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129 lines
2.9 KiB
129 lines
2.9 KiB
# YOLOv8-pose Model with TensorRT |
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The yolov8-pose model conversion route is : |
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YOLOv8 PyTorch model -> ONNX -> TensorRT Engine |
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***Notice !!!*** We don't support TensorRT API building !!! |
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# Export Orin ONNX model by ultralytics |
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You can leave this repo and use the original `ultralytics` repo for onnx export. |
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### 1. ONNX -> TensorRT |
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You can export your onnx model by `ultralytics` API. |
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``` shell |
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yolo export model=yolov8s-pose.pt format=onnx opset=11 simplify=True |
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``` |
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or run this python script: |
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```python |
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from ultralytics import YOLO |
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# Load a model |
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model = YOLO("yolov8s-pose.pt") # load a pretrained model (recommended for training) |
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success = model.export(format="onnx", opset=11, simplify=True) # export the model to onnx format |
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assert success |
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``` |
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Then build 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-pose.onnx \ |
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--saveEngine=yolov8s-pose.engine \ |
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--fp16 |
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``` |
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### 2. Direct to TensorRT (NOT RECOMMAND!!) |
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Usage: |
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```shell |
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yolo export model=yolov8s-pose.pt format=engine device=0 |
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``` |
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or run python script: |
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```python |
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from ultralytics import YOLO |
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# Load a model |
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model = YOLO("yolov8s-pose.pt") # load a pretrained model (recommended for training) |
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success = model.export(format="engine", device=0) # export the model to engine format |
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assert success |
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``` |
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After executing the above script, you will get an engine named `yolov8s-pose.engine` . |
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# Inference |
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## Infer with python script |
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You can infer images with the engine by [`infer-pose.py`](../infer-pose.py) . |
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Usage: |
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``` shell |
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python3 infer-pose.py \ |
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--engine yolov8s-pose.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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## Inference with c++ |
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You can infer with c++ in [`csrc/pose/normal`](../csrc/pose/normal) . |
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### Build: |
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Please set you own librarys in [`CMakeLists.txt`](../csrc/pose/normal/CMakeLists.txt) and modify `KPS_COLORS` |
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and `SKELETON` and `LIMB_COLORS` in [`main.cpp`](../csrc/pose/normal/main.cpp). |
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Besides, you can modify the postprocess parameters such as `score_thres` and `iou_thres` and `topk` |
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in [`main.cpp`](../csrc/pose/normal/main.cpp). |
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```c++ |
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int topk = 100; |
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float score_thres = 0.25f; |
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float iou_thres = 0.65f; |
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``` |
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And build: |
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``` shell |
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export root=${PWD} |
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cd src/pose/normal |
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mkdir build |
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cmake .. |
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make |
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mv yolov8-pose ${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-pose yolov8s-pose.engine data/bus.jpg |
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# infer images |
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./yolov8-pose yolov8s-pose.engine data |
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# infer video |
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./yolov8-pose yolov8s-pose.engine data/test.mp4 # the video path |
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```
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