449 lines
24 KiB
C++
449 lines
24 KiB
C++
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#include "InferenceModelEngine.h"
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using namespace std;
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using namespace ai_matrix;
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InferenceModelEngine::InferenceModelEngine() {}
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InferenceModelEngine::~InferenceModelEngine() {}
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APP_ERROR InferenceModelEngine::Init()
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{
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strPort0_ = engineName_ + "_" + std::to_string(engineId_) + "_0";
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//创建模型推理CUDA流
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inference_model_stream_ = new cudaStream_t;
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CUDA_CHECK(cudaStreamCreate(inference_model_stream_));
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gLogger_ = new Logger;
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//相关资源分配
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buffers_[0] = nullptr; buffers_[1] = nullptr;
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CUDA_CHECK(cudaMalloc((void**)&buffers_[0], BATCH_SIZE * 3 * INPUT_H * INPUT_W * sizeof(float))); //输入资源分配
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CUDA_CHECK(cudaMalloc((void**)&buffers_[1], BATCH_SIZE * OUTPUT_SIZE * sizeof(float))); //输出资源分配
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LogInfo << "engineId_:" << engineId_ << " InferenceModelEngine Init ok";
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return APP_ERR_OK;
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}
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APP_ERROR InferenceModelEngine::DeInit()
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{
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CUDA_CHECK(cudaStreamDestroy(*inference_model_stream_)); delete inference_model_stream_; inference_model_stream_ = nullptr; //释放模型推理CUDA流
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CUDA_CHECK(cudaFree(buffers_[0])); //释放输入数据设备端内存
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CUDA_CHECK(cudaFree(buffers_[1])); //释放输出数据设备端内存
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//析构engine引擎资源
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context_->destroy(); //析构绘话
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engine_->destroy(); //析构TensorRT引擎
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runtime_->destroy(); //析构运行时环境
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delete gLogger_; gLogger_ = nullptr;
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LogInfo << "engineId_:" << engineId_ << " InferenceModelEngine DeInit ok";
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return APP_ERR_OK;
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}
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APP_ERROR InferenceModelEngine::Process()
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{
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cudaSetDevice(DEVICE); //设置GPU
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//wts及engine模型名称
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std::string wts_name = MyYaml::GetIns()->GetStringValue("yolov5_wts_name");
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std::string engine_name = MyYaml::GetIns()->GetStringValue("yolov5_model_name");
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bool is_p6 = false; //默认不是P6模型
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/**********************************************************************************
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gw width_multiple系数: width_multiple控制网络的宽度。
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gd depth_multiple系数: depth_multiple控制网络的深度
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N模型:
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gd = 0.33;gw = 0.25;
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S模型:
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gd = 0.33;gw = 0.50;
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M模型:
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gd = 0.67;gw = 0.75;
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L模型:
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gd = 1.0;gw = 1.0;
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X模型:
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gd = 1.33;gw = 1.25;
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**********************************************************************************/
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float gd = 0.67, gw = 0.75; //默认使用M模型
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//序列化引擎
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//直接使用API创建一个模型，并将其序列化为流 编译成TensorRT引擎engine文件后无需再次调用,调用依次生成engine即可
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#if 0
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if (!wts_name.empty()) {
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IHostMemory* modelStream{ nullptr };
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APIToModel(*gLogger_, BATCH_SIZE, &modelStream, is_p6, gd, gw, wts_name);
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assert(modelStream != nullptr);
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std::ofstream p(engine_name, std::ios::binary);
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if (!p) {
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std::cerr << "could not open plan output file" << std::endl;
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return -1;
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}
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p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
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modelStream->destroy();
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}
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#endif
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//反序列化模型并运行推理
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std::ifstream file(engine_name, std::ios::binary);
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if (!file.good()) {
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LogInfo << "read " << engine_name << " error!" << std::endl;
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exit(0);
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}
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//创建tensorRT流对象trtModelStream,这个就跟文件流中的ifstream类似的
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//trtModelStream是一块内存区域,用于保存序列化的plan文件
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char *trtModelStream = nullptr;
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size_t size = 0;
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file.seekg(0, file.end); //将指针移动至距离文件末尾0处的位置
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size = file.tellg(); //获得当前字符的位置
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file.seekg(0, file.beg); //将指针移动至距离文件开头0处的位置
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trtModelStream = new char[size];
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assert(trtModelStream);
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file.read(trtModelStream, size); //将序列化engine模型(数据及数据大小)读入trtModelStream
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file.close();
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//1.设置运行时环境
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//runtime_ = new IRuntime;
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runtime_ = createInferRuntime(*gLogger_); //创建运行时环境IRuntime对象,传入gLogger用于打印信息
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assert(runtime_ != nullptr);
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//2.生成反序列化引擎
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//engine = new ICudaEngine;
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engine_ = runtime_->deserializeCudaEngine(trtModelStream, size); //反序列化引擎engine(根据trtModelStream反序列化)
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assert(engine_ != nullptr);
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//3.创建上下文环境
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//context = new IExecutionContext;
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context_ = engine_->createExecutionContext(); //创建上下文环境,主要用于inference函数中启动cuda核
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assert(context_ != nullptr);
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delete[] trtModelStream; //析构trtModelStream
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assert(engine_->getNbBindings() == 2);
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//获取绑定的输入输入
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const int inputIndex = engine_->getBindingIndex(INPUT_BLOB_NAME);
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const int outputIndex = engine_->getBindingIndex(OUTPUT_BLOB_NAME);
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std::cout<<"inputIndex: "<<inputIndex<<"\toutputIndex: "<<outputIndex<<std::endl;
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assert(inputIndex == 0);
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assert(outputIndex == 1);
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uint64_t u64count_num = 0;
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int iRet = APP_ERR_OK;
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while (!isStop_)
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{
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std::shared_ptr<void> pVoidData0 = nullptr;
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inputQueMap_[strPort0_]->pop(pVoidData0);
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if (nullptr == pVoidData0)
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{
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usleep(1*1000); //n ms
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continue;
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}
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// LogInfo << "receive from ImagePreprocessEngine's data success!";
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// std::cout<<"receive from ImagePreprocessEngine's data success!"<<std::endl;
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// std::cout<<"Enter InferenceModelEngine Thread "<<++u64count_num<<" Times!"<<std::endl;
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std::shared_ptr<InferenceData> pImagePreprocessData = std::static_pointer_cast<InferenceData>(pVoidData0);
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//将图像预处理数据拷贝到buffers_[0]
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#ifdef CUDA_MEMCPY_TIME_CONSUMING_TEST
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auto cuda_memcpy_start = std::chrono::system_clock::now(); //计时开始
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CUDA_CHECK(cudaMemcpyAsync(buffers_[0], static_cast<void *>(pImagePreprocessData->pData.get()), pImagePreprocessData->iSize, cudaMemcpyDeviceToDevice,*inference_model_stream_));
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auto cuda_memcpy_end = std::chrono::system_clock::now(); //计时结束
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std::cout<< "InferenceModelEngine cuda memcpy data size is: "<<pImagePreprocessData->iSize<<std::endl;
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std::cout << "InferenceModelEngine cuda memcpy device to device time: " << std::chrono::duration_cast<std::chrono::milliseconds>(cuda_memcpy_end - cuda_memcpy_start).count() << "ms" << std::endl;
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#else
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CUDA_CHECK(cudaMemcpyAsync(buffers_[0], static_cast<void *>(pImagePreprocessData->pData.get()), pImagePreprocessData->iSize, cudaMemcpyDeviceToDevice,*inference_model_stream_));
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#endif
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//构造推理结果数据
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void* pInferenceModelBuffer = nullptr;
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unsigned int pInferenceModelBuffer_Size = BATCH_SIZE * OUTPUT_SIZE;
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pInferenceModelBuffer = new float[pInferenceModelBuffer_Size];
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void* pSrcRGBBuffer = nullptr;
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unsigned int pSrcRGBBuffer_Size = pImagePreprocessData->iSrcSize;
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pSrcRGBBuffer = new uint8_t[pSrcRGBBuffer_Size];
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memcpy(pSrcRGBBuffer, pImagePreprocessData->pSrcData.get(), pSrcRGBBuffer_Size);
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std::shared_ptr<InferenceData> pInferenceModelData = std::make_shared<InferenceData>();
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#ifdef INFERENCE_MODEL_TIME_CONSUMING_TEST
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auto start = std::chrono::system_clock::now(); //计时开始
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doInference(*context_, *inference_model_stream_, (void**)buffers_, (float*)pInferenceModelBuffer, BATCH_SIZE); //context为推理的上下文环境,stream为注册流(用于异步推理时进行同步),buffers为传入的图像数据,pInferenceModelBuffer为推理的结果
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auto end = std::chrono::system_clock::now();
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std::cout << "inference time: " << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
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#else
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doInference(*context_, *inference_model_stream_, (void**)buffers_, (float*)pInferenceModelBuffer, BATCH_SIZE); //context为推理的上下文环境,stream为注册流(用于异步推理时进行同步),buffers为传入的图像数据,pInferenceModelBuffer为推理的结果
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#endif
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//组织数据
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pInferenceModelData->iDataSource = engineId_;
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pInferenceModelData->iSize = pInferenceModelBuffer_Size;
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pInferenceModelData->pData.reset(pInferenceModelBuffer, [](void* data){if(data){delete[] data; data = nullptr;}}); //智能指针管理内存
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pInferenceModelData->iSrcSize = pSrcRGBBuffer_Size;
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pInferenceModelData->pSrcData.reset(pSrcRGBBuffer, [](void* data){if(data){delete[] data; data = nullptr;}}); //智能指针管理内存
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pInferenceModelData->i64TimeStamp = pImagePreprocessData->i64TimeStamp;
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#if 1
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//推理结果送入下一引擎
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iRet = outputQueMap_[strPort0_]->push(std::static_pointer_cast<void>(pInferenceModelData));
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if (iRet != APP_ERR_OK){
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LogError << "push info error";
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// std::cerr<<"push the inference model data failed..."<<std::endl;
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}else{
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// std::cout<<"push the inference model data success!"<<std::endl;
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}
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#endif
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}
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}
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//获取宽度
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int InferenceModelEngine::get_width(int x, float gw, int divisor = 8) {
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return int(ceil((x * gw) / divisor)) * divisor;
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}
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//获取深度
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int InferenceModelEngine::get_depth(int x, float gd) {
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if (x == 1) return 1;
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int r = round(x * gd);
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if (x * gd - int(x * gd) == 0.5 && (int(x * gd) % 2) == 0) {
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--r;
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}
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return std::max<int>(r, 1);
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}
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//构建普通引擎(例:yolov5s,yolov5m...)
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ICudaEngine* InferenceModelEngine::build_engine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, nvinfer1::DataType dt, float& gd, float& gw, std::string& wts_name) {
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INetworkDefinition* network = builder->createNetworkV2(0U);
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// Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
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ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });
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assert(data);
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std::map<std::string, Weights> weightMap = loadWeights(wts_name);
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/* ------ yolov5 backbone------ */
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auto conv0 = convBlock(network, weightMap, *data, get_width(64, gw), 6, 2, 1, "model.0");
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assert(conv0);
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auto conv1 = convBlock(network, weightMap, *conv0->getOutput(0), get_width(128, gw), 3, 2, 1, "model.1");
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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");
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auto conv3 = convBlock(network, weightMap, *bottleneck_CSP2->getOutput(0), get_width(256, gw), 3, 2, 1, "model.3");
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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");
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auto conv5 = convBlock(network, weightMap, *bottleneck_csp4->getOutput(0), get_width(512, gw), 3, 2, 1, "model.5");
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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");
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auto conv7 = convBlock(network, weightMap, *bottleneck_csp6->getOutput(0), get_width(1024, gw), 3, 2, 1, "model.7");
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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");
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auto spp9 = SPPF(network, weightMap, *bottleneck_csp8->getOutput(0), get_width(1024, gw), get_width(1024, gw), 5, "model.9");
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/* ------ yolov5 head ------ */
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auto conv10 = convBlock(network, weightMap, *spp9->getOutput(0), get_width(512, gw), 1, 1, 1, "model.10");
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auto upsample11 = network->addResize(*conv10->getOutput(0));
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assert(upsample11);
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upsample11->setResizeMode(ResizeMode::kNEAREST);
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upsample11->setOutputDimensions(bottleneck_csp6->getOutput(0)->getDimensions());
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ITensor* inputTensors12[] = { upsample11->getOutput(0), bottleneck_csp6->getOutput(0) };
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auto cat12 = network->addConcatenation(inputTensors12, 2);
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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");
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auto conv14 = convBlock(network, weightMap, *bottleneck_csp13->getOutput(0), get_width(256, gw), 1, 1, 1, "model.14");
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auto upsample15 = network->addResize(*conv14->getOutput(0));
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assert(upsample15);
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upsample15->setResizeMode(ResizeMode::kNEAREST);
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upsample15->setOutputDimensions(bottleneck_csp4->getOutput(0)->getDimensions());
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ITensor* inputTensors16[] = { upsample15->getOutput(0), bottleneck_csp4->getOutput(0) };
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auto cat16 = network->addConcatenation(inputTensors16, 2);
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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");
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/* ------ detect ------ */
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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"]);
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auto conv18 = convBlock(network, weightMap, *bottleneck_csp17->getOutput(0), get_width(256, gw), 3, 2, 1, "model.18");
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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;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
//构建p6引擎(例:yolov5s6,yolov5m6...)
|
|||
|
|
ICudaEngine* InferenceModelEngine::build_engine_p6(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, nvinfer1::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;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
//转换模型
|
|||
|
|
void InferenceModelEngine::APIToModel(Logger gLogger, unsigned int maxBatchSize, IHostMemory** modelStream, bool& is_p6, float& gd, float& gw, std::string& wts_name) {
|
|||
|
|
// Create builder
|
|||
|
|
IBuilder* builder = createInferBuilder(gLogger); //创建builder(要传入gLogger)
|
|||
|
|
IBuilderConfig* config = builder->createBuilderConfig(); //创建builderconfig
|
|||
|
|
|
|||
|
|
// 创建模型来填充网络，然后设置输出并创建一个引擎
|
|||
|
|
// 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, nvinfer1::DataType::kFLOAT, gd, gw, wts_name);
|
|||
|
|
} else {
|
|||
|
|
engine = build_engine(maxBatchSize, builder, config, nvinfer1::DataType::kFLOAT, gd, gw, wts_name);
|
|||
|
|
}
|
|||
|
|
assert(engine != nullptr);
|
|||
|
|
|
|||
|
|
// Serialize the engine
|
|||
|
|
//序列化引擎生成模型流
|
|||
|
|
(*modelStream) = engine->serialize();
|
|||
|
|
|
|||
|
|
// Close everything down
|
|||
|
|
//释放相关资源
|
|||
|
|
engine->destroy();
|
|||
|
|
builder->destroy();
|
|||
|
|
config->destroy();
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
//执行推理
|
|||
|
|
void InferenceModelEngine::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); //执行异步推理(调用context->enqueueV2即可执行异步推理,如果用同步推理的话,可以调用context->executeV2)
|
|||
|
|
CUDA_CHECK(cudaMemcpyAsync(output, buffers[1], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream)); //把推理后的结果从GPU上拷贝到CPU上
|
|||
|
|
cudaStreamSynchronize(stream); //同步之前创建的cuda流，原因很简单，直接使用的context->enqueueV2函数是异步推理,因此需要把cuda流同步一下
|
|||
|
|
}
|