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...
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 8.0.3.4 + CUDA 11.2. Engines in both environments are converted from ONNX passed n...
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...
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...
// 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->context!=...
createExecutionContext 函数接受指定分配策略的参数 (kSTATIC, kON_PROFILE_CHANGE 和kUSER_MANAGED),以确定执行上下文设备内存的大小。对于用户管理的分配,即 kUSER_MANAGED,还需要使用额外的 API updateDeviceMemorySizeForShapes,以根据实际输入形状查询所需的大小。 减重引擎 TensorRT 10.0 支持轻量化引擎,可以实现 99...
创建一个执行上下文“IexecutionContext”,用于管理治理的执行。 nvinfer1::IExecutionContext* context = engine->createExecutionContext(); assert(context != nullptr); (4)准备输入输出缓冲区 在GPU上为输入和输出分配缓冲区。 void* buffers[engine->getNbBindings()]; ...
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(); ...
d_output=cuda.mem_alloc(h_output.nbytes)#创建cuda流stream =cuda.Stream()#创建context并进行推理with engine.create_execution_context() as context:#Transfer input data to the GPU.cuda.memcpy_htod_async(d_input, h_input, stream)#Run inference.context.execute_async_v2(bindings=[int(d_input),...