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270 lines
8.1 KiB
270 lines
8.1 KiB
# YOLOv8-TensorRT |
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`YOLOv8` using TensorRT accelerate ! |
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--- |
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[](https://github.com/triple-Mu/YOLOv8-TensorRT) |
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[](https://github.com/triple-Mu/YOLOv8-TensorRT) |
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[](https://developer.nvidia.com/tensorrt) |
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[](https://github.com/triple-Mu/YOLOv8-TensorRT) |
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[](https://github.com/triple-Mu/YOLOv8-TensorRT/blob/main/LICENSE) |
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[](https://github.com/triple-Mu/YOLOv8-TensorRT/pulls) |
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[](https://github.com/triple-Mu/YOLOv8-TensorRT) |
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--- |
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# Prepare the environment |
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1. Install `CUDA` follow [`CUDA official website`](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#download-the-nvidia-cuda-toolkit). |
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🚀 RECOMMENDED `CUDA` >= 11.4 |
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2. Install `TensorRT` follow [`TensorRT official website`](https://developer.nvidia.com/nvidia-tensorrt-8x-download). |
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🚀 RECOMMENDED `TensorRT` >= 8.4 |
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2. Install python requirements. |
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``` shell |
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pip install -r requirements.txt |
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``` |
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3. Install [`ultralytics`](https://github.com/ultralytics/ultralytics) package for ONNX export or TensorRT API building. |
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``` shell |
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pip install ultralytics |
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``` |
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5. Prepare your own PyTorch weight such as `yolov8s.pt` or `yolov8s-seg.pt`. |
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***NOTICE:*** |
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Please use the latest `CUDA` and `TensorRT`, so that you can achieve the fastest speed ! |
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If you have to use a lower version of `CUDA` and `TensorRT`, please read the relevant issues carefully ! |
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# Normal Usage |
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If you get ONNX from origin [`ultralytics`](https://github.com/ultralytics/ultralytics) repo, you should build engine by yourself. |
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You can only use the `c++` inference code to deserialize the engine and do inference. |
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You can get more information in [`Normal.md`](docs/Normal.md) ! |
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Besides, other scripts won't work. |
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# Export End2End ONNX with NMS |
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You can export your onnx model by `ultralytics` API and add postprocess such as bbox decoder and `NMS` into ONNX model at the same time. |
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``` shell |
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python3 export-det.py \ |
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--weights yolov8s.pt \ |
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--iou-thres 0.65 \ |
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--conf-thres 0.25 \ |
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--topk 100 \ |
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--opset 11 \ |
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--sim \ |
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--input-shape 1 3 640 640 \ |
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--device cuda:0 |
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``` |
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#### Description of all arguments |
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- `--weights` : The PyTorch model you trained. |
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- `--iou-thres` : IOU threshold for NMS plugin. |
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- `--conf-thres` : Confidence threshold for NMS plugin. |
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- `--topk` : Max number of detection bboxes. |
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- `--opset` : ONNX opset version, default is 11. |
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- `--sim` : Whether to simplify your onnx model. |
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- `--input-shape` : Input shape for you model, should be 4 dimensions. |
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- `--device` : The CUDA deivce you export engine . |
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You will get an onnx model whose prefix is the same as input weights. |
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### Just Taste First |
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If you just want to taste first, you can download the onnx model which are exported by `YOLOv8` package and modified by me. |
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[**YOLOv8-n**](https://triplemu-shared.oss-cn-beijing.aliyuncs.com/models/yolov8n.onnx?OSSAccessKeyId=LTAI5tNk9iiMqhFC64jCcgpv&Expires=2690974569&Signature=3ct9pnRygBduWdgAtfKOQAt4PeU%3D) |
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[**YOLOv8-s**](https://triplemu-shared.oss-cn-beijing.aliyuncs.com/models/yolov8s.onnx?OSSAccessKeyId=LTAI5tNk9iiMqhFC64jCcgpv&Expires=10000000001690974000&Signature=cbHjUwmRsYdvilcirzjBI6%2BzmvI%3D) |
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[**YOLOv8-m**](https://triplemu-shared.oss-cn-beijing.aliyuncs.com/models/yolov8m.onnx?OSSAccessKeyId=LTAI5tNk9iiMqhFC64jCcgpv&Expires=101690974603&Signature=XnJnQqbKsnJSKSgqVQ41kxoeETU%3D) |
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[**YOLOv8-l**](https://triplemu-shared.oss-cn-beijing.aliyuncs.com/models/yolov8l.onnx?OSSAccessKeyId=LTAI5tNk9iiMqhFC64jCcgpv&Expires=2690974619&Signature=djxvNzcaFosHrMS5ylWh1R0%2Ff8E%3D) |
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[**YOLOv8-x**](https://triplemu-shared.oss-cn-beijing.aliyuncs.com/models/yolov8x.onnx?OSSAccessKeyId=LTAI5tNk9iiMqhFC64jCcgpv&Expires=2690974637&Signature=DMmuT2wlfBzai%2BBpYJFcmNbkMKU%3D) |
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# Build End2End Engine from ONNX |
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### 1. Build Engine by TensorRT ONNX Python api |
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You can export TensorRT engine from ONNX by [`build.py` ](build.py). |
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Usage: |
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``` shell |
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python3 build.py \ |
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--weights yolov8s.onnx \ |
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--iou-thres 0.65 \ |
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--conf-thres 0.25 \ |
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--topk 100 \ |
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--fp16 \ |
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--device cuda:0 |
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``` |
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#### Description of all arguments |
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- `--weights` : The ONNX model you download. |
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- `--iou-thres` : IOU threshold for NMS plugin. |
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- `--conf-thres` : Confidence threshold for NMS plugin. |
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- `--topk` : Max number of detection bboxes. |
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- `--fp16` : Whether to export half-precision engine. |
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- `--device` : The CUDA deivce you export engine . |
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You can modify `iou-thres` `conf-thres` `topk` by yourself. |
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### 2. Export Engine by Trtexec Tools |
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You can export TensorRT engine by [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/main/samples/trtexec) tools. |
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Usage: |
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``` shell |
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/usr/src/tensorrt/bin/trtexec \ |
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--onnx=yolov8s.onnx \ |
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--saveEngine=yolov8s.engine \ |
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--fp16 |
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``` |
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**If you installed TensorRT by a debian package, then the installation path of `trtexec` |
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is `/usr/src/tensorrt/bin/trtexec`** |
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**If you installed TensorRT by a tar package, then the installation path of `trtexec` is under the `bin` folder in the path you decompressed** |
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# Build TensorRT Engine by TensorRT API |
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Please see more information in [`API-Build.md`](docs/API-Build.md) |
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***Notice !!!*** We don't support YOLOv8-seg model now !!! |
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# Inference |
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## 1. Infer with python script |
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You can infer images with the engine by [`infer-det.py`](infer-det.py) . |
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Usage: |
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``` shell |
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python3 infer-det.py \ |
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--engine yolov8s.engine \ |
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--imgs data \ |
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--show \ |
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--out-dir outputs \ |
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--device cuda:0 |
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``` |
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#### Description of all arguments |
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- `--engine` : The Engine you export. |
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- `--imgs` : The images path you want to detect. |
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- `--show` : Whether to show detection results. |
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- `--out-dir` : Where to save detection results images. It will not work when use `--show` flag. |
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- `--device` : The CUDA deivce you use. |
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- `--profile` : Profile the TensorRT engine. |
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## 2. Infer with C++ |
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You can infer with c++ in [`csrc/detect/end2end`](csrc/detect/end2end) . |
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### Build: |
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Please set you own librarys in [`CMakeLists.txt`](csrc/detect/end2end/CMakeLists.txt) and modify `CLASS_NAMES` and `COLORS` in [`main.cpp`](csrc/detect/end2end/main.cpp). |
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``` shell |
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export root=${PWD} |
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cd csrc/detect/end2end |
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mkdir -p build && cd build |
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cmake .. |
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make |
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mv yolov8 ${root} |
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cd ${root} |
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``` |
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Usage: |
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``` shell |
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# infer image |
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./yolov8 yolov8s.engine data/bus.jpg |
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# infer images |
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./yolov8 yolov8s.engine data |
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# infer video |
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./yolov8 yolov8s.engine data/test.mp4 # the video path |
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``` |
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# TensorRT Segment Deploy |
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Please see more information in [`Segment.md`](docs/Segment.md) |
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# TensorRT Pose Deploy |
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Please see more information in [`Pose.md`](docs/Pose.md) |
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# TensorRT Cls Deploy |
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Please see more information in [`Cls.md`](docs/Cls.md) |
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# DeepStream Detection Deploy |
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See more in [`README.md`](csrc/deepstream/README.md) |
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# Jetson Deploy |
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Only test on `Jetson-NX 4GB`. |
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See more in [`Jetson.md`](docs/Jetson.md) |
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# Profile you engine |
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If you want to profile the TensorRT engine: |
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Usage: |
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``` shell |
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python3 trt-profile.py --engine yolov8s.engine --device cuda:0 |
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``` |
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# Refuse To Use PyTorch for Model Inference !!! |
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If you need to break away from pytorch and use tensorrt inference, |
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you can get more information in [`infer-det-without-torch.py`](infer-det-without-torch.py), |
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the usage is the same as the pytorch version, but its performance is much worse. |
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You can use `cuda-python` or `pycuda` for inference. |
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Please install by such command: |
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```shell |
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pip install cuda-python |
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# or |
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pip install pycuda |
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``` |
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Usage: |
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``` shell |
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python3 infer-det-without-torch.py \ |
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--engine yolov8s.engine \ |
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--imgs data \ |
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--show \ |
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--out-dir outputs \ |
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--method cudart |
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``` |
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#### Description of all arguments |
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- `--engine` : The Engine you export. |
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- `--imgs` : The images path you want to detect. |
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- `--show` : Whether to show detection results. |
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- `--out-dir` : Where to save detection results images. It will not work when use `--show` flag. |
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- `--method` : Choose `cudart` or `pycuda`, default is `cudart`.
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