├── .gitignore ├── CMakeLists.txt ├── README.md ├── apps ├── app_mot.cpp ├── app_ptq.cpp ├── app_rtdetr.cpp ├── app_seg.cpp ├── app_sot.cpp ├── app_yolo.cpp ├── app_yolop.cpp ├── app_yolov10.cpp ├── bytetrack │ ├── BYTETracker.cpp │ ├── BYTETracker.h │ ├── STrack.cpp │ ├── STrack.h │ ├── dataType.h │ ├── kalmanFilter.cpp │ ├── kalmanFilter.h │ ├── lapjv.cpp │ ├── lapjv.h │ └── utils.cpp ├── lighttrack │ ├── LightTrack.cpp │ ├── LightTrack.hpp │ └── README.md ├── ostrack │ ├── OSTrack.cpp │ ├── OSTrack.hpp │ └── README.md ├── ppseg │ ├── ppseg.cpp │ └── ppseg.hpp ├── rtdetr │ ├── rtdetr.cpp │ ├── rtdetr.hpp │ └── rtdetr_decode.cu ├── yolo │ ├── README.md │ ├── yolo.cpp │ ├── yolo.hpp │ └── yolo_decode.cu ├── yolop │ ├── yolop.cpp │ ├── yolop.hpp │ └── yolop_decode.cu └── yolov10 │ ├── README.md │ ├── yolov10.cpp │ ├── yolov10.hpp │ └── yolov10_decode.cu ├── main.cpp ├── quant-tools ├── README.md ├── imgs │ ├── image-20230825203849049.png │ ├── image-20230825204514668.png │ ├── image-20230825204612547.png │ ├── monkey-patch-qat-conv-fp16-issue_ptq.png │ ├── monkey-patch-qat-conv-fp16-issue_qatonnx_edit.png │ └── monkey-patch-qat-maxpooling-qat.png ├── quantization │ ├── quantize.py │ └── rules.py └── scripts │ ├── draw-engine.py │ ├── qat-yolov5.py │ └── qat-yolov7.py ├── trt_common ├── cuda_tools.cpp ├── cuda_tools.hpp ├── ilogger.cpp ├── ilogger.hpp ├── infer_controller.hpp ├── preprocess_kernel.cu ├── preprocess_kernel.cuh ├── tensor_allocator.hpp ├── trt_infer.cpp ├── trt_infer.hpp ├── trt_tensor.cpp └── trt_tensor.hpp └── workspace ├── Woman ├── groundtruth_rect.txt └── img │ ├── 0001.jpg │ ├── 0002.jpg │ ├── 0003.jpg │ ├── 0004.jpg │ ├── 0005.jpg │ ├── 0006.jpg │ ├── 0007.jpg │ ├── 0008.jpg │ ├── 0009.jpg │ ├── 0010.jpg │ ├── 0011.jpg │ ├── 0012.jpg │ ├── 0013.jpg │ ├── 0014.jpg │ ├── 0015.jpg │ ├── 0016.jpg │ ├── 0017.jpg │ ├── 0018.jpg │ ├── 0019.jpg │ ├── 0020.jpg │ ├── 0021.jpg │ ├── 0022.jpg │ ├── 0023.jpg │ ├── 0024.jpg │ ├── 0025.jpg │ ├── 0026.jpg │ ├── 0027.jpg │ ├── 0028.jpg │ ├── 0029.jpg │ ├── 0030.jpg │ ├── 0031.jpg │ ├── 0032.jpg │ ├── 0033.jpg │ ├── 0034.jpg │ ├── 0035.jpg │ ├── 0036.jpg │ ├── 0037.jpg │ ├── 0038.jpg │ ├── 0039.jpg │ ├── 0040.jpg │ ├── 0041.jpg │ ├── 0042.jpg │ ├── 0043.jpg │ ├── 0044.jpg │ ├── 0045.jpg │ ├── 0046.jpg │ ├── 0047.jpg │ ├── 0048.jpg │ ├── 0049.jpg │ ├── 0050.jpg │ ├── 0051.jpg │ ├── 0052.jpg │ ├── 0053.jpg │ ├── 0054.jpg │ ├── 0055.jpg │ ├── 0056.jpg │ ├── 0057.jpg │ ├── 0058.jpg │ ├── 0059.jpg │ ├── 0060.jpg │ ├── 0061.jpg │ ├── 0062.jpg │ ├── 0063.jpg │ ├── 0064.jpg │ ├── 0065.jpg │ ├── 0066.jpg │ ├── 0067.jpg │ ├── 0068.jpg │ ├── 0069.jpg │ ├── 0070.jpg │ ├── 0071.jpg │ ├── 0072.jpg │ ├── 0073.jpg │ ├── 0074.jpg │ ├── 0075.jpg │ ├── 0076.jpg │ ├── 0077.jpg │ ├── 0078.jpg │ ├── 0079.jpg │ ├── 0080.jpg │ ├── 0081.jpg │ ├── 0082.jpg │ ├── 0083.jpg │ ├── 0084.jpg │ ├── 0085.jpg │ ├── 0086.jpg │ ├── 0087.jpg │ ├── 0088.jpg │ ├── 0089.jpg │ ├── 0090.jpg │ ├── 0091.jpg │ ├── 0092.jpg │ ├── 0093.jpg │ ├── 0094.jpg │ ├── 0095.jpg │ ├── 0096.jpg │ ├── 0097.jpg │ ├── 0098.jpg │ ├── 0099.jpg │ ├── 0100.jpg │ ├── 0101.jpg │ ├── 0102.jpg │ ├── 0103.jpg │ ├── 0104.jpg │ ├── 0105.jpg │ ├── 0106.jpg │ ├── 0107.jpg │ ├── 0108.jpg │ ├── 0109.jpg │ ├── 0110.jpg │ ├── 0111.jpg │ ├── 0112.jpg │ ├── 0113.jpg │ ├── 0114.jpg │ ├── 0115.jpg │ ├── 0116.jpg │ ├── 0117.jpg │ ├── 0118.jpg │ ├── 0119.jpg │ ├── 0120.jpg │ ├── 0121.jpg │ ├── 0122.jpg │ ├── 0123.jpg │ ├── 0124.jpg │ ├── 0125.jpg │ ├── 0126.jpg │ ├── 0127.jpg │ ├── 0128.jpg │ ├── 0129.jpg │ ├── 0130.jpg │ ├── 0131.jpg │ ├── 0132.jpg │ ├── 0133.jpg │ ├── 0134.jpg │ ├── 0135.jpg │ ├── 0136.jpg │ ├── 0137.jpg │ ├── 0138.jpg │ ├── 0139.jpg │ ├── 0140.jpg │ ├── 0141.jpg │ ├── 0142.jpg │ ├── 0143.jpg │ ├── 0144.jpg │ ├── 0145.jpg │ ├── 0146.jpg │ ├── 0147.jpg │ ├── 0148.jpg │ ├── 0149.jpg │ ├── 0150.jpg │ ├── 0151.jpg │ ├── 0152.jpg │ ├── 0153.jpg │ ├── 0154.jpg │ ├── 0155.jpg │ ├── 0156.jpg │ ├── 0157.jpg │ ├── 0158.jpg │ ├── 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├── 0226.jpg │ ├── 0227.jpg │ ├── 0228.jpg │ ├── 0229.jpg │ ├── 0230.jpg │ ├── 0231.jpg │ ├── 0232.jpg │ ├── 0233.jpg │ ├── 0234.jpg │ ├── 0235.jpg │ ├── 0236.jpg │ ├── 0237.jpg │ ├── 0238.jpg │ ├── 0239.jpg │ ├── 0240.jpg │ ├── 0241.jpg │ ├── 0242.jpg │ ├── 0243.jpg │ ├── 0244.jpg │ ├── 0245.jpg │ ├── 0246.jpg │ ├── 0247.jpg │ ├── 0248.jpg │ ├── 0249.jpg │ ├── 0250.jpg │ ├── 0251.jpg │ ├── 0252.jpg │ ├── 0253.jpg │ ├── 0254.jpg │ ├── 0255.jpg │ ├── 0256.jpg │ ├── 0257.jpg │ ├── 0258.jpg │ ├── 0259.jpg │ ├── 0260.jpg │ ├── 0261.jpg │ ├── 0262.jpg │ ├── 0263.jpg │ ├── 0264.jpg │ ├── 0265.jpg │ ├── 0266.jpg │ ├── 0267.jpg │ ├── 0268.jpg │ ├── 0269.jpg │ ├── 0270.jpg │ ├── 0271.jpg │ ├── 0272.jpg │ ├── 0273.jpg │ ├── 0274.jpg │ ├── 0275.jpg │ ├── 0276.jpg │ ├── 0277.jpg │ ├── 0278.jpg │ ├── 0279.jpg │ ├── 0280.jpg │ ├── 0281.jpg │ ├── 0282.jpg │ ├── 0283.jpg │ ├── 0284.jpg │ ├── 0285.jpg │ ├── 0286.jpg │ ├── 0287.jpg │ ├── 0288.jpg │ ├── 0289.jpg │ ├── 0290.jpg │ ├── 0291.jpg │ ├── 0292.jpg │ ├── 0293.jpg │ ├── 0294.jpg │ ├── 0295.jpg │ ├── 0296.jpg │ ├── 0297.jpg │ ├── 0298.jpg │ ├── 0299.jpg │ ├── 0300.jpg │ ├── 0301.jpg │ ├── 0302.jpg │ ├── 0303.jpg │ ├── 0304.jpg │ ├── 0305.jpg │ ├── 0306.jpg │ ├── 0307.jpg │ ├── 0308.jpg │ ├── 0309.jpg │ ├── 0310.jpg │ ├── 0311.jpg │ ├── 0312.jpg │ ├── 0313.jpg │ ├── 0314.jpg │ ├── 0315.jpg │ ├── 0316.jpg │ ├── 0317.jpg │ ├── 0318.jpg │ ├── 0319.jpg │ ├── 0320.jpg │ ├── 0321.jpg │ ├── 0322.jpg │ ├── 0323.jpg │ ├── 0324.jpg │ ├── 0325.jpg │ ├── 0326.jpg │ ├── 0327.jpg │ ├── 0328.jpg │ ├── 0329.jpg │ ├── 0330.jpg │ ├── 0331.jpg │ ├── 0332.jpg │ ├── 0333.jpg │ ├── 0334.jpg │ ├── 0335.jpg │ ├── 0336.jpg │ ├── 0337.jpg │ ├── 0338.jpg │ ├── 0339.jpg │ ├── 0340.jpg │ ├── 0341.jpg │ ├── 0342.jpg │ ├── 0343.jpg │ ├── 0344.jpg │ ├── 0345.jpg │ ├── 0346.jpg │ ├── 0347.jpg │ ├── 0348.jpg │ ├── 0349.jpg │ ├── 0350.jpg │ ├── 0351.jpg │ ├── 0352.jpg │ ├── 0353.jpg │ ├── 0354.jpg │ ├── 0355.jpg │ ├── 0356.jpg │ ├── 0357.jpg │ ├── 0358.jpg │ ├── 0359.jpg │ ├── 0360.jpg │ ├── 0361.jpg │ ├── 0362.jpg │ ├── 0363.jpg │ ├── 0364.jpg │ ├── 0365.jpg │ ├── 0366.jpg │ ├── 0367.jpg │ ├── 0368.jpg │ ├── 0369.jpg │ ├── 0370.jpg │ ├── 0371.jpg │ ├── 0372.jpg │ ├── 0373.jpg │ ├── 0374.jpg │ ├── 0375.jpg │ ├── 0376.jpg │ ├── 0377.jpg │ ├── 0378.jpg │ ├── 0379.jpg │ ├── 0380.jpg │ ├── 0381.jpg │ ├── 0382.jpg │ ├── 0383.jpg │ ├── 0384.jpg │ ├── 0385.jpg │ ├── 0386.jpg │ ├── 0387.jpg │ ├── 0388.jpg │ ├── 0389.jpg │ ├── 0390.jpg │ ├── 0391.jpg │ ├── 0392.jpg │ ├── 0393.jpg │ ├── 0394.jpg │ ├── 0395.jpg │ ├── 0396.jpg │ ├── 0397.jpg │ ├── 0398.jpg │ ├── 0399.jpg │ ├── 0400.jpg │ ├── 0401.jpg │ ├── 0402.jpg │ ├── 0403.jpg │ ├── 0404.jpg │ ├── 0405.jpg │ ├── 0406.jpg │ ├── 0407.jpg │ ├── 0408.jpg │ ├── 0409.jpg │ ├── 0410.jpg │ ├── 0411.jpg │ ├── 0412.jpg │ ├── 0413.jpg │ ├── 0414.jpg │ ├── 0415.jpg │ ├── 0416.jpg │ ├── 0417.jpg │ ├── 0418.jpg │ ├── 0419.jpg │ ├── 0420.jpg │ ├── 0421.jpg │ ├── 0422.jpg │ ├── 0423.jpg │ ├── 0424.jpg │ ├── 0425.jpg │ ├── 0426.jpg │ ├── 0427.jpg │ ├── 0428.jpg │ ├── 0429.jpg │ ├── 0430.jpg │ ├── 0431.jpg │ ├── 0432.jpg │ ├── 0433.jpg │ ├── 0434.jpg │ ├── 0435.jpg │ ├── 0436.jpg │ ├── 0437.jpg │ ├── 0438.jpg │ ├── 0439.jpg │ ├── 0440.jpg │ ├── 0441.jpg │ ├── 0442.jpg │ ├── 0443.jpg │ ├── 0444.jpg │ ├── 0445.jpg │ ├── 0446.jpg │ ├── 0447.jpg │ ├── 0448.jpg │ ├── 0449.jpg │ ├── 0450.jpg │ ├── 0451.jpg │ ├── 0452.jpg │ ├── 0453.jpg │ ├── 0454.jpg │ ├── 0455.jpg │ ├── 0456.jpg │ ├── 0457.jpg │ ├── 0458.jpg │ ├── 0459.jpg │ ├── 0460.jpg │ ├── 0461.jpg │ ├── 0462.jpg │ ├── 0463.jpg │ ├── 0464.jpg │ ├── 0465.jpg │ ├── 0466.jpg │ ├── 0467.jpg │ ├── 0468.jpg │ ├── 0469.jpg │ ├── 0470.jpg │ ├── 0471.jpg │ ├── 0472.jpg │ ├── 0473.jpg │ ├── 0474.jpg │ ├── 0475.jpg │ ├── 0476.jpg │ ├── 0477.jpg │ ├── 0478.jpg │ ├── 0479.jpg │ ├── 0480.jpg │ ├── 0481.jpg │ ├── 0482.jpg │ ├── 0483.jpg │ ├── 0484.jpg │ ├── 0485.jpg │ ├── 0486.jpg │ ├── 0487.jpg │ ├── 0488.jpg │ ├── 0489.jpg │ ├── 0490.jpg │ ├── 0491.jpg │ ├── 0492.jpg │ ├── 0493.jpg │ ├── 0494.jpg │ ├── 0495.jpg │ ├── 0496.jpg │ ├── 0497.jpg │ ├── 0498.jpg │ ├── 0499.jpg │ ├── 0500.jpg │ ├── 0501.jpg │ ├── 0502.jpg │ ├── 0503.jpg │ ├── 0504.jpg │ ├── 0505.jpg │ ├── 0506.jpg │ ├── 0507.jpg │ ├── 0508.jpg │ ├── 0509.jpg │ ├── 0510.jpg │ ├── 0511.jpg │ ├── 0512.jpg │ ├── 0513.jpg │ ├── 0514.jpg │ ├── 0515.jpg │ ├── 0516.jpg │ ├── 0517.jpg │ ├── 0518.jpg │ ├── 0519.jpg │ ├── 0520.jpg │ ├── 0521.jpg │ ├── 0522.jpg │ ├── 0523.jpg │ ├── 0524.jpg │ ├── 0525.jpg │ ├── 0526.jpg │ ├── 0527.jpg │ ├── 0528.jpg │ ├── 0529.jpg │ ├── 0530.jpg │ ├── 0531.jpg │ ├── 0532.jpg │ ├── 0533.jpg │ ├── 0534.jpg │ ├── 0535.jpg │ ├── 0536.jpg │ ├── 0537.jpg │ ├── 0538.jpg │ ├── 0539.jpg │ ├── 0540.jpg │ ├── 0541.jpg │ ├── 0542.jpg │ ├── 0543.jpg │ ├── 0544.jpg │ ├── 0545.jpg │ ├── 0546.jpg │ ├── 0547.jpg │ ├── 0548.jpg │ ├── 0549.jpg │ ├── 0550.jpg │ ├── 0551.jpg │ ├── 0552.jpg │ ├── 0553.jpg │ ├── 0554.jpg │ ├── 0555.jpg │ ├── 0556.jpg │ ├── 0557.jpg │ ├── 0558.jpg │ ├── 0559.jpg │ ├── 0560.jpg │ ├── 0561.jpg │ ├── 0562.jpg │ ├── 0563.jpg │ ├── 0564.jpg │ ├── 0565.jpg │ ├── 0566.jpg │ ├── 0567.jpg │ ├── 0568.jpg │ ├── 0569.jpg │ ├── 0570.jpg │ ├── 0571.jpg │ ├── 0572.jpg │ ├── 0573.jpg │ ├── 0574.jpg │ ├── 0575.jpg │ ├── 0576.jpg │ ├── 0577.jpg │ ├── 0578.jpg │ ├── 0579.jpg │ ├── 0580.jpg │ ├── 0581.jpg │ ├── 0582.jpg │ ├── 0583.jpg │ ├── 0584.jpg │ ├── 0585.jpg │ ├── 0586.jpg │ ├── 0587.jpg │ ├── 0588.jpg │ ├── 0589.jpg │ ├── 0590.jpg │ ├── 0591.jpg │ ├── 0592.jpg │ ├── 0593.jpg │ ├── 0594.jpg │ ├── 0595.jpg │ ├── 0596.jpg │ └── 0597.jpg ├── compile_engine.sh ├── imgs ├── 1.jpg ├── 2.jpg ├── 3.jpg ├── 4.jpg ├── 5.jpg ├── 6.jpg ├── bus.jpg ├── frame_0.jpg ├── frame_3.jpg ├── girl.jpg ├── group.jpg ├── yq.jpg ├── zand.jpg └── zgjr.jpg └── videos └── bag.avi /.gitignore: -------------------------------------------------------------------------------- 1 | **/cmake-build-debug/** 2 | **/cmake-build-release/** 3 | **/.idea/** 4 | **/.vscode/** 5 | **/build/** 6 | **/mot_imgs/** 7 | **/mot_infer_res/** 8 | **/infer_res/** 9 | **/pro 10 | **/*.so 11 | **/*.pt 12 | **/*.pth 13 | **/*.tar 14 | **/*.onnx 15 | **/*.engine 16 | **/*.trt 17 | **/*.trtmodel 18 | -------------------------------------------------------------------------------- /CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 3.10) 2 | PROJECT(Linfer VERSION 1.0.0 LANGUAGES C CXX CUDA) 3 | set(CMAKE_CXX_STANDARD 11) 4 | set(CMAKE_BUILD_TYPE Debug) 5 | #set(CMAKE_BUILD_TYPE Release) 6 | set(EXECUTABLE_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/workspace) 7 | set(LIBRARY_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/workspace) 8 | set(CMAKE_SKIP_BUILD_RPATH False) 9 | set(CMAKE_SKIP_RPATH False) 10 | set(CMAKE_BUILD_RPATH "/home/lsf/Third_party/TensorRT-8.6.1.6/lib") 11 | 12 | file(GLOB_RECURSE CPPS 13 | ${PROJECT_SOURCE_DIR}/apps/*.cpp 14 | ${PROJECT_SOURCE_DIR}/apps/*.cu 15 | ${PROJECT_SOURCE_DIR}/trt_common/*.cpp 16 | ${PROJECT_SOURCE_DIR}/trt_common/*.cu 17 | ) 18 | 19 | set(CUDA_DIR "/usr/local/cuda") 20 | set(TENSORRT_DIR "/home/lsf/Third_party/TensorRT-8.6.1.6") 21 | find_package(OpenCV REQUIRED) 22 | if(POLICY CMP0146) 23 | cmake_policy(SET CMP0146 OLD) 24 | endif() 25 | find_package(CUDA REQUIRED) 26 | include_directories( 27 | ${PROJECT_SOURCE_DIR} 28 | ${OpenCV_INCLUDE_DIRS} 29 | ${CUDA_DIR}/include 30 | ${TENSORRT_DIR}/include 31 | "/usr/include/eigen3" 32 | ) 33 | link_directories( 34 | ${CUDA_DIR}/lib64 35 | ${TENSORRT_DIR}/lib 36 | ) 37 | list(APPEND ALL_LIBS 38 | cuda cublas cudart cudnn 39 | nvinfer nvinfer_plugin nvonnxparser 40 | pthread ${OpenCV_LIBS} 41 | ) 42 | 43 | set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-deprecated-declarations -Wfatal-errors -pthread -w") 44 | set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -std=c++11 -g -O0 -Xcompiler -fPIC") 45 | 46 | add_library(${PROJECT_NAME} SHARED ${CPPS}) 47 | target_link_libraries(${PROJECT_NAME} ${ALL_LIBS}) 48 | 49 | # reference：https://developer.nvidia.com/cuda-gpus#compute 50 | set_property(TARGET ${PROJECT_NAME} PROPERTY CUDA_ARCHITECTURES 89) 51 | target_compile_options(${PROJECT_NAME} PUBLIC 52 | $<$:--default-stream per-thread -lineinfo --use_fast_math --disable-warnings>) 53 | 54 | add_executable(pro main.cpp) 55 | target_link_libraries(pro ${PROJECT_NAME} ${ALL_LIBS}) 56 | -------------------------------------------------------------------------------- /apps/app_seg.cpp: -------------------------------------------------------------------------------- 1 | 2 | #include 3 | #include 4 | #include "ppseg/ppseg.hpp" 5 | 6 | using namespace std; 7 | 8 | void performance_seg(const string& engine_file, int gpuid){ 9 | auto predictor = PPSeg::create_seg(engine_file, gpuid); 10 | if(predictor == nullptr){ 11 | printf("predictor is nullptr.\n"); 12 | return; 13 | } 14 | 15 | auto image = cv::imread("imgs/frame_0.jpg"); 16 | 17 | cv::Mat res; 18 | // warmup 19 | for(int i = 0; i < 10; ++i) 20 | res = predictor->seg(image); 21 | 22 | // 测试 100 轮 23 | const int ntest = 100; 24 | auto start = std::chrono::steady_clock::now(); 25 | for(int i = 0; i < ntest; ++i) 26 | res = predictor->seg(image); 27 | 28 | std::chrono::duration during = std::chrono::steady_clock::now() - start; 29 | double all_time = 1000.0 * during.count(); 30 | float avg_time = all_time / ntest; 31 | printf("Average time: %.2f ms, FPS: %.2f\n", engine_file.c_str(), avg_time, 1000 / avg_time); 32 | } 33 | 34 | void inference_seg(const string& engine_file, int gpuid){ 35 | auto predictor = PPSeg::create_seg(engine_file, gpuid); 36 | if(predictor == nullptr){ 37 | printf("predictor is nullptr.\n"); 38 | return; 39 | } 40 | 41 | auto image = cv::imread("imgs/frame_3.jpg"); 42 | auto res = predictor->seg(image); 43 | 44 | cv::Mat color_img(image.size(), CV_8UC3, cv::Scalar(0, 0, 0)); 45 | // 遍历每个像素点，根据类别索引应用颜色映射 46 | for (int i = 0; i < res.rows; ++i) { 47 | for (int j = 0; j < res.cols; ++j) { 48 | uchar pixel_value = res.at(i, j); 49 | color_img.at(i, j) = cv::Vec3b(PPSeg::color_map[pixel_value][2], 50 | PPSeg::color_map[pixel_value][1], 51 | PPSeg::color_map[pixel_value][0]); 52 | } 53 | } 54 | cv::Mat out_color_img(image.size(), CV_8UC3, cv::Scalar(0, 0, 0)); 55 | float alpha = 0.7; 56 | out_color_img = (1 - alpha) * image + alpha * color_img; 57 | cv::imwrite("infer_res/seg_frame_3.jpg", out_color_img); 58 | } 59 | 60 | 61 | 62 | -------------------------------------------------------------------------------- /apps/app_yolop.cpp: -------------------------------------------------------------------------------- 1 | 2 | #include 3 | #include 4 | #include "yolop/yolop.hpp" 5 | 6 | using namespace std; 7 | 8 | void performance_yolop(const string& engine_file, YoloP::Type type, int gpuid){ 9 | auto detector = YoloP::create_detector(engine_file, type, gpuid, 0.4, 0.5); 10 | if(detector == nullptr){ 11 | printf("detector is nullptr.\n"); 12 | return; 13 | } 14 | 15 | auto image = cv::imread("imgs/1.jpg"); 16 | YoloP::PTMM res; 17 | 18 | // warmup 19 | for(int i = 0; i < 10; ++i) 20 | res = detector->detect(image); 21 | 22 | // 测试 100 轮 23 | const int ntest = 100; 24 | auto start = std::chrono::steady_clock::now(); 25 | for(int i = 0; i < ntest; ++i) 26 | res = detector->detect(image); 27 | 28 | std::chrono::duration during = std::chrono::steady_clock::now() - start; 29 | double all_time = 1000.0 * during.count(); 30 | float avg_time = all_time / ntest; 31 | printf("Average time: %.2f ms, FPS: %.2f\n", engine_file.c_str(), avg_time, 1000 / avg_time); 32 | } 33 | 34 | 35 | void inference_yolop(const string& engine_file, YoloP::Type type, int gpuid){ 36 | auto detector = YoloP::create_detector(engine_file, type, gpuid, 0.4, 0.5); 37 | if(detector == nullptr){ 38 | printf("detector is nullptr.\n"); 39 | return; 40 | } 41 | 42 | auto image = cv::imread("imgs/6.jpg"); 43 | auto res = detector->detect(image); 44 | YoloP::BoxArray& boxes = get<0>(res); 45 | cv::Mat& drive_mask = get<1>(res); 46 | cv::Mat& lane_mask = get<2>(res); 47 | 48 | for(auto& ibox : boxes) 49 | cv::rectangle(image, cv::Point(ibox.left, ibox.top), 50 | cv::Point(ibox.right, ibox.bottom), 51 | {0, 0, 255}, 2); 52 | 53 | cv::imwrite("infer_res/res.jpg", image); 54 | cv::imwrite("infer_res/drive.jpg", drive_mask); 55 | cv::imwrite("infer_res/lane.jpg", lane_mask); 56 | } 57 | 58 | -------------------------------------------------------------------------------- /apps/bytetrack/BYTETracker.h: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | #include "STrack.h" 4 | 5 | struct Object 6 | { 7 | // x, y, width, height 8 | float rect[4]; 9 | int label; 10 | float prob; 11 | }; 12 | 13 | class BYTETracker 14 | { 15 | public: 16 | BYTETracker(); 17 | ~BYTETracker(); 18 | 19 | vector update(const vector& objects); 20 | tuple get_color(int idx); 21 | byte_kalman::Config& config(); 22 | 23 | private: 24 | vector joint_stracks(vector &tlista, vector &tlistb); 25 | vector joint_stracks(vector &tlista, vector &tlistb); 26 | 27 | vector sub_stracks(vector &tlista, vector &tlistb); 28 | void remove_duplicate_stracks(vector &resa, vector &resb, vector &stracksa, vector &stracksb); 29 | 30 | void linear_assignment(vector > &cost_matrix, int cost_matrix_size, int cost_matrix_size_size, float thresh, 31 | vector > &matches, vector &unmatched_a, vector &unmatched_b); 32 | vector > iou_distance(vector &atracks, vector &btracks, int &dist_size, int &dist_size_size); 33 | vector > iou_distance(vector &atracks, vector &btracks); 34 | vector > ious(vector > &atlbrs, vector > &btlbrs); 35 | 36 | double lapjv(const vector > &cost, vector &rowsol, vector &colsol, 37 | bool extend_cost = false, float cost_limit = LONG_MAX, bool return_cost = true); 38 | 39 | private: 40 | int frame_id; 41 | 42 | vector tracked_stracks; 43 | vector lost_stracks; 44 | vector removed_stracks; 45 | byte_kalman::KalmanFilter kalman_filter; 46 | byte_kalman::Config& _config = kalman_filter.config(); 47 | }; -------------------------------------------------------------------------------- /apps/bytetrack/STrack.h: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | #include "kalmanFilter.h" 4 | 5 | using namespace std; 6 | 7 | enum TrackState { New = 0, Tracked, Lost, Removed }; 8 | 9 | class STrack 10 | { 11 | public: 12 | STrack(vector tlwh_, float score); 13 | ~STrack(); 14 | 15 | vector static tlbr_to_tlwh(vector &tlbr); 16 | void static multi_predict(vector &stracks, byte_kalman::KalmanFilter &kalman_filter); 17 | void static_tlwh(); 18 | void static_tlbr(); 19 | vector tlwh_to_xyah(vector tlwh_tmp); 20 | vector to_xyah(); 21 | void mark_lost(); 22 | void mark_removed(); 23 | int next_id(); 24 | int end_frame(); 25 | 26 | void activate(byte_kalman::KalmanFilter &kalman_filter, int frame_id); 27 | void re_activate(STrack &new_track, int frame_id, bool new_id = false); 28 | void update(STrack &new_track, int frame_id); 29 | 30 | public: 31 | bool is_activated; 32 | int track_id; 33 | int state; 34 | 35 | vector _tlwh; 36 | vector tlwh; 37 | vector tlbr; 38 | int frame_id; 39 | int tracklet_len; 40 | int start_frame; 41 | 42 | KAL_MEAN mean; 43 | KAL_COVA covariance; 44 | float score; 45 | 46 | private: 47 | byte_kalman::KalmanFilter kalman_filter; 48 | }; -------------------------------------------------------------------------------- /apps/bytetrack/dataType.h: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | #include 4 | #include 5 | 6 | #include 7 | #include 8 | typedef Eigen::Matrix DETECTBOX; 9 | typedef Eigen::Matrix DETECTBOXSS; 10 | typedef Eigen::Matrix FEATURE; 11 | typedef Eigen::Matrix FEATURESS; 12 | //typedef std::vector FEATURESS; 13 | 14 | //Kalmanfilter 15 | //typedef Eigen::Matrix KAL_FILTER; 16 | typedef Eigen::Matrix KAL_MEAN; 17 | typedef Eigen::Matrix KAL_COVA; 18 | typedef Eigen::Matrix KAL_HMEAN; 19 | typedef Eigen::Matrix KAL_HCOVA; 20 | using KAL_DATA = std::pair; 21 | using KAL_HDATA = std::pair; 22 | 23 | //main 24 | using RESULT_DATA = std::pair; 25 | 26 | //tracker: 27 | using TRACKER_DATA = std::pair; 28 | using MATCH_DATA = std::pair; 29 | typedef struct t { 30 | std::vector matches; 31 | std::vector unmatched_tracks; 32 | std::vector unmatched_detections; 33 | }TRACHER_MATCHD; 34 | 35 | //linear_assignment: 36 | typedef Eigen::Matrix DYNAMICM; -------------------------------------------------------------------------------- /apps/bytetrack/kalmanFilter.h: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | #include "dataType.h" 4 | 5 | namespace byte_kalman 6 | { 7 | struct Config{ 8 | // kalman 9 | // /** 初始状态 **/ 10 | float initiate_state[8]; 11 | 12 | // /** 每一侦的运动量协方差，下一侦 = 当前帧 + 运动量 **/ 13 | float per_frame_motion[8]; 14 | 15 | // /** 测量噪声，把输入映射到测量空间中后的噪声 **/ 16 | float noise[4]; 17 | 18 | float track_thresh = 0.5; 19 | float high_thresh = 0.6; 20 | float match_thresh = 0.8; 21 | int max_time_lost = 30; 22 | 23 | Config& set_initiate_state(const std::vector& values); 24 | Config& set_per_frame_motion(const std::vector& values); 25 | Config& set_noise(const std::vector& values); 26 | Config& set_track_thresh(float value){this->track_thresh = value; return *this;}; 27 | Config& set_high_thresh(float value){this->high_thresh = value; return *this;}; 28 | Config& set_match_thresh(float value){this->match_thresh = value; return *this;}; 29 | Config& set_max_time_lost(int value){this->max_time_lost = value; return *this;}; 30 | 31 | Config(); 32 | }; 33 | 34 | class KalmanFilter 35 | { 36 | public: 37 | static const double chi2inv95[10]; 38 | KalmanFilter(); 39 | 40 | Config& config(); 41 | KAL_DATA initiate(const DETECTBOX& measurement); 42 | void predict(KAL_MEAN& mean, KAL_COVA& covariance); 43 | KAL_HDATA project(const KAL_MEAN& mean, const KAL_COVA& covariance); 44 | KAL_DATA update(const KAL_MEAN& mean, 45 | const KAL_COVA& covariance, 46 | const DETECTBOX& measurement); 47 | 48 | Eigen::Matrix gating_distance( 49 | const KAL_MEAN& mean, 50 | const KAL_COVA& covariance, 51 | const std::vector& measurements, 52 | bool only_position = false); 53 | 54 | private: 55 | Config _config; 56 | Eigen::Matrix _motion_mat; 57 | Eigen::Matrix _update_mat; 58 | float _std_weight_position; 59 | float _std_weight_velocity; 60 | }; 61 | } -------------------------------------------------------------------------------- /apps/bytetrack/lapjv.h: -------------------------------------------------------------------------------- 1 | #ifndef LAPJV_H 2 | #define LAPJV_H 3 | 4 | #define LARGE 1000000 5 | 6 | #if !defined TRUE 7 | #define TRUE 1 8 | #endif 9 | #if !defined FALSE 10 | #define FALSE 0 11 | #endif 12 | 13 | #define NEW(x, t, n) if ((x = (t *)malloc(sizeof(t) * (n))) == 0) { return -1; } 14 | #define FREE(x) if (x != 0) { free(x); x = 0; } 15 | #define SWAP_INDICES(a, b) { int_t _temp_index = a; a = b; b = _temp_index; } 16 | 17 | #if 0 18 | #include 19 | #define ASSERT(cond) assert(cond) 20 | #define PRINTF(fmt, ...) printf(fmt, ##__VA_ARGS__) 21 | #define PRINT_COST_ARRAY(a, n) \ 22 | while (1) { \ 23 | printf(#a" = ["); \ 24 | if ((n) > 0) { \ 25 | printf("%f", (a)[0]); \ 26 | for (uint_t j = 1; j < n; j++) { \ 27 | printf(", %f", (a)[j]); \ 28 | } \ 29 | } \ 30 | printf("]\n"); \ 31 | break; \ 32 | } 33 | #define PRINT_INDEX_ARRAY(a, n) \ 34 | while (1) { \ 35 | printf(#a" = ["); \ 36 | if ((n) > 0) { \ 37 | printf("%d", (a)[0]); \ 38 | for (uint_t j = 1; j < n; j++) { \ 39 | printf(", %d", (a)[j]); \ 40 | } \ 41 | } \ 42 | printf("]\n"); \ 43 | break; \ 44 | } 45 | #else 46 | #define ASSERT(cond) 47 | #define PRINTF(fmt, ...) 48 | #define PRINT_COST_ARRAY(a, n) 49 | #define PRINT_INDEX_ARRAY(a, n) 50 | #endif 51 | 52 | 53 | typedef signed int int_t; 54 | typedef unsigned int uint_t; 55 | typedef double cost_t; 56 | typedef char boolean; 57 | typedef enum fp_t { FP_1 = 1, FP_2 = 2, FP_DYNAMIC = 3 } fp_t; 58 | 59 | extern int_t lapjv_internal( 60 | const uint_t n, cost_t *cost[], 61 | int_t *x, int_t *y); 62 | 63 | #endif // LAPJV_H -------------------------------------------------------------------------------- /apps/lighttrack/LightTrack.hpp: -------------------------------------------------------------------------------- 1 | #ifndef LIGHTTRACK_HPP 2 | #define LIGHTTRACK_HPP 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | 9 | namespace LightTrack { 10 | 11 | using namespace std; 12 | 13 | class Tracker{ 14 | public: 15 | virtual void init(cv::Mat &z_img, cv::Rect &init_bbox) = 0; 16 | virtual cv::Rect track(cv::Mat& x_img) = 0; 17 | }; 18 | 19 | shared_ptr create_tracker(const std::string &z_path, const std::string &x_path, int gpuid = 0); 20 | }; 21 | 22 | 23 | #endif //LIGHTTRACK_HPP 24 | -------------------------------------------------------------------------------- /apps/lighttrack/README.md: -------------------------------------------------------------------------------- 1 | # onnx导出 2 | 3 | 1、导出模板分支： 4 | 5 | ```python 6 | net = models.LightTrackM_Subnet('back_04502514044521042540+cls_211000022+reg_100000111_ops_32', 16) 7 | net = load_pretrain(net, '../snapshot/LightTrackM/LightTrackM.pth') 8 | dummy_input = ( 9 | torch.randn(1, 3, 128, 128), 10 | ) 11 | torch.onnx.export( 12 | net.features, 13 | dummy_input, 14 | "lighttrack-z.onnx", 15 | verbose=True, 16 | opset_version=11, 17 | input_names=["z"], 18 | output_names=["zf"], 19 | ) 20 | print('----------finished exporting onnx-----------') 21 | print('----------start simplifying onnx-----------') 22 | model_sim, flag = onnxsim.simplify('lighttrack-z.onnx') 23 | if flag: 24 | onnx.save(model_sim, 'lighttrack-z.onnx') 25 | print("---------simplify onnx successfully---------") 26 | else: 27 | print("---------simplify onnx failed-----------") 28 | ``` 29 | 30 | 31 | 32 | 2、修改源码中的 `lib/models/super_model_DP.py` 中 `class Super_model_DP_retrain` 的 `forward` 函数： 33 | 34 | ```python 35 | def forward(self, zf, search): 36 | """backbone_index: which layer's feature to use""" 37 | # zf = self.features(template) 38 | xf = self.features(search) 39 | # Batch Normalization before Corr 40 | zf, xf = self.neck(zf, xf) 41 | # Point-wise Correlation 42 | feat_dict = self.feature_fusor(zf, xf) 43 | # supernet head 44 | oup = self.head(feat_dict) 45 | cls_score = nn.functional.sigmoid(oup['cls']) 46 | bbox_pred = oup['reg'] 47 | return cls_score, bbox_pred 48 | ``` 49 | 50 | 然后导出 搜索分支+neck+head： 51 | 52 | ```python 53 | net = models.LightTrackM_Subnet('back_04502514044521042540+cls_211000022+reg_100000111_ops_32', 16) 54 | net = load_pretrain(net, '../snapshot/LightTrackM/LightTrackM.pth') 55 | dummy_input = ( 56 | torch.randn(1, 96, 8, 8), 57 | torch.randn(1, 3, 256, 256), 58 | ) 59 | torch.onnx.export( 60 | net, 61 | dummy_input, 62 | "lighttrack-x-head.onnx", 63 | verbose=True, 64 | opset_version=11, 65 | input_names=["zf", "x"], 66 | output_names=["cls", "reg"], 67 | ) 68 | print('----------finished exporting onnx-----------') 69 | print('----------start simplifying onnx-----------') 70 | model_sim, flag = onnxsim.simplify("lighttrack-x-head.onnx") 71 | if flag: 72 | onnx.save(model_sim, "lighttrack-x-head.onnx") 73 | print("---------simplify onnx successfully---------") 74 | else: 75 | print("---------simplify onnx failed-----------") 76 | ``` 77 | 78 | 79 | 80 | 81 | 82 | -------------------------------------------------------------------------------- /apps/ostrack/OSTrack.hpp: -------------------------------------------------------------------------------- 1 | #ifndef OSTRACK_HPP 2 | #define OSTRACK_HPP 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | 9 | 10 | namespace OSTrack { 11 | using namespace std; 12 | 13 | class Tracker{ 14 | public: 15 | virtual void init(cv::Mat &z_img, cv::Rect &init_bbox) = 0; 16 | virtual cv::Rect track(cv::Mat& x_img) = 0; 17 | }; 18 | 19 | shared_ptr create_tracker(const std::string &engine_path, int gpuid = 0); 20 | } 21 | 22 | 23 | #endif //OSTRACK_HPP 24 | -------------------------------------------------------------------------------- /apps/ostrack/README.md: -------------------------------------------------------------------------------- 1 | # onnx导出 2 | 3 | 4 | 5 | 1、修改 `lib/models/ostrack/ostrack.py` 文件中的 `class OSTrack` 的 `forward` 函数： 6 | 7 | ```python 8 | # return out 9 | # 修改为： 10 | return out['score_map'], out['size_map'], out['offset_map'] 11 | ``` 12 | 13 | 2、在 `tracking` 文件夹上新建 `export_onnx.py` 文件如下： 14 | 15 | ```python 16 | # 注意修改权重文件路径 17 | import os 18 | import argparse 19 | import importlib 20 | import sys 21 | sys.path.append(os.getcwd()) 22 | from lib.train.base_functions import * 23 | from lib.models.ostrack import build_ostrack 24 | import onnx 25 | import onnxsim 26 | 27 | def parse_args(): 28 | parser = argparse.ArgumentParser(description='Run a train scripts in train_settings.') 29 | parser.add_argument('--script', type=str, default='ostrack') 30 | parser.add_argument('--config', type=str, default='vitb_256_mae_ce_ep300') 31 | parser.add_argument('--output_path', type=str, default='./ostrack.onnx') 32 | parser.add_argument('--prj_dir', type=str, default='/home/lsf/Object_Tracking/SOT/my_OSTrack') 33 | parser.add_argument('--checkpoint', type=str, default='checkpoints/vitb_256_mae_ce_32x4_ep300/OSTrack_ep0300.pth.tar') 34 | args = parser.parse_args() 35 | return args 36 | 37 | def main(): 38 | args = parse_args() 39 | config_module = importlib.import_module("lib.config.%s.config" % args.script) 40 | cfg = config_module.cfg 41 | cfg_file = os.path.join(args.prj_dir, 'experiments/%s/%s.yaml' % (args.script, args.config)) 42 | config_module.update_config_from_file(cfg_file) 43 | 44 | print("New configuration is shown below.") 45 | for key in cfg.keys(): 46 | print("%s configuration:" % key, cfg[key]) 47 | print('\n') 48 | 49 | # Create network 50 | net = build_ostrack(cfg, training=False) 51 | net.cpu() 52 | checkpoint = os.path.join(args.prj_dir, args.checkpoint) 53 | net.load_state_dict(torch.load(checkpoint, map_location='cpu')['net'], strict=True) 54 | net.eval() 55 | 56 | dummy_input = ( 57 | torch.randn(1, 3, 128, 128), 58 | torch.randn(1, 3, 256, 256), 59 | ) 60 | 61 | torch.onnx.export( 62 | net, 63 | dummy_input, 64 | args.output_path, 65 | verbose=False, 66 | opset_version=15, 67 | input_names=["z", "x"], 68 | output_names=["score_map", "size_map", "offset_map"] 69 | ) 70 | print('----------finished exporting onnx-----------') 71 | print('----------start simplifying onnx-----------') 72 | model_sim, flag = onnxsim.simplify(args.output_path) 73 | if flag: 74 | onnx.save(model_sim, args.output_path) 75 | print("---------simplify onnx successfully---------") 76 | else: 77 | print("---------simplify onnx failed-----------") 78 | 79 | if __name__ == "__main__": 80 | main() 81 | ``` 82 | 83 | 3、运行 84 | 85 | ```python 86 | python tracking/export_onnx.py 87 | ``` 88 | 89 | -------------------------------------------------------------------------------- /apps/ppseg/ppseg.hpp: -------------------------------------------------------------------------------- 1 | 2 | #ifndef PPSEG_HPP 3 | #define PPSEG_HPP 4 | 5 | 6 | #include 7 | #include 8 | #include 9 | #include 10 | #include "trt_common/trt_tensor.hpp" 11 | 12 | 13 | namespace PPSeg { 14 | 15 | using namespace std; 16 | 17 | inline static vector> color_map = { 18 | cv::Scalar_(128, 64, 128), // road 19 | cv::Scalar_(244, 35, 232), // sidewalk 20 | cv::Scalar_(70, 70, 70), // building 21 | cv::Scalar_(102, 102, 156), // wall 22 | cv::Scalar_(190, 153, 153), // fence 23 | cv::Scalar_(153, 153, 153), // pole 24 | cv::Scalar_(250, 170, 30), // traffic light 25 | cv::Scalar_(220, 220, 0), // traffic sign 26 | cv::Scalar_(107, 142, 35), // vegetation 植被 27 | cv::Scalar_(152, 251, 152), // terrain 28 | cv::Scalar_(70, 130, 180), // sky 29 | cv::Scalar_(220, 20, 60), // person 30 | cv::Scalar_(255, 0, 0), // rider 31 | cv::Scalar_(0, 0, 142), // car 32 | cv::Scalar_(0, 0, 70), // truck 33 | cv::Scalar_(0, 60, 100), // bus 34 | cv::Scalar_(0, 80, 100), // train 35 | cv::Scalar_(0, 0, 230), // motorcycle 36 | cv::Scalar_(119, 11, 32) // bicycle 37 | }; 38 | 39 | class Seg{ 40 | public: 41 | virtual cv::Mat seg(const cv::Mat& image) = 0; 42 | }; 43 | 44 | shared_ptr create_seg(const string& engine_file, int gpuid = 0); 45 | } 46 | 47 | 48 | #endif //PPSEG_HPP 49 | -------------------------------------------------------------------------------- /apps/rtdetr/rtdetr.hpp: -------------------------------------------------------------------------------- 1 | #ifndef LINFER_RTDETR_HPP 2 | #define LINFER_RTDETR_HPP 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | #include 9 | 10 | 11 | /// -------------------------- 封装接口类 --------------------------- 12 | 13 | 14 | namespace RTDETR{ 15 | 16 | using namespace std; 17 | 18 | struct Box{ 19 | float left, top, right, bottom, confidence; 20 | int label; 21 | 22 | Box() = default; 23 | 24 | Box(float left, float top, float right, float bottom, float confidence, int label) 25 | :left(left), top(top), right(right), bottom(bottom), confidence(confidence), label(label){} 26 | }; 27 | 28 | using BoxArray = std::vector; 29 | 30 | class Infer{ 31 | public: 32 | virtual shared_future commit(const cv::Mat& image) = 0; 33 | virtual vector> commits(const vector& images) = 0; 34 | }; 35 | 36 | shared_ptr create_infer( 37 | const string& engine_file, int gpuid, 38 | float confidence_threshold = 0.6f, int max_objects = 300, 39 | bool use_multi_preprocess_stream = false 40 | ); 41 | 42 | 43 | } // namespace RTDETR 44 | 45 | 46 | #endif //LINFER_RTDETR_HPP 47 | -------------------------------------------------------------------------------- /apps/yolo/yolo.hpp: -------------------------------------------------------------------------------- 1 | #ifndef YOLO_HPP 2 | #define YOLO_HPP 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | #include 9 | #include "trt_common/trt_tensor.hpp" 10 | 11 | 12 | /// -------------------------- 封装接口类 --------------------------- 13 | /// -------------------- 支持 YoloV5/X/V7/V8 ----------------------- 14 | 15 | namespace Yolo{ 16 | 17 | using namespace std; 18 | 19 | struct Box{ 20 | float left, top, right, bottom, confidence; 21 | int label; 22 | 23 | Box() = default; 24 | 25 | Box(float left, float top, float right, float bottom, float confidence, int label) 26 | :left(left), top(top), right(right), bottom(bottom), confidence(confidence), label(label){} 27 | }; 28 | 29 | using BoxArray = vector; 30 | 31 | enum class Type : int{ 32 | V5 = 0, 33 | X = 1, 34 | V7 = 2, 35 | V8 = 3 36 | }; 37 | 38 | enum class NMSMethod : int{ 39 | CPU = 0, 40 | CUDA = 1 41 | }; 42 | 43 | const char* type_name(Type type); 44 | 45 | class Infer{ 46 | public: 47 | virtual shared_future commit(const cv::Mat& image) = 0; 48 | virtual vector> commits(const vector& images) = 0; 49 | }; 50 | 51 | shared_ptr create_infer( 52 | const string& engine_file, Type type, int gpuid, 53 | float confidence_threshold=0.25f, float nms_threshold=0.45f, 54 | NMSMethod nms_method = NMSMethod::CUDA, int max_objects = 1024, 55 | bool use_multi_preprocess_stream = false 56 | ); 57 | 58 | } // namespace Yolo 59 | 60 | #endif // YOLO_HPP -------------------------------------------------------------------------------- /apps/yolop/yolop.hpp: -------------------------------------------------------------------------------- 1 | 2 | #ifndef YOLOP_HPP 3 | #define YOLOP_HPP 4 | 5 | 6 | #include 7 | #include 8 | #include 9 | #include 10 | #include 11 | #include "trt_common/trt_tensor.hpp" 12 | 13 | 14 | namespace YoloP{ 15 | 16 | using namespace std; 17 | 18 | struct Box{ 19 | float left, top, right, bottom, confidence; 20 | int label; 21 | 22 | Box() = default; 23 | 24 | Box(float left, float top, float right, float bottom, float confidence, int label) 25 | :left(left), top(top), right(right), bottom(bottom), confidence(confidence), label(label){} 26 | }; 27 | 28 | using BoxArray = std::vector; 29 | // detect函数的返回值类型 30 | using PTMM = std::tuple; 31 | 32 | enum class Type : int{ 33 | V1 = 0, 34 | V2 = 1, 35 | }; 36 | 37 | enum class NMSMethod : int{ 38 | CPU = 0, 39 | CUDA = 1 40 | }; 41 | 42 | class Detector{ 43 | public: 44 | virtual PTMM detect(const cv::Mat& image) = 0; 45 | }; 46 | 47 | shared_ptr create_detector( 48 | const string& engine_file, Type type, int gpuid = 0, 49 | float confidence_threshold=0.4f, float nms_threshold=0.5f, 50 | NMSMethod nms_method = NMSMethod::CUDA, int max_objects = 512 51 | ); 52 | 53 | } // namespace YoloP 54 | 55 | #endif //YOLOP_HPP 56 | -------------------------------------------------------------------------------- /apps/yolov10/README.md: -------------------------------------------------------------------------------- 1 | ## YoloV10 export onnx 2 | 3 | 1. 下载源码 4 | 5 | ```bash 6 | git clone https://github.com/THU-MIG/yolov10 7 | cd yolov10 8 | pip install -e . 9 | ``` 10 | 11 | 2. 修改 ultralytics/engine/exporter.py 如下 12 | 13 | ```python 14 | # line 369 15 | output_names = ["output0", "output1"] if isinstance(self.model, SegmentationModel) else ["output"] 16 | dynamic = True 17 | if dynamic: 18 | dynamic = {'images': {0: 'batch'}} 19 | if isinstance(self.model, SegmentationModel): 20 | dynamic['output0'] = {0: 'batch', 2: 'anchors'} 21 | dynamic['output1'] = {0: 'batch', 2: 'mask_height', 3: 'mask_width'} 22 | elif isinstance(self.model, DetectionModel): 23 | dynamic['output'] = {0: 'batch'} 24 | ``` 25 | 26 | 3. 导出onnx 27 | 28 | ```bash 29 | yolo export model=yolov10s.pt format=onnx 30 | ``` 31 | 32 | 33 | 34 | -------------------------------------------------------------------------------- /apps/yolov10/yolov10.hpp: -------------------------------------------------------------------------------- 1 | #ifndef LINFER_YOLOV10_HPP 2 | #define LINFER_YOLOV10_HPP 3 | 4 | 5 | #include 6 | #include 7 | #include 8 | #include 9 | #include 10 | 11 | 12 | namespace YOLOV10{ 13 | 14 | using namespace std; 15 | 16 | struct Box{ 17 | float left, top, right, bottom, confidence; 18 | int label; 19 | 20 | Box() = default; 21 | 22 | Box(float left, float top, float right, float bottom, float confidence, int label) 23 | :left(left), top(top), right(right), bottom(bottom), confidence(confidence), label(label){} 24 | }; 25 | 26 | using BoxArray = std::vector; 27 | 28 | class Infer{ 29 | public: 30 | virtual shared_future commit(const cv::Mat& image) = 0; 31 | virtual vector> commits(const vector& images) = 0; 32 | }; 33 | 34 | shared_ptr create_infer( 35 | const string& engine_file, int gpuid, 36 | float confidence_threshold = 0.5f, int max_objects = 300, 37 | bool use_multi_preprocess_stream = false 38 | ); 39 | 40 | 41 | } // namespace YOLOV10 42 | 43 | 44 | #endif //LINFER_YOLOV10_HPP 45 | -------------------------------------------------------------------------------- /apps/yolov10/yolov10_decode.cu: -------------------------------------------------------------------------------- 1 | 2 | #include "trt_common/cuda_tools.hpp" 3 | 4 | namespace YOLOV10{ 5 | 6 | const int NUM_BOX_ELEMENT = 7; // left, top, right, bottom, confidence, class, keepflag 7 | 8 | static __device__ void affine_project(const float* matrix, float x, float y, float* ox, float* oy){ 9 | *ox = matrix[0] * x + matrix[1] * y + matrix[2]; 10 | *oy = matrix[3] * x + matrix[4] * y + matrix[5]; 11 | } 12 | 13 | 14 | /// ------------------ 核函数定义 ------------------ 15 | static __global__ void decode_kernel_yolov10(float* predict, int num_bboxes, float confidence_threshold, 16 | float* invert_affine_matrix, float* parray, int max_objects){ 17 | 18 | int position = blockDim.x * blockIdx.x + threadIdx.x; 19 | if (position >= num_bboxes) return; 20 | 21 | float* pitem = predict + 6 * position; 22 | float confidence = pitem[4]; 23 | int label = pitem[5]; 24 | 25 | if(confidence < confidence_threshold) 26 | return; 27 | 28 | int index = atomicAdd(parray, 1); 29 | if(index >= max_objects) 30 | return; 31 | 32 | float left = *pitem++; 33 | float top = *pitem++; 34 | float right = *pitem++; 35 | float bottom = *pitem++; 36 | 37 | affine_project(invert_affine_matrix, left, top, &left, &top); 38 | affine_project(invert_affine_matrix, right, bottom, &right, &bottom); 39 | 40 | float* pout_item = parray + 1 + index * NUM_BOX_ELEMENT; 41 | *pout_item++ = left; 42 | *pout_item++ = top; 43 | *pout_item++ = right; 44 | *pout_item++ = bottom; 45 | *pout_item++ = confidence; 46 | *pout_item++ = label; 47 | *pout_item++ = 1; // 1 = keep, 0 = ignore 48 | } 49 | 50 | 51 | /// ------------------ 核函数调用 ------------------ 52 | void decode_kernel_invoker(float* predict, int num_bboxes, float confidence_threshold, 53 | float* invert_affine_matrix, float* parray, 54 | int max_objects, cudaStream_t stream){ 55 | 56 | auto grid = CUDATools::grid_dims(num_bboxes); 57 | auto block = CUDATools::block_dims(num_bboxes); 58 | checkCudaKernel(decode_kernel_yolov10<<>>( 59 | predict, num_bboxes, confidence_threshold, 60 | invert_affine_matrix, parray, max_objects)) 61 | } 62 | 63 | } 64 | -------------------------------------------------------------------------------- /quant-tools/imgs/image-20230825203849049.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/l-sf/Linfer/68d753490f08b9a4dd66bfc6ee5100d1f763c871/quant-tools/imgs/image-20230825203849049.png -------------------------------------------------------------------------------- /quant-tools/imgs/image-20230825204514668.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/l-sf/Linfer/68d753490f08b9a4dd66bfc6ee5100d1f763c871/quant-tools/imgs/image-20230825204514668.png -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/l-sf/Linfer/68d753490f08b9a4dd66bfc6ee5100d1f763c871/quant-tools/imgs/monkey-patch-qat-conv-fp16-issue_qatonnx_edit.png -------------------------------------------------------------------------------- /quant-tools/imgs/monkey-patch-qat-maxpooling-qat.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/l-sf/Linfer/68d753490f08b9a4dd66bfc6ee5100d1f763c871/quant-tools/imgs/monkey-patch-qat-maxpooling-qat.png -------------------------------------------------------------------------------- /quant-tools/scripts/draw-engine.py: -------------------------------------------------------------------------------- 1 | ################################################################################ 2 | # SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. 3 | # SPDX-License-Identifier: MIT 4 | # 5 | # Permission is hereby granted, free of charge, to any person obtaining a 6 | # copy of this software and associated documentation files (the "Software"), 7 | # to deal in the Software without restriction, including without limitation 8 | # the rights to use, copy, modify, merge, publish, distribute, sublicense, 9 | # and/or sell copies of the Software, and to permit persons to whom the 10 | # Software is furnished to do so, subject to the following conditions: 11 | # 12 | # The above copyright notice and this permission notice shall be included in 13 | # all copies or substantial portions of the Software. 14 | # 15 | # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL 18 | # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING 20 | # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER 21 | # DEALINGS IN THE SOFTWARE. 22 | ################################################################################ 23 | 24 | 25 | """ 26 | This script generates an SVG diagram of the input engine graph SVG file. 27 | Note: 28 | THIS SCRIPT DEPENDS ON LIB: https://github.com/NVIDIA/TensorRT/tree/main/tools/experimental/trt-engine-explorer 29 | this script requires graphviz which can be installed manually: 30 | $ sudo apt-get --yes install graphviz 31 | $ python3 -m pip install graphviz networkx 32 | """ 33 | 34 | import graphviz 35 | from trex import * 36 | import argparse 37 | import shutil 38 | 39 | 40 | def draw_engine(engine_json_fname: str, engine_profile_fname: str): 41 | graphviz_is_installed = shutil.which("dot") is not None 42 | if not graphviz_is_installed: 43 | print("graphviz is required but it is not installed.\n") 44 | print("To install on Ubuntu:") 45 | print("sudo apt --yes install graphviz") 46 | exit() 47 | 48 | plan = EnginePlan(engine_json_fname, engine_profile_fname) 49 | formatter = layer_type_formatter 50 | display_regions = True 51 | expand_layer_details = False 52 | 53 | graph = to_dot(plan, formatter, 54 | display_regions=display_regions, 55 | expand_layer_details=expand_layer_details) 56 | render_dot(graph, engine_json_fname, 'svg') 57 | 58 | 59 | if __name__ == "__main__": 60 | parser = argparse.ArgumentParser() 61 | parser.add_argument('--layer', help="name of engine JSON file to draw") 62 | parser.add_argument('--profile', help="name of profile JSON file to draw") 63 | args = parser.parse_args() 64 | draw_engine(engine_json_fname=args.layer, engine_profile_fname=args.profile) 65 | -------------------------------------------------------------------------------- /trt_common/cuda_tools.cpp: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | #include "cuda_tools.hpp" 5 | 6 | namespace CUDATools{ 7 | // bool check_driver(CUresult e, const char* call, int line, const char *file) { 8 | // if (e != CUDA_SUCCESS) { 9 | 10 | // const char* message = nullptr; 11 | // const char* name = nullptr; 12 | // cuGetErrorString(e, &message); 13 | // cuGetErrorName(e, &name); 14 | // INFOE("CUDA Driver error %s # %s, code = %s [ %d ] in file %s:%d", call, message, name, e, file, line); 15 | // return false; 16 | // } 17 | // return true; 18 | // } 19 | 20 | bool check_runtime(cudaError_t e, const char* call, int line, const char *file){ 21 | if (e != cudaSuccess) { 22 | INFOE("CUDA Runtime error %s # %s, code = %s [ %d ] in file %s:%d", call, cudaGetErrorString(e), cudaGetErrorName(e), e, file, line); 23 | return false; 24 | } 25 | return true; 26 | } 27 | 28 | bool check_device_id(int device_id){ 29 | int device_count = -1; 30 | checkCudaRuntime(cudaGetDeviceCount(&device_count)); 31 | if(device_id < 0 || device_id >= device_count){ 32 | INFOE("Invalid device id: %d, count = %d", device_id, device_count); 33 | return false; 34 | } 35 | return true; 36 | } 37 | 38 | int current_device_id(){ 39 | int device_id = 0; 40 | checkCudaRuntime(cudaGetDevice(&device_id)); 41 | return device_id; 42 | } 43 | 44 | dim3 grid_dims(int numJobs) { 45 | int numBlockThreads = numJobs < GPU_BLOCK_THREADS ? numJobs : GPU_BLOCK_THREADS; 46 | return dim3(((numJobs + numBlockThreads - 1) / (float)numBlockThreads)); 47 | } 48 | 49 | dim3 block_dims(int numJobs) { 50 | return numJobs < GPU_BLOCK_THREADS ? numJobs : GPU_BLOCK_THREADS; 51 | } 52 | 53 | std::string device_description(){ 54 | 55 | cudaDeviceProp prop{}; 56 | size_t free_mem, total_mem; 57 | int device_id = 0; 58 | 59 | checkCudaRuntime(cudaGetDevice(&device_id)); 60 | checkCudaRuntime(cudaGetDeviceProperties(&prop, device_id)); 61 | checkCudaRuntime(cudaMemGetInfo(&free_mem, &total_mem)); 62 | 63 | return iLogger::format( 64 | "[ID %d]<%s>[arch %d.%d][GMEM %.2f GB/%.2f GB]", 65 | device_id, prop.name, prop.major, prop.minor, 66 | free_mem / 1024.0f / 1024.0f / 1024.0f, 67 | total_mem / 1024.0f / 1024.0f / 1024.0f 68 | ); 69 | } 70 | 71 | AutoDevice::AutoDevice(int device_id){ 72 | 73 | cudaGetDevice(&old_); 74 | checkCudaRuntime(cudaSetDevice(device_id)); 75 | } 76 | 77 | AutoDevice::~AutoDevice(){ 78 | checkCudaRuntime(cudaSetDevice(old_)); 79 | } 80 | } -------------------------------------------------------------------------------- /trt_common/cuda_tools.hpp: -------------------------------------------------------------------------------- 1 | #ifndef CUDA_TOOLS_HPP 2 | #define CUDA_TOOLS_HPP 3 | 4 | 5 | /// 系统关于CUDA的功能函数 6 | 7 | 8 | #include 9 | #include 10 | #include "ilogger.hpp" 11 | 12 | #define GPU_BLOCK_THREADS 512 13 | 14 | 15 | #define KernelPositionBlock \ 16 | int position = (blockDim.x * blockIdx.x + threadIdx.x); \ 17 | if (position >= (edge)) return; 18 | 19 | 20 | #define checkCudaDriver(call) CUDATools::check_driver(call, #call, __LINE__, __FILE__) 21 | #define checkCudaRuntime(call) CUDATools::check_runtime(call, #call, __LINE__, __FILE__) 22 | 23 | #define checkCudaKernel(...) \ 24 | __VA_ARGS__; \ 25 | do{cudaError_t cudaStatus = cudaPeekAtLastError(); \ 26 | if (cudaStatus != cudaSuccess){ \ 27 | INFOE("launch failed: %s", cudaGetErrorString(cudaStatus)); \ 28 | }} while(0); 29 | 30 | 31 | #define Assert(op) \ 32 | do{ \ 33 | bool cond = !(!(op)); \ 34 | if(!cond){ \ 35 | INFOF("Assert failed, " #op); \ 36 | } \ 37 | }while(false) 38 | 39 | 40 | struct CUctx_st; 41 | struct CUstream_st; 42 | 43 | typedef CUstream_st* ICUStream; 44 | typedef CUctx_st* ICUContext; 45 | typedef void* ICUDeviceptr; 46 | typedef int DeviceID; 47 | 48 | namespace CUDATools{ 49 | bool check_driver(CUresult e, const char* call, int iLine, const char *szFile); 50 | bool check_runtime(cudaError_t e, const char* call, int iLine, const char *szFile); 51 | bool check_device_id(int device_id); 52 | int current_device_id(); 53 | 54 | dim3 grid_dims(int numJobs); 55 | dim3 block_dims(int numJobs); 56 | 57 | std::string device_description(); 58 | 59 | class AutoDevice{ 60 | public: 61 | explicit AutoDevice(int device_id = 0); 62 | virtual ~AutoDevice(); 63 | 64 | private: 65 | int old_ = -1; 66 | }; 67 | } 68 | 69 | 70 | #endif // CUDA_TOOLS_HPP -------------------------------------------------------------------------------- /trt_common/ilogger.hpp: -------------------------------------------------------------------------------- 1 | 2 | #ifndef ILOGGER_HPP 3 | #define ILOGGER_HPP 4 | 5 | #include 6 | #include 7 | #include 8 | #include 9 | 10 | 11 | namespace iLogger{ 12 | 13 | using namespace std; 14 | 15 | enum class LogLevel : int{ 16 | Debug = 5, 17 | Verbose = 4, 18 | Info = 3, 19 | Warning = 2, 20 | Error = 1, 21 | Fatal = 0 22 | }; 23 | 24 | #define INFOD(...) iLogger::__log_func(__FILE__, __LINE__, iLogger::LogLevel::Debug, __VA_ARGS__) 25 | #define INFOV(...) iLogger::__log_func(__FILE__, __LINE__, iLogger::LogLevel::Verbose, __VA_ARGS__) 26 | #define INFO(...) iLogger::__log_func(__FILE__, __LINE__, iLogger::LogLevel::Info, __VA_ARGS__) 27 | #define INFOW(...) iLogger::__log_func(__FILE__, __LINE__, iLogger::LogLevel::Warning, __VA_ARGS__) 28 | #define INFOE(...) iLogger::__log_func(__FILE__, __LINE__, iLogger::LogLevel::Error, __VA_ARGS__) 29 | #define INFOF(...) iLogger::__log_func(__FILE__, __LINE__, iLogger::LogLevel::Fatal, __VA_ARGS__) 30 | 31 | string date_now(); 32 | string time_now(); 33 | string gmtime_now(); 34 | string gmtime(time_t t); 35 | 36 | bool isfile(const string& file); 37 | bool mkdir(const string& path); 38 | bool mkdirs(const string& path); 39 | bool exists(const string& path); 40 | string format(const char* fmt, ...); 41 | FILE* fopen_mkdirs(const string& path, const string& mode); 42 | string file_name(const string& path, bool include_suffix=true); 43 | long long timestamp_now(); 44 | 45 | // return, 0-255, 0-255, 0-255 46 | tuple random_color(int id); 47 | 48 | // abcdefg.pnga *.png > false 49 | // abcdefg.png *.png > true 50 | // abcdefg.png a?cdefg.png > true 51 | bool pattern_match(const char* str, const char* matcher, bool igrnoe_case = true); 52 | vector find_files( 53 | const string& directory, 54 | const string& filter = "*", bool findDirectory = false, bool includeSubDirectory = false); 55 | 56 | string align_blank(const string& input, int align_size, char blank=' '); 57 | bool save_file(const string& file, const vector& data, bool mk_dirs = true); 58 | bool save_file(const string& file, const string& data, bool mk_dirs = true); 59 | bool save_file(const string& file, const void* data, size_t length, bool mk_dirs = true); 60 | 61 | 62 | // 关于logger的api 63 | const char* level_string(LogLevel level); 64 | void __log_func(const char* file, int line, LogLevel level, const char* fmt, ...); 65 | void destroy_logger(); 66 | 67 | inline int upbound(int n, int align = 32) { return (n + align - 1) / align * align;} 68 | } 69 | 70 | 71 | #endif // ILOGGER_HPP -------------------------------------------------------------------------------- /trt_common/preprocess_kernel.cuh: -------------------------------------------------------------------------------- 1 | #ifndef PREPROCESS_KERNEL_CUH 2 | #define PREPROCESS_KERNEL_CUH 3 | 4 | #include "cuda_tools.hpp" 5 | 6 | namespace CUDAKernel{ 7 | 8 | enum class NormType : int{ 9 | None = 0, 10 | MeanStd = 1, 11 | AlphaBeta = 2 12 | }; 13 | 14 | enum class ChannelType : int{ 15 | None = 0, 16 | Invert = 1 17 | }; 18 | 19 | struct Norm{ 20 | float mean[3]; 21 | float std[3]; 22 | float alpha, beta; 23 | NormType type = NormType::None; 24 | ChannelType channel_type = ChannelType::None; 25 | 26 | // out = (x * alpha - mean) / std 27 | static Norm mean_std(const float mean[3], const float std[3], float alpha = 1/255.0f, ChannelType channel_type=ChannelType::None); 28 | 29 | // out = x * alpha + beta 30 | static Norm alpha_beta(float alpha, float beta = 0, ChannelType channel_type=ChannelType::None); 31 | 32 | // None 33 | static Norm None(); 34 | }; 35 | 36 | 37 | void warp_affine_bilinear_and_normalize_plane( 38 | uint8_t* src, int src_line_size, int src_width, int src_height, 39 | float* dst , int dst_width, int dst_height, 40 | float* matrix_2_3, uint8_t const_value, const Norm& norm, 41 | cudaStream_t stream); 42 | 43 | }; 44 | 45 | #endif // PREPROCESS_KERNEL_CUH -------------------------------------------------------------------------------- /trt_common/trt_infer.hpp: -------------------------------------------------------------------------------- 1 | 2 | 3 | #ifndef TRT_INFER_HPP 4 | #define TRT_INFER_HPP 5 | 6 | #include 7 | #include 8 | #include 9 | #include 10 | #include "trt_tensor.hpp" 11 | 12 | namespace TRT { 13 | 14 | /// ------------------------------------------------------------------------ 15 | /// -------------- 封装接口类，对外只提供接口，具体实现逻辑在子类中 --------------- 16 | /// ------------------------------------------------------------------------ 17 | 18 | class Infer { 19 | public: 20 | virtual void forward(bool sync = true) = 0; 21 | virtual std::shared_ptr input(int index = 0) = 0; 22 | virtual std::shared_ptr output(int index = 0) = 0; 23 | virtual std::shared_ptr tensor(const std::string& name) = 0; 24 | virtual void set_input(int index, std::shared_ptr tensor) = 0; 25 | virtual void set_output(int index, std::shared_ptr tensor) = 0; 26 | virtual bool has_dynamic_dim() = 0; 27 | virtual std::vector run_dims(const std::string &name) = 0; 28 | virtual std::vector run_dims(int ibinding) = 0; 29 | virtual std::vector static_dims(const std::string &name) = 0; 30 | virtual std::vector static_dims(int ibinding) = 0; 31 | virtual bool set_run_dims(const std::string &name, const std::vector &dims) = 0; 32 | virtual bool set_run_dims(int ibinding, const std::vector &dims) = 0; 33 | virtual int num_bindings() = 0; 34 | virtual int num_input() = 0; 35 | virtual int num_output() = 0; 36 | virtual std::string get_input_name(int index = 0) = 0; 37 | virtual std::string get_output_name(int index = 0) = 0; 38 | virtual bool is_input_name(const std::string& name) = 0; 39 | virtual bool is_output_name(const std::string& name) = 0; 40 | virtual bool is_input(int ibinding) = 0; 41 | virtual void print() = 0; 42 | virtual void set_stream(CUStream stream) = 0; 43 | virtual CUStream get_stream() = 0; 44 | virtual int get_max_batch_size() = 0; 45 | virtual void synchronize() = 0; 46 | virtual int device() = 0; 47 | virtual size_t get_device_memory_size() = 0; 48 | virtual std::shared_ptr get_workspace() = 0; 49 | }; 50 | 51 | int get_device_count(); 52 | int get_device(); 53 | void set_device(int device_id); 54 | 55 | /// 加载infer，资源获取即初始化 56 | std::shared_ptr load_infer(const std::string& file); 57 | 58 | } 59 | 60 | #endif //TRT_INFER_HPP 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