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<section id="kymatio-wavelet-scattering-in-python-v0-3-0-erdre">
<h1>Kymatio: Wavelet scattering in Python - v0.3.0 “Erdre”<a class="headerlink" href="#kymatio-wavelet-scattering-in-python-v0-3-0-erdre" title="Permalink to this heading">¶</a></h1>
<p>Kymatio is an implementation of the wavelet scattering transform in the Python programming language, suitable for large-scale numerical experiments in signal processing and machine learning.
Scattering transforms are translation-invariant signal representations implemented as convolutional networks whose filters are not learned, but fixed (as wavelet filters).</p>
<a class="reference external image-reference" href="https://pypi.org/project/kymatio/"><img alt="PyPI" src="https://img.shields.io/badge/Python-3.7%2C_3.8%2C_3.9%2C_3.10-blue.svg" /></a>
<a class="reference external image-reference" href="https://opensource.org/licenses/BSD-3-Clause"><img alt="License" src="https://img.shields.io/badge/License-BSD%203--Clause-blue.svg" /></a>
<a class="reference external image-reference" href="https://github.com/kymatio/kymatio/actions/workflows/pip.yml/badge.svg"><img alt="Build status" src="https://github.com/kymatio/kymatio/actions/workflows/pip.yml/badge.svg" /></a>
<a class="reference external image-reference" href="https://pepy.tech/project/kymatio"><img alt="Downloads" src="https://pepy.tech/badge/kymatio" /></a>
<a class="reference external image-reference" href="https://codecov.io/gh/kymatio/kymatio"><img alt="codecov" src="https://codecov.io/gh/kymatio/kymatio/branch/master/graph/badge.svg" /></a>
<p>Use Kymatio if you need a library that:</p>
<ul class="simple">
<li><p>supports 1-D, 2-D, and 3-D wavelets,</p></li>
<li><p>integrates wavelet scattering in a deep learning architecture, and</p></li>
<li><p>runs seamlessly on CPU and GPU hardware, with major deep learning APIs, such
as PyTorch, TensorFlow, and Jax.</p></li>
</ul>
</section>
<section id="the-kymatio-environment">
<h1>The Kymatio environment<a class="headerlink" href="#the-kymatio-environment" title="Permalink to this heading">¶</a></h1>
<section id="flexibility">
<h2>Flexibility<a class="headerlink" href="#flexibility" title="Permalink to this heading">¶</a></h2>
<p>The Kymatio organization associates the developers of several pre-existing packages for wavelet scattering, including <code class="docutils literal notranslate"><span class="pre">ScatNet</span></code>, <code class="docutils literal notranslate"><span class="pre">scattering.m</span></code>, <code class="docutils literal notranslate"><span class="pre">PyScatWave</span></code>, <code class="docutils literal notranslate"><span class="pre">WaveletScattering.jl</span></code>, and <code class="docutils literal notranslate"><span class="pre">PyScatHarm</span></code>.</p>
<p>Interfacing Kymatio into deep learning frameworks allows the programmer to backpropagate the gradient of wavelet scattering coefficients, thus integrating them within an end-to-end trainable pipeline, such as a deep neural network.</p>
</section>
<section id="portability">
<h2>Portability<a class="headerlink" href="#portability" title="Permalink to this heading">¶</a></h2>
<p>Each of these algorithms is written in a high-level imperative paradigm, making it portable to any Python library for array operations as long as it enables complex-valued linear algebra and a fast Fourier transform (FFT).</p>
<p>Each algorithm comes packaged with a frontend and backend. The frontend takes care of
interfacing with the user. The backend defines functions necessary for
computation of the scattering transform.</p>
<p>Currently, there are eight available frontend–backend pairs, NumPy (CPU), scikit-learn (CPU), pure PyTorch (CPU and GPU), PyTorch>=1.10 (CPU and GPU), PyTorch+scikit-cuda (GPU), PyTorch>=1.10+scikit-cuda (GPU), TensorFlow (CPU and GPU), Keras (CPU and GPU), and Jax (CPU and GPU).</p>
</section>
<section id="scalability">
<h2>Scalability<a class="headerlink" href="#scalability" title="Permalink to this heading">¶</a></h2>
<p>Kymatio integrates the construction of wavelet filter banks in 1D, 2D, and 3D, as well as memory-efficient algorithms for extracting wavelet scattering coefficients, under a common application programming interface.</p>
<p>Running Kymatio on a graphics processing unit (GPU) rather than a multi-core conventional central processing unit (CPU) allows for significant speedups in computing the scattering transform.
The current speedup with respect to CPU-based MATLAB code is of the order of 10 in 1D and 3D and of the order of 100 in 2D.</p>
<p>We refer to our <a class="reference external" href="https://www.kymat.io/userguide.html#benchmarks">official benchmarks</a> for further details.</p>
</section>
<section id="how-to-cite">
<h2>How to cite<a class="headerlink" href="#how-to-cite" title="Permalink to this heading">¶</a></h2>
<p>If you use this package, please cite our paper <em>Kymatio: Scattering Transforms in Python</em>:</p>
<p>Andreux M., Angles T., Exarchakis G., Leonarduzzi R., Rochette G., Thiry L., Zarka J., Mallat S., Andén J., Belilovsky E., Bruna J., Lostanlen V., Chaudhary M., Hirn M. J., Oyallon E., Zhang S., Cella C., Eickenberg M. (2020). Kymatio: Scattering Transforms in Python. Journal of Machine Learning Research 21(60):1−6, 2020. <a class="reference external" href="http://jmlr.org/papers/v21/19-047.html">(paper)</a> <a class="reference external" href="http://jmlr.org/papers/v21/19-047.bib">(bibtex)</a></p>
</section>
</section>
<section id="installation">
<h1>Installation<a class="headerlink" href="#installation" title="Permalink to this heading">¶</a></h1>
<section id="dependencies">
<h2>Dependencies<a class="headerlink" href="#dependencies" title="Permalink to this heading">¶</a></h2>
<p>Kymatio requires:</p>
<ul class="simple">
<li><p>Python (>= 3.7)</p></li>
<li><p>SciPy (>= 0.13)</p></li>
</ul>
<section id="standard-installation">
<h3>Standard installation<a class="headerlink" href="#standard-installation" title="Permalink to this heading">¶</a></h3>
<p>We strongly recommend running Kymatio in an Anaconda environment, because this simplifies the installation of other
dependencies. You may install the latest version of Kymatio using the package manager <code class="docutils literal notranslate"><span class="pre">pip</span></code>, which will automatically download
Kymatio from the Python Package Index (PyPI):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">kymatio</span>
</pre></div>
</div>
<p>Linux and macOS are the two officially supported operating systems.</p>
</section>
</section>
</section>
<section id="frontends">
<h1>Frontends<a class="headerlink" href="#frontends" title="Permalink to this heading">¶</a></h1>
<section id="numpy">
<h2>NumPy<a class="headerlink" href="#numpy" title="Permalink to this heading">¶</a></h2>
<p>To explicitly call the NumPy frontend, run:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">kymatio.numpy</span> <span class="kn">import</span> <span class="n">Scattering2D</span>
<span class="n">scattering</span> <span class="o">=</span> <span class="n">Scattering2D</span><span class="p">(</span><span class="n">J</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">))</span>
</pre></div>
</div>
</section>
<section id="scikit-learn">
<h2>Scikit-learn<a class="headerlink" href="#scikit-learn" title="Permalink to this heading">¶</a></h2>
<p>You can call also call <code class="docutils literal notranslate"><span class="pre">Scattering2D</span></code> as a scikit-learn <code class="docutils literal notranslate"><span class="pre">Transformer</span></code> using:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">kymatio.sklearn</span> <span class="kn">import</span> <span class="n">Scattering2D</span>
<span class="n">scattering_transformer</span> <span class="o">=</span> <span class="n">Scattering2D</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">))</span>
</pre></div>
</div>
</section>
<section id="pytorch">
<h2>PyTorch<a class="headerlink" href="#pytorch" title="Permalink to this heading">¶</a></h2>
<p>Using PyTorch, you can instantiate <code class="docutils literal notranslate"><span class="pre">Scattering2D</span></code> as a <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">kymatio.torch</span> <span class="kn">import</span> <span class="n">Scattering2D</span>
<span class="n">scattering</span> <span class="o">=</span> <span class="n">Scattering2D</span><span class="p">(</span><span class="n">J</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">))</span>
</pre></div>
</div>
</section>
<section id="tensorflow-and-keras">
<h2>TensorFlow and Keras<a class="headerlink" href="#tensorflow-and-keras" title="Permalink to this heading">¶</a></h2>
<p>Similarly, in TensorFlow, you can instantiate <code class="docutils literal notranslate"><span class="pre">Scattering2D</span></code> as a <code class="docutils literal notranslate"><span class="pre">tf.Module</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">kymatio.tensorflow</span> <span class="kn">import</span> <span class="n">Scattering2D</span>
<span class="n">scattering</span> <span class="o">=</span> <span class="n">Scattering2D</span><span class="p">(</span><span class="n">J</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">))</span>
</pre></div>
</div>
<p>Alternatively, you can call <code class="docutils literal notranslate"><span class="pre">Scattering2D</span></code> as a Keras <code class="docutils literal notranslate"><span class="pre">Layer</span></code> using:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">tensorflow.keras.layers</span> <span class="kn">import</span> <span class="n">Input</span>
<span class="kn">from</span> <span class="nn">kymatio.keras</span> <span class="kn">import</span> <span class="n">Scattering2D</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">))</span>
<span class="n">scattering</span> <span class="o">=</span> <span class="n">Scattering2D</span><span class="p">(</span><span class="n">J</span><span class="o">=</span><span class="mi">2</span><span class="p">)(</span><span class="n">inputs</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="jax">
<h2>Jax<a class="headerlink" href="#jax" title="Permalink to this heading">¶</a></h2>
<p>Finally, with Jax installed, you can also instantiate a Jax <code class="docutils literal notranslate"><span class="pre">Scattering2D</span></code> object:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">kymatio.jax</span> <span class="kn">import</span> <span class="n">Scattering2D</span>
<span class="n">scattering</span> <span class="o">=</span> <span class="n">Scattering2D</span><span class="p">(</span><span class="n">J</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">))</span>
</pre></div>
</div>
</section>
</section>
<section id="installation-from-source">
<h1>Installation from source<a class="headerlink" href="#installation-from-source" title="Permalink to this heading">¶</a></h1>
<p>Assuming the Kymatio source has been downloaded, you may install it by running</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="o">-</span><span class="n">r</span> <span class="n">requirements</span><span class="o">.</span><span class="n">txt</span>
<span class="n">python</span> <span class="n">setup</span><span class="o">.</span><span class="n">py</span> <span class="n">install</span>
</pre></div>
</div>
<p>Developers can also install Kymatio via:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="o">-</span><span class="n">r</span> <span class="n">requirements</span><span class="o">.</span><span class="n">txt</span>
<span class="n">python</span> <span class="n">setup</span><span class="o">.</span><span class="n">py</span> <span class="n">develop</span>
</pre></div>
</div>
<section id="gpu-acceleration">
<h2>GPU acceleration<a class="headerlink" href="#gpu-acceleration" title="Permalink to this heading">¶</a></h2>
<p>Certain frontends, <code class="docutils literal notranslate"><span class="pre">numpy</span></code> and <code class="docutils literal notranslate"><span class="pre">sklearn</span></code>, only allow processing on the CPU and are therefore slower. The <code class="docutils literal notranslate"><span class="pre">torch</span></code>, <code class="docutils literal notranslate"><span class="pre">tensorflow</span></code>, <code class="docutils literal notranslate"><span class="pre">keras</span></code>, and <code class="docutils literal notranslate"><span class="pre">jax</span></code> frontends, however, also support GPU processing, which can significantly accelerate computations. Additionally, the <code class="docutils literal notranslate"><span class="pre">torch</span></code> backend supports an optimized <code class="docutils literal notranslate"><span class="pre">skcuda</span></code> backend which currently provides the fastest performance in computing scattering transforms.</p>
<p>To use it, you must first install the <code class="docutils literal notranslate"><span class="pre">scikit-cuda</span></code> and <code class="docutils literal notranslate"><span class="pre">cupy</span></code> dependencies:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">cuda</span> <span class="n">cupy</span>
</pre></div>
</div>
<p>Then you may instantiate a scattering object using the <code class="docutils literal notranslate"><span class="pre">backend='torch_skcuda'</span></code> argument:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">kymatio.torch</span> <span class="kn">import</span> <span class="n">Scattering2D</span>
<span class="n">scattering</span> <span class="o">=</span> <span class="n">Scattering2D</span><span class="p">(</span><span class="n">J</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">),</span> <span class="n">backend</span><span class="o">=</span><span class="s1">'torch_skcuda'</span><span class="p">)</span>
</pre></div>
</div>
</section>
</section>
<section id="documentation">
<h1>Documentation<a class="headerlink" href="#documentation" title="Permalink to this heading">¶</a></h1>
<p>The documentation of Kymatio is officially hosted on the <a class="reference external" href="https://www.kymat.io/">kymat.io</a> website.</p>
<section id="online-resources">
<h2>Online resources<a class="headerlink" href="#online-resources" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/kymatio/kymatio">GitHub repository</a></p></li>
<li><p><a class="reference external" href="https://github.com/kymatio/kymatio/issues">GitHub issue tracker</a></p></li>
<li><p><a class="reference external" href="https://github.com/kymatio/kymatio/blob/master/LICENSE.md">BSD-3-Clause license</a></p></li>
<li><p><a class="reference external" href="https://github.com/kymatio/kymatio/blob/master/AUTHORS.md">List of authors</a></p></li>
<li><p><a class="reference external" href="https://github.com/kymatio/kymatio/blob/master/CODE_OF_CONDUCT.md">Code of conduct</a></p></li>
</ul>
</section>
<section id="building-the-documentation-from-source">
<h2>Building the documentation from source<a class="headerlink" href="#building-the-documentation-from-source" title="Permalink to this heading">¶</a></h2>
<p>The documentation can also be found in the <code class="docutils literal notranslate"><span class="pre">doc/</span></code> subfolder of the GitHub repository.
To build the documentation locally, please clone this repository and run</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="o">-</span><span class="n">r</span> <span class="n">requirements_optional</span><span class="o">.</span><span class="n">txt</span>
<span class="n">cd</span> <span class="n">doc</span><span class="p">;</span> <span class="n">make</span> <span class="n">clean</span><span class="p">;</span> <span class="n">make</span> <span class="n">html</span>
</pre></div>
</div>
</section>
<section id="support">
<h2>Support<a class="headerlink" href="#support" title="Permalink to this heading">¶</a></h2>
<p>We wish to thank the Scientific Computing Core at the Flatiron Institute for the use of their computing resources for testing.</p>
<a class="reference external image-reference" href="https://www.flatironinstitute.org/"><img alt="Flatiron" src="https://kymat.io/_static/FL_Full_Logo_Mark_Small.png" /></a>
<p>We would also like to thank École Normale Supérieure for their support.</p>
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<h2>Kymatio<a class="headerlink" href="#kymatio" title="Permalink to this heading">¶</a></h2>
<p>Kyma (<em>κύμα</em>) means <em>wave</em> in Greek. By the same token, Kymatio (<em>κυμάτιο</em>) means <em>wavelet</em>.</p>
<p>Note that the organization and the library are capitalized (<em>Kymatio</em>) whereas the corresponding Python module is written in lowercase (<code class="docutils literal notranslate"><span class="pre">import</span> <span class="pre">kymatio</span></code>).</p>
<p>The recommended pronunciation for Kymatio is <em>kim-ah-tio</em>. In other words, it rhymes with patio, not with ratio.</p>
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