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  <section id="tutorial-7-training-with-custom-augmentations">
<span id="tutorial7"></span><h1>Tutorial 7: Training with custom augmentations<a class="headerlink" href="#tutorial-7-training-with-custom-augmentations" title="Permalink to this heading">¶</a></h1>
<p>In this tutorial, we’ll take a look at how you can use custom image augmentations when training a model with Slideflow. This tutorial builds off of <a class="reference internal" href="../tutorial2/#tutorial2"><span class="std std-ref">Tutorial 2: Model training (advanced)</span></a>, so if you haven’t already, you should read that tutorial first.</p>
<p>Our goal will be to train a model on a sparse outcome, such as ER status (roughly 4:1 positive:negative), with a custom augmentation that will oversample the minority class.  This tutorial will use PyTorch, but the same principles apply when using Tensorflow.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">os</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;SF_BACKEND&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;torch&#39;</span>
</pre></div>
</div>
<p>First, we’ll start by loading a project and preparing our datasets, just like in <a class="reference internal" href="../tutorial2/#tutorial2"><span class="std std-ref">Tutorial 2: Model training (advanced)</span></a>:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">slideflow</span> <span class="k">as</span> <span class="nn">sf</span>
<span class="gp">&gt;&gt;&gt; </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;/home/er_project&#39;</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">full_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="gp">... </span>  <span class="n">tile_px</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span>
<span class="gp">... </span>  <span class="n">tile_um</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span>
<span class="gp">... </span>  <span class="n">filters</span><span class="o">=</span><span class="p">{</span>
<span class="gp">... </span>    <span class="s1">&#39;er_status_by_ihc&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;Positive&#39;</span><span class="p">,</span> <span class="s1">&#39;Negative&#39;</span><span class="p">]</span>
<span class="gp">... </span><span class="p">})</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">labels</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">full_dataset</span><span class="o">.</span><span class="n">labels</span><span class="p">(</span><span class="s1">&#39;er_status_by_ihc&#39;</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">train</span><span class="p">,</span> <span class="n">val</span> <span class="o">=</span> <span class="n">full_dataset</span><span class="o">.</span><span class="n">split</span><span class="p">(</span>
<span class="gp">... </span>  <span class="n">labels</span><span class="o">=</span><span class="s1">&#39;er_status_by_ihc&#39;</span><span class="p">,</span>
<span class="gp">... </span>  <span class="n">val_strategy</span><span class="o">=</span><span class="s1">&#39;k-fold&#39;</span><span class="p">,</span>
<span class="gp">... </span>  <span class="n">val_k_fold</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
<span class="gp">... </span>  <span class="n">k_fold_iter</span><span class="o">=</span><span class="mi">1</span>
<span class="gp">... </span><span class="p">)</span>
</pre></div>
</div>
<p>If tiles have not yet been extracted from slides, do that now.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">dataset</span><span class="o">.</span><span class="n">extract_tiles</span><span class="p">(</span><span class="n">qc</span><span class="o">=</span><span class="s1">&#39;otsu&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>By default, Slideflow will equally sample from all slides / TFRecords during training, resulting in oversampling of slides with fewer tiles. In this case, we want to oversample the minority class (ER negative), so we’ll use category-level balancing. Sampling strategies are discussed in detail in the <a class="reference internal" href="../dataloaders/#balancing"><span class="std std-ref">Developer Notes</span></a>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">train</span> <span class="o">=</span> <span class="n">train</span><span class="o">.</span><span class="n">balance</span><span class="p">(</span><span class="s1">&#39;er_status_by_ihc&#39;</span><span class="p">,</span> <span class="n">strategy</span><span class="o">=</span><span class="s1">&#39;category&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>Next, we’ll set up our model hyperparameters, using the same parameters as in <a class="reference internal" href="../tutorial2/#tutorial2"><span class="std std-ref">Tutorial 2: Model training (advanced)</span></a>. We still want to use Slideflow’s default augmentation (random flip/rotation and JPEG compression), so we’ll use the hyperparameter <code class="docutils literal notranslate"><span class="pre">augment=True</span></code>. Our custom augmentation will be applied after the default augmentation.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">hp</span> <span class="o">=</span> <span class="n">sf</span><span class="o">.</span><span class="n">ModelParams</span><span class="p">(</span>
<span class="gp">... </span>  <span class="n">tile_px</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span>
<span class="gp">... </span>  <span class="n">tile_um</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span>
<span class="gp">... </span>  <span class="n">model</span><span class="o">=</span><span class="s1">&#39;xception&#39;</span><span class="p">,</span>
<span class="gp">... </span>  <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span>
<span class="gp">... </span>  <span class="n">epochs</span><span class="o">=</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
<span class="gp">... </span>  <span class="n">augment</span><span class="o">=</span><span class="kc">True</span>
<span class="gp">... </span><span class="p">)</span>
</pre></div>
</div>
<p>Now, we’ll define our custom augmentation. Augmentations are functions that take a single Tensor (<code class="xref py py-class docutils literal notranslate"><span class="pre">tf.Tensor</span></code> or <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code>) as input and return a single Tensor as output. Our training augmentation will include a random color jitter, random gaussian blur, and random auto-contrast.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">torch</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">transforms</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">augment</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
<span class="gp">... </span>    <span class="n">transforms</span><span class="o">.</span><span class="n">ColorJitter</span><span class="p">(</span><span class="n">brightness</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">contrast</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">saturation</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="mf">0.5</span><span class="p">),</span>
<span class="gp">... </span>    <span class="n">transforms</span><span class="o">.</span><span class="n">RandomAutocontrast</span><span class="p">(),</span>
<span class="gp">... </span>    <span class="n">transforms</span><span class="o">.</span><span class="n">GaussianBlur</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="gp">... </span><span class="p">])</span>
</pre></div>
</div>
<p>Transformations can be applied to training or validation data by passing a dictionary - with the keys ‘train’ and/or ‘val’ - to the <code class="docutils literal notranslate"><span class="pre">transform</span></code> argument of <code class="xref py py-class docutils literal notranslate"><span class="pre">slideflow.Trainer</span></code>. If a transformation should be applied to both training and validation, it can be passed directly to the <code class="docutils literal notranslate"><span class="pre">transform</span></code> argument. In this case, we’ll apply our custom augmentation to the training dataset only.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">trainer</span> <span class="o">=</span> <span class="n">sf</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">build_trainer</span><span class="p">(</span>
<span class="gp">... </span>  <span class="n">hp</span><span class="o">=</span><span class="n">hp</span><span class="p">,</span>
<span class="gp">... </span>  <span class="n">outdir</span><span class="o">=</span><span class="s1">&#39;/some/directory&#39;</span><span class="p">,</span>
<span class="gp">... </span>  <span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span>
<span class="gp">... </span>  <span class="n">transform</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;train&#39;</span><span class="p">:</span> <span class="n">augment</span><span class="p">},</span>
<span class="gp">... </span><span class="p">)</span>
</pre></div>
</div>
<p>Now we can start training. Pass the training and validation datasets to the <a class="reference internal" href="../model/#slideflow.model.Trainer.train" title="slideflow.model.Trainer.train"><code class="xref py py-meth docutils literal notranslate"><span class="pre">slideflow.model.Trainer.train()</span></code></a> method of our trainer, assigning the output to a new variable <code class="docutils literal notranslate"><span class="pre">results</span></code>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">results</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">val</span><span class="p">)</span>
</pre></div>
</div>
<p>And that’s it! You’ve trained a model with a custom augmentation. You can now use the model to make predictions on new data, or use the model to make predictions on the validation dataset.</p>
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