yolov5s ncnn practice

 

Tutorial - deploy YOLOv5 with ncnn

https://github.com/Tencent/ncnn/discussions/4541

 

ncnn model制作(yolov5s.pt -> ncnn.param and ncnn.bin)

 

使用ncnn库编译后生成的工具

https://github.com/Tencent/ncnn/wiki/use-ncnn-with-pytorch-or-onnx

https://ncnn.readthedocs.io/en/latest/how-to-use-and-FAQ/use-ncnn-with-pytorch-or-onnx.html

 

pt -> onnx -> ncnn

 

pytorch to onnx

python export.py --weights yolov5s.pt --include torchscript onnx

 

onnx simplify

先安装protoc库

https://blog.csdn.net/qq_45057749/article/details/115013509

# 准备基础环境
sudo apt install build-essential libopencv-dev cmake

# 编译安装protobuf依赖库
git clone https://github.com/protocolbuffers/protobuf.git  # 安装源文件
cd protobuf
git submodule update --init --recursive  # clone子模块的依赖
./autogen.sh  # 执行自动生成的shell脚本
./configure  # 配置文件shell脚本
make  # 编译
make install  # 编译安装
sudo ldconfig  # 刷新

 

安装功能库

pip install onnxsim
python -m onnxsim resnet18.onnx resnet18-sim.onnx

or

pip install onnxslim
python -m onnxslim resnet18.onnx resnet18-slim.onnx

 

onnx to ncnn

onnx2ncnn resnet18-sim.onnx resnet18.param resnet18.bin

 

优化

https://github.com/Tencent/ncnn/wiki/use-ncnnoptimize-to-optimize-model

https://ncnn.readthedocs.io/en/latest/how-to-use-and-FAQ/use-ncnnoptimize-to-optimize-model.html

ncnnoptimize mobilenet.param mobilenet.bin mobilenet-opt.param mobilenet-opt.bin 65536 

 

量化

https://ncnn.readthedocs.io/en/latest/how-to-use-and-FAQ/quantized-int8-inference.html

create calibration table file

下载图片

https://github.com/nihui/imagenet-sample-images

 

find images/ -type f > imagelist.txt
./ncnn2table mobilenet-opt.param mobilenet-opt.bin imagelist.txt mobilenet.table mean=[104,117,123] norm=[0.017,0.017,0.017] shape=[224,224,3] pixel=BGR thread=8 method=kl

 

./ncnn2int8 mobilenet-opt.param mobilenet-opt.bin mobilenet-int8.param mobilenet-int8.bin mobilenet.table

 

使用pnnx工具

pt -> torchscript / onnx -> ncnn

 

https://github.com/Tencent/ncnn/wiki/use-ncnn-with-pytorch-or-onnx

https://github.com/pnnx/pnnx

https://github.com/Tencent/ncnn/discussions/4541

setup yolov5 pytorch

# checkout yolov5 v7.0 project
git clone https://github.com/ultralytics/yolov5
cd yolov5
git checkout v7.0

# install requirements
pip install -r requirements.txt --user

# download yolov5s weight
wget https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt

# test detection with pytorch weight, result saved to runs/detect/expN folder
python detect.py --source /home/nihui/nbs.jpg --weights yolov5s.pt --view-img

export torchscript and convert it to ncnn via pnnx

# export to torchscript, result saved to yolov5s.torchscript
python export.py --weights yolov5s.pt --include torchscript

# download latest pnnx from https://github.com/pnnx/pnnx/releases
wget https://github.com/pnnx/pnnx/releases/download/20230217/pnnx-20230217-ubuntu.zip
unzip pnnx-20230217-ubuntu.zip

# convert torchscript to pnnx and ncnn, result saved to yolov5s.ncnn.param yolov5s.ncnn.bin
./pnnx-20230217-ubuntu/pnnx yolov5s.torchscript inputshape=[1,3,640,640]