This project contains data compression ops and layers for TensorFlow. The project website is at tensorflow.github.io/compression.
You can use this library 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.
Specifically, the EntropyBottleneck class in this library simplifies the process of designing rate–distortion optimized codes. During training, it acts like a likelihood model. Once training is completed, it encodes floating point tensors into optimal bit sequences by automating the design of probability tables and calling a range coder implementation behind the scenes.
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
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
To see a list of options, change to the directory and run:
cd examples 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. Training can be as simple as the following command:
python bls2017.py -v --train_glob="images/*.png" train
This will use the default settings. The most important parameter is
which controls the trade-off between bitrate and distortion that the model will
be optimized for. The number of channels per layer is important, too: models
tuned for higher bitrates (or, equivalently, lower distortion) tend to require
transforms with a greater approximation capacity (i.e. more channels), so to
optimize performance, you want to make sure that the number of channels is large
enough (or larger). This is described in more detail in:
"Efficient nonlinear transforms for lossy image compression"
If you wish, you can monitor progress with Tensorboard. To do this, create a Tensorboard instance in the background before starting the training, then point your web browser to port 6006 on your machine:
tensorboard --logdir=. &
When training has finished, the Python script can be used to compress and decompress images as follows. The same model checkpoint must be accessible to both commands.
python bls2017.py [options] compress original.png compressed.bin python bls2017.py [options] decompress compressed.bin reconstruction.png
Help & documentation
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.
Note that this is not an officially supported Google product.