Yolov5 libtorch 训练自己的数据并用liborch 部署
环境：ubuntu18.01（训练平台） ， windows / vs2017 部署平台 opencv3.4.7 （提前编译好的）cuda10.1 pytorch1.6
yolov5 项目：https://github.com/ultralytics/yolov5
yolov5 v2.0模型下载链接：https://github.com/ultralytics/yolov5/releases
训练阶段：略
libtorch部署 win10 vs2017, opencv3.4.7, libtorch 1.6：
#include
#include
#include
#include
#include
using namespace std;
std::vector
{
std::vector
for (size_t i = 0; i < preds.sizes()[0]; ++i)
{
torch::Tensor pred = preds.select(0, i);
// Filter by scores
torch::Tensor scores = pred.select(1, 4) * std::get<0>(torch::max(pred.slice(1, 5, pred.sizes()[1]), 1));
pred = torch::index_select(pred, 0, torch::nonzero(scores > score_thresh).select(1, 0));
if (pred.sizes()[0] == 0) continue;
// (center_x, center_y, w, h) to (left, top, right, bottom)
pred.select(1, 0) = pred.select(1, 0) - pred.select(1, 2) / 2;
pred.select(1, 1) = pred.select(1, 1) - pred.select(1, 3) / 2;
pred.select(1, 2) = pred.select(1, 0) + pred.select(1, 2);
pred.select(1, 3) = pred.select(1, 1) + pred.select(1, 3);
// Computing scores and classes
std::tuple
pred.select(1, 4) = pred.select(1, 4) * std::get<0>(max_tuple);
pred.select(1, 5) = std::get<1>(max_tuple);
torch::Tensor dets = pred.slice(1, 0, 6);
torch::Tensor keep = torch::empty({ dets.sizes()[0] });
torch::Tensor areas = (dets.select(1, 3) - dets.select(1, 1)) * (dets.select(1, 2) - dets.select(1, 0));
std::tuple
torch::Tensor v = std::get<0>(indexes_tuple);
torch::Tensor indexes = std::get<1>(indexes_tuple);
int count = 0;
while (indexes.sizes()[0] > 0)
{
keep[count] = (indexes[0].item().toInt());
count += 1;
// Computing overlaps
torch::Tensor lefts = torch::empty(indexes.sizes()[0] - 1);
torch::Tensor tops = torch::empty(indexes.sizes()[0] - 1);
torch::Tensor rights = torch::empty(indexes.sizes()[0] - 1);
torch::Tensor bottoms = torch::empty(indexes.sizes()[0] - 1);
torch::Tensor widths = torch::empty(indexes.sizes()[0] - 1);
torch::Tensor heights = torch::empty(indexes.sizes()[0] - 1);
for (size_t i = 0; i < indexes.sizes()[0] - 1; ++i)
{
lefts[i] = std::max(dets[indexes[0]][0].item().toFloat(), dets[indexes[i + 1]][0].item().toFloat());
tops[i] = std::max(dets[indexes[0]][1].item().toFloat(), dets[indexes[i + 1]][1].item().toFloat());
rights[i] = std::min(dets[indexes[0]][2].item().toFloat(), dets[indexes[i + 1]][2].item().toFloat());
bottoms[i] = std::min(dets[indexes[0]][3].item().toFloat(), dets[indexes[i + 1]][3].item().toFloat());
widths[i] = std::max(float(0), rights[i].item().toFloat() - lefts[i].item().toFloat());
heights[i] = std::max(float(0), bottoms[i].item().toFloat() - tops[i].item().toFloat());
}
torch::Tensor overlaps = widths * heights;
// FIlter by IOUs
torch::Tensor ious = overlaps / (areas.select(0, indexes[0].item().toInt()) + torch::index_select(areas, 0, indexes.slice(0, 1, indexes.sizes()[0])) - overlaps);
indexes = torch::index_select(indexes, 0, torch::nonzero(ious <= iou_thresh).select(1, 0) + 1);
}
keep = keep.toType(torch::kInt64);
output.push_back(torch::index_select(dets, 0, keep.slice(0, 0, count)));
}
return output;
}
int main()
{
// Loading Module
torch::jit::script::Module module = torch::jit::load("libtorchYolov5\\libtorchYolov5\\my_best.jit");
module.to(at::kCPU);
std::vector
std::ifstream f("libtorchYolov5\\libtorchYolov5\\shoes.names");
std::string name = "";
while (std::getline(f, name))
{
classnames.push_back(name);
}
/*cv::VideoCapture cap = cv::VideoCapture(0);
cap.set(cv::CAP_PROP_FRAME_WIDTH, 1920);
cap.set(cv::CAP_PROP_FRAME_HEIGHT, 1080);*/
cv::Mat frame, img;
frame = cv::imread("libtorchYolov5\\x64\\Release\\04.jpg");
clock_t start = clock();
//第一种方式
cv::resize(frame, img, cv::Size(640, 640)); //384
cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
torch::Tensor imgTensor = torch::from_blob(img.data, { img.rows, img.cols,3 }, torch::kByte);
imgTensor = imgTensor.permute({ 2,0,1 });
imgTensor = imgTensor.toType(torch::kFloat);
imgTensor = imgTensor.div(255);
imgTensor = imgTensor.unsqueeze(0);
torch::Tensor preds = module.forward({ imgTensor }).toTuple()->elements()[0].toTensor();
//第二种方式
// preds: [?, 15120, 9]
//cv::resize(frame, img, cv::Size(640, 640)); //384
//torch::DeviceType device_type = at::kCPU;
//cv::cvtColor(img, img, cv::COLOR_BGR2RGB); // BGR -> RGB
//img.convertTo(img, CV_32FC3, 1.0f / 255.0f); // normalization 1/255
//auto imgTensor = torch::from_blob(img.data, { 1, img.rows, img.cols, img.channels() }).to(device_type);
//imgTensor = imgTensor.permute({ 0, 3, 1, 2 }).contiguous(); // BHWC -> BCHW (Batch, Channel, Height, Width)
//std::vector
//inputs.emplace_back(imgTensor);
preds: [?, 15120, 9]
//torch::jit::IValue output = module.forward(inputs); // CPUFloatType{1,3,12,20,85}
//auto preds = output.toTuple()->elements()[0].toTensor();
std::vector
cout << "det:" << dets.size() << endl;
cout << "det:" << dets[0] << endl;
if (dets.size() > 0)
{
// Visualize result
for (size_t i = 0; i < dets[0].sizes()[0]; ++i)
{
float left = dets[0][i][0].item().toFloat() * frame.cols / 640;
float top = dets[0][i][1].item().toFloat() * frame.rows / 640; // 384
float right = dets[0][i][2].item().toFloat() * frame.cols / 640;
float bottom = dets[0][i][3].item().toFloat() * frame.rows / 640; //384
float score = dets[0][i][4].item().toFloat();
int classID = dets[0][i][5].item().toInt();
cv::rectangle(frame, cv::Rect(left, top, (right - left), (bottom - top)), cv::Scalar(0, 255, 0), 2);
cv::putText(frame,
classnames[classID] + ": " + cv::format("%.2f", score),
cv::Point(left, top),
cv::FONT_HERSHEY_SIMPLEX, (right - left) / 200, cv::Scalar(0, 255, 0), 2);
}
}
cv::putText(frame, "FPS: " + std::to_string(int(1e7 / (clock() - start))),
cv::Point(50, 50),
cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(0, 255, 0), 2);
cv::imwrite("libtorchYolov5\\x64\\Release\\00.jpg", frame);
cv::imshow("", frame);
cv::waitKey(0);
//if (cv::waitKey(1) == 27) break;
//}
return 0;
}
结果：
