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  <section id="self-supervised-learning-ssl">
<span id="simclr-ssl"></span><h1>Self-Supervised Learning (SSL)<a class="headerlink" href="#self-supervised-learning-ssl" title="Permalink to this heading">¶</a></h1>
<p>Slideflow provides easy access to training the self-supervised, contrastive learning framework <a class="reference external" href="https://arxiv.org/abs/2002.05709">SimCLR</a>. Self-supervised learning provides an avenue for learning useful visual representations in your dataset without requiring ground-truth labels. These visual representations can be exported as feature vectors and used for downstream analyses such as <a class="reference internal" href="../posthoc/#slidemap"><span class="std std-ref">dimensionality reduction</span></a> or <a class="reference internal" href="../mil/#mil"><span class="std std-ref">multi-instance learning</span></a>.</p>
<p>The <code class="docutils literal notranslate"><span class="pre">slideflow.simclr</span></code> module contains a <a class="reference external" href="https://github.com/jamesdolezal/simclr/">forked Tensorflow implementation</a> minimally modified to interface with Slideflow. SimCLR models can be trained with <a class="reference internal" href="../project/#slideflow.Project.train_simclr" title="slideflow.Project.train_simclr"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Project.train_simclr()</span></code></a>, and SimCLR features can be calculated as with other models using <a class="reference internal" href="../project/#slideflow.Project.generate_features" title="slideflow.Project.generate_features"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Project.generate_features()</span></code></a>.</p>
<section id="training-simclr">
<h2>Training SimCLR<a class="headerlink" href="#training-simclr" title="Permalink to this heading">¶</a></h2>
<p>First, determine the SimCLR training parameters with <a class="reference internal" href="../simclr/#slideflow.simclr.get_args" title="slideflow.simclr.get_args"><code class="xref py py-func docutils literal notranslate"><span class="pre">slideflow.simclr.get_args()</span></code></a>. This function accepts parameters via keyword arguments, such as <code class="docutils literal notranslate"><span class="pre">learning_rate</span></code> and <code class="docutils literal notranslate"><span class="pre">temperature</span></code>, and returns a configured <a class="reference internal" href="../simclr/#slideflow.simclr.SimCLR_Args" title="slideflow.simclr.SimCLR_Args"><code class="xref py py-class docutils literal notranslate"><span class="pre">slideflow.simclr.SimCLR_Args</span></code></a>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">slideflow</span> <span class="kn">import</span> <span class="n">simclr</span>

<span class="n">args</span> <span class="o">=</span> <span class="n">simclr</span><span class="o">.</span><span class="n">get_args</span><span class="p">(</span>
    <span class="n">temperature</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
    <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span>
    <span class="n">train_epochs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
    <span class="n">image_size</span><span class="o">=</span><span class="mi">299</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Next, assemble a training and (optionally) a validation dataset. The validation dataset is used to assess contrastive loss during training, but is not required.</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="c1"># Load a project and dataset</span>
<span class="n">P</span> <span class="o">=</span> <span class="n">sf</span><span class="o">.</span><span class="n">load_project</span><span class="p">(</span><span class="s1">&#39;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">299</span><span class="p">,</span> <span class="n">tile_um</span><span class="o">=</span><span class="mi">302</span><span class="p">)</span>

<span class="c1"># Split dataset into training/validation</span>
<span class="n">train_dts</span><span class="p">,</span> <span class="n">val_dts</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">split</span><span class="p">(</span>
    <span class="n">val_fraction</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span>
    <span class="n">model_type</span><span class="o">=</span><span class="s1">&#39;classification&#39;</span><span class="p">,</span>
    <span class="n">labels</span><span class="o">=</span><span class="s1">&#39;subtype&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>Finally, SimCLR can be trained with <a class="reference internal" href="../project/#slideflow.Project.train_simclr" title="slideflow.Project.train_simclr"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Project.train_simclr()</span></code></a>. You can train with a single dataset:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">P</span><span class="o">.</span><span class="n">train_simclr</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">dataset</span><span class="p">)</span>
</pre></div>
</div>
<p>You can train with an optional validation dataset:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">P</span><span class="o">.</span><span class="n">train_simclr</span><span class="p">(</span>
    <span class="n">args</span><span class="p">,</span>
    <span class="n">train_dataset</span><span class="o">=</span><span class="n">train_dts</span><span class="p">,</span>
    <span class="n">val_dataset</span><span class="o">=</span><span class="n">val_dts</span>
<span class="p">)</span>
</pre></div>
</div>
<p>And you can also optionally provide labels for training the supervised head. To train a supervised head, you’ll also need to set the SimCLR argument <code class="docutils literal notranslate"><span class="pre">lineareval_while_pretraining=True</span></code>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># SimCLR args</span>
<span class="n">args</span> <span class="o">=</span> <span class="n">simclr</span><span class="o">.</span><span class="n">get_args</span><span class="p">(</span>
    <span class="o">...</span><span class="p">,</span>
    <span class="n">lineareval_while_pretraining</span><span class="o">=</span><span class="kc">True</span>
<span class="p">)</span>

<span class="c1"># Train with validation &amp; supervised head</span>
<span class="n">P</span><span class="o">.</span><span class="n">train_simclr</span><span class="p">(</span>
    <span class="n">args</span><span class="p">,</span>
    <span class="n">train_dataset</span><span class="o">=</span><span class="n">train_dts</span><span class="p">,</span>
    <span class="n">val_dataset</span><span class="o">=</span><span class="n">val_dts</span><span class="p">,</span>
    <span class="n">outcomes</span><span class="o">=</span><span class="s1">&#39;subtype&#39;</span>
<span class="p">)</span>
</pre></div>
</div>
<p>The SimCLR model checkpoints and final saved model will be saved in the <code class="docutils literal notranslate"><span class="pre">simclr/</span></code> folder within the project root directory.</p>
</section>
<section id="training-dinov2">
<span id="dinov2"></span><h2>Training DINOv2<a class="headerlink" href="#training-dinov2" title="Permalink to this heading">¶</a></h2>
<p>A lightly modified version of <a class="reference external" href="https://arxiv.org/abs/2304.07193">DINOv2</a> with Slideflow integration is available on <a class="reference external" href="https://github.com/jamesdolezal/dinov2">GitHub</a>. This version facilitates training DINOv2 with Slideflow datasets and adds stain augmentation to the training pipeline.</p>
<p>To train DINOv2, first install the package:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/jamesdolezal/dinov2.git
</pre></div>
</div>
<p>Next, configure the training parameters and datsets by providing a configuration YAML file. This configuration file should contain a <code class="docutils literal notranslate"><span class="pre">slideflow</span></code> section, which specifies the Slideflow project and dataset to use for training. An example YAML file is shown below:</p>
<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="nt">train</span><span class="p">:</span>
<span class="w">  </span><span class="nt">dataset_path</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">slideflow</span>
<span class="w">  </span><span class="nt">batch_size_per_gpu</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">32</span>
<span class="w">  </span><span class="nt">slideflow</span><span class="p">:</span>
<span class="w">    </span><span class="nt">project</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;/mnt/data/projects/TCGA_THCA_BRAF&quot;</span>
<span class="w">    </span><span class="nt">dataset</span><span class="p">:</span>
<span class="w">      </span><span class="nt">tile_px</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">299</span>
<span class="w">      </span><span class="nt">tile_um</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">302</span>
<span class="w">      </span><span class="nt">filters</span><span class="p">:</span>
<span class="w">        </span><span class="nt">brs_class</span><span class="p">:</span>
<span class="w">        </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="s">&quot;Braf-like&quot;</span>
<span class="w">        </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="s">&quot;Ras-like&quot;</span>
<span class="w">    </span><span class="nt">seed</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">42</span>
<span class="w">    </span><span class="nt">outcome_labels</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;brs_class&quot;</span>
<span class="w">    </span><span class="nt">normalizer</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;reinhard_mask&quot;</span>
<span class="w">    </span><span class="nt">interleave_kwargs</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">null</span>
</pre></div>
</div>
<p>See the <a class="reference external" href="https://github.com/jamesdolezal/dinov2">DINOv2 README</a> for more details on the configuration file format.</p>
<p>Finally, train DINOv2 using the same command-line interface as the original DINOv2 implementation. For example, to train DINOv2 on 4 GPUs on a single node:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>torchrun<span class="w"> </span>--nproc_per_node<span class="o">=</span><span class="m">4</span><span class="w"> </span>-m<span class="w"> </span><span class="s2">&quot;dinov2.train.train&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w">    </span>--config-file<span class="w"> </span>/path/to/config.yaml<span class="w"> </span><span class="se">\</span>
<span class="w">    </span>--output-dir<span class="w"> </span>/path/to/output_dir
</pre></div>
</div>
<p>The teacher weights will be saved in <code class="docutils literal notranslate"><span class="pre">outdir/eval/.../teacher_checkpoint.pth</span></code>, and the final configuration YAML will be saved in <code class="docutils literal notranslate"><span class="pre">outdir/config.yaml</span></code>.</p>
</section>
<section id="generating-features">
<h2>Generating features<a class="headerlink" href="#generating-features" title="Permalink to this heading">¶</a></h2>
<p>Generating features from a trained SSL is straightforward - use the same <a class="reference internal" href="../project/#slideflow.Project.generate_features" title="slideflow.Project.generate_features"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.Project.generate_features()</span></code></a> and <a class="reference internal" href="../dataset_features/#slideflow.DatasetFeatures" title="slideflow.DatasetFeatures"><code class="xref py py-class docutils literal notranslate"><span class="pre">slideflow.DatasetFeatures</span></code></a> interfaces as <a class="reference internal" href="../posthoc/#dataset-features"><span class="std std-ref">previously described</span></a>, providing a path to a saved SimCLR model or checkpoint.</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="c1"># Create the SimCLR feature extractor</span>
<span class="n">simclr</span> <span class="o">=</span> <span class="n">sf</span><span class="o">.</span><span class="n">build_feature_extractor</span><span class="p">(</span>
    <span class="s1">&#39;simclr&#39;</span><span class="p">,</span>
    <span class="n">ckpt</span><span class="o">=</span><span class="s1">&#39;/path/to/simclr.ckpt&#39;</span>
<span class="p">)</span>

<span class="c1"># Calculate SimCLR features for a dataset</span>
<span class="n">features</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">generate_features</span><span class="p">(</span><span class="n">simclr</span><span class="p">,</span> <span class="o">...</span><span class="p">)</span>
</pre></div>
</div>
<p>For DINOv2 models, use <code class="docutils literal notranslate"><span class="pre">'dinov2'</span></code> as the first argument, and pass the model configuration YAML file to <code class="docutils literal notranslate"><span class="pre">cfg</span></code> and the teacher checkpoint weights to <code class="docutils literal notranslate"><span class="pre">weights</span></code>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">dinov2</span> <span class="o">=</span> <span class="n">build_feature_extractor</span><span class="p">(</span>
    <span class="s1">&#39;dinov2&#39;</span><span class="p">,</span>
    <span class="n">weights</span><span class="o">=</span><span class="s1">&#39;/path/to/teacher_checkpoint.pth&#39;</span><span class="p">,</span>
    <span class="n">cfg</span><span class="o">=</span><span class="s1">&#39;/path/to/config.yaml&#39;</span>
<span class="p">)</span>
</pre></div>
</div>
</section>
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