70 lines
2.8 KiB
Markdown
70 lines
2.8 KiB
Markdown
# yolov5
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The Pytorch implementation is [ultralytics/yolov5](https://github.com/ultralytics/yolov5).
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Currently, we support yolov5 v1.0(yolov5s only), v2.0, v3.0 and v3.1.
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- For yolov5 v3.1, please visit [yolov5 release v3.1](https://github.com/ultralytics/yolov5/releases/tag/v3.1), and use the latest commit of this repo.
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- For yolov5 v3.0, please visit [yolov5 release v3.0](https://github.com/ultralytics/yolov5/releases/tag/v3.0), and use the latest commit of this repo.
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- For yolov5 v2.0, please visit [yolov5 release v2.0](https://github.com/ultralytics/yolov5/releases/tag/v2.0), and checkout commit ['5cfa444'](https://github.com/wang-xinyu/tensorrtx/commit/5cfa4445170eabaa54acd5ad7f469ef65a8763f1) of this repo.
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- For yolov5 v1.0, please visit [yolov5 release v1.0](https://github.com/ultralytics/yolov5/releases/tag/v1.0), and checkout commit ['f09aa3b'](https://github.com/wang-xinyu/tensorrtx/commit/f09aa3bbebf4d4d37b6d3b32a1d39e1f2678a07b) of this repo.
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## Config
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- Choose the model s/m/l/x by `NET` macro in yolov5.cpp
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- Input shape defined in yololayer.h
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- Number of classes defined in yololayer.h, **DO NOT FORGET TO ADAPT THIS, If using your own model**
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- FP16/FP32 can be selected by the macro in yolov5.cpp
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- GPU id can be selected by the macro in yolov5.cpp
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- NMS thresh in yolov5.cpp
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- BBox confidence thresh in yolov5.cpp
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- Batch size in yolov5.cpp
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## How to Run, yolov5s as example
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```
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1. generate yolov5s.wts from pytorch with yolov5s.pt, or download .wts from model zoo
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git clone https://github.com/wang-xinyu/tensorrtx.git
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git clone https://github.com/ultralytics/yolov5.git
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// download its weights 'yolov5s.pt'
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// copy tensorrtx/yolov5/gen_wts.py into ultralytics/yolov5
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// ensure the file name is yolov5s.pt and yolov5s.wts in gen_wts.py
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// go to ultralytics/yolov5
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python gen_wts.py
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// a file 'yolov5s.wts' will be generated.
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2. build tensorrtx/yolov5 and run
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// put yolov5s.wts into tensorrtx/yolov5
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// go to tensorrtx/yolov5
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// ensure the macro NET in yolov5.cpp is s
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mkdir build
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cd build
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cmake ..
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make
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sudo ./yolov5 -s // serialize model to plan file i.e. 'yolov5s.engine'
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sudo ./yolov5 -d ../samples // deserialize plan file and run inference, the images in samples will be processed.
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3. check the images generated, as follows. _zidane.jpg and _bus.jpg
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4. optional, load and run the tensorrt model in python
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// install python-tensorrt, pycuda, etc.
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// ensure the yolov5s.engine and libmyplugins.so have been built
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python yolov5_trt.py
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```
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<p align="center">
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<img src="https://user-images.githubusercontent.com/15235574/78247927-4d9fac00-751e-11ea-8b1b-704a0aeb3fcf.jpg">
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</p>
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<p align="center">
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<img src="https://user-images.githubusercontent.com/15235574/78247970-60b27c00-751e-11ea-88df-41473fed4823.jpg">
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</p>
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## More Information
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See the readme in [home page.](https://github.com/wang-xinyu/tensorrtx)
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