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  <section id="generative-networks-gans">
<span id="stylegan"></span><h1>Generative Networks (GANs)<a class="headerlink" href="#generative-networks-gans" title="Permalink to this heading">¶</a></h1>
<video autoplay="True" width="100%" controls="True" preload="auto" loop="True"><source src="https://media.githubusercontent.com/media/slideflow/slideflow/master/docs/stylegan.webm" type="video/webm"></video><div class="line-block">
<div class="line"><br /></div>
</div>
<p>Slideflow includes tools to easily interface with the PyTorch implementations of <a class="reference external" href="https://github.com/jamesdolezal/stylegan2-slideflow">StyleGAN2</a> and <a class="reference external" href="https://github.com/jamesdolezal/stylegan3-slideflow">StyleGAN3</a>, allowing you to train these Generative Adversarial Networks (GANs). Slideflow additionally includes tools to assist with image generation, interpolation between class labels, and interactively visualize GAN-generated images and their predictions. See our manuscript on the use of GANs to <a class="reference external" href="https://arxiv.org/abs/2211.06522">generate synthetic histology</a> for an example of how these networks might be used.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>StyleGAN requires PyTorch &lt;0.13 and Slideflow-NonCommercial, which can be installed with:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>slideflow-noncommercial
</pre></div>
</div>
</div>
<section id="training-stylegan">
<h2>Training StyleGAN<a class="headerlink" href="#training-stylegan" title="Permalink to this heading">¶</a></h2>
<p>The easiest way to train StyleGAN2/StyleGAN3 is with <a class="reference internal" href="../project/#slideflow.Project.gan_train" title="slideflow.Project.gan_train"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Project.gan_train()</span></code></a>. Both standard and class-conditional GANs are
supported. To train a GAN, pass a <a class="reference internal" href="../dataset/#slideflow.Dataset" title="slideflow.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">slideflow.Dataset</span></code></a>, experiment label,
and StyleGAN keyword arguments to this function:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">slideflow</span> <span class="k">as</span> <span class="nn">sf</span>

<span class="n">P</span> <span class="o">=</span> <span class="n">sf</span><span class="o">.</span><span class="n">Project</span><span class="p">(</span><span class="s1">&#39;/project/path&#39;</span><span class="p">)</span>
<span class="n">dataset</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">dataset</span><span class="p">(</span><span class="n">tile_px</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span> <span class="n">tile_um</span><span class="o">=</span><span class="mi">400</span><span class="p">)</span>

<span class="n">P</span><span class="o">.</span><span class="n">gan_train</span><span class="p">(</span>
  <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">,</span>
  <span class="n">model</span><span class="o">=</span><span class="s1">&#39;stylegan3&#39;</span><span class="p">,</span>
  <span class="n">cfg</span><span class="o">=</span><span class="s1">&#39;stylegan3-r&#39;</span><span class="p">,</span>
  <span class="n">exp_label</span><span class="o">=</span><span class="s2">&quot;ExperimentLabel&quot;</span><span class="p">,</span>
  <span class="n">gpus</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
  <span class="n">batch</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span>
  <span class="o">...</span>
<span class="p">)</span>
</pre></div>
</div>
<p>The trained networks will be saved in the <code class="docutils literal notranslate"><span class="pre">gan/</span></code> subfolder in the project directory.</p>
<p>StyleGAN2/3 can only be trained on images with sizes that are powers of 2. You can crop and/or resize images from a Dataset to match this requirement by using the <code class="docutils literal notranslate"><span class="pre">crop</span></code> and/or <code class="docutils literal notranslate"><span class="pre">resize</span></code> arguments:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">dataset</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">dataset</span><span class="p">(</span><span class="n">tile_px</span><span class="o">=</span><span class="mi">299</span><span class="p">,</span> <span class="o">...</span><span class="p">)</span>

<span class="c1"># Train a GAN on images resized to 256x256</span>
<span class="n">P</span><span class="o">.</span><span class="n">gan_train</span><span class="p">(</span>
  <span class="o">...</span><span class="p">,</span>
  <span class="n">resize</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
<p>See the <a class="reference internal" href="../project/#slideflow.Project.gan_train" title="slideflow.Project.gan_train"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Project.gan_train()</span></code></a> documentation for additional
keyword arguments to customize training.</p>
<section id="class-conditioning">
<h3>Class conditioning<a class="headerlink" href="#class-conditioning" title="Permalink to this heading">¶</a></h3>
<p>GANs can also be trained with class conditioning. To train a class-conditional GAN, simply provide a list of categorical
outcome labels to the <code class="docutils literal notranslate"><span class="pre">outcomes</span></code> argument of <a class="reference internal" href="../project/#slideflow.Project.gan_train" title="slideflow.Project.gan_train"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Project.gan_train()</span></code></a>. For example, to train a GAN with class conditioning on ER status:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">P</span><span class="o">.</span><span class="n">gan_train</span><span class="p">(</span>
  <span class="o">...</span><span class="p">,</span>
  <span class="n">outcomes</span><span class="o">=</span><span class="s1">&#39;er_status&#39;</span>
<span class="p">)</span>
</pre></div>
</div>
</section>
<section id="tile-level-labels">
<h3>Tile-level labels<a class="headerlink" href="#tile-level-labels" title="Permalink to this heading">¶</a></h3>
<p>In addition to class conditioning with slide-level labels, StyleGAN2/StyleGAN3 can be trained with tile-level class conditioning. Tile-level labels can be generated through ROI annotations, as described in <a class="reference internal" href="../tile_labels/#tile-labels"><span class="std std-ref">Strong Supervision with Tile Labels</span></a>.</p>
<p>Prepare a pandas dataframe, indexed with the format <code class="docutils literal notranslate"><span class="pre">{slide}-{x}-{y}</span></code>, where <code class="docutils literal notranslate"><span class="pre">slide</span></code> is the name of the slide (without extension), <code class="docutils literal notranslate"><span class="pre">x</span></code> is the corresponding tile x-coordinate, and <code class="docutils literal notranslate"><span class="pre">y</span></code> is the tile y-coordinate. The dataframe should have a single column, <code class="docutils literal notranslate"><span class="pre">label</span></code>, containing onehot-encoded category labels. For example:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>

<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
  <span class="n">index</span><span class="o">=</span><span class="p">[</span>
    <span class="s1">&#39;slide1-251-425&#39;</span><span class="p">,</span>
    <span class="s1">&#39;slide1-560-241&#39;</span><span class="p">,</span>
    <span class="s1">&#39;slide1-321-502&#39;</span><span class="p">,</span>
    <span class="o">...</span>
  <span class="p">],</span>
  <span class="n">data</span><span class="o">=</span><span class="p">{</span>
    <span class="s1">&#39;label&#39;</span><span class="p">:</span> <span class="p">[</span>
      <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
      <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
      <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
      <span class="o">...</span>
    <span class="p">]</span>
  <span class="p">}</span>
<span class="p">)</span>
</pre></div>
</div>
<p>This dataframe can be generated, as described in <a class="reference internal" href="../tile_labels/#tile-labels"><span class="std std-ref">Strong Supervision with Tile Labels</span></a>, through the <a class="reference internal" href="../dataset/#slideflow.Dataset.get_tile_dataframe" title="slideflow.Dataset.get_tile_dataframe"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Dataset.get_tile_dataframe()</span></code></a> function. For GAN conditioning, the <code class="docutils literal notranslate"><span class="pre">label</span></code> column should be onehot-encoded.</p>
<p>Once the dataframe is complete, save it in parquet format:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">to_parquet</span><span class="p">(</span><span class="s1">&#39;tile_labels.parquet&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>And supply this file to the <code class="docutils literal notranslate"><span class="pre">tile_labels</span></code> argument of <a class="reference internal" href="../project/#slideflow.Project.gan_train" title="slideflow.Project.gan_train"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Project.gan_train()</span></code></a>:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">P</span><span class="o">.</span><span class="n">gan_train</span><span class="p">(</span>
  <span class="o">...</span><span class="p">,</span>
  <span class="n">tile_labels</span><span class="o">=</span><span class="s1">&#39;tile_labels.parquet&#39;</span>
<span class="p">)</span>
</pre></div>
</div>
</section>
</section>
<section id="generating-images">
<h2>Generating images<a class="headerlink" href="#generating-images" title="Permalink to this heading">¶</a></h2>
<p>Images can be generated from a trained GAN and exported either as loose images
in PNG or JPG format, or alternatively stored in TFRecords. Images are generated from a list
of seeds (list of int). Use the <a class="reference internal" href="../project/#slideflow.Project.gan_generate" title="slideflow.Project.gan_generate"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Project.gan_generate()</span></code></a> function
to generate images, with <code class="docutils literal notranslate"><span class="pre">out</span></code> set to a directory path if exporting loose images,
or <code class="docutils literal notranslate"><span class="pre">out</span></code> set to a filename ending in <code class="docutils literal notranslate"><span class="pre">.tfrecords</span></code> if saving images in
TFRecord format:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">network_pkl</span> <span class="o">=</span> <span class="s1">&#39;/path/to/trained/gan.pkl&#39;</span>
<span class="n">P</span><span class="o">.</span><span class="n">gan_generate</span><span class="p">(</span>
  <span class="n">network_pkl</span><span class="p">,</span>
  <span class="n">out</span><span class="o">=</span><span class="s1">&#39;target.tfrecords&#39;</span><span class="p">,</span>
  <span class="n">seeds</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">),</span>
  <span class="o">...</span>
<span class="p">)</span>
</pre></div>
</div>
<p>The image format is set with the <code class="docutils literal notranslate"><span class="pre">format</span></code> argument:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">P</span><span class="o">.</span><span class="n">gan_generate</span><span class="p">(</span>
  <span class="o">...</span><span class="p">,</span>
  <span class="nb">format</span><span class="o">=</span><span class="s1">&#39;jpg&#39;</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Class index (for class-conditional GANs) is set with <code class="docutils literal notranslate"><span class="pre">class_idx</span></code>:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">P</span><span class="o">.</span><span class="n">gan_generate</span><span class="p">(</span>
  <span class="o">...</span><span class="p">,</span>
  <span class="n">class_idx</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Finally, images can be resized after generation to match a target tile size:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">P</span><span class="o">.</span><span class="n">gan_generate</span><span class="p">(</span>
  <span class="o">...</span><span class="p">,</span>
  <span class="n">gan_px</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span>
  <span class="n">gan_um</span><span class="o">=</span><span class="mi">400</span><span class="p">,</span>
  <span class="n">target_px</span><span class="o">=</span><span class="mi">299</span><span class="p">,</span>
  <span class="n">target_um</span><span class="o">=</span><span class="mi">302</span><span class="p">,</span>
<span class="p">)</span>
</pre></div>
</div>
<section id="interactive-visualization">
<h3>Interactive visualization<a class="headerlink" href="#interactive-visualization" title="Permalink to this heading">¶</a></h3>
<p>Slideflow Studio can be used to interactively visualize GAN-generated images (see <a class="reference internal" href="../studio/#studio"><span class="std std-ref">Slideflow Studio: Live Visualization</span></a>). Images can be directly exported from this interface. This tool also enables you to visualize real-time predictions for GAN generated images when as inputs to a trained classifier.</p>
<p>For more examples of using Slideflow to work with GAN-generated images, see <a class="reference external" href="https://github.com/jamesdolezal/synthetic-histology">our GitHub repository</a> for code accompanying the previously referenced manuscript.</p>
</section>
</section>
</section>


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