Fraunhofer Neural Network Encoder/Decoder (NNCodec)

The Fraunhofer Neural Network Encoder/Decoder Software (NNCodec) is an efficient implementation of NNC (Neural Network Coding / ISO/IEC 15938-17 or MPEG-7 part 17), which is the first international standard on compression of neural networks. NNCodec provides an encoder and decoder with the following main features:

• Standard compliant implementation of the core compression technologies including, e.g., DeepCABAC
• Easy-to-use user interface
• Built-in support for common frameworks (e.g. TensorFlow and PyTorch)
• Built-in ImageNet-support for data driven compression tools
• Extensibility of data driven compression tools for arbitrary datasets

Highlights

ICML 2023 Neural Compression Workshop

Our paper titled "NNCodec: An Open Source Software Implementation of the Neural Network Coding ISO/IEC Standard" was awarded a Spotlight Paper at the ICML 2023 Neural Compression Workshop.

TL;DR - The paper presents NNCodec, analyses its coding tools with respect to the principles of information theory and gives comparative results for a broad range of neural network architectures.

The code for reproducing the experimental results of the paper and a software demo are available here:

Information

Refer to the Wiki-Page for more information:

• Installation-Guide
• Usage
• Compression-Performance
• License
• References
• Version history

Installation

The software provides python packages which can be installed using pip. However, core technologies are implemented using C++, which requires a C++ compiler for the installation process.

The software has been tested on different target platforms (Windows, Linux and MacOS).

Requirements

• python >= 3.6 (recommended versions 3.6, 3.7 and 3.8) with working pip
• Windows: Microsoft Visual Studio 2015 Update 3 or later

Package installation

From the root of the cloned repository, issue

pip install wheel
pip install -r requirements.txt
pip install .

and for CUDA11 support

pip install wheel
pip install -r requirements_cu11.txt
pip install .

for installation.

Information: On Linux/Mac the scripts create_env.sh and create_env_cu11.sh (for Cuda 11 support) set up a virtual python environment "env" and install all required packages and the software itself, automatically. For activating this environment, issue:

source env/bin/activate

Note: For further information on how to set up a virtual python environment (also on Windows) refer to https://docs.python.org/3/library/venv.html .

When successfully installed, the software outputs the line : "Successfully installed NNC-0.3.1"

Importing the main module

After installation the software can be used by importing the main python module 'nnc':

import nnc

References and Publications

If you use NNCodec in your work, please cite:

@inproceedings{becking2023nncodec,
title={{NNC}odec: An Open Source Software Implementation of the Neural Network Coding {ISO}/{IEC} Standard},
author={Daniel Becking and Paul Haase and Heiner Kirchhoffer and Karsten M{\"u}ller and Wojciech Samek and Detlev Marpe},
booktitle={ICML 2023 Workshop Neural Compression: From Information Theory to Applications},
year={2023},
url={https://openreview.net/forum?id=5VgMDKUgX0}
}

Publications

• H. Kirchhoffer, et al. "Overview of the Neural Network Compression and Representation (NNR) Standard", IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-14, July 2021, doi: 10.1109/TCSVT.2021.3095970, Open Access
• P. Haase et al., "Encoder Optimizations For The NNR Standard On Neural Network Compression", 2021 IEEE International Conference on Image Processing (ICIP), 2021, pp. 3522-3526, doi: 10.1109/ICIP42928.2021.9506655.
• K. Müller et al., "Ein internationaler KI-Standard zur Kompression Neuronaler Netze", FKT- Fachzeitschrift für Fernsehen, Film und Elektronische Medien, pp. 33-36, September 2021
• S. Wiedemann et al., "DeepCABAC: A universal compression algorithm for deep neural networks", in IEEE Journal of Selected Topics in Signal Processing, doi: 10.1109/JSTSP.2020.2969554.

Contributing

Everyone is invited to contribute. To do so:

• Fork the current-most state of the master branch
• Implement features or changes
• Add your name to AUTHORS.md
• Create a pull-request to the upstream repository

License

Please see LICENSE.txt file for the terms of the use of the contents of this repository.

For more information and bug reports, please contact: [email protected]

Copyright (c) 2019-2023, Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. & The NNCodec Authors.

All rights reserved.