* engine = builder->buildCudaEngine(*network); engine:引擎,不能跨平台和TensorRT版本移植。若要存储,需要将引擎转化为一种格式,即序列化,若要推理,需要反序列化引擎。引擎用于保存网络定义和模型参数。 IHostMemory *serializedModel = engine->serialize(); // store mo
print('network layers ',network.num_layers) engine = builder.build_cuda_engine(network) print("Completed creating Engine") with open(engine_file_path, "wb") as f: f.write(engine.serialize()) return engine if os.path.exists(engine_file_path): # If a serialized engine exists, use it in...
import pycuda.autoinit import pycuda.driver as cuda TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE) def build_engine(onnx_file_path, engine_file_path, int8_calib=None): with trt.Builder(TRT_LOGGER) as builder, builder.create_network(1) as network, \ trt.OnnxParser(network, TRT_LOGGER) as...
builder.build_cuda_engine(network):生成TensorRT引擎。 5. 加载TensorRT引擎 importpycuda.driverascudaimportpycuda.autoinit# 预初始化CUDA# 将引擎序列化并反序列化,即加载引擎defload_engine(engine_file_path):withopen(engine_file_path,"rb")asf:returntrt.Runtime(TRT_LOGGER).deserialize_cuda_engine(f.r...
) exit() # 构建TensorRT引擎 engine = builder.build_cuda_engine(network) #将TensorRT引擎保存到文件 engine_path = 'model.engine' with open(engine_path, 'wb') as f: f.write(engine.serialize()) 总结 以上步骤涵盖了从加载ONNX模型到将其转换为TensorRT引擎的全过程。确保你根据具体的硬件配置(如...
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...
ICudaEngine*engine=builder->buildCudaEngine(*network); You can also use the GIE C++ API to define the network without the Caffe parser, as Listing 2 shows. You can use the API to define any supported layer and its parameters. You can define any parameter that varies between networks, incl...
1.1 创建builder 2、继承ILogger,实例化接口 1.2 创建network 1.3 创建parse解析器 1.4 设置必要参数并创建Engine 1.5 创建Engine并序列化 2、读取序列化后TensorRT Engine 并进行推理 2.1 反序列化engine 2.2 创建runtime 2.3 创建engine 2.4 创建context
// 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 = createEngine(maxBatchSize, builder, config, DataType::kFLOAT, ...
INetworkDefinition* network = builder->createNetworkV2(1U);//此处重点1U为OU就有问题IParser* parser = createParser(*network, gLogger); parser->parseFromFile(onnx_path, static_cast<int32_t>(ILogger::Severity::kWARNING));//解析有错误将返回for(int32_t i =0; i < parser->getNbErrors()...