提示：文章写完后，目录可以自动生成，如何生成可参考右边的帮助文档
文章目录
- 前言
- 一、pytorch构建分类网络
- 基于torchvision构建resnet网络
- 获得wts文件
- 获得onnx文件
- 二、tensorrt部署resnet
- 基于wts格式采用C++ API 转tensorrt部署
- 基于onnx格式采用C++ API 转tensorrt部署
- onnx-simpiler简化onnx文件
- 部署测试展示
- 三、性能测试实验
- 四、py转engine被C调用验证
- py转engine代码
- 推理部署代码(C++)
- infer显示
- 实验结果
- 五、Linux环境下构建CMakeList文件
- 基于wts格式构建编译文件
- 基于onnx格式构建编译文件
- 六、测试结果
- 基于wts测试结果
- 基于onnx测试结果
- 总结
前言
本文通过分类网络验证基于onnx构建network和基于wts构建network方式，使用tensorrt推理存在的性能区别。为此，本文内容主要分为六个，第一个内容介绍使用python构建网络，获取pt/wts/onnx文件；第二个内容介绍基于C++ API构建engine；第三个内容介绍基于C++使用onnx构建engine；第四个内容介绍windows性能及linux性能；第五个内容介绍验证；第六个内容介绍如何在Linux环境下编译engine且运行。
代码链接-百度网盘(提取码：r63z)
一、pytorch构建分类网络
基于torchvision构建resnet网络
构建resnet分类网络，并保存pth权重，代码如下：
from torchvision.transforms import transforms
import torch
import torchvision.models as models
import struct
transform_train = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
transforms_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
def build_model():
model = models.resnet18(pretrained=True)
model = model.eval（)
model = model.cuda()
torch.save(model, "./resnet18.pth")
if __name__ == '__main__':
build_model()获得wts文件
获得wts权重格式文件，代码如下：
from torchvision.transforms import transforms
import torch
import torchvision.models as models
import struct
def get_wts(model_path='./resnet18.pth',save_wts_path="./resnet18.wts"):
net = torch.load(model_path)
net = net.cuda()
net = net.eval（)
print('model: ', net)
# print('state dict: ', net.state_dict().keys())
tmp = torch.ones(1, 3, 224, 224).cuda()
print('input: ', tmp)
out = net(tmp)
print('output:', out)
f = open(save_wts_path, 'w')
f.write("{}\n".format(len(net.state_dict().keys())))
for k, v in net.state_dict().items():
print('key: ', k)
print('value: ', v.shape)
vr = v.reshape(-1).cpu().numpy()
f.write("{} {}".format(k, len(vr)))
for vv in vr:
f.write(" ")
f.write(struct.pack(">f", float(vv)).hex())
f.write("\n")
if __name__ == '__main__':
get_wts(model_path='./resnet18.pth',save_wts_path="./resnet18.wts")获得onnx文件
获得onnx格式文件，代码如下：
from torchvision.transforms import transforms
import torch
import torchvision.models as models
import struct
def get_onnx(model_path='./resnet18.pth',save_onnx_path="./resnet18.onnx"):
# 定义静态onnx，若推理input_data格式不一致，将导致保存
input_data = torch.randn(2, 3, 224, 224).cuda()
model = torch.load(model_path).cuda()
input_names = ["data"] + ["called_%d" % i for i in range(2)]
output_names = ["prob"]
torch.onnx.export(
model,
input_data,
save_onnx_path,
verbose=True,
input_names=input_names,
output_names=output_names
)
if __name__ == '__main__':
get_onnx(model_path='./resnet18.pth', save_onnx_path="./resnet18.onnx")以上代码可复制粘贴合并到一个py文件使用。
二、tensorrt部署resnet
基于wts格式采用C++ API 转tensorrt部署
以下使用wts方法，实现引擎engine构建与推理部署，代码如下：
#include "NvInfer.h"
#include "cuda_runtime_api.h"
//#include "logging.h"
#include <fstream>
#include <iostream>
#include <map>
#include <sstream>
#include <vector>
#include <chrono>
#include <cmath>
#include <cassert>
#include<opencv2/core/core.hpp>
#include<opencv2/highgui/highgui.hpp>
#include <opencv2/opencv.hpp>
using namespace std;
#define CHECK(status) \
do\
{\
auto ret = (status);\
if (ret != 0)\
{\
std::cerr << "Cuda failure: " << ret << std::endl;\
abort();\
}\
} while (0)
// stuff we know about the network and the input/output blobs
static const int INPUT_H = 224;
static const int INPUT_W = 224;
static const int OUTPUT_SIZE = 1000;
const char* INPUT_BLOB_NAME = "data";
const char* OUTPUT_BLOB_NAME = "prob";
using namespace nvinfer1;
//static Logger gLogger;
//构建Logger
class Logger : public ILogger
{
void log(Severity severity, const char* msg) noexcept override
{
// suppress info-level messages
if (severity <= Severity::kWARNING)
std::cout << msg << std::endl;
}
} gLogger;
// Load weights from files shared with TensorRT samples.
// TensorRT weight files have a simple space delimited format:
// [type] [size] <data x size in hex>
std::map<std::string, Weights> loadWeights(const std::string file)
{
std::cout << "Loading weights: " << file << std::endl;
std::map<std::string, Weights> weightMap;
// Open weights file
std::ifstream input(file);
assert(input.is_open() && "Unable to load weight file.");
// Read number of weight blobs
int32_t count;
input >> count;
assert(count > 0 && "Invalid weight map file.");
while (count--)
{
Weights wt{ DataType::kFLOAT, nullptr, 0 };
uint32_t size;
// Read name and type of blob
std::string name;
input >> name >> std::dec >> size;
wt.type = DataType::kFLOAT;
// Load blob
uint32_t* val = reinterpret_cast<uint32_t*>(malloc(sizeof(val) * size));
for (uint32_t x = 0, y = size; x < y; ++x)
{
input >> std::hex >> val[x];
}
wt.values = val;
wt.count = size;
weightMap[name] = wt;
}
return weightMap;
}
IScaleLayer* addBatchNorm2d(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, float eps) {
float* gamma = (float*)weightMap[lname + ".weight"].values;
float* beta = (float*)weightMap[lname + ".bias"].values;
float* mean = (float*)weightMap[lname + ".running_mean"].values;
float* var = (float*)weightMap[lname + ".running_var"].values;
int len = weightMap[lname + ".running_var"].count;
std::cout << "len " << len << std::endl;
float* scval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
scval[i] = gamma[i] / sqrt(var[i] + eps);
}
Weights scale{ DataType::kFLOAT, scval, len };
float* shval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps);
}
Weights shift{ DataType::kFLOAT, shval, len };
float* pval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
pval[i] = 1.0;
}
Weights power{ DataType::kFLOAT, pval, len };
weightMap[lname + ".scale"] = scale;
weightMap[lname + ".shift"] = shift;
weightMap[lname + ".power"] = power;
IScaleLayer* scale_1 = network->addScale(input, ScaleMode::kCHANNEL, shift, scale, power);
assert(scale_1);
return scale_1;
}
IActivationLayer* basicBlock(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input, int inch, int outch, int stride, std::string lname) {
Weights emptywts{ DataType::kFLOAT, nullptr, 0 };
IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{ 3, 3 }, weightMap[lname + "conv1.weight"], emptywts);
assert(conv1);
conv1->setStrideNd(DimsHW{ stride, stride });
conv1->setPaddingNd(DimsHW{ 1, 1 });
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + "bn1", 1e-5);
IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
assert(relu1);
IConvolutionLayer* conv2 = network->addConvolutionNd(*relu1->getOutput(0), outch, DimsHW{ 3, 3 }, weightMap[lname + "conv2.weight"], emptywts);
assert(conv2);
conv2->setPaddingNd(DimsHW{ 1, 1 });
IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + "bn2", 1e-5);
IElementWiseLayer* ew1;
if (inch != outch) {
IConvolutionLayer* conv3 = network->addConvolutionNd(input, outch, DimsHW{ 1, 1 }, weightMap[lname + "downsample.0.weight"], emptywts);
assert(conv3);
conv3->setStrideNd(DimsHW{ stride, stride });
IScaleLayer* bn3 = addBatchNorm2d(network, weightMap, *conv3->getOutput(0), lname + "downsample.1", 1e-5);
ew1 = network->addElementWise(*bn3->getOutput(0), *bn2->getOutput(0), ElementWiseOperation::kSUM);
}
else {
ew1 = network->addElementWise(input, *bn2->getOutput(0), ElementWiseOperation::kSUM);
}
IActivationLayer* relu2 = network->addActivation(*ew1->getOutput(0), ActivationType::kRELU);
assert(relu2);
return relu2;
}
// Creat the engine using only the API and not any parser.
ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, string wts_path = "../resnet18.wts")
{
INetworkDefinition* network = builder->createNetworkV2(0U);
// Create input tensor of shape { 3, INPUT_H, INPUT_W } with name INPUT_BLOB_NAME
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });
assert(data);
std::map<std::string, Weights> weightMap = loadWeights(wts_path);
Weights emptywts{ DataType::kFLOAT, nullptr, 0 };
IConvolutionLayer* conv1 = network->addConvolutionNd(*data, 64, DimsHW{ 7, 7 }, weightMap["conv1.weight"], emptywts);
assert(conv1);
conv1->setStrideNd(DimsHW{ 2, 2 });
conv1->setPaddingNd(DimsHW{ 3, 3 });
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), "bn1", 1e-5);
IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
assert(relu1);
IPoolingLayer* pool1 = network->addPoolingNd(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{ 3, 3 });
assert(pool1);
pool1->setStrideNd(DimsHW{ 2, 2 });
pool1->setPaddingNd(DimsHW{ 1, 1 });
IActivationLayer* relu2 = basicBlock(network, weightMap, *pool1->getOutput(0), 64, 64, 1, "layer1.0.");
IActivationLayer* relu3 = basicBlock(network, weightMap, *relu2->getOutput(0), 64, 64, 1, "layer1.1.");
IActivationLayer* relu4 = basicBlock(network, weightMap, *relu3->getOutput(0), 64, 128, 2, "layer2.0.");
IActivationLayer* relu5 = basicBlock(network, weightMap, *relu4->getOutput(0), 128, 128, 1, "layer2.1.");
IActivationLayer* relu6 = basicBlock(network, weightMap, *relu5->getOutput(0), 128, 256, 2, "layer3.0.");
IActivationLayer* relu7 = basicBlock(network, weightMap, *relu6->getOutput(0), 256, 256, 1, "layer3.1.");
IActivationLayer* relu8 = basicBlock(network, weightMap, *relu7->getOutput(0), 256, 512, 2, "layer4.0.");
IActivationLayer* relu9 = basicBlock(network, weightMap, *relu8->getOutput(0), 512, 512, 1, "layer4.1.");
IPoolingLayer* pool2 = network->addPoolingNd(*relu9->getOutput(0), PoolingType::kAVERAGE, DimsHW{ 7, 7 });
assert(pool2);
pool2->setStrideNd(DimsHW{ 1, 1 });
IFullyConnectedLayer* fc1 = network->addFullyConnected(*pool2->getOutput(0), 1000, weightMap["fc.weight"], weightMap["fc.bias"]);
assert(fc1);
fc1->getOutput(0)->setName(OUTPUT_BLOB_NAME);
std::cout << "set name out" << std::endl;
network->markOutput(*fc1->getOutput(0));
// Build engine
builder->setMaxBatchSize(maxBatchSize);
config->setMaxWorkspaceSize(1 << 20);
//config->setFlag(nvinfer1::BuilderFlag::kFP16);
ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
std::cout << "build out" << std::endl;
// Don't need the network any more
network->destroy();
// Release host memory
for (auto& mem : weightMap)
{
free((void*)(mem.second.values));
}
return engine;
}
void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream)
{
string wts_path = "./resnet18.wts";
// 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, wts_path = wts_path);
assert(engine != nullptr);
// Serialize the engine
(*modelStream) = engine->serialize();
// Close everything down
engine->destroy();
builder->destroy();
config->destroy();
}
void doInference(IExecutionContext& context, float* input, float* output, int batchSize)
{
const ICudaEngine& engine = context.getEngine();
// Pointers to input and output device buffers to pass to engine.
// Engine requires exactly IEngine::getNbBindings() number of buffers.
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()
const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
// Create GPU buffers on device
CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float)));
CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));
// Create stream
cudaStream_t stream;
CHECK(cudaStreamCreate(&stream));
// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * 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 stream and buffers
cudaStreamDestroy(stream);
CHECK(cudaFree(buffers[inputIndex]));
CHECK(cudaFree(buffers[outputIndex]));
}
//加工图片变成拥有batch的输入， tensorrt输入需要的格式，为一个维度
void ProcessImage(cv::Mat image, float input_data[]) {
//只处理一张图片,总之结果为一维[batch*3*INPUT_W*INPUT_H]
//以下代码为投机取巧了
cv::resize(image, image, cv::Size(INPUT_W, INPUT_H), 0, 0, cv::INTER_LINEAR);
std::vector<cv::Mat> InputImage;
InputImage.push_back(image);
int ImgCount = InputImage.size();
//float input_data[BatchSize * 3 * INPUT_H * INPUT_W];
for (int b = 0; b < ImgCount; b++) {
cv::Mat img = InputImage.at(b);
int w = img.cols;
int h = img.rows;
int i = 0;
for (int row = 0; row < h; ++row) {
uchar* uc_pixel = img.data + row * img.step;
for (int col = 0; col < INPUT_W; ++col) {
input_data[b * 3 * INPUT_H * INPUT_W + i] = (float)uc_pixel[2] / 255.0;
input_data[b * 3 * INPUT_H * INPUT_W + i + INPUT_H * INPUT_W] = (float)uc_pixel[1] / 255.0;
input_data[b * 3 * INPUT_H * INPUT_W + i + 2 * INPUT_H * INPUT_W] = (float)uc_pixel[0] / 255.0;
uc_pixel += 3;
++i;
}
}
}
}
int get_trtengine() {
IHostMemory* modelStream{ nullptr };
APIToModel(1, &modelStream);
assert(modelStream != nullptr);
std::ofstream p("./resnet18.engine", std::ios::binary);
if (!p)
{
std::cerr << "could not open plan output file" << std::endl;
return -1;
}
p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
modelStream->destroy();
return 0;
}
int infer() {
//加载engine引擎
char* trtModelStream{ nullptr };
size_t size{ 0 };
std::ifstream file("./resnet18.engine", std::ios::binary);
if (file.good()) {
file.seekg(0, file.end);
size = file.tellg();
file.seekg(0, file.beg);
trtModelStream = new char[size];
assert(trtModelStream);
file.read(trtModelStream, size);
file.close();
}
//反序列为engine，创建context
IRuntime* runtime = createInferRuntime(gLogger);
assert(runtime != nullptr);
ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size, nullptr);
assert(engine != nullptr);
IExecutionContext* context = engine->createExecutionContext();
assert(context != nullptr);
delete[] trtModelStream;
//*********************推理*********************//
// 循环推理
float time_read_img = 0.0;
float time_infer = 0.0;
static float prob[OUTPUT_SIZE];
for (int i = 0; i < 1000; i++) {
// 处理图片为固定输出
auto start = std::chrono::system_clock::now(); //时间函数
std::string path = "./1.jpg";
std::cout << "img_path=" << path << endl;
static float data[3 * INPUT_H * INPUT_W];
cv::Mat img = cv::imread(path);
ProcessImage(img, data);
auto end = std::chrono::system_clock::now();
time_read_img = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() + time_read_img;
//Run inference
start = std::chrono::system_clock::now(); //时间函数
doInference(*context, data, prob, 1);
end = std::chrono::system_clock::now();
time_infer = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() + time_infer;
std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
//输出后处理
//std::cout <<"prob="<<prob << std::endl;
float cls_float = prob[0];
int cls_id = 0;
for (int i = 0; i < OUTPUT_SIZE; i++) {
if (cls_float < prob[i]) {
cls_float = prob[i];
cls_id = i;
}
}
std::cout << "i=" << i << "\tcls_id=" << cls_id << "\t cls_float=" << cls_float << std::endl;
}
std::cout << "C++2engine" << "mean read img time =" << time_read_img / 1000 << "ms\t" << "mean infer img time =" << time_infer / 1000 << "ms" << std::endl;
// Destroy the engine
context->destroy();
engine->destroy();
runtime->destroy();
return 0;
}
int main(int argc, char** argv)
{
//string mode = argv[1];
string mode = "-d"; //适用windows编译，固定指定参数
//if (std::string(argv[1]) == "-s") {
if (mode == "-s") {
get_trtengine();
}
//else if (std::string(argv[1]) == "-d") {
else if (mode == "-d") {
infer();
}
else {
return -1;
}
return 0;
}基于onnx格式采用C++ API 转tensorrt部署
本代码基于onnx格式，使用visual studio编译器，实现resnet分类网络部署，代码如下：
#include "NvInfer.h"
#include "cuda_runtime_api.h"
//#include "logging.h"
#include <fstream>
#include <iostream>
#include <map>
#include <sstream>
#include <vector>
#include <chrono>
#include <cmath>
#include <cassert>
#include<opencv2/core/core.hpp>
#include<opencv2/highgui/highgui.hpp>
#include <opencv2/opencv.hpp>
using namespace std;
#define CHECK(status) \
do\
{\
auto ret = (status);\
if (ret != 0)\
{\
std::cerr << "Cuda failure: " << ret << std::endl;\
abort();\
}\
} while (0)
// stuff we know about the network and the input/output blobs
static const int INPUT_H = 224;
static const int INPUT_W = 224;
static const int OUTPUT_SIZE = 1000;
const char* INPUT_BLOB_NAME = "data";
const char* OUTPUT_BLOB_NAME = "prob";
using namespace nvinfer1;
//static Logger gLogger;
//构建Logger
class Logger : public ILogger
{
void log(Severity severity, const char* msg) noexcept override
{
// suppress info-level messages
if (severity <= Severity::kWARNING)
std::cout << msg << std::endl;
}
} gLogger;
// Load weights from files shared with TensorRT samples.
// TensorRT weight files have a simple space delimited format:
// [type] [size] <data x size in hex>
std::map<std::string, Weights> loadWeights(const std::string file)
{
std::cout << "Loading weights: " << file << std::endl;
std::map<std::string, Weights> weightMap;
// Open weights file
std::ifstream input(file);
assert(input.is_open() && "Unable to load weight file.");
// Read number of weight blobs
int32_t count;
input >> count;
assert(count > 0 && "Invalid weight map file.");
while (count--)
{
Weights wt{ DataType::kFLOAT, nullptr, 0 };
uint32_t size;
// Read name and type of blob
std::string name;
input >> name >> std::dec >> size;
wt.type = DataType::kFLOAT;
// Load blob
uint32_t* val = reinterpret_cast<uint32_t*>(malloc(sizeof(val) * size));
for (uint32_t x = 0, y = size; x < y; ++x)
{
input >> std::hex >> val[x];
}
wt.values = val;
wt.count = size;
weightMap[name] = wt;
}
return weightMap;
}
IScaleLayer* addBatchNorm2d(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, float eps) {
float* gamma = (float*)weightMap[lname + ".weight"].values;
float* beta = (float*)weightMap[lname + ".bias"].values;
float* mean = (float*)weightMap[lname + ".running_mean"].values;
float* var = (float*)weightMap[lname + ".running_var"].values;
int len = weightMap[lname + ".running_var"].count;
std::cout << "len " << len << std::endl;
float* scval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
scval[i] = gamma[i] / sqrt(var[i] + eps);
}
Weights scale{ DataType::kFLOAT, scval, len };
float* shval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps);
}
Weights shift{ DataType::kFLOAT, shval, len };
float* pval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
for (int i = 0; i < len; i++) {
pval[i] = 1.0;
}
Weights power{ DataType::kFLOAT, pval, len };
weightMap[lname + ".scale"] = scale;
weightMap[lname + ".shift"] = shift;
weightMap[lname + ".power"] = power;
IScaleLayer* scale_1 = network->addScale(input, ScaleMode::kCHANNEL, shift, scale, power);
assert(scale_1);
return scale_1;
}
IActivationLayer* basicBlock(INetworkDefinition* network, std::map<std::string, Weights>& weightMap, ITensor& input, int inch, int outch, int stride, std::string lname) {
Weights emptywts{ DataType::kFLOAT, nullptr, 0 };
IConvolutionLayer* conv1 = network->addConvolutionNd(input, outch, DimsHW{ 3, 3 }, weightMap[lname + "conv1.weight"], emptywts);
assert(conv1);
conv1->setStrideNd(DimsHW{ stride, stride });
conv1->setPaddingNd(DimsHW{ 1, 1 });
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + "bn1", 1e-5);
IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
assert(relu1);
IConvolutionLayer* conv2 = network->addConvolutionNd(*relu1->getOutput(0), outch, DimsHW{ 3, 3 }, weightMap[lname + "conv2.weight"], emptywts);
assert(conv2);
conv2->setPaddingNd(DimsHW{ 1, 1 });
IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + "bn2", 1e-5);
IElementWiseLayer* ew1;
if (inch != outch) {
IConvolutionLayer* conv3 = network->addConvolutionNd(input, outch, DimsHW{ 1, 1 }, weightMap[lname + "downsample.0.weight"], emptywts);
assert(conv3);
conv3->setStrideNd(DimsHW{ stride, stride });
IScaleLayer* bn3 = addBatchNorm2d(network, weightMap, *conv3->getOutput(0), lname + "downsample.1", 1e-5);
ew1 = network->addElementWise(*bn3->getOutput(0), *bn2->getOutput(0), ElementWiseOperation::kSUM);
}
else {
ew1 = network->addElementWise(input, *bn2->getOutput(0), ElementWiseOperation::kSUM);
}
IActivationLayer* relu2 = network->addActivation(*ew1->getOutput(0), ActivationType::kRELU);
assert(relu2);
return relu2;
}
// Creat the engine using only the API and not any parser.
ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, string wts_path = "../resnet18.wts")
{
INetworkDefinition* network = builder->createNetworkV2(0U);
// Create input tensor of shape { 3, INPUT_H, INPUT_W } with name INPUT_BLOB_NAME
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });
assert(data);
std::map<std::string, Weights> weightMap = loadWeights(wts_path);
Weights emptywts{ DataType::kFLOAT, nullptr, 0 };
IConvolutionLayer* conv1 = network->addConvolutionNd(*data, 64, DimsHW{ 7, 7 }, weightMap["conv1.weight"], emptywts);
assert(conv1);
conv1->setStrideNd(DimsHW{ 2, 2 });
conv1->setPaddingNd(DimsHW{ 3, 3 });
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), "bn1", 1e-5);
IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
assert(relu1);
IPoolingLayer* pool1 = network->addPoolingNd(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{ 3, 3 });
assert(pool1);
pool1->setStrideNd(DimsHW{ 2, 2 });
pool1->setPaddingNd(DimsHW{ 1, 1 });
IActivationLayer* relu2 = basicBlock(network, weightMap, *pool1->getOutput(0), 64, 64, 1, "layer1.0.");
IActivationLayer* relu3 = basicBlock(network, weightMap, *relu2->getOutput(0), 64, 64, 1, "layer1.1.");
IActivationLayer* relu4 = basicBlock(network, weightMap, *relu3->getOutput(0), 64, 128, 2, "layer2.0.");
IActivationLayer* relu5 = basicBlock(network, weightMap, *relu4->getOutput(0), 128, 128, 1, "layer2.1.");
IActivationLayer* relu6 = basicBlock(network, weightMap, *relu5->getOutput(0), 128, 256, 2, "layer3.0.");
IActivationLayer* relu7 = basicBlock(network, weightMap, *relu6->getOutput(0), 256, 256, 1, "layer3.1.");
IActivationLayer* relu8 = basicBlock(network, weightMap, *relu7->getOutput(0), 256, 512, 2, "layer4.0.");
IActivationLayer* relu9 = basicBlock(network, weightMap, *relu8->getOutput(0), 512, 512, 1, "layer4.1.");
IPoolingLayer* pool2 = network->addPoolingNd(*relu9->getOutput(0), PoolingType::kAVERAGE, DimsHW{ 7, 7 });
assert(pool2);
pool2->setStrideNd(DimsHW{ 1, 1 });
IFullyConnectedLayer* fc1 = network->addFullyConnected(*pool2->getOutput(0), 1000, weightMap["fc.weight"], weightMap["fc.bias"]);
assert(fc1);
fc1->getOutput(0)->setName(OUTPUT_BLOB_NAME);
std::cout << "set name out" << std::endl;
network->markOutput(*fc1->getOutput(0));
// Build engine
builder->setMaxBatchSize(maxBatchSize);
config->setMaxWorkspaceSize(1 << 20);
//config->setFlag(nvinfer1::BuilderFlag::kFP16);
ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
std::cout << "build out" << std::endl;
// Don't need the network any more
network->destroy();
// Release host memory
for (auto& mem : weightMap)
{
free((void*)(mem.second.values));
}
return engine;
}
void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream)
{
string wts_path = "./resnet18.wts";
// 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, wts_path = wts_path);
assert(engine != nullptr);
// Serialize the engine
(*modelStream) = engine->serialize();
// Close everything down
engine->destroy();
builder->destroy();
config->destroy();
}
void doInference(IExecutionContext& context, float* input, float* output, int batchSize)
{
const ICudaEngine& engine = context.getEngine();
// Pointers to input and output device buffers to pass to engine.
// Engine requires exactly IEngine::getNbBindings() number of buffers.
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()
const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
// Create GPU buffers on device
CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float)));
CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));
// Create stream
cudaStream_t stream;
CHECK(cudaStreamCreate(&stream));
// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * 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 stream and buffers
cudaStreamDestroy(stream);
CHECK(cudaFree(buffers[inputIndex]));
CHECK(cudaFree(buffers[outputIndex]));
}
//加工图片变成拥有batch的输入， tensorrt输入需要的格式，为一个维度
void ProcessImage(cv::Mat image, float input_data[]) {
//只处理一张图片,总之结果为一维[batch*3*INPUT_W*INPUT_H]
//以下代码为投机取巧了
cv::resize(image, image, cv::Size(INPUT_W, INPUT_H), 0, 0, cv::INTER_LINEAR);
std::vector<cv::Mat> InputImage;
InputImage.push_back(image);
int ImgCount = InputImage.size();
//float input_data[BatchSize * 3 * INPUT_H * INPUT_W];
for (int b = 0; b < ImgCount; b++) {
cv::Mat img = InputImage.at(b);
int w = img.cols;
int h = img.rows;
int i = 0;
for (int row = 0; row < h; ++row) {
uchar* uc_pixel = img.data + row * img.step;
for (int col = 0; col < INPUT_W; ++col) {
input_data[b * 3 * INPUT_H * INPUT_W + i] = (float)uc_pixel[2] / 255.0;
input_data[b * 3 * INPUT_H * INPUT_W + i + INPUT_H * INPUT_W] = (float)uc_pixel[1] / 255.0;
input_data[b * 3 * INPUT_H * INPUT_W + i + 2 * INPUT_H * INPUT_W] = (float)uc_pixel[0] / 255.0;
uc_pixel += 3;
++i;
}
}
}
}
int get_trtengine() {
IHostMemory* modelStream{ nullptr };
APIToModel(1, &modelStream);
assert(modelStream != nullptr);
std::ofstream p("./resnet18.engine", std::ios::binary);
if (!p)
{
std::cerr << "could not open plan output file" << std::endl;
return -1;
}
p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
modelStream->destroy();
return 0;
}
int infer() {
//加载engine引擎
char* trtModelStream{ nullptr };
size_t size{ 0 };
std::ifstream file("./resnet18.engine", std::ios::binary);
if (file.good()) {
file.seekg(0, file.end);
size = file.tellg();
file.seekg(0, file.beg);
trtModelStream = new char[size];
assert(trtModelStream);
file.read(trtModelStream, size);
file.close();
}
//反序列为engine，创建context
IRuntime* runtime = createInferRuntime(gLogger);
assert(runtime != nullptr);
ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size, nullptr);
assert(engine != nullptr);
IExecutionContext* context = engine->createExecutionContext();
assert(context != nullptr);
delete[] trtModelStream;
//*********************推理*********************//
// 循环推理
float time_read_img = 0.0;
float time_infer = 0.0;
static float prob[OUTPUT_SIZE];
for (int i = 0; i < 1000; i++) {
// 处理图片为固定输出
auto start = std::chrono::system_clock::now(); //时间函数
std::string path = "./1.jpg";
std::cout << "img_path=" << path << endl;
static float data[3 * INPUT_H * INPUT_W];
cv::Mat img = cv::imread(path);
ProcessImage(img, data);
auto end = std::chrono::system_clock::now();
time_read_img = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() + time_read_img;
//Run inference
start = std::chrono::system_clock::now(); //时间函数
doInference(*context, data, prob, 1);
end = std::chrono::system_clock::now();
time_infer = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() + time_infer;
std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
//输出后处理
//std::cout <<"prob="<<prob << std::endl;
float cls_float = prob[0];
int cls_id = 0;
for (int i = 0; i < OUTPUT_SIZE; i++) {
if (cls_float < prob[i]) {
cls_float = prob[i];
cls_id = i;
}
}
std::cout << "i=" << i << "\tcls_id=" << cls_id << "\t cls_float=" << cls_float << std::endl;
}
std::cout << "C++2engine" << "mean read img time =" << time_read_img / 1000 << "ms\t" << "mean infer img time =" << time_infer / 1000 << "ms" << std::endl;
// Destroy the engine
context->destroy();
engine->destroy();
runtime->destroy();
return 0;
}
int main(int argc, char** argv)
{
//string mode = argv[1];
string mode = "-d"; //适用windows编译，固定指定参数
//if (std::string(argv[1]) == "-s") {
if (mode == "-s") {
get_trtengine();
}
//else if (std::string(argv[1]) == "-d") {
else if (mode == "-d") {
infer();
}
else {
return -1;
}
return 0;
}onnx-simpiler简化onnx文件
使用onnx-simpiler 进行优化onnx，但已是最简化，但若能简化，猜想预测会更快一些。
onnxsim ./resnet18.onnx ./resnet18.onnx
部署测试展示
总之测试2张图基本在一个大类中，应该没啥错误。
windows使用visual studio测试结果：
linux服务器测试结果：
三、性能测试实验
性能测试结果(测试平台：windows10 cuda11.4 tensorrt8.4 RTX 2060)：
性能测试结果(测试平台：Linux ubuntu18.4 cuda11.3 tensorrt8.2 RTX 2060)(添加：20220914)：
注：检测1000张的平均时间
说明 window10与ubuntu是2个独立设备(电脑)，读图主要是CPU处理代码，后期可改成CUDA处理提速。
四、py转engine被C调用验证
使用python将onnx转为engine引擎，使用C++调用验证
py转engine代码
python代码将其转为engine库，注：使用同样的tensorrt版本
我将这部分代码丢失，读者可参考网络方法，进行转换。
推理部署代码(C++)
将转换的engine文件通过tensorrt部署推理，代码如下:
#include "NvInfer.h"
#include "cuda_runtime_api.h"
#include <fstream>
#include <iostream>
#include <map>
#include <sstream>
#include <vector>
#include <chrono>
#include <cmath>
#include <cassert>
#include<opencv2/core/core.hpp>
#include<opencv2/highgui/highgui.hpp>
#include <opencv2/opencv.hpp>
using namespace std;
#define CHECK(status) \
do\
{\
auto ret = (status);\
if (ret != 0)\
{\
std::cerr << "Cuda failure: " << ret << std::endl;\
abort();\
}\
} while (0)
// stuff we know about the network and the input/output blobs
static const int INPUT_H = 224;
static const int INPUT_W = 224;
static const int OUTPUT_SIZE = 1000;
const char* INPUT_BLOB_NAME = "data";
const char* OUTPUT_BLOB_NAME = "prob";
using namespace nvinfer1;
//static Logger gLogger;
//构建Logger
class Logger : public ILogger
{
void log(Severity severity, const char* msg) noexcept override
{
// suppress info-level messages
if (severity <= Severity::kWARNING)
std::cout << msg << std::endl;
}
} gLogger;
void doInference(IExecutionContext& context, float* input, float* output, int batchSize)
{
const ICudaEngine& engine = context.getEngine();
// Pointers to input and output device buffers to pass to engine.
// Engine requires exactly IEngine::getNbBindings() number of buffers.
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()
const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
// Create GPU buffers on device
CHECK(cudaMalloc(&buffers[inputIndex], batchSize * 3 * INPUT_H * INPUT_W * sizeof(float)));
CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));
// Create stream
cudaStream_t stream;
CHECK(cudaStreamCreate(&stream));
// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * 3 * 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 stream and buffers
cudaStreamDestroy(stream);
CHECK(cudaFree(buffers[inputIndex]));
CHECK(cudaFree(buffers[outputIndex]));
}
//加工图片变成拥有batch的输入， tensorrt输入需要的格式，为一个维度
void ProcessImage(cv::Mat image, float input_data[]) {
//只处理一张图片,总之结果为一维[batch*3*INPUT_W*INPUT_H]
//以下代码为投机取巧了
cv::resize(image, image, cv::Size(INPUT_W, INPUT_H), 0, 0, cv::INTER_LINEAR);
std::vector<cv::Mat> InputImage;
InputImage.push_back(image);
int ImgCount = InputImage.size();
//float input_data[BatchSize * 3 * INPUT_H * INPUT_W];
for (int b = 0; b < ImgCount; b++) {
cv::Mat img = InputImage.at(b);
int w = img.cols;
int h = img.rows;
int i = 0;
for (int row = 0; row < h; ++row) {
uchar* uc_pixel = img.data + row * img.step;
for (int col = 0; col < INPUT_W; ++col) {
input_data[b * 3 * INPUT_H * INPUT_W + i] = (float)uc_pixel[2] / 255.0;
input_data[b * 3 * INPUT_H * INPUT_W + i + INPUT_H * INPUT_W] = (float)uc_pixel[1] / 255.0;
input_data[b * 3 * INPUT_H * INPUT_W + i + 2 * INPUT_H * INPUT_W] = (float)uc_pixel[0] / 255.0;
uc_pixel += 3;
++i;
}
}
}
}
int infer() {
//加载engine引擎
char* trtModelStream{ nullptr };
size_t size{ 0 };
std::ifstream file("./resnet18.engine", std::ios::binary);
if (file.good()) {
file.seekg(0, file.end);
size = file.tellg();
file.seekg(0, file.beg);
trtModelStream = new char[size];
assert(trtModelStream);
file.read(trtModelStream, size);
file.close();
}
//反序列为engine，创建context
IRuntime* runtime = createInferRuntime(gLogger);
assert(runtime != nullptr);
ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size, nullptr);
assert(engine != nullptr);
IExecutionContext* context = engine->createExecutionContext();
assert(context != nullptr);
delete[] trtModelStream;
//*********************推理*********************//
// 循环推理
float time_read_img = 0.0;
float time_infer = 0.0;
static float prob[OUTPUT_SIZE];
for (int i = 0; i < 1000; i++) {
// 处理图片为固定输出
auto start = std::chrono::system_clock::now(); //时间函数
std::string path = "./1.jpg";
std::cout << "img_path=" << path << endl;
static float data[3 * INPUT_H * INPUT_W];
cv::Mat img = cv::imread(path);
ProcessImage(img, data);
auto end = std::chrono::system_clock::now();
time_read_img = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() + time_read_img;
//Run inference
start = std::chrono::system_clock::now(); //时间函数
doInference(*context, data, prob, 1);
end = std::chrono::system_clock::now();
time_infer = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() + time_infer;
std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
//输出后处理
//std::cout <<"prob="<<prob << std::endl;
float cls_float = prob[0];
int cls_id = 0;
for (int i = 0; i < OUTPUT_SIZE; i++) {
if (cls_float < prob[i]) {
cls_float = prob[i];
cls_id = i;
}
}
std::cout << "i=" << i << "\tcls_id=" << cls_id << "\t cls_float=" << cls_float << std::endl;
}
std::cout << "C++2engine" << "mean read img time =" << time_read_img / 1000 << "ms\t" << "mean infer img time =" << time_infer / 1000 << "ms" << std::endl;
// Destroy the engine
context->destroy();
engine->destroy();
runtime->destroy();
return 0;
}
int main(int argc, char** argv)
{
infer();
return 0;
}infer显示
以下为python转engine后，通过C++直接使用转换的engine推理效果如下：
实验结果
windows系统 可行！ 很令人兴奋，意味着使用python转换为engine，将可以使用C++调用，无需再使用C++创建engine。
注:推理时间变长了快2倍。
五、Linux环境下构建CMakeList文件
本节介绍如何使用编译命令在ubuntu(linux)环境中运行，本节将介绍主要介绍CMakeLists.txt文件的构建：
基于wts格式构建编译文件
CMakeList.txt文件：
cmake_minimum_required(VERSION 2.6)
project(resnet)
add_definitions(-std=c++11)
option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_BUILD_TYPE Debug)
include_directories(${PROJECT_SOURCE_DIR}/include)
# include and link dirs of cuda and tensorrt, you need adapt them if yours are different
# cuda
include_directories(/usr/local/cuda/include)
link_directories(/usr/local/cuda/lib64)
# tensorrt
include_directories(/home/ubuntu/soft/TensorRT-8.2.5.1/include/)
link_directories(/home/ubuntu/soft/TensorRT-8.2.5.1/lib/)
#include_directories(/usr/include/x86_64-linux-gnu/)
#link_directories(/usr/lib/x86_64-linux-gnu/)
# opencv
find_package(OpenCV REQUIRED)
include_directories(${OpenCV_INCLUDE_DIRS})
add_executable(resnet18 ${PROJECT_SOURCE_DIR}/main.cpp)
target_link_libraries(resnet18 nvinfer)
target_link_libraries(resnet18 cudart)
target_link_libraries(resnet18 ${OpenCV_LIBS})
add_definitions(-O2 -pthread)基于onnx格式构建编译文件
CMakeList.txt文件：
cmake_minimum_required(VERSION 2.6)
project(resnet)
add_definitions(-std=c++11)
option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_BUILD_TYPE Debug)
include_directories(${PROJECT_SOURCE_DIR}/include)
# include and link dirs of cuda and tensorrt, you need adapt them if yours are different
# cuda
include_directories(/usr/local/cuda/include)
link_directories(/usr/local/cuda/lib64)
# tensorrt
include_directories(/home/ubuntu/soft/TensorRT-8.2.5.1/include/)
link_directories(/home/ubuntu/soft/TensorRT-8.2.5.1/lib/)
include_directories(/home/ubuntu/soft/TensorRT-8.2.5.1/samples/common/)
#link_directories(/home/ubuntu/soft/TensorRT-8.2.5.1/lib/stubs/)
# opencv
find_package(OpenCV REQUIRED)
include_directories(${OpenCV_INCLUDE_DIRS})
add_executable(resnet18 ${PROJECT_SOURCE_DIR}/main.cpp)
target_link_libraries(resnet18 nvinfer)
target_link_libraries(resnet18 cudart)
target_link_libraries(resnet18 ${OpenCV_LIBS})
target_link_libraries(resnet18 /home/ubuntu/soft/TensorRT-8.2.5.1/lib/stubs/libnvonnxparser.so
)
add_definitions(-O2 -pthread)以上为ONNX及C++构建engine的cmakelists的语句，主要在于库的链接或头文件之类，相关可看其它博客或网上资料。
附带说明:以上Onnx的CmakeLists.txt语句已经在yolov5、yolov7中验证，可以编译运行。
ResNet代码在上面已有说明，我将不放在本博客中，其中细节代码在我发布的链接中可下载使用。
六、测试结果
本节展示linux服务器上，分别基于wts与onnx方法构建分类网络resnet测试结果比较。
从以下图中，可知onnx转engine速度更快，但我个人觉得可能因为网络不够复杂等，导致与预期不一致现象。为此，此结论仅作为分类网络resnet测试参考。
基于wts测试结果
基于onnx测试结果
总结
本文实现基于wts与onnx部署方法与性能测试。
本文章为转载内容，我们尊重原作者对文章享有的著作权。如有内容错误或侵权问题，欢迎原作者联系我们进行内容更正或删除文章。
