yolo_standard_libray/tensorrtx-master/yolov7/main.cpp

#include "config.h"
#include "model.h"
#include "cuda_utils.h"
#include "logging.h"
#include "utils.h"
#include "preprocess.h"
#include "postprocess.h"
#include <chrono>
#include <fstream>

using namespace nvinfer1;

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

void serialize_engine(unsigned int maxBatchSize, std::string& wts_name, std::string& sub_type, 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 (sub_type == "t") {
    serialized_engine = build_engine_yolov7_tiny(maxBatchSize, builder, config, DataType::kFLOAT, wts_name);
  } else if (sub_type == "v7") {
    serialized_engine = build_engine_yolov7(maxBatchSize, builder, config, DataType::kFLOAT, wts_name);
  } else if (sub_type == "x") {
    serialized_engine = build_engine_yolov7x(maxBatchSize, builder, config, DataType::kFLOAT, wts_name);
  } else if (sub_type == "w6") {
    serialized_engine = build_engine_yolov7w6(maxBatchSize, builder, config, DataType::kFLOAT, wts_name);
  } else if (sub_type == "e6") {
    serialized_engine = build_engine_yolov7e6(maxBatchSize, builder, config, DataType::kFLOAT, wts_name);
  } else if (sub_type == "d6") {
    serialized_engine = build_engine_yolov7d6(maxBatchSize, builder, config, DataType::kFLOAT, wts_name);
  } else if (sub_type == "e6e") {
    serialized_engine = build_engine_yolov7e6e(maxBatchSize, builder, config, DataType::kFLOAT, wts_name);
  }
  assert(serialized_engine != nullptr);

  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());

  delete config;
  delete serialized_engine;
  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
  context.enqueue(batchSize, buffers, stream, nullptr);
  CUDA_CHECK(cudaMemcpyAsync(output, buffers[1], batchSize * kOutputSize * sizeof(float), cudaMemcpyDeviceToHost, stream));
  CUDA_CHECK(cudaStreamSynchronize(stream));
}

bool parse_args(int argc, char** argv, std::string& wts, std::string& engine, std::string& img_dir, std::string& sub_type) {
  if (argc < 4) return false;
  if (std::string(argv[1]) == "-s" && argc == 5) {
    wts = std::string(argv[2]);
    engine = std::string(argv[3]);
    sub_type = std::string(argv[4]);
  } 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(kGpuId);

  std::string wts_name = "";
  std::string engine_name = "";
  std::string img_dir;
  std::string sub_type = "";

  if (!parse_args(argc, argv, wts_name, engine_name, img_dir, sub_type)) {
    std::cerr << "Arguments not right!" << std::endl;
    std::cerr << "./yolov7 -s [.wts] [.engine] [t/v7/x/w6/e6/d6/e6e]  // serialize model to plan file" << std::endl;
    std::cerr << "./yolov7 -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(kBatchSize, wts_name, sub_type, 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));

  cuda_preprocess_init(kMaxInputImageSize);

  // Prepare cpu and gpu buffers
  float* device_buffers[2];
  float* output_buffer_host = nullptr;
  prepare_buffer(engine, &device_buffers[0], &device_buffers[1], &output_buffer_host);

  // 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, device_buffers[0], kInputW, kInputH, stream);

    // Run inference
    auto start = std::chrono::system_clock::now();
    infer(*context, stream, (void**)device_buffers, output_buffer_host, 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, output_buffer_host, img_batch.size(), kOutputSize, kConfThresh, kNmsThresh);

    // 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]));
  delete[] output_buffer_host;
  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;
}