more
      network->destroy();
  
      // Release host memory
      for (auto& mem : weightMap)
      {
          free((void*)(mem.second.values));
      }
  
      return engine;
  }
  
  ICudaEngine* build_engine_p6(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, float& gd, float& gw, std::string& wts_name) {
      INetworkDefinition* network = builder->createNetworkV2(0U);
      // Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
      ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });
      assert(data);
      
      std::map<std::string, Weights> weightMap = loadWeights(wts_name);
  
      /* ------ yolov5 backbone------ */
      auto conv0 = convBlock(network, weightMap, *data,  get_width(64, gw), 6, 2, 1,  "model.0");
      auto conv1 = convBlock(network, weightMap, *conv0->getOutput(0), get_width(128, gw), 3, 2, 1, "model.1");
      auto c3_2 = C3(network, weightMap, *conv1->getOutput(0), get_width(128, gw), get_width(128, gw), get_depth(3, gd), true, 1, 0.5, "model.2");
      auto conv3 = convBlock(network, weightMap, *c3_2->getOutput(0), get_width(256, gw), 3, 2, 1, "model.3");
      auto c3_4 = C3(network, weightMap, *conv3->getOutput(0), get_width(256, gw), get_width(256, gw), get_depth(6, gd), true, 1, 0.5, "model.4");
      auto conv5 = convBlock(network, weightMap, *c3_4->getOutput(0), get_width(512, gw), 3, 2, 1, "model.5");
      auto c3_6 = C3(network, weightMap, *conv5->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(9, gd), true, 1, 0.5, "model.6");
      auto conv7 = convBlock(network, weightMap, *c3_6->getOutput(0), get_width(768, gw), 3, 2, 1, "model.7");
      auto c3_8 = C3(network, weightMap, *conv7->getOutput(0), get_width(768, gw), get_width(768, gw), get_depth(3, gd), true, 1, 0.5, "model.8");
      auto conv9 = convBlock(network, weightMap, *c3_8->getOutput(0), get_width(1024, gw), 3, 2, 1, "model.9");
      auto c3_10 = C3(network, weightMap, *conv9->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), true, 1, 0.5, "model.10");
      auto sppf11 = SPPF(network, weightMap, *c3_10->getOutput(0), get_width(1024, gw), get_width(1024, gw), 5, "model.11");
  
      /* ------ yolov5 head ------ */
      auto conv12 = convBlock(network, weightMap, *sppf11->getOutput(0), get_width(768, gw), 1, 1, 1, "model.12");
      auto upsample13 = network->addResize(*conv12->getOutput(0));
      assert(upsample13);
      upsample13->setResizeMode(ResizeMode::kNEAREST);
      upsample13->setOutputDimensions(c3_8->getOutput(0)->getDimensions());
      ITensor* inputTensors14[] = { upsample13->getOutput(0), c3_8->getOutput(0) };
      auto cat14 = network->addConcatenation(inputTensors14, 2);
      auto c3_15 = C3(network, weightMap, *cat14->getOutput(0), get_width(1536, gw), get_width(768, gw), get_depth(3, gd), false, 1, 0.5, "model.15");
  
      auto conv16 = convBlock(network, weightMap, *c3_15->getOutput(0), get_width(512, gw), 1, 1, 1, "model.16");
      auto upsample17 = network->addResize(*conv16->getOutput(0));
      assert(upsample17);
      upsample17->setResizeMode(ResizeMode::kNEAREST);
      upsample17->setOutputDimensions(c3_6->getOutput(0)->getDimensions());
      ITensor* inputTensors18[] = { upsample17->getOutput(0), c3_6->getOutput(0) };
      auto cat18 = network->addConcatenation(inputTensors18, 2);
      auto c3_19 = C3(network, weightMap, *cat18->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.19");
  
      auto conv20 = convBlock(network, weightMap, *c3_19->getOutput(0), get_width(256, gw), 1, 1, 1, "model.20");
      auto upsample21 = network->addResize(*conv20->getOutput(0));
      assert(upsample21);
      upsample21->setResizeMode(ResizeMode::kNEAREST);
      upsample21->setOutputDimensions(c3_4->getOutput(0)->getDimensions());
      ITensor* inputTensors21[] = { upsample21->getOutput(0), c3_4->getOutput(0) };
      auto cat22 = network->addConcatenation(inputTensors21, 2);
      auto c3_23 = C3(network, weightMap, *cat22->getOutput(0), get_width(512, gw), get_width(256, gw), get_depth(3, gd), false, 1, 0.5, "model.23");
  
      auto conv24 = convBlock(network, weightMap, *c3_23->getOutput(0), get_width(256, gw), 3, 2, 1, "model.24");
      ITensor* inputTensors25[] = { conv24->getOutput(0), conv20->getOutput(0) };
      auto cat25 = network->addConcatenation(inputTensors25, 2);
      auto c3_26 = C3(network, weightMap, *cat25->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.26");
  
      auto conv27 = convBlock(network, weightMap, *c3_26->getOutput(0), get_width(512, gw), 3, 2, 1, "model.27");
      ITensor* inputTensors28[] = { conv27->getOutput(0), conv16->getOutput(0) };
      auto cat28 = network->addConcatenation(inputTensors28, 2);
      auto c3_29 = C3(network, weightMap, *cat28->getOutput(0), get_width(1536, gw), get_width(768, gw), get_depth(3, gd), false, 1, 0.5, "model.29");
  
      auto conv30 = convBlock(network, weightMap, *c3_29->getOutput(0), get_width(768, gw), 3, 2, 1, "model.30");
      ITensor* inputTensors31[] = { conv30->getOutput(0), conv12->getOutput(0) };
      auto cat31 = network->addConcatenation(inputTensors31, 2);
      auto c3_32 = C3(network, weightMap, *cat31->getOutput(0), get_width(2048, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.32");
  
      /* ------ detect ------ */
      IConvolutionLayer* det0 = network->addConvolutionNd(*c3_23->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.0.weight"], weightMap["model.33.m.0.bias"]);
      IConvolutionLayer* det1 = network->addConvolutionNd(*c3_26->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.1.weight"], weightMap["model.33.m.1.bias"]);
      IConvolutionLayer* det2 = network->addConvolutionNd(*c3_29->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.2.weight"], weightMap["model.33.m.2.bias"]);
      IConvolutionLayer* det3 = network->addConvolutionNd(*c3_32->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.3.weight"], weightMap["model.33.m.3.bias"]);
  
      auto yolo = addYoLoLayer(network, weightMap, "model.33", std::vector<IConvolutionLayer*>{det0, det1, det2, det3});
      yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);
      network->markOutput(*yolo->getOutput(0));
  
      // Build engine
      builder->setMaxBatchSize(maxBatchSize);
      config->setMaxWorkspaceSize(16 * (1 << 20));  // 16MB
  #if defined(USE_FP16)
      config->setFlag(BuilderFlag::kFP16);
  #elif defined(USE_INT8)
      std::cout << "Your platform support int8: " << (builder->platformHasFastInt8() ? "true" : "false") << std::endl;
      assert(builder->platformHasFastInt8());
      config->setFlag(BuilderFlag::kINT8);
      Int8EntropyCalibrator2* calibrator = new Int8EntropyCalibrator2(1, INPUT_W, INPUT_H, "./coco_calib/", "int8calib.table", INPUT_BLOB_NAME);
      config->setInt8Calibrator(calibrator);
  #endif
  
      std::cout << "Building engine, please wait for a while..." << std::endl;
      ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
      std::cout << "Build engine successfully!" << std::endl;
  
      // Don't need the network any more
      network->destroy();
  
      // Release host memory
      for (auto& mem : weightMap)
      {
          free((void*)(mem.second.values));
      }
  
      return engine;
  }
  
  void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream, bool& is_p6, float& gd, float& gw, std::string& wts_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
      ICudaEngine *engine = nullptr;
      if (is_p6) {
          engine = build_engine_p6(maxBatchSize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
      } else {
          engine = build_engine(maxBatchSize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
      }
      assert(engine != nullptr);
  
      // Serialize the engine
      (*modelStream) = engine->serialize();
  
      // Close everything down
      engine->destroy();
      builder->destroy();
      config->destroy();
  }
  
  void doInference(IExecutionContext& context, cudaStream_t& stream, void **buffers, float* output, int batchSize) {
      // infer on the batch asynchronously, and DMA output back to host
      context.enqueue(batchSize, buffers, stream, nullptr);
      CUDA_CHECK(cudaMemcpyAsync(output, buffers[1], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
      cudaStreamSynchronize(stream);
  }
  
  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;
  }
  
  int main(int argc, char** argv) {
      cudaSetDevice(DEVICE);
  
      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 -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 -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 stream
      if (!wts_name.empty()) {
          IHostMemory* modelStream{ nullptr };
          APIToModel(BATCH_SIZE, &modelStream, is_p6, gd, gw, wts_name);
          assert(modelStream != nullptr);
          std::ofstream p(engine_name, std::ios::binary);
          if (!p) {
              std::cerr << "could not open plan output file" << std::endl;
              return -1;
          }
          p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
          modelStream->destroy();
          return 0;
      }
  
      // deserialize the .engine and run inference
      std::ifstream file(engine_name, std::ios::binary);
      if (!file.good()) {
          std::cerr << "read " << engine_name << " error!" << std::endl;
          return -1;
      }
      char *trtModelStream = nullptr;
      size_t size = 0;
      file.seekg(0, file.end);
      size = file.tellg();
      file.seekg(0, file.beg);
      trtModelStream = new char[size];
      assert(trtModelStream);
      file.read(trtModelStream, size);
      file.close();
  
      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;
      }
  
      static float prob[BATCH_SIZE * OUTPUT_SIZE];
      IRuntime* runtime = createInferRuntime(gLogger);
      assert(runtime != nullptr);
      ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size);
      assert(engine != nullptr);
      IExecutionContext* context = engine->createExecutionContext();
      assert(context != nullptr);
      delete[] trtModelStream;
      assert(engine->getNbBindings() == 2);
      float* 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);
      assert(inputIndex == 0);
      assert(outputIndex == 1);
      // Create GPU buffers on device
      CUDA_CHECK(cudaMalloc((void**)&buffers[inputIndex], BATCH_SIZE * 3 * INPUT_H * INPUT_W * sizeof(float)));
      CUDA_CHECK(cudaMalloc((void**)&buffers[outputIndex], BATCH_SIZE * OUTPUT_SIZE * sizeof(float)));
  
      // Create stream
      cudaStream_t stream;
      CUDA_CHECK(cudaStreamCreate(&stream));
      uint8_t* img_host = nullptr;
      uint8_t* img_device = nullptr;
      // prepare input data cache in pinned memory 
      CUDA_CHECK(cudaMallocHost((void**)&img_host, MAX_IMAGE_INPUT_SIZE_THRESH * 3));
      // prepare input data cache in device memory
      CUDA_CHECK(cudaMalloc((void**)&img_device, MAX_IMAGE_INPUT_SIZE_THRESH * 3));
      int fcount = 0;
      std::vector<cv::Mat> imgs_buffer(BATCH_SIZE);
      for (int f = 0; f < (int)file_names.size(); f++) {
          fcount++;
          if (fcount < BATCH_SIZE && f + 1 != (int)file_names.size()) continue;
          //auto start = std::chrono::system_clock::now();
          float* buffer_idx = (float*)buffers[inputIndex];
          for (int b = 0; b < fcount; b++) {
              cv::Mat img = cv::imread(img_dir + "/" + file_names[f - fcount + 1 + b]);
              if (img.empty()) continue;
              imgs_buffer[b] = img;
              size_t  size_image = img.cols * img.rows * 3;
              size_t  size_image_dst = INPUT_H * INPUT_W * 3;
              //copy data to pinned memory
              memcpy(img_host,img.data,size_image);
              //copy data to device memory
              CUDA_CHECK(cudaMemcpyAsync(img_device,img_host,size_image,cudaMemcpyHostToDevice,stream));
              preprocess_kernel_img(img_device, img.cols, img.rows, buffer_idx, INPUT_W, INPUT_H, stream);       
              buffer_idx += size_image_dst;
          }
          // Run inference
          auto start = std::chrono::system_clock::now();
          doInference(*context, stream, (void**)buffers, prob, BATCH_SIZE);
          auto end = std::chrono::system_clock::now();
          std::cout << "inference time: " << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
          std::vector<std::vector<Yolo::Detection>> batch_res(fcount);
          for (int b = 0; b < fcount; b++) {
              auto& res = batch_res[b];
              nms(res, &prob[b * OUTPUT_SIZE], CONF_THRESH, NMS_THRESH);
          }
          for (int b = 0; b < fcount; b++) {
              auto& res = batch_res[b];
              cv::Mat img = imgs_buffer[b];
              for (size_t j = 0; j < res.size(); j++) {
                  cv::Rect r = get_rect(img, res[j].bbox);
                  cv::rectangle(img, r, cv::Scalar(0x27, 0xC1, 0x36), 2);
                  cv::putText(img, std::to_string((int)res[j].class_id), cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 2);
              }
              cv::imwrite("_" + file_names[f - fcount + 1 + b], img);
          }
          fcount = 0;
      }
  
      // Release stream and buffers
      cudaStreamDestroy(stream);
      CUDA_CHECK(cudaFree(img_device));
      CUDA_CHECK(cudaFreeHost(img_host));
      CUDA_CHECK(cudaFree(buffers[inputIndex]));
      CUDA_CHECK(cudaFree(buffers[outputIndex]));
      // Destroy the engine
      context->destroy();
      engine->destroy();
      runtime->destroy();
  
  
      // Print histogram of the output distribution
      //std::cout << "\nOutput:\n\n";
      //for (unsigned int i = 0; i < OUTPUT_SIZE; i++)
      //{
      //    std::cout << prob[i] << ", ";
      //    if (i % 10 == 0) std::cout << std::endl;
      //}
      //std::cout << std::endl;
  
      return 0;
  }
  