百度官方网站登录,国内很多网站不是响应式,石家庄网站定制开发,中国建设网站首页之前在 YOLOv8 classify介绍_yolov8分类模型-CSDN博客 中介绍过使用YOLOv8模型进行分类#xff0c;当时只给出了Python的实现#xff0c;包括训练和预测#xff0c;这里基于训练生成的模型#xff0c;分别给出C的opencv dnn、libtorch、onnruntime的实现。 opencv dnn实现测… 之前在 YOLOv8 classify介绍_yolov8分类模型-CSDN博客 中介绍过使用YOLOv8模型进行分类当时只给出了Python的实现包括训练和预测这里基于训练生成的模型分别给出C的opencv dnn、libtorch、onnruntime的实现。 opencv dnn实现测试代码如下
int test_yolov8_classify_opencv()
{auto net cv::dnn::readNetFromONNX(onnx_file);if (net.empty()) {std::cerr Error: there are no layers in the network: onnx_file std::endl;return -1;}if (cuda_enabled) {net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);} else {net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);}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;}constexpr int imgsz{ 224 };for (const auto [key, val] : get_dir_images(images_dir)) {cv::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 modify_image_size(frame); // top left padding//cv::Mat bgr;//letter_box(frame, bgr, std::vectorint{imgsz, imgsz}); // center paddingcv::Mat blob;cv::dnn::blobFromImage(bgr, blob, 1.0 / 255.0, cv::Size(imgsz, imgsz), cv::Scalar(), true, false);net.setInput(blob);std::vectorcv::Mat outputs;net.forward(outputs, net.getUnconnectedOutLayersNames());double max_val{ 0. };cv::Point max_idx{};cv::minMaxLoc(outputs[0], 0, max_val, 0, max_idx);std::cout image name: val , category: classes[max_idx.x] , conf: std::format({:.4f},max_val) std::endl;}return 0;
} 执行结果如下图所示图像填充方式有左上角填充和中心填充两种方式两种方式的均可正确分类但置信度多少有些差异 libtorch实现测试代码如下
int test_yolov8_classify_libtorch()
{if (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;}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);constexpr int imgsz{ 224 };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, std::vectorint{imgsz, imgsz});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::vectortorch::jit::IValue inputs{ tensor };// inferencetorch::Tensor output model.forward(inputs).toTensor().cpu();auto idx std::get1(output.max(1, true)).itemint();std::cout image name: val , category: classes[idx] , conf: std::format({:.4f}, torch::softmax(output, 1)[0][idx].itemfloat()) std::endl;}}catch (const c10::Error e) {std::cerr Error: e.msg() std::endl;return -1;}return 0;
} 执行结果如下图所示 onnxruntime实现测试代码如下
int test_yolov8_classify_onnxruntime()
{try {Ort::Env env Ort::Env(ORT_LOGGING_LEVEL_WARNING, Yolo);Ort::SessionOptions session_option;if (cuda_enabled) {OrtCUDAProviderOptions cuda_option;cuda_option.device_id 0;session_option.AppendExecutionProvider_CUDA(cuda_option);}session_option.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);session_option.SetIntraOpNumThreads(1);session_option.SetLogSeverityLevel(3);Ort::Session session(env, ctow(onnx_file).c_str(), session_option);Ort::AllocatorWithDefaultOptions allocator;std::vectorconst char* input_node_names, output_node_names;std::vectorstd::string input_node_names_, output_node_names_;for (auto i 0; i session.GetInputCount(); i) {Ort::AllocatedStringPtr input_node_name session.GetInputNameAllocated(i, allocator);input_node_names_.emplace_back(input_node_name.get());}for (auto i 0; i session.GetOutputCount(); i) {Ort::AllocatedStringPtr output_node_name session.GetOutputNameAllocated(i, allocator);output_node_names_.emplace_back(output_node_name.get());}for (auto i 0; i input_node_names_.size(); i)input_node_names.emplace_back(input_node_names_[i].c_str());for (auto i 0; i output_node_names_.size(); i)output_node_names.emplace_back(output_node_names_[i].c_str());constexpr int imgsz{ 224 };Ort::RunOptions options(nullptr);std::unique_ptrfloat[] blob(new float[imgsz * imgsz * 3]);std::vectorint64_t input_node_dims{ 1, 3, imgsz, imgsz };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;}for (const auto [key, val] : get_dir_images(images_dir)) {cv::Mat frame cv::imread(val, cv::IMREAD_COLOR);if (frame.empty()) {std::cerr Warning: unable to load image: val std::endl;continue;}cv::Mat rgb;letter_box(frame, rgb, std::vectorint{imgsz, imgsz});cv::cvtColor(rgb, rgb, cv::COLOR_BGR2RGB);image_to_blob(rgb, blob.get());Ort::Value input_tensor Ort::Value::CreateTensorfloat(Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU), blob.get(), 3 * imgsz * imgsz, input_node_dims.data(), input_node_dims.size());auto output_tensors session.Run(options, input_node_names.data(), input_tensor, input_node_names.size(), output_node_names.data(), output_node_names.size());Ort::Value output_tensor output_tensors[0];float* output_data output_tensor.GetTensorMutableDatafloat();auto shape output_tensor.GetTensorTypeAndShapeInfo().GetShape();auto num shape.size() 1 ? shape[1] : shape[0];float conf{ 0. };int idx{ -1 };for (auto i 0; i num; i) {if (output_data[i] conf) {conf output_data[i];idx static_castint(i);}}std::cout image name: val , category: classes[idx] , conf: std::format({:.4f}, conf) std::endl;}}catch (const std::exception e) {std::cerr Error: e.what() std::endl;return -1;}return 0;
} 执行结果如下图所示 注虽然三种方式均可对所有测试图像进行正确分类但它们的置信度不同 GitHubhttps://github.com/fengbingchun/NN_Test
