hukaixuan19970627 · ChristoCai · Apr 23, 2023 · Apr 23, 2023
diff --git a/CMakeLists.txt b/CMakeLists.txt
@@ -0,0 +1,61 @@
+cmake_minimum_required(VERSION 3.10)
+
+project(yolov5_obb)
+
+add_definitions(-std=c++11)
+add_definitions(-DAPI_EXPORTS)
+option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
+set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_BUILD_TYPE Release)
+
+find_package(CUDA REQUIRED)
+
+if(WIN32)
+enable_language(CUDA)
+endif(WIN32)
+
+
+
+include_directories(${PROJECT_SOURCE_DIR}/include)
+find_package(OpenCV)
+include_directories(${OpenCV_INCLUDE_DIRS})
+
+find_package(CUDA REQUIRED)
+message(STATUS " libraries: ${CUDA_LIBRARIES}")
+message(STATUS " include path: ${CUDA_INCLUDE_DIRS}")
+include_directories(${CUDA_INCLUDE_DIRS})
+enable_language(CUDA) 
+
+# include and link dirs of cuda and tensorrt, you need adapt them if yours are different
+# cuda
+#include_directories(/usr/local/cuda/include)
+#link_directories(/usr/local/cuda/lib64)
+# tensorrt
+#include_directories(/usr/include/x86_64-linux-gnu/)
+#link_directories(/usr/lib/x86_64-linux-gnu/)
+
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -Ofast -g  -D_MWAITXINTRIN_H_INCLUDED")#-Wfatal-errors
+cuda_add_library(myplugins SHARED yololayer.cu)
+target_link_libraries(myplugins nvinfer cudart)
+
+
+cuda_add_executable(yolov5_gen  yolov5_gen.cpp)
+
+target_link_libraries(yolov5_gen nvinfer)
+target_link_libraries(yolov5_gen cudart)
+target_link_libraries(yolov5_gen myplugins)
+target_link_libraries(yolov5_gen ${OpenCV_LIBS})
+
+cuda_add_executable(yolov5_use yolov5_use.cpp)
+
+target_link_libraries(yolov5_use nvinfer)
+target_link_libraries(yolov5_use cudart)
+target_link_libraries(yolov5_use myplugins)
+target_link_libraries(yolov5_use ${OpenCV_LIBS})
+
+
+if(UNIX)
+add_definitions(-O2 -pthread)
+endif(UNIX)
+
+
diff --git a/README.md b/README.md
@@ -1,70 +1,44 @@
-# Yolov5 for Oriented Object Detection
-![图片](./docs/detection.png)
-![train_batch0.jpg](./docs/train_batch6.jpg)
-![results.png](./docs/results.png)
+# yolov5_for_oriented_object_detection
 
-The code for the implementation of “[Yolov5](https://github.com/ultralytics/yolov5) + [Circular Smooth Label](https://arxiv.org/abs/2003.05597v2)”. 
+The Pytorch implementation is [yolov5_obb](https://github.com/hukaixuan19970627/yolov5_obb )
 
-# Results and Models
-The results on **DOTA_subsize1024_gap200_rate1.0** test-dev set are shown in the table below. (**password: yolo**)
+actually ,This repo is based on [yolov5](https://github.com/ultralytics/yolov5/tree/v6.0/)  and only used at Linux PC (partial dependency install hardly at windows PC)
 
- |Model<br><sup>(download link) |Size<br><sup>(pixels) | TTA<br><sup>(multi-scale/<br>rotate testing) | OBB mAP<sup>test<br><sup>0.5<br>DOTAv1.0 | OBB mAP<sup>test<br><sup>0.5<br>DOTAv1.5 | OBB mAP<sup>test<br><sup>0.5<br>DOTAv2.0 | Speed<br><sup>CPU b1<br>(ms)|Speed<br><sup>2080Ti b1<br>(ms) |Speed<br><sup>2080Ti b16<br>(ms) |params<br><sup>(M) |FLOPs<br><sup>@640 (B) 
- | ----                                                                                                                                                           | ---  | ---   | ---      | ---   | ---   | ---   | ---   | --- | --- | ---
- |yolov5m [[baidu](https://pan.baidu.com/s/1UPNaMuQ_gNce9167FZx8-w)/[google](https://drive.google.com/file/d/1NMgxcN98cmBg9_nVK4axxqfiq4pYh-as/view?usp=sharing)]  |1024  | ×     |**77.3** |**73.2** |**58.0**  |**328.2**      |**16.9**     |**11.3**      |**21.6**   |**50.5**   
- |yolov5s [[baidu](https://pan.baidu.com/s/1Lqw42xlSZxZn-2gNniBpmw?pwd=yolo)]    |1024  | ×     |**76.8**   |-      |-      |-      |**15.6**  | -     |**7.5**     |**17.5**    
- |yolov5n [[baidu](https://pan.baidu.com/s/1Lqw42xlSZxZn-2gNniBpmw?pwd=yolo)]    |1024  | ×     |**73.3**   |-      |-      |-      |**15.2**  | -     |**2.0**     |**5.0**
+## How to Run, yolov5s as example
 
+1. generate .wts from pytorch with .pt
 
-<details>
-  <summary>Table Notes (click to expand / **点我看更多**)</summary>
+```
+// clone code according to above #Different versions of yolov5
+train and gen best.pt at runs/train/exp[]/weights/
+cp gen_wts.py {yolov5_obb}
+python gen_wts.py -w runs/train/exp[]/weights/best.pt -o yolov5s.wts
+// a file 'yolov5s.wts' will be generated.
+```
 
-* All checkpoints are trained to 300 epochs with [COCO pre-trained checkpoints](https://github.com/ultralytics/yolov5/releases/tag/v6.0), default settings and hyperparameters.
-* **mAP<sup>test dota</sup>** values are for single-model single-scale on [DOTA](https://captain-whu.github.io/DOTA/index.html)(1024,1024,200,1.0) dataset.<br>Reproduce Example:
- ```shell
- python val.py --data 'data/dotav15_poly.yaml' --img 1024 --conf 0.01 --iou 0.4 --task 'test' --batch 16 --save-json --name 'dotav15_test_split'
- python tools/TestJson2VocClassTxt.py --json_path 'runs/val/dotav15_test_split/best_obb_predictions.json' --save_path 'runs/val/dotav15_test_split/obb_predictions_Txt'
- python DOTA_devkit/ResultMerge_multi_process.py --scrpath 'runs/val/dotav15_test_split/obb_predictions_Txt' --dstpath 'runs/val/dotav15_test_split/obb_predictions_Txt_Merged'
- zip the poly format results files and submit it to https://captain-whu.github.io/DOTA/evaluation.html
- ```
-* **Speed** averaged over DOTAv1.5 val_split_subsize1024_gap200 images using a 2080Ti gpu. NMS + pre-process times is included.<br>Reproduce by `python val.py --data 'data/dotav15_poly.yaml' --img 1024 --task speed --batch 1`
+2. build **tensorr engine **
 
+```
+// update CLASS_NUM in yololayer.h 
+// caution:CLASS_NUM= your classes +180(180 for angle classes)
+mkdir build
+cd build
+cp {yolov5_obb}/yolov5s.wts ../yolov5s.wts
+cmake ..
+make
+sudo ./yolov5_gen -s [.wts] [.engine] [n/s/m/l/x]  // serialize model to plan file
+// For example yolov5s
+sudo ./yolov5_gen -s ../yolov5s.wts ../yolov5s.engine s
+```
 
-</details>
+3. use **tensorr engine**
 
-# [Updates](./docs/ChangeLog.md)
-- [2022/1/7] : **Faster and stronger**, some bugs fixed, yolov5 base version updated.
 
+```
+sudo ./yolov5_use ../../yolov5s.engine ../../images/mytest.png
+```
 
-# Installation
-Please refer to [install.md](./docs/install.md) for installation and dataset preparation.
+![image-20220402142552499](C:\Users\Administrator\AppData\Roaming\Typora\typora-user-images\image-20220402142552499.png)
 
-# Getting Started 
-This repo is based on [yolov5](https://github.com/ultralytics/yolov5). 
 
-And this repo has been rebuilt, Please see [GetStart.md](./docs/GetStart.md) for the Oriented Detection latest basic usage.
-
-#  Acknowledgements
-I have used utility functions from other wonderful open-source projects. Espeicially thank the authors of:
-
-* [ultralytics/yolov5](https://github.com/ultralytics/yolov5).
-* [Thinklab-SJTU/CSL_RetinaNet_Tensorflow](https://github.com/Thinklab-SJTU/CSL_RetinaNet_Tensorflow).
-* [jbwang1997/OBBDetection](https://github.com/jbwang1997/OBBDetection)
-* [CAPTAIN-WHU/DOTA_devkit](https://github.com/CAPTAIN-WHU/DOTA_devkit)
-## More detailed explanation
-想要了解相关实现的细节和原理可以看我的知乎文章:   
-* [自己改建YOLOv5旋转目标的踩坑记录](https://www.zhihu.com/column/c_1358464959123390464).
-
-## 有问题反馈
-在使用中有任何问题，建议先按照[install.md](./docs/install.md)检查环境依赖项，再按照[GetStart.md](./docs/GetStart.md)检查使用流程是否正确，善用搜索引擎和github中的issue搜索框，可以极大程度上节省你的时间。
-
-若遇到的是新问题，可以用以下联系方式跟我交流，为了提高沟通效率，请尽可能地提供相关信息以便我复现该问题。
-
-* 知乎（@[略略略](https://www.zhihu.com/people/lue-lue-lue-3-92-86)）
-* 代码问题提issues,其他问题请知乎上联系
-
-## 关于作者
-
-```javascript
-  Name  : "胡凯旋"
-  describe myself："咸鱼一枚"