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marcoslucianops
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YOLOv5.md
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YOLOv5.md
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# YOLOv5
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NVIDIA DeepStream SDK 5.0.1 configuration for YOLOv5 models
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Thanks [DanaHan](https://github.com/DanaHan/Yolov5-in-Deepstream-5.0), [wang-xinyu](https://github.com/wang-xinyu/tensorrtx) and [Ultralytics](https://github.com/ultralytics/yolov5)
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##
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* [Requirements](#requirements)
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* [Convert PyTorch model to wts file](#convert-pytorch-model-to-wts-file)
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* [Convert wts file to TensorRT model](#convert-wts-file-to-tensorrt-model)
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* [Compile nvdsinfer_custom_impl_Yolo](#compile-nvdsinfer_custom_impl_yolo)
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* [Testing model](#testing-model)
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##
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### Requirements
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* Python3
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```
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sudo apt-get install python3 python3-dev python3-pip
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pip3 install --upgrade pip
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```
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* OpenCV Python
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```
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sudo apt-get install libopencv-dev
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pip3 install opencv-python
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```
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* Matplotlib
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```
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pip3 install matplotlib
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```
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* Scipy
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```
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pip3 install scipy
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```
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* tqdm
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```
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pip3 install tqdm
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```
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* PyTorch
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```
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pip3 install torch torchvision
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```
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* PyTorch (for Jetson plataform)
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```
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wget https://nvidia.box.com/shared/static/9eptse6jyly1ggt9axbja2yrmj6pbarc.whl -O torch-1.6.0-cp36-cp36m-linux_aarch64.whl
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sudo apt-get install python3-pip libopenblas-base libopenmpi-dev
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pip3 install torch-1.6.0-cp36-cp36m-linux_aarch64.whl
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```
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* TorchVision (for Jetson platform)
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```
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git clone -b v0.7.0 https://github.com/pytorch/vision torchvision
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sudo apt-get install libjpeg-dev zlib1g-dev python3-pip
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cd torchvision
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export BUILD_VERSION=0.7.0
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sudo python3 setup.py install
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```
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##
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### Convert PyTorch model to wts file
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1. Download repositories
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```
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git clone https://github.com/DanaHan/Yolov5-in-Deepstream-5.0.git yolov5converter
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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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```
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2. Download latest YoloV5 (YOLOv5s, YOLOv5m, YOLOv5l or YOLOv5x) weights to yolov5/weights directory (example for YOLOv5s)
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```
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wget https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5s.pt -P yolov5/weights/
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```
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3. Copy gen_wts.py file (from tensorrtx/yolov5 folder) to yolov5 (ultralytics) folder
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```
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cp tensorrtx/yolov5/gen_wts.py yolov5/gen_wts.py
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```
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4. Generate wts file
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```
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cd yolov5
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python3 gen_wts.py
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```
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yolov5s.wts file will be generated in yolov5 folder
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<br />
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Note: if you want to generate wts file to another YOLOv5 model (YOLOv5m, YOLOv5l or YOLOv5x), edit get_wts.py file changing yolov5s to your model name
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```
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model = torch.load('weights/yolov5s.pt', map_location=device)['model'].float() # load to FP32
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model.to(device).eval()
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f = open('yolov5s.wts', 'w')
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```
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##
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### Convert wts file to TensorRT model
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1. Replace yololayer files from tensorrtx/yolov5 folder to yololayer and hardswish files from yolov5converter
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```
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mv yolov5converter/yololayer.cu tensorrtx/yolov5/yololayer.cu
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mv yolov5converter/yololayer.h tensorrtx/yolov5/yololayer.h
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mv yolov5converter/hardswish.cu tensorrtx/yolov5/hardswish.cu
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mv yolov5converter/hardswish.h tensorrtx/yolov5/hardswish.h
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```
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2. Move generated yolov5s.wts file to tensorrtx/yolov5 folder (example for YOLOv5s)
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```
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cp yolov5/yolov5s.wts tensorrtx/yolov5/yolov5s.wts
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```
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3. Build tensorrtx/yolov5
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```
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cd tensorrtx/yolov5
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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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```
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4. Convert to TensorRT model (yolov5s.engine and libmyplugins.so files will be generated in tensorrtx/yolov5/build folder)
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```
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sudo ./yolov5 -s
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```
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5. Create a custom yolo folder and copy generated files (example for YOLOv5s)
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```
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mkdir /opt/nvidia/deepstream/deepstream-5.0/sources/yolo
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cp yolov5s.engine /opt/nvidia/deepstream/deepstream-5.0/sources/yolo/yolov5s.engine
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cp libmyplugins.so /opt/nvidia/deepstream/deepstream-5.0/sources/yolo/libmyplugins.so
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```
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<br />
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Note: by default, yolov5 script generate model with batch size = 1, FP16 mode and s model.
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```
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#define USE_FP16 // comment out this if want to use FP32
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#define DEVICE 0 // GPU id
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#define NMS_THRESH 0.4
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#define CONF_THRESH 0.5
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#define BATCH_SIZE 1
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#define NET s // s m l x
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```
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Edit yolov5.cpp file before compile if you want to change this parameters.
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##
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### Compile nvdsinfer_custom_impl_Yolo
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1. Donwload [my external/yolov5 folder](https://github.com/marcoslucianops/DeepStream-Yolo/tree/master/external/yolov5) and move files to created yolo folder
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2. Compile lib
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```
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cd /opt/nvidia/deepstream/deepstream-5.0/sources/yolo
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CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
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```
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##
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### Testing model
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Use my edited [deepstream_app_config.txt](https://raw.githubusercontent.com/marcoslucianops/DeepStream-Yolo/master/external/yolov5/deepstream_app_config.txt) and [config_infer_primary.txt](https://raw.githubusercontent.com/marcoslucianops/DeepStream-Yolo/master/external/yolov5/config_infer_primary.txt) files available in [my external/yolov5 folder](https://github.com/marcoslucianops/DeepStream-Yolo/tree/master/external/yolov5)
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Run command
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```
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LD_PRELOAD=./libmyplugins.so deepstream-app -c deepstream_app_config.txt
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```
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<br />
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Note: based on selected model, edit config_infer_primary.txt file
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For example, if you using YOLOv5x
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```
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model-engine-file=yolov5s.engine
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```
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to
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```
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model-engine-file=yolov5x.engine
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```
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To change NMS_THRESH and THRESH_CONF, edit nvdsinfer_custom_impl_Yolo/nvdsparsebbox_Yolo.cpp file and recompile
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```
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#define NMS_THRESH 0.45
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#define CONF_THRESH 0.25
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```
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