2021/09/27: Release of v1.9.1
2021/09/27: Support for using different devices between training and test
2021/09/06: Improved accuracy of time measurement using GPU
2021/06/19: Release of v1.9.0
2021/03/29: Release of v1.8.1
2021/03/18: Implementation of Discriminator from DCGAN
2021/03/17: Implementation of AE1d
2021/03/16: Release of v1.8.0
2021/03/15: Implementation of YOLOv2
2021/02/11: Implementation of YOLOv1
2021/01/21: Release of v1.7.1
2020/10/30: Release of v1.7.0
2020/10/04: Implementation of Skip-GANomaly2d
2020/10/03: Implementation of GANomaly2d
2020/09/29: Implementation of EGBAD2d
2020/09/28: Implementation of AnoGAN2d
2020/09/27: Implementation of SegNet
2020/09/26: Implementation of DAE2d
2020/09/13: Implementation of ResNet
2020/09/07: Implementation of VGGNet
2020/09/05: Implementation of AlexNet
2020/09/02: Implementation of WAE2d GAN and WAE2d MMD
2020/08/30: Release of v1.6.0
2020/06/26: Implementation of DAGMM2d
2020/06/26: Release of v1.5.1
2020/06/26: Implementation of VAE2d and DCGAN
2020/06/01: Implementation of pix2pix
2020/05/29: Implementation of U-Net Classification
2020/05/26: Implementation of U-Net Regression
2020/04/24: Release of v1.5.0
2020/03/23: Implementation of AE2d

Multiclass Classification

Dimensionality Reduction

Generative Modeling

Image-to-Image Translation

Semantic Segmentation

Object Detection

Anomaly Detection

1. PyTorch C++

Please select the environment to use as follows on PyTorch official.
PyTorch official : https://pytorch.org/

PyTorch Build : Stable (2.0.0)
Your OS : Linux
Package : LibTorch
Language : C++ / Java
Run this Command : Download here (cxx11 ABI)
CUDA 11.7 : https://download.pytorch.org/libtorch/cu117/libtorch-cxx11-abi-shared-with-deps-2.0.0%2Bcu117.zip
CUDA 11.8 : https://download.pytorch.org/libtorch/cu118/libtorch-cxx11-abi-shared-with-deps-2.0.0%2Bcu118.zip
CPU : https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-2.0.0%2Bcpu.zip

2. OpenCV

version : 3.0.0 or more
This is used for pre-processing and post-processing.
Please refer to other sites for more detailed installation method.

3. OpenMP

This is used to load data in parallel.
(It may be installed on standard Linux OS.)

4. Boost

This is used for command line arguments, etc.

$ sudo apt install libboost-dev libboost-all-dev

5. Gnuplot

This is used to display loss graph.

$ sudo apt install gnuplot

6. libpng/png++/zlib

This is used to load and save index-color image in semantic segmentation.

$ sudo apt install libpng-dev libpng++-dev zlib1g-dev

1. Git Clone

$ git clone https://github.com/koba-jon/pytorch_cpp.git
$ cd pytorch_cpp

2. Path Setting

$ vi utils/CMakeLists.txt

Please change the 4th line of "CMakeLists.txt" according to the path of the directory "libtorch".
The following is an example where the directory "libtorch" is located directly under the directory "HOME".

3: # LibTorch
4: set(LIBTORCH_DIR $ENV{HOME}/libtorch)
5: list(APPEND CMAKE_PREFIX_PATH ${LIBTORCH_DIR})

3. Compiler Install

If you don't have g++ version 8 or above, install it.

$ sudo apt install g++-8

4. Execution

Please move to the directory of each model and refer to "README.md".

1. Making Original Dataset

Please create a link for the original dataset.
The following is an example of "AE2d" using "celebA" Dataset.

$ cd Dimensionality_Reduction/AE2d/datasets
$ ln -s <dataset_path> ./celebA_org

You should substitute the path of dataset for "<dataset_path>".
Please make sure you have training or test data directly under "<dataset_path>".

$ vi ../../../scripts/hold_out.sh

Please edit the file for original dataset.

#!/bin/bash

SCRIPT_DIR=$(cd $(dirname $0); pwd)

python3 ${SCRIPT_DIR}/hold_out.py \
    --input_dir "celebA_org" \
    --output_dir "celebA" \
    --train_rate 9 \
    --valid_rate 1

By running this file, you can split it into training and validation data.

$ sudo apt install python3 python3-pip
$ pip3 install natsort
$ sh ../../../scripts/hold_out.sh
$ cd ../../..

2. Data Input System

There are transform, dataset and dataloader for data input in this repository.
It corresponds to the following source code in the directory, and we can add new function to the source code below.

• transforms.cpp
• transforms.hpp
• datasets.cpp
• datasets.hpp
• dataloader.cpp
• dataloader.hpp

3. Check Progress

There are a feature to check progress for training in this repository.
We can watch the number of epoch, loss, time and speed in training.

It corresponds to the following source code in the directory.

• progress.cpp
• progress.hpp

4. Monitoring System

There are monitoring system for training in this repository.
We can watch output image and loss graph.
The feature to watch output image is in the "samples" in the directory "checkpoints" created during training.
The feature to watch loss graph is in the "graph" in the directory "checkpoints" created during training.

It corresponds to the following source code in the directory.

• visualizer.cpp
• visualizer.hpp

You can feel free to use all source code in this repository.
(Click here for details.)

But if you exploit external libraries (e.g. redistribution), you should be careful.
At a minimum, the license notation at the following URL is required.
In addition, third party copyrights belong to their respective owners.

• PyTorch
  Official : https://pytorch.org/
  License : https://github.com/pytorch/pytorch/blob/master/LICENSE
  

• OpenCV
  Official : https://opencv.org/
  License : https://opencv.org/license/
  

• OpenMP
  Official : https://www.openmp.org/
  License : https://gcc.gnu.org/onlinedocs/
  

• Boost
  Official : https://www.boost.org/
  License : https://www.boost.org/users/license.html
  

• Gnuplot
  Official : http://www.gnuplot.info/
  License : https://sourceforge.net/p/gnuplot/gnuplot-main/ci/master/tree/Copyright
  

• libpng/png++/zlib
  Official (libpng) : http://www.libpng.org/pub/png/libpng.html
  License (libpng) : http://www.libpng.org/pub/png/src/libpng-LICENSE.txt
  Official (png++) : https://www.nongnu.org/pngpp/
  License (png++) : https://www.nongnu.org/pngpp/license.html
  Official (zlib) : https://zlib.net/
  License (zlib) : https://zlib.net/zlib_license.html