tensorrt部署olov9-ros源码+演示视频+项目说明.zip

<h1 align="center"><span>TensorRT-YOLOv9-ROS</span></h1> + ROS version of [YOLOv9](https://github.com/WongKinYiu/yolov9) accelerated with [TensorRT](https://github.com/NVIDIA/TensorRT) API + This repository is a merely re-implementation with `ROS` of the: + 👏 [TensorRT-YOLOv9-C++](https://github.com/spacewalk01/TensorRT-YOLOv9), which is based on + [YOLOv9](https://github.com/WongKinYiu/yolov9) - `YOLOv9`: Learning What You Want to Learn Using Programmable Gradient Information. + [TensorRT](https://github.com/NVIDIA/TensorRT/tree/release/8.6/samples) - `TensorRT` samples and api documentation. + [TensorRTx](https://github.com/wang-xinyu/tensorrtx) - Implementation of popular deep learning networks with TensorRT network definition API. https://github.com/engcang/TensorRT_YOLOv9_ROS/assets/34734707/0dff22cc-ec12-45fb-a931-fb0c90181fd7 <br> ### Known issues / notes + The resolution of image to be trained should be multiplication of 64 + [2024-05-12] - **Now supporting `TensorRT` >= 10** + **Check the paths of TensorRT in CMakeLists.txt's line 25, 26** <br> ## Dependencies + `ROS` (currently supporting only `ROS1`) + `C++` >= 17 + `cmake` >= 3.14 + `OpenCV` >= 4.2 + `TensorRT`, `CUDA`, `cuDNN` + `.engine` file generated with `TensorRT` + Tested versions: + Desktop with i9-10900k, RTX 3080 - `CUDA` 11.5, `cuDNN` 8.3.2.44, `TensorRT` 8.4.0.6 </details> <br> ## You may want to: <details><summary> ■ Unfold here to see how to install CUDA, cuDNN and TensorRT </summary> ### ● **Note that apt install with deb is preferred to run file and source file build for both of `CUDA` and `cuDNN`** + Download and install `CUDA` following instructions at here - https://developer.nvidia.com/cuda-downloads + Download and install `cuDNN` following instructions at here - https://developer.nvidia.com/cudnn-downloads + If you want, also refer to here - https://docs.nvidia.com/deeplearning/cudnn/installation/linux.html# + Set up environmental paths ```bash gedit ~/.bashrc *** Type and save below, CUDA_PATH should be like /usr/local/cuda-11.5, depending on your version *** export PATH=CUDA_PATH/bin:$PATH export LD_LIBRARY_PATH=CUDA_PATH/lib64:$LD_LIBRARY_PATH . ~/.bashrc gedit ~/.profile *** Type and save below, CUDA_PATH should be like /usr/local/cuda-11.5, depending on your version *** export PATH=CUDA_PATH/bin:$PATH export LD_LIBRARY_PATH=CUDA_PATH/lib64:$LD_LIBRARY_PATH . ~/.profile ``` + Verify, if installed properly ```bash # Verify dpkg -l | grep cuda dpkg -l | grep cudnn nvcc --version ``` <br> ### ● **Note that apt install with deb is preferred to other methods for `TensorRT`** + Download `TensorRT` at here - https://developer.nvidia.com/tensorrt-download + Follow the instructions at here - https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html#installing-debian + Installing full packages is recommended, which means: ```bash sudo apt install tensorrt sudo apt install python3-libnvinfer-dev sudo apt install onnx-graphsurgeon ``` <br> </details> <details><summary> ■ Unfold here to see how to train custom data / generate TensorRT engine file with safe Python3 virtual environment </summary> <br> ### ● Common step for training / engine file 0. Make sure that you have installed all dependencies properly. + Particularly, you should install full packages of `TensorRT`: `tensorrt`, `python3-libnvinfer-dev`, `onnx-graphsurgeon` 1. Install and make `Python3` virtual env ```bash python3 -m pip install virtualenv virtualenvwrapper cd <PATH YOU WANT TO SAVE VIRTUAL ENVIRONMENT> virtualenv -p python3 <NAME YOU WANT> *** Now you can activate with source <PATH YOU SAVED>/<NAME YOU WANT>/bin/activate *** Deactivate with deactivate ``` 2. (While virtual env being activated), clone `YOLOv9` repo and install requirements ```bash git clone https://github.com/WongKinYiu/yolov9 cd yolov9 pip install -r requirements.txt ``` <br> ### ● Converting .pt to .onnx, and then .engine 0. (While virtual env being activated) 1. Get trained `YOLOv9` weight file as `.pt` by training your own data or downloading the pre-trained model at here - https://github.com/WongKinYiu/yolov9/releases 2. Reparameterize the `.pt` file (saving computation, memory, and size by trimming unnecessary parts for inference but necessary only for training) ```bash cd yolov9 # cloned at above step wget https://raw.githubusercontent.com/engcang/TensorRT_YOLOv9_ROS/main/reparameterize.py *** Change the number of classes in the reparameterize.py in line 8 (nc=80) python reparameterize.py yolov9-c.pt yolov9-c-reparameterized.pt # input.pt output.put ``` 3. Export `.pt` file as `.onnx` ```bash python export.py --weights yolov9-c-reparameterized.pt --include onnx ``` 4. Then `.onnx` to `.engine` ```bash /usr/src/tensorrt/bin/trtexec --onnx=yolov9-c-reparameterized.onnx --saveEngine=yolov9-c.engine #for faster, less accurate /usr/src/tensorrt/bin/trtexec --onnx=yolov9-c-reparameterized.onnx --saveEngine=yolov9-c-fp16.engine --fp16 #not recommended - much faster, much less accurate /usr/src/tensorrt/bin/trtexec --onnx=yolov9-c-reparameterized.onnx --saveEngine=yolov9-c-int8.engine --int8 ``` <br> ### ● Training your own data 0. (While virtual env being activated) + `YOLOv9` is cloned already, requirements are installed already 1. Prepare data and labels in `YOLO format`. + You may want to use this - https://github.com/AlexeyAB/Yolo_mark + Or `roboflow` - https://docs.ultralytics.com/yolov5/tutorials/roboflow_datasets_integration/ 2. Make proper `data.yaml` file by copying and editing `yolov9/data/coco.yaml` as follows: ```yaml path: training # dataset root dir (relative from train.py file) train: train # train images folder (relative to 'path') val: val # val images folder (relative to 'path') test: test # test images folder (relative to 'path') # Classes names: 0: Transmission tower 1: Insulator ``` 3. Make proper `yolov9.yaml` file by copying and editing `yolov9/models/detect/yolov9.yaml or yolov9-c, yolov9-e, etc.` ```yaml # parameters nc: 2 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple #activation: nn.LeakyReLU(0.1) #activation: nn.ReLU() # anchors anchors: 3 # YOLOv9 backbone backbone: [ [-1, 1, Silence, []], # conv down [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 ... ] ``` 4. Edit learning parameters by editing `yolov9/data/hyps/hyp.scratch-high.yaml` 5. **Put all of files properly in the `yolov9` folder. If outside the `yolov9` folder, error occurs!** ``` yolov9 │ ... ├─ data # Reference folder │ ├─ coco.yaml │ └─ hyps │ └─ hyp.scratch-high.yaml ├─ models # Reference folder │ ... │ ├─ detect │ ... │ │ ├─ yolov9-c.yaml │ │ ├─ yolov9-e.yaml │ │ └─ yolov9.yaml ├─ runs # Output saved folder │ ... ├─ train.py # Using this file for GELAN ├─ train_dual.py # Using this file for YOLOv9 ├─ training # Using this folder │ ├─ yolov9-c.pt │ ├─ data.yaml │ ├─ yolov9.yaml │ ├─ test │ │ ├─ 02001.jpg │ │ ├─ 02001.txt │ │ └─ ... │ ├─ train │ │ ├─ 00001.jpg │ │ ├─ 00001.txt │ │ └─ ... │ ├─ val │ │ ├─ 04000.jpg │ │ ├─ 04000.txt │ │ └─ ... └─ └─ ... ``` 6. Train ```bash cd yolov9 *** Using pretrained model (yolov9-c.pt here), fine-tuning: python train_dual.py --batch-size 4 --epochs 100 --img 640 --device 0 --close-mosaic 15 \ --data training/data.yaml --weights training/yolov9-c.pt --cfg training/yolov9.yaml --hyp data/hyps/hyp.scratch-high.yaml *** From the scratch: python train_dual.py --batch-s