1、训练代码
2、导出onnx
3、onnx转换为onnx-sim
# 安装onnx-simplifer
pip install onnx-simplifer
python -m onnxsim yolov5s.onnx nora.onnx
4、onnx-sim转换为ncnn模型
> cd protobuf-3.4.0
> mkdir buildd
> cd buildd
> cmake -G"NMake Makefiles" -DCMAKE_BUILD_TYPE= Release -DCMAKE_INSTALL_PREFIX= %cd%/install -Dprotobuf_BUILD_TESTS= OFF -Dprotobuf_MSVC_STATIC_RUNTIME= OFF .. /cmake
> nmake
> nmake install
> cd ncnn-master
> mkdir build
> cd build
> cmake -G"NMake Makefiles" -DCMAKE_BUILD_TYPE= Release -DCMAKE_INSTALL_PREFIX= %cd%/install -DProtobuf_INCLUDE_DIR= E:/PythonProject/202205/protobuf-3.4.0/buildd/install/include -DProtobuf_LIBRARIES= E:/PythonProject/202205/protobuf-3.4.0/buildd/install/lib/libprotobuf.lib -DProtobuf_PROTOC_EXECUTABLE= E:/PythonProject/202205/protobuf-3.4.0/buildd/install/bin/protoc.exe -DNCNN_VULKAN= OFF ..
> nmake
> nmake install
> cd tools
> onnx2ncnn.exe E:/PythonProject/202204/yolov5-master/runs/train/exp18/weights/nara.onnx E:/PythonProject/202204/yolov5-master/runs/train/exp18/weights/nora.param E:/PythonProject/202204/yolov5-master/runs/train/exp18/weights/nora.bin
转换成功，如图
5、修改输出grid数的限制，修改为-1
6、用netron查看onnx模型结构，找出输出的3个output，分别对应8-16-32stride的输出。在官方提供的代码中修改对应的
#include "layer.h"
#include "net.h"
#include
#include
#include
#include
#include
#include
class YoloV5Focus : public ncnn::Layer
{
public:
YoloV5Focus()
{
one_blob_only = true;
}
virtual int forward(const ncnn::Mat& bottom_blob, ncnn::Mat& top_blob, const ncnn::Option& opt) const
{
int w = bottom_blob.w;
int h = bottom_blob.h;
int channels = bottom_blob.c;
int outw = w / 2;
int outh = h / 2;
int outc = channels * 4;
top_blob.create(outw, outh, outc, 4u, 1, opt.blob_allocator);
if (top_blob.empty())
return -100;
#pragma omp parallel for num_threads(opt.num_threads)
for (int p = 0; p < outc; p++)
{
const float* ptr = bottom_blob.channel(p % channels).row((p / channels) % 2) + ((p / channels) / 2);
float* outptr = top_blob.channel(p);
for (int i = 0; i < outh; i++)
{
for (int j = 0; j < outw; j++)
{
*outptr = *ptr;
outptr += 1;
ptr += 2;
}
ptr += w;
}
}
return 0;
}
};
DEFINE_LAYER_CREATOR(YoloV5Focus)
struct Object
{
cv::Rect_ rect;
int label;
float prob;
};
static inline float intersection_area(const Object& a, const Object& b)
{
cv::Rect_ inter = a.rect & b.rect;
return inter.area();
}
static void qsort_descent_inplace(std::vector& faceobjects, int left, int right)
{
int i = left;
int j = right;
float p = faceobjects[(left + right) / 2].prob;
while (i <= j)
{
while (faceobjects[i].prob > p)
i++;
while (faceobjects[j].prob < p)
j--;
if (i <= j)
{
// swap
std::swap(faceobjects[i], faceobjects[j]);
i++;
j--;
}
}
#pragma omp parallel sections
{
#pragma omp section
{
if (left < j) qsort_descent_inplace(faceobjects, left, j);
}
#pragma omp section
{
if (i < right) qsort_descent_inplace(faceobjects, i, right);
}
}
}
static void qsort_descent_inplace(std::vector& faceobjects)
{
if (faceobjects.empty())
return;
qsort_descent_inplace(faceobjects, 0, faceobjects.size() - 1);
}
static void nms_sorted_bboxes(const std::vector& faceobjects, std::vector& picked, float nms_threshold)
{
picked.clear();
const int n = faceobjects.size();
std::vector areas(n);
for (int i = 0; i < n; i++)
{
areas[i] = faceobjects[i].rect.area();
}
for (int i = 0; i < n; i++)
{
const Object& a = faceobjects[i];
int keep = 1;
for (int j = 0; j < (int)picked.size(); j++)
{
const Object& b = faceobjects[picked[j]];
// intersection over union
float inter_area = intersection_area(a, b);
float union_area = areas[i] + areas[picked[j]] - inter_area;
// float IoU = inter_area / union_area
if (inter_area / union_area > nms_threshold)
keep = 0;
}
if (keep)
picked.push_back(i);
}
}
static inline float sigmoid(float x)
{
return static_cast(1.f / (1.f + exp(-x)));
}
static void generate_proposals(const ncnn::Mat& anchors, int stride, const ncnn::Mat& in_pad, const ncnn::Mat& feat_blob, float prob_threshold, std::vector& objects)
{
const int num_grid = feat_blob.h;
int num_grid_x;
int num_grid_y;
if (in_pad.w > in_pad.h)
{
num_grid_x = in_pad.w / stride;
num_grid_y = num_grid / num_grid_x;
}
else
{
num_grid_y = in_pad.h / stride;
num_grid_x = num_grid / num_grid_y;
}
const int num_class = feat_blob.w - 5;
const int num_anchors = anchors.w / 2;
for (int q = 0; q < num_anchors; q++)
{
const float anchor_w = anchors[q * 2];
const float anchor_h = anchors[q * 2 + 1];
const ncnn::Mat feat = feat_blob.channel(q);
for (int i = 0; i < num_grid_y; i++)
{
for (int j = 0; j < num_grid_x; j++)
{
const float* featptr = feat.row(i * num_grid_x + j);
// find class index with max class score
int class_index = 0;
float class_score = -FLT_MAX;
for (int k = 0; k < num_class; k++)
{
float score = featptr[5 + k]; // 这里是获取每一个类别的分数 featptr[4 + k]是物体置信度 [5 + k] 之后是类别置信度
if (score > class_score)
{
class_index = k;
class_score = score;
}
}
float box_score = featptr[4];
float confidence = sigmoid(box_score) * sigmoid(class_score);
if (confidence >= prob_threshold)
{
// yolov5/models/yolo.py Detect forward
// y = x[i].sigmoid()
// y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] # xy
// y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
float dx = sigmoid(featptr[0]);
float dy = sigmoid(featptr[1]);
float dw = sigmoid(featptr[2]);
float dh = sigmoid(featptr[3]);
float pb_cx = (dx * 2.f - 0.5f + j) * stride;
float pb_cy = (dy * 2.f - 0.5f + i) * stride;
float pb_w = pow(dw * 2.f, 2) * anchor_w;
float pb_h = pow(dh * 2.f, 2) * anchor_h;
float x0 = pb_cx - pb_w * 0.5f;
float y0 = pb_cy - pb_h * 0.5f;
float x1 = pb_cx + pb_w * 0.5f;
float y1 = pb_cy + pb_h * 0.5f;
Object obj;
obj.rect.x = x0;
obj.rect.y = y0;
obj.rect.width = x1 - x0;
obj.rect.height = y1 - y0;
obj.label = class_index;
obj.prob = confidence;
objects.push_back(obj);
}
}
}
}
std::cout << "objects.size() : " << objects.size() << std::endl;
}
static int detect_yolov5(const cv::Mat& bgr, std::vector& objects)
{
ncnn::Net yolov5;
// yolov5.opt.use_vulkan_compute = true;
yolov5.opt.num_threads = 8;
yolov5.opt.use_int8_inference = true;
// yolov5.opt.use_bf16_storage = true;
// 添加这个的原因是因为之前版本有遇到在转换为ncnn过程中出现 "Unsupported slice step"这个，但是我在转换过程中没出现，所以不需要添加
//yolov5.register_custom_layer("YoloV5Focus", YoloV5Focus_layer_creator);
// original pretrained model from https://github.com/ultralytics/yolov5
// the ncnn model https://github.com/nihui/ncnn-assets/tree/master/models
yolov5.load_param("E:\\c++Project\\202205\\yolov5_android_vs2019\\ghostyolo\\nora.param");
yolov5.load_model("E:\\c++Project\\202205\\yolov5_android_vs2019\\ghostyolo\\nora.bin");
//yolov5.load_param("E:/c++Project/202205/nora.param");
//yolov5.load_model("E:/c++Project/202205/nora.bin");
const int target_size = 416; // 使用416 减少运算开支
const float prob_threshold = 0.25f;
const float nms_threshold = 0.45f;
int img_w = bgr.cols;
int img_h = bgr.rows;
// letterbox pad to multiple of 32
int w = img_w;
int h = img_h;
float scale = 1.f;
if (w > h)
{
scale = (float)target_size / w;
w = target_size;
h = h * scale;
}
else
{
scale = (float)target_size / h;
h = target_size;
w = w * scale;
}
ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR2RGB, img_w, img_h, w, h);
// pad to target_size rectangle
// yolov5/utils/datasets.py letterbox
int wpad = (w + 31) / 32 * 32 - w;
int hpad = (h + 31) / 32 * 32 - h;
ncnn::Mat in_pad;
ncnn::copy_make_border(in, in_pad, hpad / 2, hpad - hpad / 2, wpad / 2, wpad - wpad / 2, ncnn::BORDER_CONSTANT, 114.f);
const float norm_vals[3] = { 1 / 255.f, 1 / 255.f, 1 / 255.f };
in_pad.substract_mean_normalize(0, norm_vals);
ncnn::Extractor ex = yolov5.create_extractor();
ex.input("images", in_pad);
std::vector proposals;
// anchor setting from yolov5/models/yolov5s.yaml
// stride 8
{
ncnn::Mat out1;
ex.extract("output", out1); // 根据netron查看模型的3个输出对应的输出
ncnn::Mat anchors(6);
anchors[0] = 10.f;
anchors[1] = 13.f;
anchors[2] = 16.f;
anchors[3] = 30.f;
anchors[4] = 33.f;
anchors[5] = 23.f;
std::vector objects8;
generate_proposals(anchors, 8, in_pad, out1, prob_threshold, objects8);
proposals.insert(proposals.end(), objects8.begin(), objects8.end());
}
// stride 16
{
ncnn::Mat out2;
ex.extract("491", out2); // 根据netron查看模型的3个输出对应的输出
ncnn::Mat anchors(6);
anchors[0] = 30.f;
anchors[1] = 61.f;
anchors[2] = 62.f;
anchors[3] = 45.f;
anchors[4] = 59.f;
anchors[5] = 119.f;
std::vector objects16;
generate_proposals(anchors, 16, in_pad, out2, prob_threshold, objects16);
proposals.insert(proposals.end(), objects16.begin(), objects16.end());
}
// stride 32
{
ncnn::Mat out3;
ex.extract("505", out3); // 根据netron查看模型的3个输出对应的输出
ncnn::Mat anchors(6);
anchors[0] = 116.f;
anchors[1] = 90.f;
anchors[2] = 156.f;
anchors[3] = 198.f;
anchors[4] = 373.f;
anchors[5] = 326.f;
std::vector objects32;
generate_proposals(anchors, 32, in_pad, out3, prob_threshold, objects32);
proposals.insert(proposals.end(), objects32.begin(), objects32.end());
}
// sort all proposals by score from highest to lowest
qsort_descent_inplace(proposals);
// apply nms with nms_threshold
std::vector picked;
nms_sorted_bboxes(proposals, picked, nms_threshold);
int count = picked.size();
objects.resize(count);
for (int i = 0; i < count; i++)
{
objects[i] = proposals[picked[i]];
// adjust offset to original unpadded
float x0 = (objects[i].rect.x - (wpad / 2)) / scale;
float y0 = (objects[i].rect.y - (hpad / 2)) / scale;
float x1 = (objects[i].rect.x + objects[i].rect.width - (wpad / 2)) / scale;
float y1 = (objects[i].rect.y + objects[i].rect.height - (hpad / 2)) / scale;
// clip
x0 = std::max(std::min(x0, (float)(img_w - 1)), 0.f);
y0 = std::max(std::min(y0, (float)(img_h - 1)), 0.f);
x1 = std::max(std::min(x1, (float)(img_w - 1)), 0.f);
y1 = std::max(std::min(y1, (float)(img_h - 1)), 0.f);
objects[i].rect.x = x0;
objects[i].rect.y = y0;
objects[i].rect.width = x1 - x0;
objects[i].rect.height = y1 - y0;
}
return 0;
}
static void draw_objects(const cv::Mat& bgr, const std::vector& objects)
{
static const char* class_names[] = {"nora"};
cv::Mat image = bgr.clone();
for (size_t i = 0; i < objects.size(); i++)
{
const Object& obj = objects[i];
fprintf_s(stderr, "%d = %.5f at %.2f %.2f %.2f x %.2f\n", obj.label, obj.prob,
obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height);
cv::rectangle(image, obj.rect, cv::Scalar(255, 0, 0));
char text[256];
sprintf_s(text, "%s %.1f%%", class_names[obj.label], obj.prob * 100);
int baseLine = 0;
cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
int x = obj.rect.x;
int y = obj.rect.y - label_size.height - baseLine;
if (y < 0)
y = 0;
if (x + label_size.width > image.cols)
x = image.cols - label_size.width;
cv::rectangle(image, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)),
cv::Scalar(255, 255, 255), -1);
cv::putText(image, text, cv::Point(x, y + label_size.height),
cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));
}
cv::imwrite("E:/c++Project/202205/yolov5_android_vs2019/yolov51.jpg", image);
cv::imshow("image", image);
cv::waitKey(0);
}
int main(int argc, char** argv)
{
//if (argc != 2)
//{
// fprintf(stderr, "Usage: %s [imagepath]\n", argv[0]);
// return -1;
//}
std::string imagepath = "E:/c++Project/202205/yolov5_android_vs2019/1.jpg";
//const char* imagepath = argv[1];
cv::Mat m = cv::imread(imagepath.c_str(), 1);
if (m.empty())
{
fprintf(stderr, "cv::imread %s failed\n", imagepath);
return -1;
}
std::vector objects;
detect_yolov5(m, objects);
draw_objects(m, objects);
return 0;
}
7、vs2019运行结果
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