jaxwt 0.1.0

Differentiable and gpu enabled fast wavelet transforms in JAX

Differentiable and GPU-enabled fast wavelet transforms in JAX.

Features

• 1d analysis and synthesis transforms are implemented in src/jaxwt/conv_fwt.py. Try wavedec and waverec.

• 2d analysis and synthesis transforms are part of the src/jaxwt/conv_fwt_2d.py module. The two functions are called wavedec2 and waverec2.

• Furthermore, 3d transforms are provided by the wavedec3 and waverec3 functions.

• cwt-function supports 1d continuous wavelet transforms.

• The WaveletPacket object supports 1d wavelet packet transforms.

• WaveletPacket2d implements two-dimensional wavelet packet transforms.

• swt computes a single dimensional stationary transform iswt inverts it.

This toolbox extends PyWavelets . jaxwt additionally provides GPU and gradient support via a Jax backend.

Installation

To install Jax, head over to https://github.com/google/jax#installation and follow the procedure described there. Afterward, type pip install jaxwt to install the Jax-Wavelet-Toolbox. You can uninstall it later by typing pip uninstall jaxwt.

Documentation

The documentation is available at: https://jax-wavelet-toolbox.readthedocs.io/en/latest/jaxwt.html .

Transform Examples:

One-dimensional fast wavelet transform:

import pywt
import numpy as np;
import jax.numpy as jnp
import jaxwt as jwt
# generate an input of even length.
data = jnp.array([0., 1, 2, 3, 4, 5, 6, 7, 7, 6, 5, 4, 3, 2, 1, 0])
wavelet = pywt.Wavelet('haar')

# compare the forward fwt coefficients
print(pywt.wavedec(np.array(data), wavelet, mode='zero', level=2))
print(jwt.wavedec(data, wavelet, mode='zero', level=2))

# invert the fwt.
print(jwt.waverec(jwt.wavedec(data, wavelet, mode='zero', level=2),
                  wavelet))

Two-dimensional fast wavelet transform:

import pywt, scipy.datasets
import jaxwt as jwt
import jax.numpy as jnp
face = jnp.transpose(
    scipy.datasets.face(), [2, 0, 1]).astype(jnp.float64)
transformed = jwt.wavedec2(face, pywt.Wavelet("haar"),
                           level=2, mode="reflect")
reconstruction = jwt.waverec2(transformed, pywt.Wavelet("haar"))
jnp.max(jnp.abs(face - reconstruction))

Testing

Unit tests are handled by nox. Clone the repository and run it with the following:

$ pip install nox
$ git clone https://github.com/v0lta/Jax-Wavelet-Toolbox
$ cd Jax-Wavelet-Toolbox
$ nox -s test

Goals

• In the spirit of Jax, the aim is to be 100% pywt compatible. Whenever possible, interfaces should be the same results identical.

64-Bit floating-point numbers

If you need 64-bit floating point support, set the Jax config flag:

from jax.config import config
config.update("jax_enable_x64", True)

Citation

If you use this work in a scientific context, please cite:

@phdthesis{handle:20.500.11811/9245,
  urn: https://nbn-resolving.org/urn:nbn:de:hbz:5-63361,
  author = {{Moritz Wolter}},
  title = {Frequency Domain Methods in Recurrent Neural Networks for Sequential Data Processing},
  school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
  year = 2021,
  month = jul,
  url = {https://hdl.handle.net/20.500.11811/9245}
}