MNIST CNN using libtorch & pytorch

You can train your MNIST CNN model by using pytorch (python3), and you can predict a handwritten digit by using libtorch (C++11).

Requirements

	version
OS	Ubuntu 18.04.2 LTS
gcc	7.4.0
miniconda3	4.6.14
python	3.7.1
opencv	4.1.0
pytorch & libtorch	1.1.0

prepare

clone repository

1. clone repository

   $ cd ${HOME}
   $ git clone https://github.com/nmatsui/libtorch_pytorch_mnist.git
   

install required libraries

1. install libraries

   $ sudo apt install -y build-essential cmake unzip pkg-config wget
   $ sudo apt install -y qt5-default libvtk6-dev zlib1g-dev libwebp-dev \
                         libopenexr-dev libgdal-dev libjpeg-dev libpng-dev \
                         libtiff-dev libtiff5-dev libv4l-dev libavcodec-dev \
                         libavformat-dev libswscale-dev libxine2-dev \
                         libxvidcore-dev libx264-dev libdc1394-22-dev \
                         libtheora-dev libvorbis-dev libgtk-3-dev libtbb-dev \
                         libatlas-base-dev libopencore-amrnb-dev  \
                         libopencore-amrwb-dev libeigen3-dev gfortran yasm
   

2. install opencv

   $ cd ${HOME}
   $ wget -O opencv-4.1.0.zip https://github.com/opencv/opencv/archive/4.1.0.zip
   $ unzip opencv-4.1.0.zip
   $ cd opencv-4.1.0
   $ mkdir build && cd build
   $ cmake -DCMAKE_BUILD_TYPE=RELEASE \
           -DCMAKE_INSTALL_PREFIX=/usr/local \
           -DCMAKE_CXX_FLAGS=-D_GLIBCXX_USE_CXX11_ABI=0 \
           -DWITH_QT=ON -DWITH_OPENGL=ON -DFORCE_VTK=ON -DWITH_TBB=ON -DWITH_GDAL=ON \
           -DWITH_XINE=ON -DBUILD_EXAMPLES=ON -DENABLE_PRECOMPILED_HEADERS=OFF \
           ..
   $ make -j4
   $ sudo make install
   $ sudo ldconfig
   

3. install libtorch

   $ cd ${HOME}
   $ wget -O libtorch-1.1.zip https://download.pytorch.org/libtorch/cpu/libtorch-shared-with-deps-latest.zip
   $ unzip libtorch-1.1.zip
   $ sudo cp -r libtorch/include/* /usr/local/include/
   $ sudo cp -r libtorch/lib/* /usr/local/lib/
   $ sudo cp -r libtorch/share/* /usr/local/share/
   $ sudo ldconfig
   

build C++ programs

1. cd libtorch directory

   $ cd ${HOME}/libtorch_pytorch_mnist/libtorch
   

2. prepare build directory

   $ mkdir build && cd build
   

3. build

   $ cmake ..
   $ make
   $ cp pimage/predict_image ..
   $ cp pcamera/predict_camera ..
   

How to train (python)

1. go to the python source directory

   $ cd ${HOME}/libtorch_pytorch_mnist/pytorch
   

2. create virtualenv and install required package by using conda

   $ conda env create --file conda-linux.yaml
   $ conda activate pytorch_mnist
   

3. train data

   $ ./train.py --epochs 12 ../models/mnist_py.pt ../data
   

   • 1st argument: the model weights file to be trained

   • 2nd argument: the root directory to be saved MNIST data

   • when training is complete, the loss and accuracy will be displayed like below:

     Test set: Average loss: 0.050700, Accuracy: 9847/10000 (98.47%)
     

4. convert the model weights file to be able to use by c++

   $ ./convert_model.py ../models/mnist_py.pt ../models/mnist_cpp.pt
   

   • 1st argument: trained model weights file
   • 2nd argument: the model weights file to be converted for c++

How to predict a handwritten digit from a image file (python)

1. go to the python source directory

   $ cd ${HOME}/libtorch_pytorch_mnist/pytorch
   

2. predict a handwitten digit like below:

   $ ./predict.py ../models/mnist_py.pt ../digit_images/5.png 
   

   • 1st argument: trained model weights file
   • 2nd argument: a handwritten digit image file

How to predict a handwritten digit from a image file (c++)

1. go to the c++ source directory

   $ cd ${HOME}/libtorch_pytorch_mnist/libtorch
   

2. predict a handwitten digit like below:

   $ ./predict_image ../models/mnist_cpp.pt ../digit_images/5.png
   

   • 1st argument: converted model weights file for c++
   • 2nd argument: a handwritten digit image file

How to predict a handwritten digit continuously from USB camera frame (c++)

1. go to the c++ source directory

   $ cd ${HOME}/libtorch_pytorch_mnist/libtorch
   

2. start camera preview like below:

   $ ./predict_camera ../models/mnist_cpp.pt 0 0.9
   

   • 1st argument: converted model weights file for c++
   • 2nd argument: camera device id
   • 3rd argument: predict the handwritten digit when the calcurated probability is greater than this float

3. predict the digit when a character is displayed in the green box

License

Apache License 2.0

Copyright

Copyright (c) 2019 Nobuyuki Matsui nobuyuki.matsui@gmail.com