深度学习模型部署(三)Onnxruntime部署yolov5实战
yolov5
yolov5 - Ultralytics YOLOv8的前身，是一个用于目标检测、图像分割和图像分类任务的先进模型。
项目地址：https://gitcode.com/gh_mirrors/yo/yolov5
免费下载资源
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模型分析
先使用yolov5下的yolo.py输出查看一下yolov5s的结构
python ./yolov5/models/yolo.py
YOLOv5 🚀 v7.0-287-g574331f9 Python-3.8.18 torch-2.2.1+cu118 CUDA:0 (NVIDIA GeForce RTX 3060 Laptop GPU, 6144MiB)
## 这里面的from是指输入来自哪一层，-1表示来自上一层，6表示来自第6层
from n params module arguments
0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 18816 models.common.C3 [64, 64, 1]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 2 115712 models.common.C3 [128, 128, 2]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 3 625152 models.common.C3 [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 1182720 models.common.C3 [512, 512, 1]
9 -1 1 656896 models.common.SPPF [512, 512, 5]
10 -1 1 131584 models.common.Conv [512, 256, 1, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 models.common.Concat [1]
13 -1 1 361984 models.common.C3 [512, 256, 1, False]
14 -1 1 33024 models.common.Conv [256, 128, 1, 1]
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
16 [-1, 4] 1 0 models.common.Concat [1]
17 -1 1 90880 models.common.C3 [256, 128, 1, False]
18 -1 1 147712 models.common.Conv [128, 128, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 1 296448 models.common.C3 [256, 256, 1, False]
21 -1 1 590336 models.common.Conv [256, 256, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 1 1182720 models.common.C3 [512, 512, 1, False]
24 [17, 20, 23] 1 229245 Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
YOLOv5s summary: 214 layers, 7235389 parameters, 7235389 gradients, 16.6 GFLOPs
Fusing layers...
YOLOv5s summary: 157 layers, 7225885 parameters, 7225885 gradients, 16.4 GFLOPs
可以看到yolov5s一共214层，层融合后还有157层
其中的C3层的结构是：
这里面的ConvBNSiLU就是指Conv+BN+SiLU，SiLU是ReLU的改进版激活函数，可以简单理解为y=x*sigmoid(x)。
SPPF的结构如下，SPPF的作用是图像金字塔池化，进行多尺度特征融合：
目标检测的三件套：Backbone，Neck，Head，
yolov5的Backbone就是CSP-DarkNet53
Neck是PANet
head比较简单，就是三个尺度各一个Conv卷积层
(这是6.0版本的图，我们用的是7.0版本的模型，将就着看，反正大致结构差不多)
不过这些对于我们部署来说不重要，我们只需要看数据流以及模型的计算图就行，至于哪一部分叫什么名字无所谓，不care。
另外还可以看一下fusing layers到底fuse了哪些层
def fuse(self):
"""Fuses Conv2d() and BatchNorm2d() layers in the model to improve inference speed."""
LOGGER.info("Fusing layers... ")
for m in self.model.modules():
if isinstance(m, (Conv, DWConv)) and hasattr(m, "bn"):
m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
delattr(m, "bn") # remove batchnorm
m.forward = m.forward_fuse # update forward
self.info()
return self
我们可以看到，yolov5是将卷积和BN层融合到了一起
DWConv是指深度卷积depth-wise conv，相较于传统卷积的区别是一个输入channel卷积后对应一个输出channel，而不是多个输入channel卷积加和到一起对应一个输出channel，减少了计算量和参数量
具体fuse的代码如下,跟我们前面常见算子融合blog中讲的原理一模一样，不过并没有用torch自带的方法，而是自己实现的方法：
def fuse_conv_and_bn(conv, bn):
"""
Fuses Conv2d and BatchNorm2d layers into a single Conv2d layer.
See https://tehnokv.com/posts/fusing-batchnorm-and-conv/.
"""
fusedconv = (
nn.Conv2d(
conv.in_channels,
conv.out_channels,
kernel_size=conv.kernel_size,
stride=conv.stride,
padding=conv.padding,
dilation=conv.dilation,
groups=conv.groups,
bias=True,
)
.requires_grad_(False)
.to(conv.weight.device)
)
# Prepare filters
w_conv = conv.weight.clone().view(conv.out_channels, -1)
# 提取卷积的参数
w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
# 这里就是计算γ除以根号σ方，再加一个小常数防止分母为0
fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))
# 卷积的参数乘上算出来的w_bn，然后再reshape一下
# Prepare spatial bias
b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
# 计算偏差，跟上面差不多，具体原理可以见blog
return fusedconv
可以看一下pt文件中的结构：
可以看出在pt中是25层，将C3这种视为一层来看。
再看看导出的onnx文件中的模型结构：
这图简直没法看，这是因为导出为onnx，它可不认你那套C3了，BottleNeck了的，这里也体现了一个我们之前谈到的问题：模型有多少种格式？他们直接的算子是不互通的，如何让一个框架训练出来的模型能为另一个框架所用？
yolov5导出onnx文件也非常简单，在export文件中有详细的用法简介，可以自行阅读。
模型部署
模型部署分为三部分：预处理，推理，后处理
先定义好Yolo模型类：
#include <fstream>
#include <sstream>
#include <iostream>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
//#include <cuda_provider_factory.h>
#include <onnxruntime/onnxruntime_cxx_api.h>
#include<iomanip>
using namespace std;
using namespace cv;
using namespace Ort;
struct Net_config
{
float confThreshold; // 置信度阈值，小于阈值认为该框中物体不是这个class
float nmsThreshold; // NMS非极大值抑制阈值
float objThreshold; // 物体检测阈值，小于该阈值认为框中没有物体
string modelpath; //模型文件地址
};
typedef struct BoxInfo
{
float x1;
float y1;
float x2;
float y2;
float score;
int label;
} BoxInfo;
int endsWith(string s, string sub) {
return s.rfind(sub) == (s.length() - sub.length()) ? 1 : 0;
}
const float anchors_640[3][6] = { {10.0, 13.0, 16.0, 30.0, 33.0, 23.0},
{30.0, 61.0, 62.0, 45.0, 59.0, 119.0},
{116.0, 90.0, 156.0, 198.0, 373.0, 326.0} };
class YOLO
{
public:
YOLO(Net_config config);
Mat detect(Mat& frame);
private:
float* anchors; //anchor框,yolo中预置了640分辨率的anchor尺寸，每两个数表示一个anchor的size，例如(10,13)，yolo中有三个尺度的输出，每个尺度的anchor数为3
int num_stride; // stride的数量，yolo中有三个尺度的输出，每个尺度的stride为8,16,32
int inpWidth; //输入宽度
int inpHeight; //输入高度
int nout; //输出通道数
int num_proposal; //输出的每个proposal的数据数，为85
vector<string> class_names; //类别名称
int num_class; //类别数量
int seg_num_class; //分割类别数量，用不到
float confThreshold; // 置信度阈值，小于阈值认为该框中物体不是这个class
float nmsThreshold; // NMS非极大值抑制阈值
float objThreshold; // 物体检测阈值，小于该阈值认为框中没有物体
const bool keep_ratio = true; //是否保持原图比例
vector<float> input_image_; //输入图像
void normalize_(Mat img); //归一化
void nms(vector<BoxInfo>& input_boxes); //非极大值抑制
Mat resize_image(Mat srcimg, int *newh, int *neww, int *top, int *left); //图像缩放到固定输入尺寸
Env env = Env(ORT_LOGGING_LEVEL_ERROR, "yolov5-7"); //初始化环境
Ort::Session *ort_session = nullptr; //模型session
SessionOptions sessionOptions = SessionOptions(); //模型session配置
vector<string> input_names; //输入节点名称
vector<string> output_names; //输出节点名称
vector<vector<int64_t>> input_node_dims; // 输入节点维度
vector<vector<int64_t>> output_node_dims; // 输出节点维度
};
YOLO::YOLO(Net_config config)
{
this->confThreshold = config.confThreshold;
this->nmsThreshold = config.nmsThreshold;
this->objThreshold = config.objThreshold;
string classesFile = "/home/wyq/hobby/model_deploy/onnx/onnxruntime/YoloV5/class.names"; //类别名称文件
string model_path = config.modelpath;
sessionOptions.SetGraphOptimizationLevel(ORT_ENABLE_BASIC);
std::vector<std::string> avaliable_providers = GetAvailableProviders();
auto cuda_provider = std::find(avaliable_providers.begin(), avaliable_providers.end(), "CUDA");
if(cuda_provider != avaliable_providers.end())
{
cout<<"cuda provider is available"<<endl;
OrtCUDAProviderOptions cuda_options = OrtCUDAProviderOptions{}; //使用cuda推理
sessionOptions.AppendExecutionProvider_CUDA(cuda_options);
}
else
{
cout<<"cuda provider is not available"<<endl;
}
ort_session = new Session(env, model_path.c_str(), sessionOptions);
size_t numInputNodes = ort_session->GetInputCount();
size_t numOutputNodes = ort_session->GetOutputCount();
AllocatorWithDefaultOptions allocator;
cout<<numInputNodes<<endl;
cout<<numOutputNodes<<endl;
for (int i = 0; i < numInputNodes; i++) //获取输入节点信息
{
auto name = ort_session->GetInputNameAllocated(i, allocator);
input_names.push_back(string(name.get()));
cout<<input_names[i]<<endl;
Ort::TypeInfo input_type_info = ort_session->GetInputTypeInfo(i);
auto input_tensor_info = input_type_info.GetTensorTypeAndShapeInfo();
auto input_dims = input_tensor_info.GetShape();
input_node_dims.push_back(input_dims);
}
for (int i = 0; i < numOutputNodes; i++) //获取输出节点信息
{
auto name = ort_session->GetOutputNameAllocated(i, allocator);
output_names.push_back(string(name.get()));
cout<<output_names[i]<<endl;
Ort::TypeInfo output_type_info = ort_session->GetOutputTypeInfo(i);
auto output_tensor_info = output_type_info.GetTensorTypeAndShapeInfo();
auto output_dims = output_tensor_info.GetShape();
output_node_dims.push_back(output_dims);
}
this->inpHeight = input_node_dims[0][2];
this->inpWidth = input_node_dims[0][3];
this->nout = output_node_dims[0][2];
this->num_proposal = output_node_dims[0][1];
ifstream ifs(classesFile.c_str());
string line;
while (getline(ifs, line)) this->class_names.push_back(line);
this->num_class = class_names.size();
this->anchors = (float*)anchors_640;
this->num_stride = 3; //设置stride数量
}
预处理部分即：将输入resize到固定尺寸，并进行归一化。其中归一化部分可以用cuda来实现，速度会快很多，这个后续再讲，现在我们的目的是先run起来
Mat YOLO::resize_image(Mat srcimg, int *newh, int *neww, int *top, int *left)
{
int srch = srcimg.rows, srcw = srcimg.cols;
*newh = this->inpHeight;
*neww = this->inpWidth;
Mat dstimg;
if (this->keep_ratio && srch != srcw) {
float hw_scale = (float)srch / srcw;
if (hw_scale > 1) {
*newh = this->inpHeight;
*neww = int(this->inpWidth / hw_scale);
resize(srcimg, dstimg, Size(*neww, *newh), INTER_AREA);
*left = int((this->inpWidth - *neww) * 0.5);
copyMakeBorder(dstimg, dstimg, 0, 0, *left, this->inpWidth - *neww - *left, BORDER_CONSTANT, 114);
}
else {
*newh = (int)this->inpHeight * hw_scale;
*neww = this->inpWidth;
resize(srcimg, dstimg, Size(*neww, *newh), INTER_AREA);
*top = (int)(this->inpHeight - *newh) * 0.5;
copyMakeBorder(dstimg, dstimg, *top, this->inpHeight - *newh - *top, 0, 0, BORDER_CONSTANT, 114);
}
}
else {
resize(srcimg, dstimg, Size(*neww, *newh), INTER_AREA);
}
return dstimg;
}
void YOLO::normalize_(Mat img)
{
// img.convertTo(img, CV_32F);
int row = img.rows;
int col = img.cols;
this->input_image_.resize(row * col * img.channels());
for (int c = 0; c < 3; c++)
{
for (int i = 0; i < row; i++)
{
for (int j = 0; j < col; j++)
{
float pix = img.ptr<uchar>(i)[j * 3 + 2 - c];
this->input_image_[c * row * col + i * col + j] = pix / 255.0;
}
}
}
}
推理部分：
Mat YOLO::detect(Mat& frame)
{
int newh = 0, neww = 0, padh = 0, padw = 0;
Mat dstimg = this->resize_image(frame, &newh, &neww, &padh, &padw);
this->normalize_(dstimg);
array<int64_t, 4> input_shape_{ 1, 3, this->inpHeight, this->inpWidth };
auto allocator_info = MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
Value input_tensor_ = Value::CreateTensor<float>(allocator_info, input_image_.data(), input_image_.size(), input_shape_.data(), input_shape_.size());
//vector<Value> ort_outputs = ort_session->Run(RunOptions{ nullptr }, &input_names[0], &input_tensor_, 1, output_names.data(), output_names.size());
const array<const char*,1> input_names_array = { input_names[0].c_str() };
const array<const char*,1> output_names_array = { output_names[0].c_str()};
vector<Value> ort_outputs = ort_session->Run(RunOptions{ nullptr }, input_names_array.data(), &input_tensor_, 1, output_names_array.data(), output_names_array.size());
//输出的组成：每个proposal由5个部分组成，分别是xmin,ymin,xmax,ymax,box_score,然后是类别的score，一共80个类别，所以一共85个值，
/generate proposals
vector<BoxInfo> generate_boxes; //存储所有的box
float ratioh = (float)frame.rows / newh, ratiow = (float)frame.cols / neww; //计算原图和resize后图像的比例，用于将box坐标映射到原图
const float* pdata = ort_outputs[0].GetTensorMutableData<float>();
for (int n = 0; n < this->num_stride; n++)
{
const float stride = pow(2, n + 3); //计算stride步长，不同的尺度对应不同的stride
int num_grid_x = (int)ceil((this->inpWidth / stride)); //计算x方向的网格数量
int num_grid_y = (int)ceil((this->inpHeight / stride)); //计算y方向的网格数量
for (int q = 0; q < 3; q++) ///anchor，每个尺度有三个anchor
{
const float anchor_w = this->anchors[n * 6 + q * 2]; //计算anchor的宽度
const float anchor_h = this->anchors[n * 6 + q * 2 + 1]; //计算anchor的高度
for (int i = 0; i < num_grid_y; i++) //遍历y方向的网格
{
for (int j = 0; j < num_grid_x; j++) //遍历x方向的网格
{
float box_score = pdata[4]; //输出的第四个值是box的置信度
if (box_score > this->objThreshold) //如果置信度大于阈值，才认为检测到了物体
{
int max_ind = 0;
float max_class_socre = 0;
for (int k = 0; k < num_class; k++) //遍历80个类别，找到最大的类别得分
{
if (pdata[k + 5] > max_class_socre)
{
max_class_socre = pdata[k + 5];
max_ind = k;
}
}
max_class_socre *= box_score; //类别得分乘以box的置信度，得到最终的得分
if (max_class_socre > this->confThreshold) //如果最终得分大于阈值，才认为检测到了物体，还原box坐标到原图
{
float cx = (pdata[0] * 2.f - 0.5f + j) * stride; ///cx
float cy = (pdata[1] * 2.f - 0.5f + i) * stride; ///cy
float w = powf(pdata[2] * 2.f, 2.f) * anchor_w; ///w
float h = powf(pdata[3] * 2.f, 2.f) * anchor_h; ///h
float xmin = (cx - padw - 0.5 * w)*ratiow;
float ymin = (cy - padh - 0.5 * h)*ratioh;
float xmax = (cx - padw + 0.5 * w)*ratiow;
float ymax = (cy - padh + 0.5 * h)*ratioh;
generate_boxes.push_back(BoxInfo{ xmin, ymin, xmax, ymax, max_class_socre, max_ind });
}
}
pdata += nout; //移动到下一个proposal
}
}
}
}
// Perform non maximum suppression to eliminate redundant overlapping boxes with
// lower confidences
nms(generate_boxes);
for (size_t i = 0; i < generate_boxes.size(); ++i) //画框
{
int xmin = int(generate_boxes[i].x1);
int ymin = int(generate_boxes[i].y1);
rectangle(frame, Point(xmin, ymin), Point(int(generate_boxes[i].x2), int(generate_boxes[i].y2)), Scalar(0, 0, 255), 2);
string label = format("%.2f", generate_boxes[i].score);
label = this->class_names[generate_boxes[i].label] + ":" + label;
putText(frame, label, Point(xmin, ymin - 5), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0, 255, 0), 1);
}
return frame; //返回画好框的图像,其实不用返回也可以，因为是引用传递
}
后处理nms：
void YOLO::nms(vector<BoxInfo>& input_boxes)
{
sort(input_boxes.begin(), input_boxes.end(), [](BoxInfo a, BoxInfo b) { return a.score > b.score; }); //按照score降序排列
vector<float> vArea(input_boxes.size()); //存储每个box的面积
for (int i = 0; i < int(input_boxes.size()); ++i)
{
vArea[i] = (input_boxes.at(i).x2 - input_boxes.at(i).x1 + 1)
* (input_boxes.at(i).y2 - input_boxes.at(i).y1 + 1);
}
vector<bool> isSuppressed(input_boxes.size(), false); //存储每个box是否被抑制
for (int i = 0; i < int(input_boxes.size()); ++i) //遍历所有box
{
if (isSuppressed[i]) { continue; }
for (int j = i + 1; j < int(input_boxes.size()); ++j) //计算当前box与其它box的IOU
{
if (isSuppressed[j]) { continue; }
float xx1 = (max)(input_boxes[i].x1, input_boxes[j].x1);
float yy1 = (max)(input_boxes[i].y1, input_boxes[j].y1);
float xx2 = (min)(input_boxes[i].x2, input_boxes[j].x2);
float yy2 = (min)(input_boxes[i].y2, input_boxes[j].y2);
float w = (max)(float(0), xx2 - xx1 + 1);
float h = (max)(float(0), yy2 - yy1 + 1);
float inter = w * h;
float ovr = inter / (vArea[i] + vArea[j] - inter);
if (ovr >= this->nmsThreshold)
{
isSuppressed[j] = true; //抑制IOU大于阈值的box，也就是这个box和box[i]重叠度很高
}
}
}
// return post_nms;
int idx_t = 0;
input_boxes.erase(remove_if(input_boxes.begin(), input_boxes.end(), [&idx_t, &isSuppressed](const BoxInfo& f) { return isSuppressed[idx_t++]; }), input_boxes.end());
//这里用到了C++11中的新特性lambda，匿名函数，可以自己去了解一下，推荐深入理解C++11：C++11新特性解析与应用这本书，对于C++11讲解的很好。
}
主函数：
int main()
{
Net_config yolo_nets = { 0.3, 0.5, 0.3,"/home/wyq/hobby/model_deploy/onnx/onnxruntime/YoloV5/build/yolov5s.onnx" };
YOLO yolo_model(yolo_nets);
Mat srcimg;
// VideoCapture cap("/home/wyq/hobby/model_deploy/video.mp4");
VideoCapture cap=VideoCapture(0);
cap.set(CAP_PROP_FOURCC, VideoWriter::fourcc('M', 'J', 'P', 'G'));
cap.set(CAP_PROP_FRAME_WIDTH, 640);
cap.set(CAP_PROP_FRAME_HEIGHT, 480);
cap.set(CAP_PROP_FPS, 60);
while(true)
{
double inference_time = 0;
double fps = 0.0;
cap >> srcimg;
if(srcimg.empty())
{
cout<<"can not load image"<<endl;
break;
}
double begin = static_cast<double>(getTickCount());
yolo_model.detect(srcimg);
inference_time = (static_cast<double>(getTickCount()) - begin) / getTickFrequency();
cout<<"inference time:"<<inference_time<<endl;
fps = 1.0f / inference_time;
putText(srcimg, "FPS:"+to_string(fps), Point(16, 32),FONT_HERSHEY_COMPLEX, 0.8, Scalar(0, 0, 255));
imshow("yolo", srcimg);
cout<<"fps:"<<fps<<endl;
if(waitKey(1) == 27)
break;
}
}
本文用到的模型文件以及标签文件都在下面链接：
链接：https://pan.baidu.com/s/1agn2iAPqcs0f5wFLw7hhew?pwd=byvw
提取码：byvw
GitHub 加速计划 / yo / yolov5
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yolov5 - Ultralytics YOLOv8的前身，是一个用于目标检测、图像分割和图像分类任务的先进模型。
最近提交(Master分支：3 个月前 )
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* Update Integrations table
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Update README.md
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Update README.zh-CN.md
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> 1 个月前
94a62456
* fix: quad training
* fix: quad training in segmentation 1 个月前
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