本文主要是介绍使用libtorch加载YOLOv8生成的torchscript文件进行目标检测，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
在网上下载了60多幅包含西瓜和冬瓜的图像组成melon数据集，使用 LabelMe 工具进行标注，然后使用 labelme2yolov8 脚本将json文件转换成YOLOv8支持的.txt文件，并自动生成YOLOv8支持的目录结构，包括melon.yaml文件，其内容如下：
path: ../datasets/melon # dataset root dir
train: images/train # train images (relative to 'path')
val: images/val # val images (relative to 'path')
test: # test images (optional)# Classes
names:0: watermelon1: wintermelon使用以下python脚本进行训练生成torchscript文件：
import argparse
import colorama
from ultralytics import YOLOdef parse_args():parser = argparse.ArgumentParser(description="YOLOv8 object detect")parser.add_argument("--yaml", required=True, type=str, help="yaml file")parser.add_argument("--epochs", required=True, type=int, help="number of training")args = parser.parse_args()return argsdef train(yaml, epochs):model = YOLO("yolov8n.pt") # load a pretrained modelresults = model.train(data=yaml, epochs=epochs, imgsz=640) # train the modelmetrics = model.val() # It'll automatically evaluate the data you trained, no arguments needed, dataset and settings rememberedmodel.export(format="onnx") #, dynamic=True) # export the model, cannot specify dynamic=True, opencv does not support# model.export(format="onnx", opset=12, simplify=True, dynamic=False, imgsz=640)model.export(format="torchscript") # libtorchif __name__ == "__main__":colorama.init()args = parse_args()train(args.yaml, args.epochs)print(colorama.Fore.GREEN + "====== execution completed ======")以下是使用libtorch接口加载torchscript文件进行目标检测的实现代码：
namespace {constexpr bool cuda_enabled{ false };
constexpr int image_size[2]{ 640, 640 }; // {height,width}, input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 6, 8400)
constexpr float model_score_threshold{ 0.45 }; // confidence threshold
constexpr float model_nms_threshold{ 0.50 }; // iou threshold#ifdef _MSC_VER
constexpr char* onnx_file{ "../../../data/best.onnx" };
constexpr char* torchscript_file{ "../../../data/best.torchscript" };
constexpr char* images_dir{ "../../../data/images/predict" };
constexpr char* result_dir{ "../../../data/result" };
constexpr char* classes_file{ "../../../data/images/labels.txt" };
#else
constexpr char* onnx_file{ "data/best.onnx" };
constexpr char* torchscript_file{ "data/best.torchscript" };
constexpr char* images_dir{ "data/images/predict" };
constexpr char* result_dir{ "data/result" };
constexpr char* classes_file{ "data/images/labels.txt" };
#endifstd::vector<std::string> parse_classes_file(const char* name)
{std::vector<std::string> classes;std::ifstream file(name);if (!file.is_open()) {std::cerr << "Error: fail to open classes file: " << name << std::endl;return classes;}std::string line;while (std::getline(file, line)) {auto pos = line.find_first_of(" ");classes.emplace_back(line.substr(0, pos));}file.close();return classes;
}auto get_dir_images(const char* name)
{std::map<std::string, std::string> images; // image name, image path + image namefor (auto const& dir_entry : std::filesystem::directory_iterator(name)) {if (dir_entry.is_regular_file())images[dir_entry.path().filename().string()] = dir_entry.path().string();}return images;
}void draw_boxes(const std::vector<std::string>& classes, const std::vector<int>& ids, const std::vector<float>& confidences,const std::vector<cv::Rect>& boxes, const std::string& name, cv::Mat& frame)
{if (ids.size() != confidences.size() || ids.size() != boxes.size() || confidences.size() != boxes.size()) {std::cerr << "Error: their lengths are inconsistent: " << ids.size() << ", " << confidences.size() << ", " << boxes.size() << std::endl;return;}std::cout << "image name: " << name << ", number of detections: " << ids.size() << std::endl;std::random_device rd;std::mt19937 gen(rd());std::uniform_int_distribution<int> dis(100, 255);for (auto i = 0; i < ids.size(); ++i) {auto color = cv::Scalar(dis(gen), dis(gen), dis(gen));cv::rectangle(frame, boxes[i], color, 2);std::string class_string = classes[ids[i]] + ' ' + std::to_string(confidences[i]).substr(0, 4);cv::Size text_size = cv::getTextSize(class_string, cv::FONT_HERSHEY_DUPLEX, 1, 2, 0);cv::Rect text_box(boxes[i].x, boxes[i].y - 40, text_size.width + 10, text_size.height + 20);cv::rectangle(frame, text_box, color, cv::FILLED);cv::putText(frame, class_string, cv::Point(boxes[i].x + 5, boxes[i].y - 10), cv::FONT_HERSHEY_DUPLEX, 1, cv::Scalar(0, 0, 0), 2, 0);}cv::imshow("Inference", frame);cv::waitKey(-1);std::string path(result_dir);path += "/" + name;cv::imwrite(path, frame);
}float letter_box(const cv::Mat& src, cv::Mat& dst, const std::vector<int>& imgsz)
{if (src.cols == imgsz[1] && src.rows == imgsz[0]) {if (src.data == dst.data) {return 1.;} else {dst = src.clone();return 1.;}}auto resize_scale = std::min(imgsz[0] * 1. / src.rows, imgsz[1] * 1. / src.cols);int new_shape_w = std::round(src.cols * resize_scale);int new_shape_h = std::round(src.rows * resize_scale);float padw = (imgsz[1] - new_shape_w) / 2.;float padh = (imgsz[0] - new_shape_h) / 2.;int top = std::round(padh - 0.1);int bottom = std::round(padh + 0.1);int left = std::round(padw - 0.1);int right = std::round(padw + 0.1);cv::resize(src, dst, cv::Size(new_shape_w, new_shape_h), 0, 0, cv::INTER_AREA);cv::copyMakeBorder(dst, dst, top, bottom, left, right, cv::BORDER_CONSTANT, cv::Scalar(114.));return resize_scale;
}torch::Tensor xywh2xyxy(const torch::Tensor& x)
{auto y = torch::empty_like(x);auto dw = x.index({ "...", 2 }).div(2);auto dh = x.index({ "...", 3 }).div(2);y.index_put_({ "...", 0 }, x.index({ "...", 0 }) - dw);y.index_put_({ "...", 1 }, x.index({ "...", 1 }) - dh);y.index_put_({ "...", 2 }, x.index({ "...", 0 }) + dw);y.index_put_({ "...", 3 }, x.index({ "...", 1 }) + dh);return y;
}// reference: https://github.com/pytorch/vision/blob/main/torchvision/csrc/ops/cpu/nms_kernel.cpp
torch::Tensor nms(const torch::Tensor& bboxes, const torch::Tensor& scores, float iou_threshold)
{if (bboxes.numel() == 0)return torch::empty({ 0 }, bboxes.options().dtype(torch::kLong));auto x1_t = bboxes.select(1, 0).contiguous();auto y1_t = bboxes.select(1, 1).contiguous();auto x2_t = bboxes.select(1, 2).contiguous();auto y2_t = bboxes.select(1, 3).contiguous();torch::Tensor areas_t = (x2_t - x1_t) * (y2_t - y1_t);auto order_t = std::get<1>(scores.sort(/*stable=*/true, /*dim=*/0, /* descending=*/true));auto ndets = bboxes.size(0);torch::Tensor suppressed_t = torch::zeros({ ndets }, bboxes.options().dtype(torch::kByte));torch::Tensor keep_t = torch::zeros({ ndets }, bboxes.options().dtype(torch::kLong));auto suppressed = suppressed_t.data_ptr<uint8_t>();auto keep = keep_t.data_ptr<int64_t>();auto order = order_t.data_ptr<int64_t>();auto x1 = x1_t.data_ptr<float>();auto y1 = y1_t.data_ptr<float>();auto x2 = x2_t.data_ptr<float>();auto y2 = y2_t.data_ptr<float>();auto areas = areas_t.data_ptr<float>();int64_t num_to_keep = 0;for (int64_t _i = 0; _i < ndets; _i++) {auto i = order[_i];if (suppressed[i] == 1)continue;keep[num_to_keep++] = i;auto ix1 = x1[i];auto iy1 = y1[i];auto ix2 = x2[i];auto iy2 = y2[i];auto iarea = areas[i];for (int64_t _j = _i + 1; _j < ndets; _j++) {auto j = order[_j];if (suppressed[j] == 1)continue;auto xx1 = std::max(ix1, x1[j]);auto yy1 = std::max(iy1, y1[j]);auto xx2 = std::min(ix2, x2[j]);auto yy2 = std::min(iy2, y2[j]);auto w = std::max(static_cast<float>(0), xx2 - xx1);auto h = std::max(static_cast<float>(0), yy2 - yy1);auto inter = w * h;auto ovr = inter / (iarea + areas[j] - inter);if (ovr > iou_threshold)suppressed[j] = 1;}}return keep_t.narrow(0, 0, num_to_keep);
}torch::Tensor non_max_suppression(torch::Tensor& prediction, float conf_thres = 0.25, float iou_thres = 0.45, int max_det = 300)
{using torch::indexing::Slice;using torch::indexing::None;auto bs = prediction.size(0);auto nc = prediction.size(1) - 4;auto nm = prediction.size(1) - nc - 4;auto mi = 4 + nc;auto xc = prediction.index({ Slice(), Slice(4, mi) }).amax(1) > conf_thres;prediction = prediction.transpose(-1, -2);prediction.index_put_({ "...", Slice({None, 4}) }, xywh2xyxy(prediction.index({ "...", Slice(None, 4) })));std::vector<torch::Tensor> output;for (int i = 0; i < bs; i++) {output.push_back(torch::zeros({ 0, 6 + nm }, prediction.device()));}for (int xi = 0; xi < prediction.size(0); xi++) {auto x = prediction[xi];x = x.index({ xc[xi] });auto x_split = x.split({ 4, nc, nm }, 1);auto box = x_split[0], cls = x_split[1], mask = x_split[2];auto [conf, j] = cls.max(1, true);x = torch::cat({ box, conf, j.toType(torch::kFloat), mask }, 1);x = x.index({ conf.view(-1) > conf_thres });int n = x.size(0);if (!n) { continue; }// NMSauto c = x.index({ Slice(), Slice{5, 6} }) * 7680;auto boxes = x.index({ Slice(), Slice(None, 4) }) + c;auto scores = x.index({ Slice(), 4 });auto i = nms(boxes, scores, iou_thres);i = i.index({ Slice(None, max_det) });output[xi] = x.index({ i });}return torch::stack(output);
}} // namespaceint test_yolov8_detect_libtorch()
{// reference: ultralytics/examples/YOLOv8-LibTorch-CPP-Inferenceif (auto flag = torch::cuda::is_available(); flag == true)std::cout << "cuda is available" << std::endl;elsestd::cout << "cuda is not available" << std::endl;torch::Device device(torch::cuda::is_available() ? torch::kCUDA : torch::kCPU);auto classes = parse_classes_file(classes_file);if (classes.size() == 0) {std::cerr << "Error: fail to parse classes file: " << classes_file << std::endl;return -1;}std::cout << "classes: ";for (const auto& val : classes) {std::cout << val << " ";}std::cout << std::endl;try {// load modeltorch::jit::script::Module model;if (torch::cuda::is_available() == true)model = torch::jit::load(torchscript_file, torch::kCUDA);elsemodel = torch::jit::load(torchscript_file, torch::kCPU);model.eval();// note: cpu is normal; gpu is abnormal: the model may not be fully placed on the gpu // model = torch::jit::load(file); model.to(torch::kCUDA) ==> model = torch::jit::load(file, torch::kCUDA)// model.to(device, torch::kFloat32);for (const auto& [key, val] : get_dir_images(images_dir)) {// load image and preprocesscv::Mat frame = cv::imread(val, cv::IMREAD_COLOR);if (frame.empty()) {std::cerr << "Warning: unable to load image: " << val << std::endl;continue;}cv::Mat bgr;letter_box(frame, bgr, {image_size[0], image_size[1]});torch::Tensor tensor = torch::from_blob(bgr.data, { bgr.rows, bgr.cols, 3 }, torch::kByte).to(device);tensor = tensor.toType(torch::kFloat32).div(255);tensor = tensor.permute({ 2, 0, 1 });tensor = tensor.unsqueeze(0);std::vector<torch::jit::IValue> inputs{ tensor };// inferencetorch::Tensor output = model.forward(inputs).toTensor().cpu();// NMSauto keep = non_max_suppression(output, 0.1f, 0.1f, 300)[0];std::vector<int> ids;std::vector<float> confidences;std::vector<cv::Rect> boxes;for (auto i = 0; i < keep.size(0); ++i) {int x1 = keep[i][0].item().toFloat();int y1 = keep[i][1].item().toFloat();int x2 = keep[i][2].item().toFloat();int y2 = keep[i][3].item().toFloat();boxes.emplace_back(cv::Rect(x1, y1, x2 - x1, y2 - y1));confidences.emplace_back(keep[i][4].item().toFloat());ids.emplace_back(keep[i][5].item().toInt());}draw_boxes(classes, ids, confidences, boxes, key, bgr);}} catch (const c10::Error& e) {std::cerr << "Error: " << e.msg() << std::endl;}return 0;
}labels.txt文件内容如下：仅2类
watermelon 0
wintermelon 1说明：
1.这里使用的libtorch版本为2.2.2；
2.通过函数torch::cuda::is_available()判断执行cpu还是gpu
3.通过非cmake构建项目时，调用torch::cuda::is_available()时即使在gpu下也会返回false，解决方法：项目属性：链接器 --> 命令行：其他选项中添加如下语句：
/INCLUDE:?warp_size@cuda@at@@YAHXZ4.gpu下，语句model.to(torch::kCUDA)有问题，好像并不能将模型全部放置在gpu上，应调整为如下语句：
model = torch::jit::load(torchscript_file, torch::kCUDA);执行结果如下图所示：同样的预测图像集，结果不如使用 opencv dnn 方法好，它们的前处理和后处理方式不同
其中一幅图像的检测结果如下图所示：
GitHub：https://github.com/fengbingchun/NN_Test
这篇关于使用libtorch加载YOLOv8生成的torchscript文件进行目标检测的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
