msnh2012 / Msnhnet

🔥 (yolov3 yolov4 yolov5 unet ...)A mini pytorch inference framework which inspired from darknet.

🔥 Msnhnet(V2.0 Focusing on Robot Vision)🔥

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A mini pytorch inference framework which inspired from darknet.

OS supported (you can check other OS by yourself)

	windows	linux	mac	Loongnix
checked
gpu

CPU checked

	Intel i7	raspberry 3B	raspberry 4B	Jeston NX	Loongson
checked

Features

• C++ Only. 3rdparty blas lib is optional, also you can use OpenBlas.
• OS supported: Windows, Linux(Ubuntu checked) and Mac os(unchecked).
• CPU supported: Intel X86, AMD(unchecked) and ARM(checked: armv7 armv8 arrch64).
• x86 avx2 supported.(Working....)
• arm neon supported.(Working....)
• A cv lib like opencv is supported for msnhnet.(MsnhCV)
• conv2d 3x3s1 3x3s2 winograd3x3s1 is supported(Arm)
• Keras to Msnhnet is supported. (Keras 2 and tensorflow 1.x)
• GPU cuda supported.(Checked GTX1080Ti, Jetson NX)
• GPU cudnn supported.(Checked GTX1080Ti, Jetson NX)
• GPU fp16 mode supported.(Checked GTX1080Ti, Jetson NX.)
• ps. Please check your card wheather fp16 full speed is supported.
• c_api supported.
• keras 2 msnhnet supported.(Keras 2 and tensorflow 1.x, part of op)
• pytorch 2 msnhnet supported.(Part of op, working on it)
• MsnhnetSharp supported.
• A viewer for msnhnet is supported.(netron like)
• Working on it...(Weekend Only (╮（╯＿╰）╭))

Tested networks

• lenet5
• lenet5_bn
• alexnet(torchvision)
• vgg16(torchvision)
• vgg16_bn(torchvision)
• resnet18(torchvision)
• resnet34(torchvision)
• resnet50(torchvision)
• resnet101(torchvision)
• resnet152(torchvision)
• darknet53(Pytorch_Darknet53)
• googLenet(torchvision)
• mobilenetv2(torchvision)
• yolov3(u版yolov3)
• yolov3_spp(u版yolov3)
• yolov3_tiny(u版yolov3)
• yolov4(u版yolov3)
• fcns(pytorch-FCN-easiest-demo)
• unet(bbuf keras)
• deeplabv3(torchvision)
• yolov5s🔥(U版yolov5, for params)
• yolov5m🔥(U版yolov5,for params)
  ==============================================================
• mobilenetv2_yolov3_lite (cudnn does not work with GTX10** Pascal Card, please use GPU model only)
• mobilenetv2_yolov3_nano (cudnn does not work with GTX10** Pascal Card, please use GPU model only)
• yoloface100k (cudnn does not work with GTX10** Pascal Card, please use GPU model only)
• yoloface500k (cudnn does not work with GTX10** Pascal Card, please use GPU model only)
• Thanks: https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3 ==============================================================
• pretrained models. 链接：https://pan.baidu.com/s/1mBaJvGx7tp2ZsLKzT5ifOg 提取码：x53z
• pretrained models. Link : Google Drive
• Examples here.

Yolo Test

• Win10 MSVC 2017 I7-10700F

  net	yolov3	yolov3_tiny	yolov4
  time	380ms	50ms	432ms

• ARM(Yolov3Tiny cpu)

  cpu	raspberry 3B	raspberry 4B	Jeston NX
  with neon asm	?	0.432s	?

Yolo GPU Test

• Ubuntu16.04 GCC Cuda10.1 GTX1080Ti

  net	yolov3	yolov3_tiny	yolov4
  time	30ms	8ms	30ms

• Jetson NX

  net	yolov3	yolov3_tiny	yolov4
  time	200ms	20ms	210ms

Yolo GPU cuDnn FP16 Test

• Jetson NX

  net	yolov3	yolov4
  time	115ms	120ms

Yolov5s GPU Test

• Ubuntu18.04 GCC Cuda10.1 GTX2080Ti

  net	yolov5s	yolov5s_fp16
  time	9.57ms	8.57ms

Mobilenet Yolo GPU cuDnn Test

• Jetson NX

  net	yoloface100k	yoloface500k	mobilenetv2_yolov3_nano	mobilenetv2_yolov3_lite
  time	7ms	20ms	20ms	30ms

DeepLabv3 GPU Test

• Ubuntu18.04 GCC Cuda10.1 GTX2080Ti

  net	deeplabv3_resnet101	deeplabv3_resnet50
  time	22.51ms	16.46ms

Requirements

• OpenCV4 (optional) https://github.com/opencv/opencv
• Qt5 (optional. for Msnhnet viewer) http://download.qt.io/archive/qt/
• opengl(optional. for MsnhCV GUI) .
• glew(optional. for MsnhCV GUI) http://glew.sourceforge.net/ .
• glfw3(optional. for MsnhCV GUI) https://www.glfw.org/.
• cuda10+ cudnn 7.0+.(optional. for GPU)

Video tutorials(bilibili)

• Build on Linux
• Build on Windows
• Pytorch Params to msnhbin

How to build

• With CMake 3.15+

• Viewer can not build with GPU.

• Options
  
  ps. You can change omp threads by unchecking OMP_MAX_THREAD and modifying "num" val at CMakeLists.txt:52
  

• Windows

1. Compile opencv4 (optional)
2. Config environment. Add "OpenCV_DIR" (optional)
3. Get qt5 and install. http://download.qt.io/ (optional)
4. Add qt5 bin path to environment (optional).
5. Get glew for MsnhCV Gui.http://glew.sourceforge.net/ (optional).
6. Get glfw3 for MsnhCV Gui.https://www.glfw.org/ (optional).
7. Extract glew, add glew path to "CMAKE_PREFIX_PATH" (optional).
8. Compile gflw3 with cmake, add gflw3 cmake dir to "GLFW_DIR" (optional).
9. Then use cmake-gui tool and visual studio to make or use vcpkg.

• Linux(Ubuntu)

ps. If you want to build with Jetson, please uncheck NNPACK, OPENBLAS, NEON.

sudo apt-get install build-essential
sudo apt-get install qt5-default      #optional
sudo apt-get install libqt5svg5-dev   #optional
sudo apt-get install libopencv-dev    #optional
sudo apt-get install libgl1-mesa-dev libglfw3-dev libglfw3 libglew-dev #optional


#config 
sudo echo /usr/local/lib > /etc/ld.so.conf.d/usrlib.conf
sudo ldconfig

# build Msnhnet
git clone https://github.com/msnh2012/Msnhnet.git
mkdir build 

cd Msnhnet/build
cmake -DCMAKE_BUILD_TYPE=Release ..  
make -j4
sudo make install

vim ~/.bashrc # Last line add: export PATH=/usr/local/bin:$PATH
sudo ldconfig

• MacOS(MacOS Catalina) Without viewer

PS: XCode should be pre-installed.

Please download cmake from official website with gui support and the source code of yaml and opencv.

# install cmake

vim .bash_profile
export CMAKE_ROOT=/Applications/CMake.app/Contents/bin/
export PATH=$CMAKE_ROOT:$PATH
source .bash_profile

# install brew to install necessary libraries

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)"

brew install wget
brew install openjpeg
brew install hdf5
brew install gflags
brew install glog
brew install eigen
brew install libomp

# build yaml-cpp
git clone https://github.com/jbeder/yaml-cpp.git
cd yaml-cpp
mkdir build
source .bash_profile
cmake-gui
Set the source code path: ./yaml-cpp
Set the build binary path: ./yaml-cpp/build
configure
CMAKE_BUILD_TYPE = Release
uncheck YAML_CPP_BUILD_TESTS
configure (and continue to debug)
generate
cd ./yaml-cpp/build
sudo make install -j8

# build opencv
# download opencv.zip from official website(Remember to download opencv-contrib together)
cd opencv-4.4.0
mkdir build
source .bash_profile
cmake-gui


Set the source code path: ./opencv-4.4.0
Set the build binary path: ./opencv-4.4.0/build
configure (use default)
search for OPENCV_ENABLE_NONFREE and enable it
seach for OPENCV_EXTRA_MODULES_PATH to the path of opencv-contrib
configure (and continue to debug)
generate
cd ./opencv-4.4.0/build/
sudo make install -j8


# build Msnhnet
git clone https://github.com/msnh2012/Msnhnet.git
mkdir build 

cd Msnhnet/build
cmake -DCMAKE_BUILD_TYPE=Release ..  
make -j4
sudo make install

Test Msnhnet

• 1. Download pretrained model and extract. eg.D:/models.
• 2. Open terminal and cd "Msnhnet install bin". eg. D:/Msnhnet/bin
• 3. Test yolov3 "yolov3 D:/models".
• 4. Test yolov3tiny_video "yolov3tiny_video D:/models".
• 5. Test classify "classify D:/models".
     

View Msnhnet

• 1. Open terminal and cd "Msnhnet install bin" eg. D:/Msnhnet/bin
• 2. run "MsnhnetViewer"

PS. You can double click "ResBlock Res2Block AddBlock ConcatBlock" node to view more detail
ResBlock

Res2Block

AddBlock

ConcatBlock

How to convert your own pytorch network

• pytorch2msnhnet
• ps:
• 1 . Please check out OPs which supported by pytorch2msnhnet before trans.
• 2 . Maybe some model can not be translated.
• 3 . If your model contains preprocessors and postprocessors which are quite complicated, please trans backbone first and then add some OPs manually.
• 4 . As for yolov3 & yolov4, just follow this video. You can find "pytorch2msnhbin" tool here.

About Train

• Just use pytorch to train your model, and export as msnhbin.
• eg. yolov3/v4 https://github.com/ultralytics/yolov3

Enjoy it! :D

Acknowledgement

Msnhnet got ideas and developed based on these projects：

• DarkNet
• NCNN
• ACL

3rdparty Libs

• stb_image
• yaml-cpp
• imGui
• mpeg

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