“IRuntime”: 未声明的标识符
“IRuntime”: 未声明的标识符
完整用法:
TensorRT 系列 (1)模型推理_洪流之源的博客-CSDN博客_tensorrt 推理
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// tensorRT include
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#include <NvInfer.h>
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#include <NvInferRuntime.h>
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// cuda include
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#include <cuda_runtime.h>
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// system include
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#include <stdio.h>
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#include <math.h>
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#include <iostream>
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#include <fstream>
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#include <vector>
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using namespace std;
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// 上一节的代码
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class TRTLogger : public nvinfer1::ILogger
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{
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public:
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virtual void log(Severity severity, nvinfer1::AsciiChar const* msg) noexcept override
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{
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if(severity <= Severity::kINFO)
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{
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printf("%d: %s\n", severity, msg);
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}
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}
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} logger;
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nvinfer1::Weights make_weights(float* ptr, int n)
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{
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nvinfer1::Weights w;
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w.count = n;
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w.type = nvinfer1::DataType::kFLOAT;
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w.values = ptr;
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return w;
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}
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bool build_model()
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{
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TRTLogger logger;
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// 这是基本需要的组件
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nvinfer1::IBuilder* builder = nvinfer1::createInferBuilder(logger);
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nvinfer1::IBuilderConfig* config = builder->createBuilderConfig();
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nvinfer1::INetworkDefinition* network = builder->createNetworkV2(1);
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// 构建一个模型
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/*
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Network definition:
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image
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|
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linear (fully connected) input = 3, output = 2, bias = True w=[[1.0, 2.0, 0.5], [0.1, 0.2, 0.5]], b=[0.3, 0.8]
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|
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sigmoid
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|
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prob
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*/
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const int num_input = 3;
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const int num_output = 2;
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float layer1_weight_values[] = {1.0, 2.0, 0.5, 0.1, 0.2, 0.5};
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float layer1_bias_values[] = {0.3, 0.8};
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nvinfer1::ITensor* input = network->addInput("image", nvinfer1::DataType::kFLOAT, nvinfer1::Dims4(1, num_input, 1, 1));
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nvinfer1::Weights layer1_weight = make_weights(layer1_weight_values, 6);
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nvinfer1::Weights layer1_bias = make_weights(layer1_bias_values, 2);
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auto layer1 = network->addFullyConnected(*input, num_output, layer1_weight, layer1_bias);
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auto prob = network->addActivation(*layer1->getOutput(0), nvinfer1::ActivationType::kSIGMOID);
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// 将我们需要的prob标记为输出
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network->markOutput(*prob->getOutput(0));
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printf("Workspace Size = %.2f MB\n", (1 << 28) / 1024.0f / 1024.0f);
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config->setMaxWorkspaceSize(1 << 28);
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builder->setMaxBatchSize(1);
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nvinfer1::ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
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if(engine == nullptr)
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{
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printf("Build engine failed.\n");
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return false;
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}
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// 将模型序列化,并储存为文件
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nvinfer1::IHostMemory* model_data = engine->serialize();
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FILE* f = fopen("engine.trtmodel", "wb");
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fwrite(model_data->data(), 1, model_data->size(), f);
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fclose(f);
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// 卸载顺序按照构建顺序倒序
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model_data->destroy();
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engine->destroy();
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network->destroy();
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config->destroy();
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builder->destroy();
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printf("Done.\n");
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return true;
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}
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vector<unsigned char> load_file(const string& file)
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{
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ifstream in(file, ios::in | ios::binary);
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if (!in.is_open())
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return {};
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in.seekg(0, ios::end);
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size_t length = in.tellg();
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std::vector<uint8_t> data;
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if (length > 0){
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in.seekg(0, ios::beg);
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data.resize(length);
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in.read((char*)&data[0], length);
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}
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in.close();
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return data;
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}
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void inference(){
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// ------------------------------ 1. 准备模型并加载 ----------------------------
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TRTLogger logger;
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auto engine_data = load_file("engine.trtmodel");
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// 执行推理前,需要创建一个推理的runtime接口实例。与builer一样,runtime需要logger:
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nvinfer1::IRuntime* runtime = nvinfer1::createInferRuntime(logger);
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// 将模型从读取到engine_data中,则可以对其进行反序列化以获得engine
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nvinfer1::ICudaEngine* engine = runtime->deserializeCudaEngine(engine_data.data(), engine_data.size());
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if(engine == nullptr){
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printf("Deserialize cuda engine failed.\n");
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runtime->destroy();
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return;
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}
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nvinfer1::IExecutionContext* execution_context = engine->createExecutionContext();
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cudaStream_t stream = nullptr;
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// 创建CUDA流,以确定这个batch的推理是独立的
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cudaStreamCreate(&stream);
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/*
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Network definition:
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image
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|
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linear (fully connected) input = 3, output = 2, bias = True w=[[1.0, 2.0, 0.5], [0.1, 0.2, 0.5]], b=[0.3, 0.8]
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|
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sigmoid
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|
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prob
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*/
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// ------------------------------ 2. 准备好要推理的数据并搬运到GPU ----------------------------
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float input_data_host[] = {1, 2, 3};
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float* input_data_device = nullptr;
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float output_data_host[2];
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float* output_data_device = nullptr;
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cudaMalloc(&input_data_device, sizeof(input_data_host));
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cudaMalloc(&output_data_device, sizeof(output_data_host));
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cudaMemcpyAsync(input_data_device, input_data_host, sizeof(input_data_host), cudaMemcpyHostToDevice, stream);
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// 用一个指针数组指定input和output在gpu中的指针。
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float* bindings[] = {input_data_device, output_data_device};
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// ------------------------------ 3. 推理并将结果搬运回CPU ----------------------------
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bool success = execution_context->enqueueV2((void**)bindings, stream, nullptr);
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cudaMemcpyAsync(output_data_host, output_data_device, sizeof(output_data_host), cudaMemcpyDeviceToHost, stream);
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cudaStreamSynchronize(stream);
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printf("output_data_host = %f, %f\n", output_data_host[0], output_data_host[1]);
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// ------------------------------ 4. 释放内存 ----------------------------
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printf("Clean memory\n");
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cudaStreamDestroy(stream);
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execution_context->destroy();
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engine->destroy();
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runtime->destroy();
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// ------------------------------ 5. 手动推理进行验证 ----------------------------
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const int num_input = 3;
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const int num_output = 2;
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float layer1_weight_values[] = {1.0, 2.0, 0.5, 0.1, 0.2, 0.5};
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float layer1_bias_values[] = {0.3, 0.8};
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printf("手动验证计算结果:\n");
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for(int io = 0; io < num_output; ++io)
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{
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float output_host = layer1_bias_values[io];
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for(int ii = 0; ii < num_input; ++ii)
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{
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output_host += layer1_weight_values[io * num_input + ii] * input_data_host[ii];
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}
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// sigmoid
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float prob = 1 / (1 + exp(-output_host));
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printf("output_prob[%d] = %f\n", io, prob);
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}
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}
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int main()
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{
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if(!build_model())
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{
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return -1;
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}
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inference();
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return 0;
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}
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原文链接:https://blog.csdn.net/weicao1990/article/details/125034572
makefile:
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cc := g++
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name := pro
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workdir := workspace
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srcdir := src
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objdir := objs
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stdcpp := c++11
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cuda_home := /home/liuhongyuan/miniconda3/envs/trtpy/lib/python3.8/site-packages/trtpy/trt8cuda112cudnn8
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syslib := /home/liuhongyuan/miniconda3/envs/trtpy/lib/python3.8/site-packages/trtpy/lib
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cpp_pkg := /home/liuhongyuan/miniconda3/envs/trtpy/lib/python3.8/site-packages/trtpy/cpp-packages
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cuda_arch :=
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nvcc := $(cuda_home)/bin/nvcc -ccbin=$(cc)
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# 定义cpp的路径查找和依赖项mk文件
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cpp_srcs := $(shell find $(srcdir) -name "*.cpp")
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cpp_objs := $(cpp_srcs:.cpp=.cpp.o)
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cpp_objs := $(cpp_objs:$(srcdir)/%=$(objdir)/%)
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cpp_mk := $(cpp_objs:.cpp.o=.cpp.mk)
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# 定义cu文件的路径查找和依赖项mk文件
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cu_srcs := $(shell find $(srcdir) -name "*.cu")
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cu_objs := $(cu_srcs:.cu=.cu.o)
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cu_objs := $(cu_objs:$(srcdir)/%=$(objdir)/%)
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cu_mk := $(cu_objs:.cu.o=.cu.mk)
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# 定义opencv和cuda需要用到的库文件
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link_cuda := cudart cudnn
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link_trtpro :=
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link_tensorRT := nvinfer
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link_opencv :=
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link_sys := stdc++ dl
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link_librarys := $(link_cuda) $(link_tensorRT) $(link_sys) $(link_opencv)
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# 定义头文件路径,请注意斜杠后边不能有空格
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# 只需要写路径,不需要写-I
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include_paths := src \
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$(cuda_home)/include/cuda \
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$(cuda_home)/include/tensorRT \
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$(cpp_pkg)/opencv4.2/include
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# 定义库文件路径,只需要写路径,不需要写-L
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library_paths := $(cuda_home)/lib64 $(syslib) $(cpp_pkg)/opencv4.2/lib
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# 把library path给拼接为一个字符串,例如a b c => a:b:c
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# 然后使得LD_LIBRARY_PATH=a:b:c
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empty :=
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library_path_export := $(subst $(empty) $(empty),:,$(library_paths))
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# 把库路径和头文件路径拼接起来成一个,批量自动加-I、-L、-l
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run_paths := $(foreach item,$(library_paths),-Wl,-rpath=$(item))
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include_paths := $(foreach item,$(include_paths),-I$(item))
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library_paths := $(foreach item,$(library_paths),-L$(item))
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link_librarys := $(foreach item,$(link_librarys),-l$(item))
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# 如果是其他显卡,请修改-gencode=arch=compute_75,code=sm_75为对应显卡的能力
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# 显卡对应的号码参考这里:https://developer.nvidia.com/zh-cn/cuda-gpus#compute
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# 如果是 jetson nano,提示找不到-m64指令,请删掉 -m64选项。不影响结果
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cpp_compile_flags := -std=$(stdcpp) -w -g -O0 -m64 -fPIC -fopenmp -pthread
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cu_compile_flags := -std=$(stdcpp) -w -g -O0 -m64 $(cuda_arch) -Xcompiler "$(cpp_compile_flags)"
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link_flags := -pthread -fopenmp -Wl,-rpath='$$ORIGIN'
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cpp_compile_flags += $(include_paths)
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cu_compile_flags += $(include_paths)
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link_flags += $(library_paths) $(link_librarys) $(run_paths)
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# 如果头文件修改了,这里的指令可以让他自动编译依赖的cpp或者cu文件
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ifneq ($(MAKECMDGOALS), clean)
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-include $(cpp_mk) $(cu_mk)
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endif
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$(name) : $(workdir)/$(name)
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all : $(name)
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run : $(name)
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@cd $(workdir) && ./$(name) $(run_args)
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$(workdir)/$(name) : $(cpp_objs) $(cu_objs)
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@echo Link $@
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@mkdir -p $(dir $@)
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@$(cc) $^ -o $@ $(link_flags)
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$(objdir)/%.cpp.o : $(srcdir)/%.cpp
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@echo Compile CXX $<
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@mkdir -p $(dir $@)
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@$(cc) -c $< -o $@ $(cpp_compile_flags)
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$(objdir)/%.cu.o : $(srcdir)/%.cu
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@echo Compile CUDA $<
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@mkdir -p $(dir $@)
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@$(nvcc) -c $< -o $@ $(cu_compile_flags)
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# 编译cpp依赖项,生成mk文件
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$(objdir)/%.cpp.mk : $(srcdir)/%.cpp
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@echo Compile depends C++ $<
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@mkdir -p $(dir $@)
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@$(cc) -M $< -MF $@ -MT $(@:.cpp.mk=.cpp.o) $(cpp_compile_flags)
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# 编译cu文件的依赖项,生成cumk文件
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$(objdir)/%.cu.mk : $(srcdir)/%.cu
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@echo Compile depends CUDA $<
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@mkdir -p $(dir $@)
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@$(nvcc) -M $< -MF $@ -MT $(@:.cu.mk=.cu.o) $(cu_compile_flags)
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# 定义清理指令
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clean :
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@rm -rf $(objdir) $(workdir)/$(name) $(workdir)/*.trtmodel
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# 防止符号被当做文件
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.PHONY : clean run $(name)
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# 导出依赖库路径,使得能够运行起来
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export LD_LIBRARY_PATH:=$(library_path_export)
原文链接:https://blog.csdn.net/weicao1990/article/details/125034572
重点提炼:
1. 必须使用createNetworkV2,并指定为1(表示显性batch),createNetwork已经废弃,非显性batch官方不推荐,这个方式直接影响推理时enqueue还是enqueueV2;
2. builder、config等指针,记得释放,否则会有内存泄漏,使用ptr->destroy()释放;
3. markOutput表示是该模型的输出节点,mark几次,就有几个输出,addInput几次就有几个输入;
4. workspaceSize是工作空间大小,某些layer需要使用额外存储时,不会自己分配空间,而是为了内存复用,直接找tensorRT要workspace空间;
5. 一定要记住,保存的模型只能适配编译时的trt版本、编译时指定的设备,也只能保证在这种配置下是最优的。如果用trt跨不同设备执行,有时候可以运行,但不是最优的,也不推荐;
6. bindings是tensorRT对输入输出张量的描述,bindings = input-tensor + output-tensor。比如input有a,output有b, c, d,那么bindings = [a, b, c, d],bindings[0] = a,bindings[2] = c;
7. enqueueV2是异步推理,加入到stream队列等待执行。输入的bindings则是tensors的指针(注意是device pointer);
8. createExecutionContext可以执行多次,允许一个引擎具有多个执行上下文。
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版权声明:本文为CSDN博主「洪流之源」的原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接及本声明。
原文链接:https://blog.csdn.net/weicao1990/article/details/125034572
文章来源: blog.csdn.net,作者:AI视觉网奇,版权归原作者所有,如需转载,请联系作者。
原文链接:blog.csdn.net/jacke121/article/details/125962123
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