Msnhnet

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A mini pytorch inference framework which inspired from darknet.

TODO:
1.GPU
2.neon

OS supported (you can check other OS by yourself)

Yolo Test (Win10 MSVC 2017 I7-10700F)

Tested networks

• lenet5
• lenet5_bn
• alexnet
• vgg16
• vgg16_bn
• resnet18
• resnet34
• resnet50
• resnet101
• resnet152
• darknet53
• googLenet
• yolov3
• yolov3_spp
• yolov3_tiny
• yolov4
• pretrained models 链接：https://pan.baidu.com/s/1WElMhBhaN5EnPJnD8S1P3w 提取码：1hlm

Requirements

• OpenCV4 https://github.com/opencv/opencv
• yaml-cpp https://github.com/jbeder/yaml-cpp
• Qt5 (optional. for Msnhnet viewer) http://download.qt.io/archive/qt/

How to build

• With CMake 3.10+

• Options
  
  ps. You can change omp threads by unchecking OMP_MAX_THREAD and modifying "num" val at CMakeLists.txt:43
  

• Windows

1. Compile opencv4 and yaml-cpp.
2. Config environment. Add "OpenCV_DIR" and "yaml-cpp_DIR"
3. Get qt5 and install. http://download.qt.io/ (optional)
4. Add qt5 bin path to environment.
5. Then use cmake-gui tool and visual studio to make or use vcpkg.

• Linux(Ubuntu)

sudo apt-get install qt5-default      #optional
sudo apt-get install libqt5svg5-dev   #optional
sudo apt-get install libopencv-dev

# build yaml-cpp
git clone https://github.com/jbeder/yaml-cpp.git
cd yaml-cpp
mdir build 
cd build 
cmake ..
make -j4
sudo make install 

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

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

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

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

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

1. Use pytorch to load network

import torchvision.models as models
import torch
from torchsummary import summary 

md = models.resnet18(pretrained = True)
md.to("cpu")
md.eval()

print(md, file = open("net.txt", "a"))

summary(md, (3, 224, 224),device='cpu')

2. Write msnhnet file according to net.txt and summary result.(Manually :o. Like darnet cfg)
3. Export msnhbin

val = []
dd = 0
for name in md.state_dict():
        if "num_batches_tracked" not in name:
                c = md.state_dict()[name].data.flatten().numpy().tolist()
                dd = dd + len(c)
                print(name, ":", len(c))
                val.extend(c)

with open("alexnet.msnhbin","wb") as f:
    for i in val :
        f.write(pack('f',i))

Ps. More detail in file "pytorch2msnhbin/pytorch2msnhbin.py"

Enjoy it! :D