Support for YOLOv5 4.0
Added support for YOLOv5 4.0
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Marcos Luciano
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YOLOv5-4.0.md
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YOLOv5-4.0.md
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# YOLOv5
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NVIDIA DeepStream SDK 5.1 configuration for YOLOv5 4.0 models
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Thanks [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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* [TensorRTX](https://github.com/wang-xinyu/tensorrtx/blob/master/tutorials/install.md)
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* [Ultralytics](https://github.com/ultralytics/yolov5/blob/v4.0/requirements.txt)
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* Matplotlib (for Jetson plataform)
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```
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sudo apt-get install python3-matplotlib
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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 -b yolov5-v4.0 https://github.com/wang-xinyu/tensorrtx.git
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git clone -b v4.0 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 folder (example for YOLOv5s)
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```
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wget https://github.com/ultralytics/yolov5/releases/download/v4.0/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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##
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### Convert wts file to TensorRT model
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1. 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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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/build/yolov5s.wts
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```
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3. Convert to TensorRT model (yolov5s.engine file will be generated in tensorrtx/yolov5/build folder)
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```
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sudo ./yolov5 -s yolov5s.wts yolov5s.engine s
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```
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4. Create a custom yolo folder and copy generated file (example for YOLOv5s)
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```
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mkdir /opt/nvidia/deepstream/deepstream-5.1/sources/yolo
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cp yolov5s.engine /opt/nvidia/deepstream/deepstream-5.1/sources/yolo/yolov5s.engine
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```
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<br />
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Note: by default, yolov5 script generate model with batch size = 1 and FP16 mode.
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```
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#define USE_FP32 // set USE_INT8 or USE_FP16 or 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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```
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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. Run command
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```
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sudo chmod -R 777 /opt/nvidia/deepstream/deepstream-5.1/sources/
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```
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2. Donwload [my external/yolov5-4.0 folder](https://github.com/marcoslucianops/DeepStream-Yolo/tree/master/external/yolov5-4.0) and move files to created yolo folder
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3. Compile lib
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* x86 platform
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```
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cd /opt/nvidia/deepstream/deepstream-5.1/sources/yolo
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CUDA_VER=11.1 make -C nvdsinfer_custom_impl_Yolo
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```
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* Jetson platform
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```
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cd /opt/nvidia/deepstream/deepstream-5.1/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/yolov5-4.0/deepstream_app_config.txt) and [config_infer_primary.txt](https://raw.githubusercontent.com/marcoslucianops/DeepStream-Yolo/master/external/yolov5-4.0/config_infer_primary.txt) files available in [my external/yolov5-4.0 folder](https://github.com/marcoslucianops/DeepStream-Yolo/tree/master/external/yolov5-4.0)
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Run command
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```
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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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##
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To change NMS_THRESH, edit nvdsinfer_custom_impl_Yolo/nvdsparsebbox_Yolo.cpp file and recompile
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```
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#define kNMS_THRESH 0.45
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```
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To change CONF_THRESH, edit config_infer_primary.txt file
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```
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[class-attrs-all]
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pre-cluster-threshold=0.25
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```
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