TensorRT 10.0还增强了运行时内存分配的功能。通过createExecutionContext函数,用户可以指定执行上下文设备内存的分配策略。对于用户管理的分配方式,TensorRT提供了额外的API,以便根据实际输入形状查询所需的大小,从而更精细地管理内存资源。 减重引擎(Weight-stripped engines)与重量流 (Weight streaming) 为了应对大型模型的部...
ICudaEngine对象中存放着经过TensorRT优化后的模型,不过如果要用模型进行推理则还需要通过createExecutionContext()函数去创建一个IExecutionContext对象来管理推理的过程: nvinfer1::IExecutionContext *context = engine->createExecutionContext(); 现在让我们先来看一下使用TensorRT框架进行模型推理的完整流程: 对输入图像...
ICudaEngine对象中存放着经过TensorRT优化后的模型,不过如果要用模型进行推理则还需要通过createExecutionContext()函数去创建一个IExecutionContext对象来管理推理的过程: nvinfer1::IExecutionContext *context = engine->createExecutionContext(); 现在让我们...
AI代码解释 // 设置当前设备为GPU 0cudaSetDevice(0);// 初始化TensorRTthis->runtime=createInferRuntime(gLogger);assert(this->runtime!=nullptr);this->engine=runtime->deserializeCudaEngine(trtModelStream,size);assert(this->engine!=nullptr);this->context=engine->createExecutionContext();assert(this-...
In order to run inference, use the interfaceIExecutionContext. In order to create an object of typeIExecutionContext, first create an object of typeICudaEngine(the engine). The builder or runtime will be created with the GPU context associated with the creating thread.Even though it is possi...
步骤2:加载engine模型,创建context,确定输入的固定尺寸 nvinfer1::ICudaEngine* engine_infer = engine_runtime->deserializeCudaEngine(data.get(), length, nullptr); nvinfer1::IExecutionContext* engine_context = engine_infer->createExecutionContext(); ...
Description TrtUniquePtr<nvinfer1::IExecutionContext> context(m_engine->createExecutionContext()); This line of code run normally with TensorRT 7.2.3.4 + CUDA 11.1, takes about 2 ms. But it takes 300 ms with TensorRT 8…
IExecutionContext* context = engine->createExecutionContext();if(!context) { std::cerr<<"Failed to create execution context"<<std::endl;return-1; }//Load an image using OpenCVcv::Mat img = cv::imread("face.jpg"); cv::resize(img, img, cv::Size(640,640)); ...
*runtime=createInferRuntime(gLogger);assert(runtime!=nullptr);ICudaEngine*engine=runtime->deserializeCudaEngine(trtModelStream->data(),trtModelStream->size(),nullptr);assert(engine!=nullptr);trtModelStream->destroy();IExecutionContext*context=engine->createExecutionContext();assert(context!=nullptr...
context = engine.create_execution_context()# 创建执行上下文,用于执行推理,可以理解为GPU进程 context.set_input_shape(lTensorName[0], (1, 1, 28, 28))# 设置输入形状 hInput = np.random.random((1, 1, 28, 28)).astype(np.float32)# 创建输入数据,host端 ...