AI代码解释 // The execution context is responsible for launching the// compute kernels 创建上下文环境 context,用于启动kernelIExecutionContext*context=engine->createExecutionContext();// In order to bind the buffers, we
stream = cuda.Stream() # pycuda 操作缓冲区 with engine.create_execution_context() as context: total_duration = 0. total_compute_duration = 0. total_pre_duration = 0. total_post_duration = 0. for iteration in range(num_iters): pre_t = time.time() # set host data img = torch.fro...
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...
1.3、创建engine的context代码 代码语言:javascript 代码运行次数:0 运行 AI代码解释 # Create the contextforthisengine context=engine.create_execution_context()print("Context executed ",type(context))# Allocate buffersforinput and output inputs,outputs,bindings,stream=allocate_buffers(engine)# input,output...
context = engine.create_execution_context() 3.3 构建 Buffer 相关 构建Buffer 相关,主要涉及数据的准备,包括 Host 端和 Device 端,以及数据的拷贝,如执行推理前需要将 CPU 数据拷贝到 GPU 上,即 Host -> Device;当推理完成后,需要将结果数据从 GPU 拷出到 CPU,也即 Device -> Host。一些相关的示例代码如...
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: 对输入图像数据做与模型训练时一样的预处理操作。 把模型的输入数据从CPU拷贝到GPU中。 调用模型推理接口进行推理...
创建IExecutionContext对象,例如create_execution_context()或create_execution_context_without_device_memory() 序列化 Engine,serialize,大概用法就是open(filename, "wb").write(engine.serialize()) 这里要单独介绍一下binding相关内容 概念:可理解为端口,用于表示输入tensor与输出tensor。
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 ...
context = engine.create_execution_context() # Allocate buffers for input and output inputs, outputs, bindings, stream = allocate_buffers(engine) # input, output: host # bindings # Do inference shape_of_output = (max_batch_size, 1000) ...
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 APIupdateDeviceMemorySizeForShapes,以根据实际输入形状查询所需的大小。