text: #include <iostream>
  #include <chrono>
  #include "cuda_runtime_api.h"
  #include "logging.h"
  #include "common.hpp"
  #define DEVICE 0
  #define NET s  // s m l x
  #define NETSTRUCT(str) createEngine_##str
  #define CREATENET(net) NETSTRUCT(net)
  #define STR1(x) #x
  #define STR2(x) STR1(x)
  // #define USE_FP16  // comment out this if want to use FP16
  #define CONF_THRESH 0.5
  #define BATCH_SIZE 1
  // stuff we know about the network and the input/output blobs
  static const int INPUT_H = 816;
  static const int INPUT_W = 672;
  static const int OUTPUT_SIZE = 672*816;
  
  const char* INPUT_BLOB_NAME = "data";
  const char* OUTPUT_BLOB_NAME = "prob";
  
  using namespace nvinfer1;
  
  static Logger gLogger;
  
  
  cv::Mat preprocess_img(cv::Mat& img) {
      int w, h, x, y;
      float r_w = INPUT_W / (img.cols*1.0);
      float r_h = INPUT_H / (img.rows*1.0);
      if (r_h > r_w) {
          w = INPUT_W;
          h = r_w * img.rows;
          x = 0;
          y = (INPUT_H - h) / 2;
      } else {
          w = r_h* img.cols;
          h = INPUT_H;
          x = (INPUT_W - w) / 2;
          y = 0;
      }
      cv::Mat re(h, w, CV_8UC3);
      cv::resize(img, re, re.size(), 0, 0, cv::INTER_CUBIC);
      cv::Mat out(INPUT_H, INPUT_W, CV_8UC3, cv::Scalar(128, 128, 128));
      re.copyTo(out(cv::Rect(x, y, re.cols, re.rows)));
      return out;
  }
  
  
  
  ILayer* doubleConv(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int ksize, std::string lname, int midch){
      // Weights emptywts{DataType::kFLOAT, nullptr, 0};
      // int p = ksize / 2;
      // if (midch==NULL){
      //     midch = outch;
      // }
      IConvolutionLayer* conv1 = network->addConvolutionNd(input, midch, DimsHW{ksize, ksize}, weightMap[lname + ".double_conv.0.weight"], weightMap[lname + ".double_conv.0.bias"]);
      conv1->setStrideNd(DimsHW{1, 1});
      conv1->setPaddingNd(DimsHW{1, 1});
      conv1->setNbGroups(1);
      IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".double_conv.1", 0);
      IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kLEAKY_RELU);
      IConvolutionLayer* conv2 = network->addConvolutionNd(*relu1->getOutput(0), outch, DimsHW{3, 3}, weightMap[lname + ".double_conv.3.weight"], weightMap[lname + ".double_conv.3.bias"]);
      conv2->setStrideNd(DimsHW{1, 1});
      conv2->setPaddingNd(DimsHW{1, 1});
      conv2->setNbGroups(1);
      IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + ".double_conv.4", 0);
      IActivationLayer* relu2 = network->addActivation(*bn2->getOutput(0), ActivationType::kLEAKY_RELU);
      assert(relu2);
      return relu2;
  }
  
  ILayer* down(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input,  int outch, int p, std::string lname){
      
      IPoolingLayer* pool1 = network->addPoolingNd(input, PoolingType::kMAX, DimsHW{2, 2});
      assert(pool1);
      ILayer* dcov1 = doubleConv(network,weightMap,*pool1->getOutput(0),outch,3,lname+".maxpool_conv.1",outch);
      assert(dcov1);
      return dcov1;
  }
  
  ILayer* up(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input1,  ITensor& input2, int resize,  int outch, int midch, std::string lname){
      float *deval = reinterpret_cast<float*>(malloc(sizeof(float) * resize * 2 * 2));
      for (int i = 0; i < resize * 2 * 2; i++) {
          deval[i] = 1.0;
      }
      Weights emptywts{DataType::kFLOAT, nullptr, 0};
      Weights deconvwts1{DataType::kFLOAT, deval, resize * 2 * 2};
      IDeconvolutionLayer* deconv1 = network->addDeconvolutionNd(input1, resize, DimsHW{2, 2}, deconvwts1, emptywts);
      deconv1->setStrideNd(DimsHW{2, 2});
      deconv1->setNbGroups(resize);
      weightMap["deconvwts."+lname] = deconvwts1;
  
      int diffx = input2.getDimensions().d[1]-deconv1->getOutput(0)->getDimensions().d[1];
      int diffy = input2.getDimensions().d[2]-deconv1->getOutput(0)->getDimensions().d[2];
      // IPoolingLayer* pool1 = network->addPooling(dcov1, PoolingType::kMAX, DimsHW{2, 2});
      // pool1->setStrideNd(DimsHW{2, 2});
      // dcov1->add_pading
      ILayer* pad1 = network->addPaddingNd(*deconv1->getOutput(0),DimsHW{diffx / 2, diffy / 2},DimsHW{diffx - (diffx / 2), diffy - (diffy / 2)});
      // dcov1->setPaddingNd(DimsHW{diffx / 2, diffx - diffx / 2},DimsHW{diffy / 2, diffy - diffy / 2});
      ITensor* inputTensors[] = {&input2,pad1->getOutput(0)};
      auto cat = network->addConcatenation(inputTensors, 2);
      assert(cat);
      if (midch==64){
          ILayer* dcov1 = doubleConv(network,weightMap,*cat->getOutput(0),outch,3,lname+".conv",outch);
          assert(dcov1);
          return dcov1;
      }else{
          int midch1 = outch/2;
          ILayer* dcov1 = doubleConv(network,weightMap,*cat->getOutput(0),midch1,3,lname+".conv",outch);
          assert(dcov1);
          return dcov1;
      }
      
      // assert(dcov1);
  
      // return dcov1;
  }
  
  ILayer* outConv(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input,  int outch, std::string lname){
      // Weights emptywts{DataType::kFLOAT, nullptr, 0};
  
      IConvolutionLayer* conv1 = network->addConvolutionNd(input, 1, DimsHW{1, 1}, weightMap[lname + ".conv.weight"], weightMap[lname + ".conv.bias"]);
      assert(conv1);
      conv1->setStrideNd(DimsHW{1, 1});
      conv1->setPaddingNd(DimsHW{0, 0});
      conv1->setNbGroups(1);
      return conv1;
  }
  
  
  
  ICudaEngine* createEngine_l(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt) {
      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("/home/sycv/workplace/pengyuzhou/tensorrtx/unet/unet_816_672.wts");
      Weights emptywts{DataType::kFLOAT, nullptr, 0};
  
      // build network
      auto x1 = doubleConv(network,weightMap,*data,64,3,"inc",64);
      auto x2 = down(network,weightMap,*x1->getOutput(0),128,1,"down1");
      auto x3 = down(network,weightMap,*x2->getOutput(0),256,1,"down2");
      auto x4 = down(network,weightMap,*x3->getOutput(0),512,1,"down3");
      auto x5 = down(network,weightMap,*x4->getOutput(0),512,1,"down4");
      ILayer* x6 = up(network,weightMap,*x5->getOutput(0),*x4->getOutput(0),512,512,512,"up1");
      ILayer* x7 = up(network,weightMap,*x6->getOutput(0),*x3->getOutput(0),256,256,256,"up2");
      ILayer* x8 = up(network,weightMap,*x7->getOutput(0),*x2->getOutput(0),128,128,128,"up3");
      ILayer* x9 = up(network,weightMap,*x8->getOutput(0),*x1->getOutput(0),64,64,64,"up4");
      ILayer* x10 = outConv(network,weightMap,*x9->getOutput(0),OUTPUT_SIZE,"outc");
      std::cout << "set name out" << std::endl;
      x10->getOutput(0)->setName(OUTPUT_BLOB_NAME);
      network->markOutput(*x10->getOutput(0));
  
      // Build engine
      builder->setMaxBatchSize(maxBatchSize);
      config->setMaxWorkspaceSize(16 * (1 << 20));  // 16MB
  #ifdef USE_FP16
      config->setFlag(BuilderFlag::kFP16);
  #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) {
      // 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 = (CREATENET(NET))(maxBatchSize, builder, config, DataType::kFLOAT);
      ICudaEngine* engine = createEngine_l(maxBatchSize, builder, config, DataType::kFLOAT);
      assert(engine != nullptr);
  
      // Serialize the engine
      (*modelStream) = engine->serialize();
  
      // Close everything down
      engine->destroy();
      builder->destroy();
  }
  
  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()
      const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
      const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
  
      // Create GPU buffers on device
      CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * 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 * 3 * 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));
      //流同步：通过cudaStreamSynchronize()来协调。
      cudaStreamSynchronize(stream);
  
      // Release stream and buffers
      cudaStreamDestroy(stream);
      CHECK(cudaFree(buffers[inputIndex]));
      CHECK(cudaFree(buffers[outputIndex]));
  }
  
  struct  Detection{        
      float mask[INPUT_W*INPUT_H*1];
      };
  
  float sigmoid(float x)
  {
      return (1 / (1 + exp(-x)));
  }
  
  void process_cls_result(Detection &res, float *output) {    
      for(int i=0;i<INPUT_W*INPUT_H*1;i++){
          res.mask[i] = sigmoid(*(output+i));
          }
      }
  
  int main(int argc, char** argv) {
      cudaSetDevice(DEVICE);
      // create a model using the API directly and serialize it to a stream
      char *trtModelStream{nullptr};
      size_t size{0};
      std::string engine_name = "unet.engine";
      if (argc == 2 && std::string(argv[1]) == "-s") {
          IHostMemory* modelStream{nullptr};
          APIToModel(BATCH_SIZE, &modelStream);
          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;
      } else if (argc == 3 && std::string(argv[1]) == "-d") {
          std::ifstream file(engine_name, std::ios::binary);
          if (file.good()) {
              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();
          }
      } else {
          std::cerr << "arguments not right!" << std::endl;
          std::cerr << "./unet -s  // serialize model to plan file" << std::endl;
          std::cerr << "./unet -d ../samples  // deserialize plan file and run inference" << std::endl;
          return -1;
      }
  
      std::vector<std::string> file_names;
      if (read_files_in_dir(argv[2], file_names) < 0) {
          std::cout << "read_files_in_dir failed." << std::endl;
          return -1;
      }
  
      // prepare input data ---------------------------
      static float data[BATCH_SIZE * 3 * INPUT_H * INPUT_W];
      //for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++)
      //    data[i] = 1.0;
      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;
  
      int fcount = 0;
      for (int f = 0; f < (int)file_names.size(); f++) {
          fcount++;
          if (fcount < BATCH_SIZE && f + 1 != (int)file_names.size()) continue;
          for (int b = 0; b < fcount; b++) {
              cv::Mat img = cv::imread(std::string(argv[2]) + "/" + file_names[f - fcount + 1 + b]);
              if (img.empty()) continue;
              cv::Mat pr_img = preprocess_img(img); // letterbox BGR to RGB
              // cv::imwrite("s_o" + file_names[f - fcount + 1 + b] + "_unet.jpg", pr_img); 
              int i = 0;
              for (int row = 0; row < INPUT_H; ++row) {
                  uchar* uc_pixel = pr_img.data + row * pr_img.step;
                  for (int col = 0; col < INPUT_W; ++col) {
                      data[b * 3 * INPUT_H * INPUT_W + i] = (float)uc_pixel[2] / 255.0;
                      data[b * 3 * INPUT_H * INPUT_W + i + INPUT_H * INPUT_W] = (float)uc_pixel[1] / 255.0;
                      data[b * 3 * INPUT_H * INPUT_W + i + 2 * INPUT_H * INPUT_W] = (float)uc_pixel[0] / 255.0;
                      uc_pixel += 3;
                      ++i;
                  }
              }
          }
  
          // Run inference
          auto start = std::chrono::system_clock::now();
          doInference(*context, data, prob, BATCH_SIZE);
          auto end = std::chrono::system_clock::now();
          std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
  
          
  
          std::vector<Detection> batch_res(fcount);
          for (int b = 0; b < fcount; b++) {
              auto& res = batch_res[b];
              process_cls_result(res, &prob[b * OUTPUT_SIZE]);
          }
  
          std::cout << fcount << std::endl;
  
          for (int b = 0; b < fcount; b++) {
              auto& res = batch_res[b];
              float * mask = res.mask;
              cv::Mat mask_mat = cv::Mat(INPUT_H,INPUT_W,CV_8UC1); 
              uchar *ptmp = NULL;
              for(int i =0; i< INPUT_H ;i++){
                  ptmp = mask_mat.ptr<uchar>(i);
                  for(int j=0;j<INPUT_W;j++){
                      float * pixcel = mask+i*INPUT_W+j;
                      // std::cout << *pixcel << std::endl;
                      if(*pixcel > CONF_THRESH){
                          ptmp[j] = 255;
                      }
                      else{
                          ptmp[j]=0;
                      } 
                  }            
              }
  
              cv::imwrite("s_" + file_names[f - fcount + 1 + b] + "_unet.jpg", mask_mat); 
          }
          fcount = 0;
      }
  
      // Destroy the engine
      context->destroy();
      engine->destroy();
      runtime->destroy();
  
      return 0;
  }
  