mnist tensorrt 运行_TensorRT程序分析

写在前面

首先，部署TensorRT进行推理，我们需要有一个训练好的网络，

需要它的网络结构文件*.prototxt

需要它的网络权值文件*caffemodel

我们要用一个cuda程序来部署，那么这个在TensorRT中的cuda程序如何编写呢，我们还是从TensorRT给的实例来分析

simpleMNIST.cpp

Part1 caffe model 到 TensorRT model的转换 –>gieModelStream

// create a GIE model from the caffe model and serialize it to a stream

std::stringstream gieModelStream;

caffeToGIEModel("mnist.prototxt", "mnist.caffemodel", std::vector < std::string > { OUTPUT_BLOB_NAME }, 1, gieModelStream);

main函数 上来调用caffeToGIEModel函数 把caffe网络模型转化为GIE(即TensorRT)网络模型，并将其序列化，那么到底是如何操作的呢

//从入口参数也可以看得到，它需要caffe网络模型的 网络结构文件和网络权值文件，

//指定batchsize 最后是转换后的TensorRT model

void caffeToGIEModel(const std::string& deployFile, // name for caffe prototxt

const std::string& modelFile, // name for model

const std::vector<:string>& outputs, // network outputs

unsigned int maxBatchSize, // batch size - NB must be at least as large as the batch we want to run with)

std::ostream& gieModelStream) // output stream for the GIE model

{

// create the builder

IBuilder* builder = createInferBuilder(gLogger);

// parse the caffe model to populate the network, then set the outputs

INetworkDefinition* network = builder->createNetwork();

ICaffeParser* parser = createCaffeParser();

const IBlobNameToTensor* blobNameToTensor = parser->parse(locateFile(deployFile).c_str(),

locateFile(modelFile).c_str(),

*network,

DataType::kFLOAT);

// specify which tensors are outputs

for (auto& s : outputs)

network->markOutput(*blobNameToTensor->find(s.c_str()));

// Build the engine

builder->setMaxBatchSize(maxBatchSize);

builder->setMaxWorkspaceSize(1 << 20);

ICudaEngine* engine = builder->buildCudaEngine(*network);

assert(engine);

// we don't need the network any more, and we can destroy the parser

network->destroy();

parser->destroy();

// serialize the engine, then close everything down

engine->serialize(gieModelStream);

engine->destroy();

builder->destroy();

shutdownProtobufLibrary();

}

//创建了一个builder，并利用builder创建一个createNetwork

//解析caffe model

//指明TensorRT的输出

//创建engine

//序列化engine

Part2 输入 输入文件与均值文件作差 —->data[INPUT_H*INPUT_W]

//因为这个实例是手写数字的识别，所以它随机产生了一个十以内的数字，并且print出来

//获取均值文件

//把均值文件与输入的文件作差

// read a random digit file

srand(unsigned(time(nullptr)));

uint8_t fileData[INPUT_H*INPUT_W];

readPGMFile(std::to_string(rand() % 10) + ".pgm", fileData);

// print an ascii representation

std::cout << "\n\n\n---------------------------" << "\n\n\n" << std::endl;

for (int i = 0; i < INPUT_H*INPUT_W; i++)

std::cout << (" .:-=+*#%@"[fileData[i] / 26]) << (((i + 1) % INPUT_W) ? "" : "\n");

// parse the mean file and subtract it from the image

ICaffeParser* parser = createCaffeParser();

IBinaryProtoBlob* meanBlob = parser->parseBinaryProto(locateFile("mnist_mean.binaryproto").c_str());

parser->destroy();

const float *meanData = reinterpret_cast(meanBlob->getData());

float data[INPUT_H*INPUT_W];

for (int i = 0; i < INPUT_H*INPUT_W; i++)

data[i] = float(fileData[i])-meanData[i];

meanBlob->destroy();

Part3 反序列化引擎 得到—->IExecutionContext *context

// deserialize the engine

gieModelStream.seekg(0, gieModelStream.beg);

IRuntime* runtime = createInferRuntime(gLogger);

ICudaEngine* engine = runtime->deserializeCudaEngine(gieModelStream);

IExecutionContext *context = engine->createExecutionContext();

//创建runtime

//反序列化引擎

//创建可执行上下文

Part4 执行推理，输出结果—–>doInference【context, data, prob, 1】—>prob

// run inference

float prob[OUTPUT_SIZE];

doInference(*context, data, prob, 1);

// destroy the engine

context->destroy();

engine->destroy();

runtime->destroy();

// print a histogram of the output distribution

std::cout << "\n\n";

for (unsigned int i = 0; i < 10; i++)

std::cout << i << ": " << std::string(int(std::floor(prob[i] * 10 + 0.5f)), '*') << "\n";

std::cout << std::endl;

//执行推理

//输出结果

所以着重看一下怎么执行推理的， doInference(*context, data, prob, 1);

从函数的入口参数也能看出，*context, data分别是Part3/2的结果 prob是最终的推理结果，batchsize

创建GPU buffer CHECK(cudaMalloc(&buffers[inputIndex], batchSize *

INPUT_H * INPUT_W * sizeof(float)));

CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE *

sizeof(float)));

执行推理 cudaStream_t stream; CHECK(cudaStreamCreate(&stream));

// DMA the input to the GPU, execute the batch asynchronously, and

DMA it back: CHECK(cudaMemcpyAsync(buffers[inputIndex], input,

batchSize * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice,

stream)); context.enqueue(batchSize, buffers, stream, nullptr);

CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize *

OUTPUT_SIZE*sizeof(float), cudaMemcpyDeviceToHost, stream));

cudaStreamSynchronize(stream);

void doInference(IExecutionContext& context, float* input, float* output, int batchSize)

{

const ICudaEngine& engine = context.getEngine();

// input and output buffer pointers that we pass to the engine - the engine requires exactly IEngine::getNbBindings(),

// of these, but in this case we know that there is exactly one input and one output.

assert(engine.getNbBindings() == 2);

void* buffers[2];

// In order to bind the buffers, we need to know the names of the input and output tensors.

// note that indices are guaranteed to be less than IEngine::getNbBindings()

int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME),

outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);

// create GPU buffers and a stream

CHECK(cudaMalloc(&buffers[inputIndex], batchSize * INPUT_H * INPUT_W * sizeof(float)));

CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));

cudaStream_t stream;

CHECK(cudaStreamCreate(&stream));

// DMA the input to the GPU, execute the batch asynchronously, and DMA it back:

CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));

context.enqueue(batchSize, buffers, stream, nullptr);

CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE*sizeof(float), cudaMemcpyDeviceToHost, stream));

cudaStreamSynchronize(stream);

// release the stream and the buffers

cudaStreamDestroy(stream);

CHECK(cudaFree(buffers[inputIndex]));

CHECK(cudaFree(buffers[outputIndex]));

}