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<h1>Source code for dscript.models.contact</h1><div class="highlight"><pre>
<span></span><span class="sd">"""</span>
<span class="sd">Contact model classes.</span>
<span class="sd">"""</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch.functional</span> <span class="k">as</span> <span class="nn">F</span>
<div class="viewcode-block" id="FullyConnected"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.contact.FullyConnected">[docs]</a><span class="k">class</span> <span class="nc">FullyConnected</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Performs part 1 of Contact Prediction Module. Takes embeddings from Projection module and produces broadcast tensor.</span>
<span class="sd"> Input embeddings of dimension :math:`d` are combined into a :math:`2d` length MLP input :math:`z_{cat}`, where :math:`z_{cat} = [z_0 \\ominus z_1 | z_0 \\odot z_1]`</span>
<span class="sd"> :param embed_dim: Output dimension of `dscript.models.embedding <#module-dscript.models.embedding>`_ model :math:`d` [default: 100]</span>
<span class="sd"> :type embed_dim: int</span>
<span class="sd"> :param hidden_dim: Hidden dimension :math:`h` [default: 50]</span>
<span class="sd"> :type hidden_dim: int</span>
<span class="sd"> :param activation: Activation function for broadcast tensor [default: torch.nn.ReLU()]</span>
<span class="sd"> :type activation: torch.nn.Module</span>
<span class="sd"> """</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">embed_dim</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">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">FullyConnected</span><span class="p">,</span> <span class="bp">self</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">D</span> <span class="o">=</span> <span class="n">embed_dim</span>
<span class="bp">self</span><span class="o">.</span><span class="n">H</span> <span class="o">=</span> <span class="n">hidden_dim</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">D</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">H</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">batchnorm</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">H</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">activation</span> <span class="o">=</span> <span class="n">activation</span>
<div class="viewcode-block" id="FullyConnected.forward"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.contact.FullyConnected.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">z0</span><span class="p">,</span> <span class="n">z1</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> :param z0: Projection module embedding :math:`(b \\times N \\times d)`</span>
<span class="sd"> :type z0: torch.Tensor</span>
<span class="sd"> :param z1: Projection module embedding :math:`(b \\times M \\times d)`</span>
<span class="sd"> :type z1: torch.Tensor</span>
<span class="sd"> :return: Predicted broadcast tensor :math:`(b \\times N \\times M \\times h)`</span>
<span class="sd"> :rtype: torch.Tensor</span>
<span class="sd"> """</span>
<span class="c1"># z0 is (b,N,d), z1 is (b,M,d)</span>
<span class="n">z0</span> <span class="o">=</span> <span class="n">z0</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">z1</span> <span class="o">=</span> <span class="n">z1</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="c1"># z0 is (b,d,N), z1 is (b,d,M)</span>
<span class="n">z_dif</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">z0</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="o">-</span> <span class="n">z1</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
<span class="n">z_mul</span> <span class="o">=</span> <span class="n">z0</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="o">*</span> <span class="n">z1</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="n">z_cat</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">z_dif</span><span class="p">,</span> <span class="n">z_mul</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">z_cat</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">batchnorm</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
<span class="k">return</span> <span class="n">b</span></div></div>
<div class="viewcode-block" id="ContactCNN"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.contact.ContactCNN">[docs]</a><span class="k">class</span> <span class="nc">ContactCNN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Residue Contact Prediction Module. Takes embeddings from Projection module and produces contact map, output of Contact module.</span>
<span class="sd"> :param embed_dim: Output dimension of `dscript.models.embedding <#module-dscript.models.embedding>`_ model :math:`d` [default: 100]</span>
<span class="sd"> :type embed_dim: int</span>
<span class="sd"> :param hidden_dim: Hidden dimension :math:`h` [default: 50]</span>
<span class="sd"> :type hidden_dim: int</span>
<span class="sd"> :param width: Width of convolutional filter :math:`2w+1` [default: 7]</span>
<span class="sd"> :type width: int</span>
<span class="sd"> :param activation: Activation function for final contact map [default: torch.nn.Sigmoid()]</span>
<span class="sd"> :type activation: torch.nn.Module</span>
<span class="sd"> """</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">embed_dim</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">hidden_dim</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">Sigmoid</span><span class="p">()):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">ContactCNN</span><span class="p">,</span> <span class="bp">self</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">hidden</span> <span class="o">=</span> <span class="n">FullyConnected</span><span class="p">(</span><span class="n">embed_dim</span><span class="p">,</span> <span class="n">hidden_dim</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">hidden_dim</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="n">width</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">batchnorm</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</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">activation</span> <span class="o">=</span> <span class="n">activation</span>
<span class="bp">self</span><span class="o">.</span><span class="n">clip</span><span class="p">()</span>
<div class="viewcode-block" id="ContactCNN.clip"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.contact.ContactCNN.clip">[docs]</a> <span class="k">def</span> <span class="nf">clip</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Force the convolutional layer to be transpose invariant.</span>
<span class="sd"> :meta private:</span>
<span class="sd"> """</span>
<span class="n">w</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="o">.</span><span class="n">weight</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">[:]</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span><span class="n">w</span> <span class="o">+</span> <span class="n">w</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span></div>
<div class="viewcode-block" id="ContactCNN.forward"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.contact.ContactCNN.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">z0</span><span class="p">,</span> <span class="n">z1</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> :param z0: Projection module embedding :math:`(b \\times N \\times d)`</span>
<span class="sd"> :type z0: torch.Tensor</span>
<span class="sd"> :param z1: Projection module embedding :math:`(b \\times M \\times d)`</span>
<span class="sd"> :type z1: torch.Tensor</span>
<span class="sd"> :return: Predicted contact map :math:`(b \\times N \\times M)`</span>
<span class="sd"> :rtype: torch.Tensor</span>
<span class="sd"> """</span>
<span class="n">B</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">z0</span><span class="p">,</span> <span class="n">z1</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">C</span><span class="p">)</span></div>
<div class="viewcode-block" id="ContactCNN.broadcast"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.contact.ContactCNN.broadcast">[docs]</a> <span class="k">def</span> <span class="nf">broadcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">z0</span><span class="p">,</span> <span class="n">z1</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Calls `dscript.models.contact.FullyConnected <#module-dscript.models.contact.FullyConnected>`_.</span>
<span class="sd"> :param z0: Projection module embedding :math:`(b \\times N \\times d)`</span>
<span class="sd"> :type z0: torch.Tensor</span>
<span class="sd"> :param z1: Projection module embedding :math:`(b \\times M \\times d)`</span>
<span class="sd"> :type z1: torch.Tensor</span>
<span class="sd"> :return: Predicted contact broadcast tensor :math:`(b \\times N \\times M \\times h)`</span>
<span class="sd"> :rtype: torch.Tensor</span>
<span class="sd"> """</span>
<span class="n">B</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden</span><span class="p">(</span><span class="n">z0</span><span class="p">,</span> <span class="n">z1</span><span class="p">)</span>
<span class="k">return</span> <span class="n">B</span></div>
<div class="viewcode-block" id="ContactCNN.predict"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.contact.ContactCNN.predict">[docs]</a> <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">B</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Predict contact map from broadcast tensor.</span>
<span class="sd"> :param B: Predicted contact broadcast :math:`(b \\times N \\times M \\times h)`</span>
<span class="sd"> :type B: torch.Tensor</span>
<span class="sd"> :return: Predicted contact map :math:`(b \\times N \\times M)`</span>
<span class="sd"> :rtype: torch.Tensor</span>
<span class="sd"> """</span>
<span class="n">C</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">B</span><span class="p">)</span>
<span class="n">C</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">batchnorm</span><span class="p">(</span><span class="n">C</span><span class="p">)</span>
<span class="n">C</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation</span><span class="p">(</span><span class="n">C</span><span class="p">)</span>
<span class="k">return</span> <span class="n">C</span></div></div>
</pre></div>
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