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  <section id="tutorial-3-using-a-custom-architecture">
<span id="tutorial3"></span><h1>Tutorial 3: Using a custom architecture<a class="headerlink" href="#tutorial-3-using-a-custom-architecture" title="Permalink to this heading">¶</a></h1>
<p>Out of the box, Slideflow includes support for 21 model architectures in the Tensorflow backend and 17 with the PyTorch backend. In this tutorial, we will demonstrate how to train a custom model architecture (ViT) in either backend.</p>
<section id="custom-tensorflow-model">
<h2>Custom Tensorflow model<a class="headerlink" href="#custom-tensorflow-model" title="Permalink to this heading">¶</a></h2>
<p>Any Tensorflow/Keras model (<code class="xref py py-class docutils literal notranslate"><span class="pre">tf.keras.Model</span></code>) can be trained in Slideflow by setting the <code class="docutils literal notranslate"><span class="pre">model</span></code> parameter of a <a class="reference internal" href="../model_params/#slideflow.ModelParams" title="slideflow.ModelParams"><code class="xref py py-class docutils literal notranslate"><span class="pre">slideflow.ModelParams</span></code></a> object to a function which initalizes the model.</p>
<p>First, define the model in a file that can be imported. In this example, we will define a vision transformer (ViT) model in a file <code class="docutils literal notranslate"><span class="pre">vit_tensorflow.py</span></code>:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># From:</span>
<span class="c1"># https://github.com/ashishpatel26/Vision-Transformer-Keras-Tensorflow-Pytorch-Examples/blob/main/Vision_Transformer_with_tf2.ipynb</span>

<span class="kn">import</span> <span class="nn">math</span>
<span class="kn">import</span> <span class="nn">six</span>
<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="kn">from</span> <span class="nn">einops.layers.tensorflow</span> <span class="kn">import</span> <span class="n">Rearrange</span>


<span class="k">def</span> <span class="nf">gelu</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;Gaussian Error Linear Unit.</span>
<span class="sd">    This is a smoother version of the RELU.</span>
<span class="sd">    Original paper: https://arxiv.org/abs/1606.08415</span>
<span class="sd">    Args:</span>
<span class="sd">        x: float Tensor to perform activation.</span>
<span class="sd">    Returns:</span>
<span class="sd">        `x` with the GELU activation applied.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">cdf</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">+</span> <span class="n">tf</span><span class="o">.</span><span class="n">tanh</span><span class="p">(</span>
        <span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="mf">0.044715</span> <span class="o">*</span> <span class="n">tf</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))))</span>
    <span class="k">return</span> <span class="n">x</span> <span class="o">*</span> <span class="n">cdf</span>


<span class="k">def</span> <span class="nf">get_activation</span><span class="p">(</span><span class="n">identifier</span><span class="p">):</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;Maps a identifier to a Python function, e.g., &quot;relu&quot; =&gt; `tf.nn.relu`.</span>
<span class="sd">    It checks string first and if it is one of customized activation not in TF,</span>
<span class="sd">    the corresponding activation will be returned. For non-customized activation</span>
<span class="sd">    names and callable identifiers, always fallback to tf.keras.activations.get.</span>
<span class="sd">    Args:</span>
<span class="sd">        identifier: String name of the activation function or callable.</span>
<span class="sd">    Returns:</span>
<span class="sd">        A Python function corresponding to the activation function.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">identifier</span><span class="p">,</span> <span class="n">six</span><span class="o">.</span><span class="n">string_types</span><span class="p">):</span>
        <span class="n">name_to_fn</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;gelu&quot;</span><span class="p">:</span> <span class="n">gelu</span><span class="p">}</span>
        <span class="n">identifier</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">identifier</span><span class="p">)</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
        <span class="k">if</span> <span class="n">identifier</span> <span class="ow">in</span> <span class="n">name_to_fn</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">activations</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">name_to_fn</span><span class="p">[</span><span class="n">identifier</span><span class="p">])</span>
    <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">activations</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">identifier</span><span class="p">)</span>


<span class="k">class</span> <span class="nc">Residual</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">Model</span><span class="p">):</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fn</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fn</span> <span class="o">=</span> <span class="n">fn</span>

    <span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fn</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="n">x</span>


<span class="k">class</span> <span class="nc">PreNorm</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">Model</span><span class="p">):</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">fn</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">norm</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">LayerNormalization</span><span class="p">(</span><span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fn</span> <span class="o">=</span> <span class="n">fn</span>

    <span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fn</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>


<span class="k">class</span> <span class="nc">FeedForward</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">Model</span><span class="p">):</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">hidden_dim</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">Sequential</span><span class="p">([</span>
            <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span>
                <span class="n">hidden_dim</span><span class="p">,</span>
                <span class="n">activation</span><span class="o">=</span><span class="n">get_activation</span><span class="p">(</span><span class="s1">&#39;gelu&#39;</span><span class="p">)</span>
            <span class="p">),</span>
            <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="n">dim</span><span class="p">)]</span>
        <span class="p">)</span>

    <span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>


<span class="k">class</span> <span class="nc">Attention</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">Model</span><span class="p">):</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">heads</span><span class="o">=</span><span class="mi">8</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">heads</span> <span class="o">=</span> <span class="n">heads</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">scale</span> <span class="o">=</span> <span class="n">dim</span> <span class="o">**</span> <span class="o">-</span><span class="mf">0.5</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">to_qkv</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="n">dim</span> <span class="o">*</span> <span class="mi">3</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">to_out</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">rearrange_qkv</span> <span class="o">=</span> <span class="n">Rearrange</span><span class="p">(</span>
            <span class="s1">&#39;b n (qkv h d) -&gt; qkv b h n d&#39;</span><span class="p">,</span>
            <span class="n">qkv</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
            <span class="n">h</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">heads</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">rearrange_out</span> <span class="o">=</span> <span class="n">Rearrange</span><span class="p">(</span><span class="s1">&#39;b h n d -&gt; b n (h d)&#39;</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="n">qkv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">to_qkv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">qkv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rearrange_qkv</span><span class="p">(</span><span class="n">qkv</span><span class="p">)</span>
        <span class="n">q</span> <span class="o">=</span> <span class="n">qkv</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">k</span> <span class="o">=</span> <span class="n">qkv</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
        <span class="n">v</span> <span class="o">=</span> <span class="n">qkv</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
        <span class="n">dots</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;bhid,bhjd-&gt;bhij&#39;</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span>
        <span class="n">attn</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">dots</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;bhij,bhjd-&gt;bhid&#39;</span><span class="p">,</span> <span class="n">attn</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rearrange_out</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">to_out</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">out</span>


<span class="k">class</span> <span class="nc">Transformer</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">Model</span><span class="p">):</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">depth</span><span class="p">,</span> <span class="n">heads</span><span class="p">,</span> <span class="n">mlp_dim</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="n">layers</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">depth</span><span class="p">):</span>
            <span class="n">layers</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span>
                <span class="n">Residual</span><span class="p">(</span><span class="n">PreNorm</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="n">Attention</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="n">heads</span><span class="o">=</span><span class="n">heads</span><span class="p">))),</span>
                <span class="n">Residual</span><span class="p">(</span><span class="n">PreNorm</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="n">FeedForward</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="n">mlp_dim</span><span class="p">)))</span>
            <span class="p">])</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">layers</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>


<span class="k">class</span> <span class="nc">ViT</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">Model</span><span class="p">):</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">image_size</span><span class="p">,</span> <span class="n">patch_size</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">,</span>
                <span class="n">dim</span><span class="p">,</span> <span class="n">depth</span><span class="p">,</span> <span class="n">heads</span><span class="p">,</span> <span class="n">mlp_dim</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="n">image_size</span> <span class="o">%</span> <span class="n">patch_size</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;image dimensions must be divisible by the &#39;</span>
                             <span class="s1">&#39;patch size&#39;</span><span class="p">)</span>
        <span class="n">num_patches</span> <span class="o">=</span> <span class="p">(</span><span class="n">image_size</span> <span class="o">//</span> <span class="n">patch_size</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">patch_size</span> <span class="o">=</span> <span class="n">patch_size</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dim</span> <span class="o">=</span> <span class="n">dim</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">pos_embedding</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">add_weight</span><span class="p">(</span>
            <span class="s2">&quot;position_embeddings&quot;</span><span class="p">,</span>
            <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="n">num_patches</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">dim</span><span class="p">],</span>
            <span class="n">initializer</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">initializers</span><span class="o">.</span><span class="n">RandomNormal</span><span class="p">(),</span>
            <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">patch_to_embedding</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">cls_token</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">add_weight</span><span class="p">(</span>
            <span class="s2">&quot;cls_token&quot;</span><span class="p">,</span>
            <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">dim</span><span class="p">],</span>
            <span class="n">initializer</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">initializers</span><span class="o">.</span><span class="n">RandomNormal</span><span class="p">(),</span>
            <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">rearrange</span> <span class="o">=</span> <span class="n">Rearrange</span><span class="p">(</span>
            <span class="s1">&#39;b (h p1) (w p2) c -&gt; b (h w) (p1 p2 c)&#39;</span><span class="p">,</span>
            <span class="n">p1</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">patch_size</span><span class="p">,</span>
            <span class="n">p2</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">patch_size</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">transformer</span> <span class="o">=</span> <span class="n">Transformer</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="n">depth</span><span class="p">,</span> <span class="n">heads</span><span class="p">,</span> <span class="n">mlp_dim</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">to_cls_token</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">identity</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">mlp_head</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">Sequential</span><span class="p">([</span>
            <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="n">mlp_dim</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="n">get_activation</span><span class="p">(</span><span class="s1">&#39;gelu&#39;</span><span class="p">)),</span>
            <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="n">num_classes</span><span class="p">)</span>
        <span class="p">])</span>

    <span class="nd">@tf</span><span class="o">.</span><span class="n">function</span>
    <span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">img</span><span class="p">):</span>
        <span class="n">shapes</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rearrange</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">patch_to_embedding</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">cls_tokens</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cls_token</span><span class="p">,</span> <span class="p">(</span><span class="n">shapes</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="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">cls_tokens</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos_embedding</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformer</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">to_cls_token</span><span class="p">(</span><span class="n">x</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">])</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">mlp_head</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>Next, define a function that accepts any combination of the keyword arguments <code class="docutils literal notranslate"><span class="pre">input_shape</span></code>, <code class="docutils literal notranslate"><span class="pre">include_top</span></code>, <code class="docutils literal notranslate"><span class="pre">pooling</span></code>, and/or <code class="docutils literal notranslate"><span class="pre">weights</span></code> and returns an instanced model.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">vit_tensorflow</span> <span class="kn">import</span> <span class="n">ViT</span>

<span class="k">def</span> <span class="nf">vit_model</span><span class="p">(</span><span class="n">image_shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">ViT</span><span class="p">(</span>
        <span class="n">image_size</span><span class="o">=</span><span class="n">input_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
        <span class="n">patch_size</span><span class="o">=</span><span class="mi">23</span><span class="p">,</span>
        <span class="n">num_classes</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
        <span class="n">dim</span><span class="o">=</span><span class="mi">1024</span><span class="p">,</span>
        <span class="n">depth</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
        <span class="n">heads</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
        <span class="n">mlp_dim</span><span class="o">=</span><span class="mi">2048</span>
    <span class="p">)</span>
</pre></div>
</div>
<p>Then, create a <a class="reference internal" href="../model_params/#slideflow.ModelParams" title="slideflow.ModelParams"><code class="xref py py-class docutils literal notranslate"><span class="pre">slideflow.ModelParams</span></code></a> object with your training parameters, setting the <code class="docutils literal notranslate"><span class="pre">model</span></code> argument equal to the function you just defined:</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="kn">from</span> <span class="nn">vit_tensorflow</span> <span class="n">impport</span> <span class="n">ViT</span>

<span class="k">def</span> <span class="nf">vit_model</span><span class="p">(</span><span class="n">image_shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="o">...</span>

<span class="n">hp</span> <span class="o">=</span> <span class="n">ModelParams</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="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span>
    <span class="n">model</span><span class="o">=</span><span class="n">vit_model</span><span class="p">,</span>
    <span class="o">...</span>
<span class="p">)</span>
</pre></div>
</div>
<p>You can now train the model as described in <a class="reference internal" href="../tutorial1/#tutorial1"><span class="std std-ref">Tutorial 1: Model training (simple)</span></a>.</p>
</section>
<section id="custom-pytorch-model">
<h2>Custom PyTorch model<a class="headerlink" href="#custom-pytorch-model" title="Permalink to this heading">¶</a></h2>
<p>The process is very similar when using PyTorch. In this example, instead of defining the architecture in a separate file, we will use an implementation of ViT available via PyPI:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip3</span> <span class="n">install</span> <span class="n">vit</span><span class="o">-</span><span class="n">pytorch</span>
</pre></div>
</div>
<p>Next, define a function which accepts any combination of the keyword arguments <code class="docutils literal notranslate"><span class="pre">image_size</span></code> and/or <code class="docutils literal notranslate"><span class="pre">pretrained</span></code> and returns an instanced model.</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="kn">from</span> <span class="nn">vit_pytorch</span> <span class="n">impport</span> <span class="n">ViT</span>

<span class="k">def</span> <span class="nf">vit_model</span><span class="p">(</span><span class="n">image_shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">ViT</span><span class="p">(</span>
        <span class="n">image_size</span><span class="o">=</span><span class="n">image_size</span><span class="p">,</span>
        <span class="n">patch_size</span><span class="o">=</span><span class="mi">23</span><span class="p">,</span>
        <span class="n">num_classes</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
        <span class="n">dim</span><span class="o">=</span><span class="mi">1024</span><span class="p">,</span>
        <span class="n">depth</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
        <span class="n">heads</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
        <span class="n">mlp_dim</span><span class="o">=</span><span class="mi">2048</span><span class="p">,</span>
        <span class="n">dropout</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
        <span class="n">emb_dropout</span><span class="o">=</span><span class="mf">0.1</span>
    <span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">out_features</span> <span class="o">=</span> <span class="mi">1000</span>
    <span class="k">return</span> <span class="n">model</span>
</pre></div>
</div>
<p>Finally, set the <code class="docutils literal notranslate"><span class="pre">model</span></code> argument of a <a class="reference internal" href="../model_params/#slideflow.ModelParams" title="slideflow.ModelParams"><code class="xref py py-class docutils literal notranslate"><span class="pre">slideflow.ModelParams</span></code></a> object equal 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="kn">from</span> <span class="nn">vit_pytorch</span> <span class="n">impport</span> <span class="n">ViT</span>

<span class="k">def</span> <span class="nf">vit_model</span><span class="p">(</span><span class="n">image_shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="o">...</span>

<span class="n">hp</span> <span class="o">=</span> <span class="n">ModelParams</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="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span>
    <span class="n">model</span><span class="o">=</span><span class="n">vit_model</span><span class="p">,</span>
    <span class="o">...</span>
<span class="p">)</span>
</pre></div>
</div>
<p>You can now train the model as described in <a class="reference internal" href="../tutorial1/#tutorial1"><span class="std std-ref">Tutorial 1: Model training (simple)</span></a>.</p>
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