使用Lite.AI.ToolKit 🚀🚀🌟 C++工具箱来跑YOLOX的一些案例(https://github.com/DefTruth/lite.ai.toolkit) , 包含ONNXRuntime C++、MNN、TNN和NCNN版本。
若是有用,❤️不妨给个⭐️🌟支持一下吧~ 🙃🤪🍀
YOLOX C++ 版本的源码包含ONNXRuntime、MNN、TNN和NCNN四个版本,包括YOLOX的旧版本模型和新版本模型(YOLOX-v0.1.1)的推理。YOLOX-v0.1.1和旧版本的YOLOX模型在预处理和模型输入输出的节点名称有所不同。源码可以在 lite.ai.toolkit 工具箱中找到。本项目主要介绍如何基于 lite.ai.toolkit 工具箱,直接使用YOLOX来跑目标检测。需要说明的是,本项目是基于MacOS下编译的 liblite.ai.toolkit.v0.1.0.dylib 来实现的,对于使用MacOS的用户,可以直接下载本项目包含的liblite.ai.toolkit.v0.1.0动态库和其他依赖库进行使用。而非MacOS用户,则需要从lite.ai.toolkit 中下载源码进行编译。lite.ai.toolkit c++工具箱目前包含70+流行的开源模型。
- yolox.cpp
- yolox.h
- mnn_yolox.cpp
- mnn_yolox.h
- tnn_yolox.cpp
- tnn_yolox.h
- ncnn_yolox.cpp
- ncnn_yolox.h
- yolox_v0.1.1.cpp
- yolox_v0.1.1.h
- mnn_yolox_v0.1.1.cpp
- mnn_yolox_v0.1.1.h
- tnn_yolox_v0.1.1.cpp
- tnn_yolox_v0.1.1.h
- ncnn_yolox_v0.1.1.cpp
- ncnn_yolox_v0.1.1.h
ONNXRuntime C++、MNN、TNN和NCNN版本的推理实现均已测试通过,欢迎白嫖~
可以从我提供的链接下载 (Baidu Drive code: 8gin) , 也可以从本直接仓库下载。
Class | Pretrained ONNX Files | Rename or Converted From (Repo) | Size |
---|---|---|---|
lite::cv::detection::YoloX | yolox_x.onnx | YOLOX | 378Mb |
lite::cv::detection::YoloX | yolox_l.onnx | YOLOX | 207Mb |
lite::cv::detection::YoloX | yolox_m.onnx | YOLOX | 97Mb |
lite::cv::detection::YoloX | yolox_s.onnx | YOLOX | 34Mb |
lite::cv::detection::YoloX | yolox_tiny.onnx | YOLOX | 19Mb |
lite::cv::detection::YoloX | yolox_nano.onnx | YOLOX | 3.5Mb |
lite::cv::detection::YoloX_V_0_1_1 | yolox_x_v0.1.1.onnx | YOLOX | 378Mb |
lite::cv::detection::YoloX_V_0_1_1 | yolox_l_v0.1.1.onnx | YOLOX | 207Mb |
lite::cv::detection::YoloX_V_0_1_1 | yolox_m_v0.1.1.onnx | YOLOX | 97Mb |
lite::cv::detection::YoloX_V_0_1_1 | yolox_s_v0.1.1.onnx | YOLOX | 34Mb |
lite::cv::detection::YoloX_V_0_1_1 | yolox_tiny_v0.1.1.onnx | YOLOX | 19Mb |
lite::cv::detection::YoloX_V_0_1_1 | yolox_nano_v0.1.1.onnx | YOLOX | 3.5Mb |
MNN模型文件下载地址,(Baidu Drive code: 9v63), 也可以从本直接仓库下载。
Class | Pretrained MNN Files | Rename or Converted From (Repo) | Size |
---|---|---|---|
lite::mnn::cv::detection::YoloX | yolox_x.mnn | YOLOX | 378Mb |
lite::mnn::cv::detection::YoloX | yolox_l.mnn | YOLOX | 207Mb |
lite::mnn::cv::detection::YoloX | yolox_m.mnn | YOLOX | 97Mb |
lite::mnn::cv::detection::YoloX | yolox_s.mnn | YOLOX | 34Mb |
lite::mnn::cv::detection::YoloX | yolox_tiny.mnn | YOLOX | 19Mb |
lite::mnn::cv::detection::YoloX | yolox_nano.mnn | YOLOX | 3.5Mb |
lite::mnn::cv::detection::YoloX_V_0_1_1 | yolox_x_v0.1.1.mnn | YOLOX | 378Mb |
lite::mnn::cv::detection::YoloX_V_0_1_1 | yolox_l_v0.1.1.mnn | YOLOX | 207Mb |
lite::mnn::cv::detection::YoloX_V_0_1_1 | yolox_m_v0.1.1.mnn | YOLOX | 97Mb |
lite::mnn::cv::detection::YoloX_V_0_1_1 | yolox_s_v0.1.1.mnn | YOLOX | 34Mb |
lite::mnn::cv::detection::YoloX_V_0_1_1 | yolox_tiny_v0.1.1.mnn | YOLOX | 19Mb |
lite::mnn::cv::detection::YoloX_V_0_1_1 | yolox_nano_v0.1.1.mnn | YOLOX | 3.5Mb |
TNN模型文件下载地址,(Baidu Drive code: 6o6k), 也可以从本直接仓库下载。
Class | Pretrained TNN Files | Rename or Converted From (Repo) | Size |
---|---|---|---|
lite::tnn::cv::detection::YoloX | yolox_x.opt.tnnproto&tnnmodel | YOLOX | 378Mb |
lite::tnn::cv::detection::YoloX | yolox_l.opt.tnnproto&tnnmodel | YOLOX | 207Mb |
lite::tnn::cv::detection::YoloX | yolox_m.opt.tnnproto&tnnmodel | YOLOX | 97Mb |
lite::tnn::cv::detection::YoloX | yolox_s.opt.tnnproto&tnnmodel | YOLOX | 34Mb |
lite::tnn::cv::detection::YoloX | yolox_tiny.opt.tnnproto&tnnmodel | YOLOX | 19Mb |
lite::tnn::cv::detection::YoloX | yolox_nano.opt.tnnproto&tnnmodel | YOLOX | 3.5Mb |
lite::tnn::cv::detection::YoloX_V_0_1_1 | yolox_x_v0.1.1.opt.tnnproto&tnnmodel | YOLOX | 378Mb |
lite::tnn::cv::detection::YoloX_V_0_1_1 | yolox_l_v0.1.1.opt.tnnproto&tnnmodel | YOLOX | 207Mb |
lite::tnn::cv::detection::YoloX_V_0_1_1 | yolox_m_v0.1.1.opt.tnnproto&tnnmodel | YOLOX | 97Mb |
lite::tnn::cv::detection::YoloX_V_0_1_1 | yolox_s_v0.1.1.opt.tnnproto&tnnmodel | YOLOX | 34Mb |
lite::tnn::cv::detection::YoloX_V_0_1_1 | yolox_tiny_v0.1.1.opt.tnnproto&tnnmodel | YOLOX | 19Mb |
lite::tnn::cv::detection::YoloX_V_0_1_1 | yolox_nano_v0.1.1.opt.tnnproto&tnnmodel | YOLOX | 3.5Mb |
NCNN模型文件下载地址,(Baidu Drive code: sc7f), 也可以从本直接仓库下载。
Class | Pretrained NCNN Files | Rename or Converted From (Repo) | Size |
---|---|---|---|
lite::ncnn::cv::detection::YoloX | yolox_x.opt.param&bin | YOLOX | 378Mb |
lite::ncnn::cv::detection::YoloX | yolox_l.opt.param&bin | YOLOX | 207Mb |
lite::ncnn::cv::detection::YoloX | yolox_m.opt.param&bin | YOLOX | 97Mb |
lite::ncnn::cv::detection::YoloX | yolox_s.opt.param&bin | YOLOX | 34Mb |
lite::ncnn::cv::detection::YoloX | yolox_tiny.opt.param&bin | YOLOX | 19Mb |
lite::ncnn::cv::detection::YoloX | yolox_nano.opt.param&bin | YOLOX | 3.5Mb |
lite::ncnn::cv::detection::YoloX_V_0_1_1 | yolox_x_v0.1.1.opt.param&bin | YOLOX | 378Mb |
lite::ncnn::cv::detection::YoloX_V_0_1_1 | yolox_l_v0.1.1.opt.param&bin | YOLOX | 207Mb |
lite::ncnn::cv::detection::YoloX_V_0_1_1 | yolox_m_v0.1.1.opt.param&bin | YOLOX | 97Mb |
lite::ncnn::cv::detection::YoloX_V_0_1_1 | yolox_s_v0.1.1.opt.param&bin | YOLOX | 34Mb |
lite::ncnn::cv::detection::YoloX_V_0_1_1 | yolox_tiny_v0.1.1.opt.param&bin | YOLOX | 19Mb |
lite::ncnn::cv::detection::YoloX_V_0_1_1 | yolox_nano_v0.1.1.opt.param&bin | YOLOX | 3.5Mb |
在lite.ai.toolkit 中,YOLOX的实现类为:
class LITE_EXPORTS lite::cv::detection::YoloX;
class LITE_EXPORTS lite::mnn::cv::detection::YoloX;
class LITE_EXPORTS lite::tnn::cv::detection::YoloX;
class LITE_EXPORTS lite::ncnn::cv::detection::YoloX;
class LITE_EXPORTS lite::cv::detection::YoloX_V_0_1_1; // YOLOX-v0.1.1 (latest)
class LITE_EXPORTS lite::mnn::cv::detection::YoloX_V_0_1_1;
class LITE_EXPORTS lite::tnn::cv::detection::YoloX_V_0_1_1;
class LITE_EXPORTS lite::ncnn::cv::detection::YoloX_V_0_1_1;
该类型目前包含1公共接口detect
用于进行目标检测。
public:
/**
* @param mat cv::Mat BGR format
* @param detected_boxes vector of Boxf to catch detected boxes.
* @param score_threshold default 0.45f, only keep the result which >= score_threshold.
* @param iou_threshold default 0.3f, iou threshold for NMS.
* @param topk default 100, maximum output boxes after NMS.
* @param nms_type the method.
*/
void detect(const cv::Mat &mat, std::vector<types::Boxf> &detected_boxes,
float score_threshold = 0.45f, float iou_threshold = 0.3f,
unsigned int topk = 100, unsigned int nms_type = NMS::OFFSET);
detect
接口的输入参数说明:
- mat: cv::Mat类型,BGR格式。
- detected_boxes: Boxf向量,包含被检测到的框,Boxf中包含x1,y1,x2,y2,label,score等成员
- score_threshold:分类得分(质量得分)阈值,默认0.45,小于该阈值的框将被丢弃。
- iou_threshold:NMS中的iou阈值,默认0.3。
- topk:默认100,只保留前k个检测到的结果。
- nms_type:NMS算法的类型,默认为不同的类别各自做NMS。
这里测试使用的是yolox_s.onnx版本(20210819之前)的模型,你可以尝试使用YOLOX-v0.1.1版本的模型。
#include "lite/lite.h"
static void test_default()
{
std::string onnx_path = "../hub/onnx/cv/yolox_s.onnx";
std::string test_img_path = "../resources/5.jpg";
std::string save_img_path = "../logs/5.jpg";
// 1. Test Default Engine ONNXRuntime
auto *yolox = new lite::cv::detection::YoloX(onnx_path); // default
std::vector<lite::types::Boxf> detected_boxes;
cv::Mat img_bgr = cv::imread(test_img_path);
yolox->detect(img_bgr, detected_boxes);
lite::utils::draw_boxes_inplace(img_bgr, detected_boxes);
cv::imwrite(save_img_path, img_bgr);
std::cout << "Default Version Detected Boxes Num: " << detected_boxes.size() << std::endl;
delete yolox;
}
#include "lite/lite.h"
static void test_mnn()
{
#ifdef ENABLE_MNN
std::string mnn_path = "../hub/mnn/cv/yolox_s.mnn";
std::string test_img_path = "../resources/7.jpg";
std::string save_img_path = "../logs/7.jpg";
// 3. Test Specific Engine MNN
auto *yolox = new lite::mnn::cv::detection::YoloX(mnn_path);
std::vector<lite::types::Boxf> detected_boxes;
cv::Mat img_bgr = cv::imread(test_img_path);
yolox->detect(img_bgr, detected_boxes);
lite::utils::draw_boxes_inplace(img_bgr, detected_boxes);
cv::imwrite(save_img_path, img_bgr);
std::cout << "MNN Version Detected Boxes Num: " << detected_boxes.size() << std::endl;
delete yolox;
#endif
}
#include "lite/lite.h"
static void test_tnn()
{
#ifdef ENABLE_TNN
std::string proto_path = "../hub/tnn/cv/yolox_s.opt.tnnproto";
std::string model_path = "../hub/tnn/cv/yolox_s.opt.tnnmodel";
std::string test_img_path = "../resources/9.jpg";
std::string save_img_path = "../logs/9.jpg";
// 5. Test Specific Engine TNN
auto *yolox = new lite::tnn::cv::detection::YoloX(proto_path, model_path);
std::vector<lite::types::Boxf> detected_boxes;
cv::Mat img_bgr = cv::imread(test_img_path);
yolox->detect(img_bgr, detected_boxes);
lite::utils::draw_boxes_inplace(img_bgr, detected_boxes);
cv::imwrite(save_img_path, img_bgr);
std::cout << "TNN Version Detected Boxes Num: " << detected_boxes.size() << std::endl;
delete yolox;
#endif
}
#include "lite/lite.h"
static void test_ncnn()
{
#ifdef ENABLE_NCNN
std::string param_path = "../hub/ncnn/cv/yolox_s.opt.param";
std::string bin_path = "../hub/ncnn/cv/yolox_s.opt.bin";
std::string test_img_path = "../resources/5.jpg";
std::string save_img_path = "../logs/5.jpg";
// 4. Test Specific Engine NCNN
auto *yolox = new lite::ncnn::cv::detection::YoloX(param_path, bin_path);
std::vector<lite::types::Boxf> detected_boxes;
cv::Mat img_bgr = cv::imread(test_img_path);
yolox->detect(img_bgr, detected_boxes);
lite::utils::draw_boxes_inplace(img_bgr, detected_boxes);
cv::imwrite(save_img_path, img_bgr);
std::cout << "NCNN Version Detected Boxes Num: " << detected_boxes.size() << std::endl;
delete yolox;
#endif
}
- 输出结果为:
在MacOS下可以直接编译运行本项目,无需下载其他依赖库。其他系统则需要从lite.ai.toolkit 中下载源码先编译lite.ai.toolkit.v0.1.0动态库。
git clone --depth=1 https://github.com/DefTruth/yolox.lite.ai.toolkit.git
cd yolox.lite.ai.toolkit
sh ./build.sh
- CMakeLists.txt设置
cmake_minimum_required(VERSION 3.17)
project(yolox.lite.ai.toolkit)
set(CMAKE_CXX_STANDARD 11)
# setting up lite.ai.toolkit
set(LITE_AI_DIR ${CMAKE_SOURCE_DIR}/lite.ai.toolkit)
set(LITE_AI_INCLUDE_DIR ${LITE_AI_DIR}/include)
set(LITE_AI_LIBRARY_DIR ${LITE_AI_DIR}/lib)
include_directories(${LITE_AI_INCLUDE_DIR})
link_directories(${LITE_AI_LIBRARY_DIR})
set(OpenCV_LIBS
opencv_highgui
opencv_core
opencv_imgcodecs
opencv_imgproc
opencv_video
opencv_videoio
)
# add your executable
set(EXECUTABLE_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/examples/build)
add_executable(lite_yolox examples/test_lite_yolox.cpp)
target_link_libraries(lite_yolox
lite.ai.toolkit
onnxruntime
MNN # need, if built lite.ai.toolkit with ENABLE_MNN=ON, default OFF
ncnn # need, if built lite.ai.toolkit with ENABLE_NCNN=ON, default OFF
TNN # need, if built lite.ai.toolkit with ENABLE_TNN=ON, default OFF
${OpenCV_LIBS}) # link lite.ai.toolkit & other libs.
- building && testing information:
[ 50%] Building CXX object CMakeFiles/lite_yolox.dir/examples/test_lite_yolox.cpp.o
[100%] Linking CXX executable lite_yolox
[100%] Built target lite_yolox
Testing Start ...
LITEORT_DEBUG LogId: ../hub/onnx/cv/yolox_s.onnx
=============== Input-Dims ==============
input_node_dims: 1
input_node_dims: 3
input_node_dims: 640
input_node_dims: 640
=============== Output-Dims ==============
Output: 0 Name: outputs Dim: 0 :1
Output: 0 Name: outputs Dim: 1 :8400
Output: 0 Name: outputs Dim: 2 :85
========================================
detected num_anchors: 8400
generate_bboxes num: 96
Default Version Detected Boxes Num: 11
LITEORT_DEBUG LogId: ../hub/onnx/cv/yolox_s.onnx
=============== Input-Dims ==============
input_node_dims: 1
input_node_dims: 3
input_node_dims: 640
input_node_dims: 640
=============== Output-Dims ==============
Output: 0 Name: outputs Dim: 0 :1
Output: 0 Name: outputs Dim: 1 :8400
Output: 0 Name: outputs Dim: 2 :85
========================================
detected num_anchors: 8400
generate_bboxes num: 195
ONNXRuntime Version Detected Boxes Num: 26
LITEMNN_DEBUG LogId: ../hub/mnn/cv/yolox_s.mnn
=============== Input-Dims ==============
**Tensor shape**: 1, 3, 640, 640,
Dimension Type: (CAFFE/PyTorch/ONNX)NCHW
=============== Output-Dims ==============
getSessionOutputAll done!
Output: outputs: **Tensor shape**: 1, 8400, 85,
========================================
detected num_anchors: 8400
generate_bboxes num: 146
MNN Version Detected Boxes Num: 17
LITENCNN_DEBUG LogId: ../hub/ncnn/cv/yolox_s.opt.param
=============== Input-Dims ==============
Input: inputs: shape: c=0 h=0 w=0
=============== Output-Dims ==============
Output: outputs: shape: c=0 h=0 w=0
========================================
detected num_anchors: 8400
generate_bboxes num: 296
NCNN Version Detected Boxes Num: 40
LITETNN_DEBUG LogId: ../hub/tnn/cv/yolox_s.opt.tnnproto
=============== Input-Dims ==============
inputs: [1 3 640 640 ]
Input Data Format: NCHW
=============== Output-Dims ==============
outputs: [1 8400 85 1 ]
========================================
detected num_anchors: 8400
generate_bboxes num: 108
TNN Version Detected Boxes Num: 13
Testing Successful !