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https://github.com/koba-jon/pytorch_cpp

Deep Learning sample programs using PyTorch in C++
https://github.com/koba-jon/pytorch_cpp

anomaly-detection autoencoder convolutional-autoencoder cpp dagmm dcgan deep-learning dimensionality-reduction generative-modeling image-to-image-translation libtorch linux multiclass-classification object-detection pix2pix pytorch semantic-segmentation u-net vae yolo

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Deep Learning sample programs using PyTorch in C++

Awesome Lists containing this project

README 

          


  

# 🔥 PyTorch C++ Samples 🔥

  

[![Language](https://img.shields.io/badge/Language-C++-blue)]()

[![LibTorch](https://img.shields.io/badge/LibTorch-2.10.0-orange)]()

[![OS](https://img.shields.io/badge/OS-Ubuntu-yellow)]()

[![OS](https://img.shields.io/badge/License-MIT-green)]()

![sample1](sample1.png)

![sample2](sample2.gif)






## 🚀 Quick Start (Details: Library, Run)

Requirements: `LibTorch`, `OpenCV`, `OpenMP`, `Boost`, `Gnuplot`, `libpng/png++/zlib` 



### 1. Git Clone



~~~

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

$ cd pytorch_cpp

$ sudo apt install g++-8

~~~



### 2. Run



**(1) Change Directory** (Model: AE1d)

~~~

$ cd Dimensionality_Reduction/AE1d

~~~



**(2) Build**

~~~

$ mkdir build

$ cd build

$ cmake ..

$ make -j4

$ cd ..

~~~



**(3) Dataset Setting** (Dataset: Normal Distribution Dataset)

~~~

$ cd datasets

$ git clone https://huggingface.co/datasets/koba-jon/normal_distribution_dataset

$ ln -s normal_distribution_dataset/NormalDistribution ./NormalDistribution

$ cd ..

~~~



**(4) Training**

~~~

$ sh scripts/train.sh

~~~



**(5) Test**

~~~

$ sh scripts/test.sh

~~~



## 🔄 Updates (MM/DD/YYYY)



01/22/2026: Release of `v2.10.0` 


12/22/2025: Implementation of `AdaIN` 


12/20/2025: Implementation of `NST` 


12/06/2025: Release of `v2.9.1.4` 


12/01/2025: Release of `v2.9.1.3` 


12/01/2025: Implementation of `PatchCore` 


11/29/2025: Release of `v2.9.1.2` 


11/29/2025: Implementation of `PaDiM` 


11/27/2025: Implementation of `WideResNet` 


11/27/2025: Release of `v2.9.1.1` 



See more...

  

11/24/2025: Implementation of `ESRGAN` 


11/21/2025: Implementation of `SRGAN` 


11/19/2025: Implementation of `DiT` 


11/14/2025: Release of `v2.9.1` 


11/01/2025: Implementation of `NeRF` and `3DGS` 


10/16/2025: Release of `v2.9.0` 


10/16/2025: Implementation of `PixelSNAIL-Gray` and `PixelSNAIL-RGB` 


10/14/2025: Implementation of `YOLOv8` 


10/13/2025: Implementation of `YOLOv5` 


10/09/2025: Implementation of `RF2d` 


10/08/2025: Implementation of `FM2d` 


10/08/2025: Implementation of `LDM` 


10/04/2025: Implementation of `Glow` 


10/01/2025: Implementation of `Real-NVP2d` 


09/28/2025: Implementation of `Planar-Flow2d` and `Radial-Flow2d` 


09/25/2025: Release of `v2.8.0.2` 


09/22/2025: Implementation of `PixelCNN-Gray` and `PixelCNN-RGB` 


09/18/2025: Implementation of `VQ-VAE-2` 


09/16/2025: Implementation of `VQ-VAE` 


09/14/2025: Implementation of `PNDM2d` 


09/14/2025: Release of `v2.8.0.1` 


09/12/2025: Implementation of `SimCLR` 


09/11/2025: Implementation of `MAE` 


09/10/2025: Implementation of EMA for `DDPM2d` and `DDIM2d` 


09/08/2025: Implementation of `EfficientNet` 


09/07/2025: Implementation of `CycleGAN` 


09/05/2025: Implementation of `ViT` 


09/04/2025: Release of `v2.8.0` 


09/04/2025: Implementation of `DDIM2d` 


09/04/2025: Implementation of `DDPM2d` 


06/27/2023: Release of `v2.0.1` 


06/27/2023: Create the heatmap for Anomaly Detection 


05/07/2023: Release of `v2.0.0` 


03/01/2023: Release of `v1.13.1` 


09/12/2022: Release of `v1.12.1` 


08/04/2022: Release of `v1.12.0` 


03/18/2022: Release of `v1.11.0` 


02/10/2022: Release of `v1.10.2` 


02/09/2022: Implementation of `YOLOv3` 


01/09/2022: Release of `v1.10.1` 


01/09/2022: Fixed execution error in test on CPU package 


11/12/2021: Release of `v1.10.0` 


09/27/2021: Release of `v1.9.1` 


09/27/2021: Support for using different devices between training and test 


09/06/2021: Improved accuracy of time measurement using GPU 


06/19/2021: Release of `v1.9.0` 


03/29/2021: Release of `v1.8.1` 


03/18/2021: Implementation of `Discriminator` from DCGAN 


03/17/2021: Implementation of `AE1d` 


03/16/2021: Release of `v1.8.0` 


03/15/2021: Implementation of `YOLOv2` 


02/11/2021: Implementation of `YOLOv1` 


01/21/2021: Release of `v1.7.1` 


10/30/2020: Release of `v1.7.0` 


10/04/2020: Implementation of `Skip-GANomaly2d` 


10/03/2020: Implementation of `GANomaly2d` 


09/29/2020: Implementation of `EGBAD2d` 


09/28/2020: Implementation of `AnoGAN2d` 


09/27/2020: Implementation of `SegNet` 


09/26/2020: Implementation of `DAE2d` 


09/13/2020: Implementation of `ResNet` 


09/07/2020: Implementation of `VGGNet` 


09/05/2020: Implementation of `AlexNet` 


09/02/2020: Implementation of `WAE2d GAN` and `WAE2d MMD` 


08/30/2020: Release of `v1.6.0` 


06/26/2020: Implementation of `DAGMM2d` 


06/26/2020: Release of `v1.5.1` 


06/26/2020: Implementation of `VAE2d` and `DCGAN` 


06/01/2020: Implementation of `Pix2Pix` 


05/29/2020: Implementation of `U-Net Classification` 


05/26/2020: Implementation of `U-Net Regression` 


04/24/2020: Release of `v1.5.0` 


03/23/2020: Implementation of `AE2d` 


  



## 🏗️ Implementation



### 📊 Multiclass Classification

  



  

    Category

    Model

    Paper

    Conference/Journal

    Code

  

  

    CNNs

    AlexNet

    A. Krizhevsky et al.

    NeurIPS 2012

    AlexNet

  

  

    VGGNet

    K. Simonyan et al.

    ICLR 2015

    VGGNet

  

  

    ResNet

    K. He et al.

    CVPR 2016

    ResNet

  

  

    WideResNet

    S. Zagoruyko et al.

    arXiv 2016

    WideResNet

  

  

    Discriminator

    A. Radford et al.

    ICLR 2016

    Discriminator

  

  

    EfficientNet

    M. Tan et al.

    ICML 2019

    EfficientNet

  

  

    Transformers

    Vision Transformer

    A. Dosovitskiy et al.

    ICLR 2021

    ViT

  



  

### 🔽 Dimensionality Reduction



  

    Model

    Paper

    Conference/Journal

    Code

  

  

    Autoencoder

    G. E. Hinton et al.

    Science 2006

    AE1d

  

  

    AE2d

  

  

    Denoising Autoencoder

    P. Vincent et al.

    ICML 2008

    DAE2d

  



### 🎨 Generative Modeling



  

    Category

    Model

    Paper

    Conference/Journal

    Code

  

  

    VAEs

    Variational Autoencoder

    D. P. Kingma et al.

    ICLR 2014

    VAE2d

  

  

    Wasserstein Autoencoder

    I. Tolstikhin et al.

    ICLR 2018

    WAE2d GAN

  

  

    WAE2d MMD

  

  

    VQ-VAE

    A. v. d. Oord et al.

    NeurIPS 2017

    VQ-VAE

  

  

    VQ-VAE-2

    A. Razavi et al.

    NeurIPS 2019

    VQ-VAE-2

  

  

    GANs

    DCGAN

    A. Radford et al.

    ICLR 2016

    DCGAN

  

  

    Flows

    Planar Flow

    D. Rezende et al.

    ICML 2015

    Planar-Flow2d

  

  

    Radial Flow

    D. Rezende et al.

    ICML 2015

    Radial-Flow2d

  

  

    Real NVP

    L. Dinh et al.

    ICLR 2017

    Real-NVP2d

  

  

    Glow

    D. P. Kingma et al.

    NeurIPS 2018

    Glow

  

  

    Diffusion Models

    DDPM

    J. Ho et al.

    NeurIPS 2020

    DDPM2d

  

  

    DDIM

    J. Song et al.

    ICLR 2021

    DDIM2d

  

  

    PNDM

    L. Liu et al.

    ICLR 2022

    PNDM2d

  

  

    LDM

    R. Rombach et al.

    CVPR 2022

    LDM

  

  

    Diffusion Transformer

    W. Peebles et al.

    ICCV 2023

    DiT

  

  

    Flow Matching

    Flow Matching

    Y. Lipman et al.

    ICLR 2023

    FM2d

  

  

    Rectified Flow

    X. Liu et al.

    ICLR 2023

    RF2d

  

  

    Autoregressive Models

    PixelCNN

    A. v. d. Oord et al.

    ICML 2016

    PixelCNN-Gray

  

  

    PixelCNN-RGB

  

    PixelSNAIL

    X. Chen et al.

    ICML 2018

    PixelSNAIL-Gray

  

  

    PixelSNAIL-RGB

  



### 🖼️ Image-to-Image Translation



  

    Model

    Paper

    Conference/Journal

    Code

  

  

    U-Net

    O. Ronneberger et al.

    MICCAI 2015

    U-Net Regression

  

  

    Pix2Pix

    P. Isola et al.

    CVPR 2017

    Pix2Pix

  

  

    CycleGAN

    J.-Y. Zhu et al.

    ICCV 2017

    CycleGAN

  



### 🔍 Super Resolution



  

    Model

    Paper

    Conference/Journal

    Code

  

  

    SRGAN

    C. Ledig et al.

    CVPR 2017

    SRGAN

  

  

    ESRGAN

    X. Wang et al.

    ECCV 2018

    ESRGAN

  



### 🖌️ Style Transfer



  

    Model

    Paper

    Conference/Journal

    Code

  

  

    Neural Style Transfer

    L. A. Gatys et al.

    CVPR 2016

    NST

  

  

    Adaptive Instance Normalization

    X. Huang et al.

    ICCV 2017

    AdaIN

  



### 🧩 Semantic Segmentation



  

    Model

    Paper

    Conference/Journal

    Code

  

  

    SegNet

    V. Badrinarayanan et al.

    CVPR 2015

    SegNet

  

  

    U-Net

    O. Ronneberger et al.

    MICCAI 2015

    U-Net Classification

  



### 🎯 Object Detection



  

    Model

    Paper

    Conference/Journal

    Code

  

  

    YOLOv1

    J. Redmon et al.

    CVPR 2016

    YOLOv1

  

  

    YOLOv2

    J. Redmon et al.

    CVPR 2017

    YOLOv2

  

  

    YOLOv3

    J. Redmon et al.

    arXiv 2018

    YOLOv3

  

  

    YOLOv5

    Ultralytics

    -

    YOLOv5

  

  

    YOLOv8

    Ultralytics

    -

    YOLOv8

  



### 🧠 Representation Learning



  

    Model

    Paper

    Conference/Journal

    Code

  

  

    SimCLR

    T. Chen et al.

    ICML 2020

    SimCLR

  

  

    Masked Autoencoder

    K. He et al.

    CVPR 2022

    MAE

  



### 🌐 View Synthesis



  

    Model

    Paper

    Conference/Journal

    Code

  

  

    Neural Radiance Field

    B. Mildenhall et al.

    ECCV 2020

    NeRF

  

  

    3D Gaussian Splatting

    B. Kerbl et al.

    SIGGRAPH 2023

    3DGS

  



### 🚨 Anomaly Detection



  

    Model

    Paper

    Conference/Journal

    Code

  

  

    AnoGAN

    T. Schlegl et al.

    IPMI 2017

    AnoGAN2d

  

  

    DAGMM

    B. Zong et al.

    ICLR 2018

    DAGMM2d

  

  

    EGBAD

    H. Zenati et al.

    ICLR Workshop 2018

    EGBAD2d

  

  

    GANomaly

    S. Akçay et al.

    ACCV 2018

    GANomaly2d

  

  

    Skip-GANomaly

    S. Akçay et al.

    IJCNN 2019

    Skip-GANomaly2d

  

  

    PaDiM

    T. Defard et al.

    ICPR Workshops 2020

    PaDiM

  

  

    PatchCore

    K. Roth et al.

    CVPR 2022

    PatchCore

  



  

## 📦 Requirement (Library)



Details



### 1. PyTorch C++

Please select the environment to use as follows on PyTorch official. 


PyTorch official : https://pytorch.org/ 


***

PyTorch Build : Stable (2.10.0) 


Your OS : Linux 


Package : LibTorch 


Language : C++ / Java 


Run this Command : Download here (cxx11 ABI) 


CUDA 12.6 : https://download.pytorch.org/libtorch/cu126/libtorch-shared-with-deps-2.10.0%2Bcu126.zip 


CUDA 12.8 : https://download.pytorch.org/libtorch/cu128/libtorch-shared-with-deps-2.10.0%2Bcu128.zip 


CUDA 13.0 : https://download.pytorch.org/libtorch/cu130/libtorch-shared-with-deps-2.10.0%2Bcu130.zip 


CPU : https://download.pytorch.org/libtorch/cpu/libtorch-shared-with-deps-2.10.0%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

~~~



## 🏃 Preparation (Run)



Details



### 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".



  

## 🛠️ Utility



Details



### 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  ./celebA_org

~~~

You should substitute the path of dataset for "".


Please make sure you have training or test data directly under "".

~~~

$ 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.


![util1](https://user-images.githubusercontent.com/56967584/88464264-3f720300-cef4-11ea-85fd-360cb3a424d1.png)


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.


![util2](https://user-images.githubusercontent.com/56967584/88464268-40a33000-cef4-11ea-8a3c-da42d4c803b6.png)


It corresponds to the following source code in the directory.

- visualizer.cpp

- visualizer.hpp



## ⚖️ License

  



Details

  

You can feel free to use all source code in this repository.


(Click [here](LICENSE) 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 



  

## 🎉 Conclusion

PyTorch is famous as a kind of Deep Learning Frameworks.


Among them, Python source code is overflowing on the Web, so we can easily write the source code of Deep Learning in Python.


However, there is very little source code written in C++ of compiler language.


I hope this repository will help many programmers by providing PyTorch sample programs written in C++.


If you have any problems with the source code of this repository, please feel free to "issue".


Let's have a good development and research life!