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机器视觉系列5:C++部署pytorch模型onnxruntime

系列文章目录

第一章:Visual Studio 2019 动态链接库DLL建立

第二章:VS动态链接库DLL调试

第三章:VS2019 OpenCV环境配置

第四章:C++部署pytorch模型Libtorch

第五章:C++部署pytorch模型onnxruntime


前言

环境:visual studio 2019;OpenCV4.5.5;pytorch1.8;onnxruntime1.8.1;

一、C++部署pytorch?

pytorch模型在C++部署,上一章是使用pytorch对应版本的Libtorch部署。其实转onnx部署可能更方便,之前语义分割精度相差太大是因为数据预处理的问题,一般图像在输入网络之前需要标准化和转RGB等相关操作。onnx部署的好处是微软中间格式,兼容各种平台,比较方便。如果部署平台有GPU可能用tensorRT会更好。

二、onnxruntime配置

注意事项:注意pt模型转onnx时的版本,onnxruntime版本对应

1.下载onnxruntime

官网:NuGet Gallery | Home

onnxruntime-win-x64-1.8.1链接如下:

https://objects.githubusercontent.com/github-production-release-asset-2e65be/156939672/7bc42680-deed-11eb-89d9-81963670045b?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220809%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220809T093837Z&X-Amz-Expires=300&X-Amz-Signature=4c9cc7ec4ba93d83ccb3307533b47e5c40303c43c003b2a791d4d321570dc5b8&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=156939672&response-content-disposition=attachment%3B%20filename%3Donnxruntime-win-x64-1.8.1.zip&response-content-type=application%2Foctet-streamhttps://objects.githubusercontent.com/github-production-release-asset-2e65be/156939672/7bc42680-deed-11eb-89d9-81963670045b?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220809%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220809T093837Z&X-Amz-Expires=300&X-Amz-Signature=4c9cc7ec4ba93d83ccb3307533b47e5c40303c43c003b2a791d4d321570dc5b8&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=156939672&response-content-disposition=attachment%3B%20filename%3Donnxruntime-win-x64-1.8.1.zip&response-content-type=application%2Foctet-stream

2.VS2019配置onnxruntime

2.1配置VC++目录

首先配置包含目录和库目录,对应opencv一样的方法。

2.2配置链接器

依赖项添加所有lib,cmd中进入lib目录,使用**dir /b *.lib>1.txt**命令可生成目录,复制使用。

2.3 onnxruntime环境变量配置

把所有DLL复制进Release或者Debug目录。

三、pytorch模型转换为onnx

注意事项:模型输入BCHW,推理模式model.eval(),输出版本opset_version=11

import torch

x = torch.randn(1, 3, 512, 512, device="cpu")
model = torch.load('best_model.pth', map_location=torch.device('cpu'))
model.eval()
input_names = ["input"]
output_names = ["output"]

torch.onnx.export(model, x, "GlandUnet.onnx", verbose=True, input_names=input_names, output_names=output_names, opset_version=11)

四、C++中onnxruntime的使用

注意事项:1,一定要注意图像输入模型之前的预处理,是否标准化,是否转RGB

              2,C++中onnxruntime推理输出为数组的首地址,可以用指针取出生成Mat
/****************************************
@brief     :  分割onnxruntime
@input       :  图像
@output    :  掩膜
*****************************************/
void SegmentAIONNX(Mat& imgSrc, int width, int height)
{
    模型信息/
    Ort::Env env(ORT_LOGGING_LEVEL_WARNING, "OnnxModel");
    Ort::SessionOptions session_options;
    session_options.SetIntraOpNumThreads(1);
#ifdef _WIN32
    const wchar_t* model_path = L"GlandUnet.onnx";
#else
    const char* model_path = "RedUnet.onnx";
#endif
    Ort::Session session(env, model_path, session_options);
    Ort::AllocatorWithDefaultOptions allocator;
    size_t num_input_nodes = session.GetInputCount();    //batchsize
    size_t num_output_nodes = session.GetOutputCount();
    const char* input_name = session.GetInputName(0, allocator);
    const char* output_name = session.GetOutputName(0, allocator);
    auto input_dims = session.GetInputTypeInfo(0).GetTensorTypeAndShapeInfo().GetShape();    //输入输出维度
    auto output_dims = session.GetOutputTypeInfo(0).GetTensorTypeAndShapeInfo().GetShape();
    std::vector<const char*> input_names{ input_name };
    std::vector<const char*> output_names = { output_name };
    输入处理//
    Mat imgBGR = imgSrc;    //输入图片预处理
    Mat imgBGRresize;
    resize(imgBGR, imgBGRresize, Size(input_dims[3], input_dims[2]), InterpolationFlags::INTER_CUBIC);
    Mat imgRGBresize = imgBGRresize;
    //cvtColor(imgBGRresize, imgRGBresize, COLOR_BGR2RGB);    //smp未转RGB
    Mat resize_img;
    imgRGBresize.convertTo(resize_img, CV_32F, 1.0 / 255);  //divided by 255转float
    cv::Mat channels[3]; //分离通道进行HWC->CHW
    cv::split(resize_img, channels);
    std::vector<float> inputTensorValues;
    float mean[] = { 0.485f, 0.456f, 0.406f };    //
    float std_val[] = { 0.229f, 0.224f, 0.225f };
    for (int i = 0; i < resize_img.channels(); i++)    //标准化ImageNet
    {
        channels[i] -= mean[i];  // mean均值
        channels[i] /= std_val[i];   // std方差
    }
    for (int i = 0; i < resize_img.channels(); i++)  //HWC->CHW
    {
        std::vector<float> data = std::vector<float>(channels[i].reshape(1, resize_img.cols * resize_img.rows));
        inputTensorValues.insert(inputTensorValues.end(), data.begin(), data.end());
    }
    Ort::MemoryInfo memoryInfo = Ort::MemoryInfo::CreateCpu(OrtAllocatorType::OrtArenaAllocator, OrtMemType::OrtMemTypeDefault);
    vector<Ort::Value> inputTensors;
    inputTensors.push_back(Ort::Value::CreateTensor<float>(memoryInfo, inputTensorValues.data(), inputTensorValues.size(), input_dims.data(), input_dims.size()));
    //clock_t startTime, endTime;    //计算推理时间
    //startTime = clock();
    auto outputTensor = session.Run(Ort::RunOptions{ nullptr }, input_names.data(), inputTensors.data(), 1, output_names.data(), 1);   // 开始推理
    //endTime = clock();
    打印模型信息/
    //printf("Using Onnxruntime C++ API\n");
    //printf("Number of inputs = %zu\n", num_input_nodes);
    //printf("Number of output = %zu\n", num_output_nodes);
    //std::cout << "input_name:" << input_name << std::endl;
    //std::cout << "output_name: " << output_name << std::endl;
    //std::cout << "input_dims:" << input_dims[0] << input_dims[1] << input_dims[2] << input_dims[3] << std::endl;
    //std::cout << "output_dims:" << output_dims[0] << output_dims[1] << output_dims[2] << output_dims[3] << std::endl;
    //std::cout << "The run time is:" << (double)(endTime - startTime) / CLOCKS_PER_SEC << "s" << std::endl;
    
    //输出处理//
    float* mask_ptr = outputTensor[0].GetTensorMutableData<float>(); //outtensor首地址
    vector< unsigned char >results(512 * 512);
    for (int i = 0; i < 512 * 512; i++)
    {
        if (mask_ptr[i] >= 0.5)
        {
            results[i] = 0;
        }
        else
        {
            results[i] = 255;
        }
    }
    unsigned char* ptr = &results[0];
    Mat mask = Mat(output_dims[2], output_dims[3], CV_8U, ptr);
    resize(mask, imgSrc, Size(imgBGR.cols, imgBGR.rows));
    //原图展示分割结果//
    //cvtColor(imgSrc, imgSrc, COLOR_GRAY2BGR);
    //Mat imgAdd;
    //addWeighted(imgBGR, 1, imgSrc, 0.3, 0, imgAdd);
}

五、python中onnxruntime的使用

# -*- coding:utf-8 -*-
import cv2
import numpy as np
import onnxruntime as ort
import imgviz
import time

class_names = ['_background_', 'conjunctiva_area']
### 定义一些数据前后处理的工具
def preprocess(input_data):
    # convert the input data into the float32 input
    img_data = input_data.astype('float32')
    # normalize
    mean_vec = np.array([0.485, 0.456, 0.406])
    stddev_vec = np.array([0.229, 0.224, 0.225])
    norm_img_data = np.zeros(img_data.shape).astype('float32')
    for i in range(img_data.shape[0]):
        norm_img_data[i, :, :] = (img_data[i, :, :] / 255 - mean_vec[i]) / stddev_vec[i]
    # add batch channel
    norm_img_data = norm_img_data.reshape(1, 3, 512, 512).astype('float32')
    return norm_img_data

def softmax(x):
    x = x.reshape(-1)
    e_x = np.exp(x - np.max(x))
    return e_x / e_x.sum(axis=0)

def postprocess(result):
    return softmax(np.array(result)).tolist()

session = ort.InferenceSession('GlandUnet.onnx')
img0 = cv2.imread('test.bmp')
h0, w0 = img0.shape[0:2]
img = cv2.cvtColor(img0, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, [512, 512])
image_data = np.array(img).transpose(2, 0, 1) # HWC->CHW
input_data = preprocess(image_data)
time_start = time.time()  # 记录开始时间
raw_result = session.run([], {'input': input_data})
time_end = time.time()  # 记录结束时间
time_sum = time_end - time_start  # 计算的时间差为程序的执行时间,单位为秒/s
print(time_sum)
# label_result = np.argmax(raw_result, dim=1)  # 缺argmax
# out = np.squeeze(raw_result)
# result_img = np.array(out, dtype=np.uint8)
# result_img = cv2.resize(result_img, (w0, h0))

out1 = raw_result[0][0]

cv2.imshow('2', out1[1])
cv2.waitKey(0)

参考文献

https://onnxruntime.ai/

https://github.com/leimao/ONNX-Runtime-Inference/blob/main/src/inference.cpp

神经网络语义分割模型C++部署(VS2019+ONNXRuntime+OpenCV)_Shijunfeng00的博客-CSDN博客

opencv 图片HWC格式转CHW格式_wuqingshan2010的博客-CSDN博客


本文转载自: https://blog.csdn.net/weixin_42748604/article/details/127078298
版权归原作者 Arvin L 所有, 如有侵权,请联系我们删除。

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