This project contains data compression ops and layers for TensorFlow. The project website is at tensorflow.github.io/compression.

What does this library do, you ask?

In a nutshell, you can use it to build your own ML models with optimized lossy data compression built in. It's useful to find storage-efficient representations of your data (features, examples, images, etc.) while only sacrificing a tiny fraction of model performance. It can compress any floating point tensor to a much smaller sequence of bits.

For an introduction to lossy data compression with machine learning, take a look at @jonycgn's talk on Learned Image Compression.

Please note: You need TensorFlow 1.9 (or the master branch as of May 2018) or later installed.

Clone the repository to a filesystem location of your choice, or download the ZIP file and unpack it. Then include the root directory in your PYTHONPATH environment variable:

cd <target directory>
git clone https://github.com/tensorflow/compression.git tensorflow_compression
export PYTHONPATH="$PWD/tensorflow_compression:$PYTHONPATH"

To make sure the library imports succeed, try running the unit tests:

cd tensorflow_compression
for i in tensorflow_compression/python/*/*_test.py; do
  python $i
done

We recommend importing the library from your Python code as follows:

import tensorflow as tf
import tensorflow_compression as tfc

The examples directory contains an implementation of the image compression model described in:

"End-to-end optimized image compression"
J. Ballé, V. Laparra, E. P. Simoncelli
https://arxiv.org/abs/1611.01704

To see a list of options, change to the directory and run:

python bls2017.py -h

To train the model, you need to supply it with a dataset of RGB training images. They should be provided in PNG format and must all have the same shape. Following training, the Python script can be used to compress and decompress images as follows:

python bls2017.py [options] compress original.png compressed.bin
python bls2017.py [options] decompress compressed.bin reconstruction.png

For usage questions and discussions, please head over to our Google group.

Refer to the API documentation for a complete description of the Keras layers and TensorFlow ops this package implements.

There's also an introduction to our EntropyBottleneck class here, and a description of the range coding operators here.

Johannes Ballé (github: jonycgn), Sung Jin Hwang (github: ssjhv), and Nick Johnston (github: nmjohn)

Note that this is not an officially supported Google product.