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<h1>Source code for dscript.models.embedding</h1><div class="highlight"><pre> |
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<span></span><span class="sd">"""</span> |
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<span class="sd">Embedding model classes.</span> |
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<span class="sd">"""</span> |
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<span class="kn">import</span> <span class="nn">torch</span> |
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<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span> |
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<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span> |
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<span class="kn">from</span> <span class="nn">torch.nn.utils.rnn</span> <span class="kn">import</span> <span class="n">PackedSequence</span> |
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<div class="viewcode-block" id="IdentityEmbed"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.embedding.IdentityEmbed">[docs]</a><span class="k">class</span> <span class="nc">IdentityEmbed</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span> |
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<span class="sd">"""</span> |
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<span class="sd"> Does not reduce the dimension of the language model embeddings, just passes them through to the contact model.</span> |
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<span class="sd"> """</span> |
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<div class="viewcode-block" id="IdentityEmbed.forward"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.embedding.IdentityEmbed.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">x</span><span class="p">):</span> |
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<span class="sd">"""</span> |
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<span class="sd"> :param x: Input language model embedding :math:`(b \\times N \\times d_0)`</span> |
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<span class="sd"> :type x: torch.Tensor</span> |
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<span class="sd"> :return: Same embedding</span> |
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<span class="sd"> :rtype: torch.Tensor</span> |
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<span class="sd"> """</span> |
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<span class="k">return</span> <span class="n">x</span></div></div> |
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<div class="viewcode-block" id="FullyConnectedEmbed"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.embedding.FullyConnectedEmbed">[docs]</a><span class="k">class</span> <span class="nc">FullyConnectedEmbed</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span> |
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<span class="sd">"""</span> |
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<span class="sd"> Protein Projection Module. Takes embedding from language model and outputs low-dimensional interaction aware projection.</span> |
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<span class="sd"> :param nin: Size of language model output</span> |
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<span class="sd"> :type nin: int</span> |
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<span class="sd"> :param nout: Dimension of projection</span> |
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<span class="sd"> :type nout: int</span> |
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<span class="sd"> :param dropout: Proportion of weights to drop out [default: 0.5]</span> |
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<span class="sd"> :type dropout: float</span> |
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<span class="sd"> :param activation: Activation for linear projection model</span> |
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<span class="sd"> :type activation: torch.nn.Module</span> |
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<span class="sd"> """</span> |
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<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">nin</span><span class="p">,</span> <span class="n">nout</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.5</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> |
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<span class="nb">super</span><span class="p">(</span><span class="n">FullyConnectedEmbed</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> |
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<span class="bp">self</span><span class="o">.</span><span class="n">nin</span> <span class="o">=</span> <span class="n">nin</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">nout</span> <span class="o">=</span> <span class="n">nout</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">dropout_p</span> <span class="o">=</span> <span class="n">dropout</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">nin</span><span class="p">,</span> <span class="n">nout</span><span class="p">)</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">drop</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dropout_p</span><span class="p">)</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">activation</span> <span class="o">=</span> <span class="n">activation</span> |
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<div class="viewcode-block" id="FullyConnectedEmbed.forward"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.embedding.FullyConnectedEmbed.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">x</span><span class="p">):</span> |
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<span class="sd">"""</span> |
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<span class="sd"> :param x: Input language model embedding :math:`(b \\times N \\times d_0)`</span> |
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<span class="sd"> :type x: torch.Tensor</span> |
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<span class="sd"> :return: Low dimensional projection of embedding</span> |
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<span class="sd"> :rtype: torch.Tensor</span> |
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<span class="sd"> """</span> |
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<span class="n">t</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
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<span class="n">t</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">t</span><span class="p">)</span> |
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<span class="n">t</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> |
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<span class="k">return</span> <span class="n">t</span></div></div> |
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<div class="viewcode-block" id="SkipLSTM"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.embedding.SkipLSTM">[docs]</a><span class="k">class</span> <span class="nc">SkipLSTM</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span> |
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<span class="sd">"""</span> |
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<span class="sd"> Language model from `Bepler & Berger <https://github.com/tbepler/protein-sequence-embedding-iclr2019>`_.</span> |
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<span class="sd"> Loaded with pre-trained weights in embedding function.</span> |
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<span class="sd"> :param nin: Input dimension of amino acid one-hot [default: 21]</span> |
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<span class="sd"> :type nin: int</span> |
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<span class="sd"> :param nout: Output dimension of final layer [default: 100]</span> |
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<span class="sd"> :type nout: int</span> |
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<span class="sd"> :param hidden_dim: Size of hidden dimension [default: 1024]</span> |
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<span class="sd"> :type hidden_dim: int</span> |
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<span class="sd"> :param num_layers: Number of stacked LSTM models [default: 3]</span> |
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<span class="sd"> :type num_layers: int</span> |
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<span class="sd"> :param dropout: Proportion of weights to drop out [default: 0]</span> |
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<span class="sd"> :type dropout: float</span> |
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<span class="sd"> :param bidirectional: Whether to use biLSTM vs. LSTM</span> |
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<span class="sd"> :type bidirectional: bool</span> |
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<span class="sd"> """</span> |
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<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">nin</span><span class="o">=</span><span class="mi">21</span><span class="p">,</span> <span class="n">nout</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">1024</span><span class="p">,</span> <span class="n">num_layers</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">bidirectional</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
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<span class="nb">super</span><span class="p">(</span><span class="n">SkipLSTM</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> |
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<span class="bp">self</span><span class="o">.</span><span class="n">nin</span> <span class="o">=</span> <span class="n">nin</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">nout</span> <span class="o">=</span> <span class="n">nout</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="n">dropout</span><span class="p">)</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">layers</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">()</span> |
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<span class="n">dim</span> <span class="o">=</span> <span class="n">nin</span> |
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<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_layers</span><span class="p">):</span> |
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<span class="n">f</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LSTM</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="mi">1</span><span class="p">,</span> <span class="n">batch_first</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">bidirectional</span><span class="o">=</span><span class="n">bidirectional</span><span class="p">)</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> |
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<span class="k">if</span> <span class="n">bidirectional</span><span class="p">:</span> |
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<span class="n">dim</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">hidden_dim</span> |
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<span class="k">else</span><span class="p">:</span> |
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<span class="n">dim</span> <span class="o">=</span> <span class="n">hidden_dim</span> |
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<span class="n">n</span> <span class="o">=</span> <span class="n">hidden_dim</span> <span class="o">*</span> <span class="n">num_layers</span> <span class="o">+</span> <span class="n">nin</span> |
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<span class="k">if</span> <span class="n">bidirectional</span><span class="p">:</span> |
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<span class="n">n</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">hidden_dim</span> <span class="o">*</span> <span class="n">num_layers</span> <span class="o">+</span> <span class="n">nin</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">proj</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">nout</span><span class="p">)</span> |
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<div class="viewcode-block" id="SkipLSTM.to_one_hot"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.embedding.SkipLSTM.to_one_hot">[docs]</a> <span class="k">def</span> <span class="nf">to_one_hot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
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<span class="sd">"""</span> |
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<span class="sd"> Transform numeric encoded amino acid vector to one-hot encoded vector</span> |
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<span class="sd"> :param x: Input numeric amino acid encoding :math:`(N)`</span> |
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<span class="sd"> :type x: torch.Tensor</span> |
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<span class="sd"> :return: One-hot encoding vector :math:`(N \\times n_{in})`</span> |
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<span class="sd"> :rtype: torch.Tensor</span> |
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<span class="sd"> """</span> |
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<span class="n">packed</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="ow">is</span> <span class="n">PackedSequence</span> |
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<span class="k">if</span> <span class="n">packed</span><span class="p">:</span> |
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<span class="n">one_hot</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">new</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">nin</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span> |
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<span class="n">one_hot</span><span class="o">.</span><span class="n">scatter_</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span> |
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<span class="n">one_hot</span> <span class="o">=</span> <span class="n">PackedSequence</span><span class="p">(</span><span class="n">one_hot</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">batch_sizes</span><span class="p">)</span> |
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<span class="k">else</span><span class="p">:</span> |
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<span class="n">one_hot</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">new</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">x</span><span class="o">.</span><span class="n">size</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">nin</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span> |
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<span class="n">one_hot</span><span class="o">.</span><span class="n">scatter_</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">x</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="mi">1</span><span class="p">)</span> |
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<span class="k">return</span> <span class="n">one_hot</span></div> |
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<div class="viewcode-block" id="SkipLSTM.transform"><a class="viewcode-back" href="../../../api/dscript.models.html#dscript.models.embedding.SkipLSTM.transform">[docs]</a> <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
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<span class="sd">"""</span> |
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<span class="sd"> :param x: Input numeric amino acid encoding :math:`(N)`</span> |
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<span class="sd"> :type x: torch.Tensor</span> |
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<span class="sd"> :return: Concatenation of all hidden layers :math:`(N \\times (n_{in} + 2 \\times \\text{num_layers} \\times \\text{hidden_dim}))`</span> |
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<span class="sd"> :rtype: torch.Tensor</span> |
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<span class="sd"> """</span> |
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<span class="n">one_hot</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">to_one_hot</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
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<span class="n">hs</span> <span class="o">=</span> <span class="p">[</span><span class="n">one_hot</span><span class="p">]</span> <span class="c1"># []</span> |
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<span class="n">h_</span> <span class="o">=</span> <span class="n">one_hot</span> |
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<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">:</span> |
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<span class="n">h</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">h_</span><span class="p">)</span> |
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<span class="c1"># h = self.dropout(h)</span> |
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<span class="n">hs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">h</span><span class="p">)</span> |
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<span class="n">h_</span> <span class="o">=</span> <span class="n">h</span> |
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<span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="ow">is</span> <span class="n">PackedSequence</span><span class="p">:</span> |
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<span class="n">h</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</span><span class="o">.</span><span class="n">data</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">hs</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span> |
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<span class="n">h</span> <span class="o">=</span> <span class="n">PackedSequence</span><span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">batch_sizes</span><span class="p">)</span> |
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<span class="k">else</span><span class="p">:</span> |
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<span class="n">h</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</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">hs</span><span class="p">],</span> <span class="mi">2</span><span class="p">)</span> |
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<span class="k">return</span> <span class="n">h</span></div> |
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<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">x</span><span class="p">):</span> |
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<span class="sd">"""</span> |
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<span class="sd"> :meta private:</span> |
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<span class="sd"> """</span> |
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<span class="n">one_hot</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">to_one_hot</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
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<span class="n">hs</span> <span class="o">=</span> <span class="p">[</span><span class="n">one_hot</span><span class="p">]</span> |
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<span class="n">h_</span> <span class="o">=</span> <span class="n">one_hot</span> |
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<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">:</span> |
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<span class="n">h</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">h_</span><span class="p">)</span> |
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<span class="c1"># h = self.dropout(h)</span> |
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<span class="n">hs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">h</span><span class="p">)</span> |
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<span class="n">h_</span> <span class="o">=</span> <span class="n">h</span> |
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<span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="ow">is</span> <span class="n">PackedSequence</span><span class="p">:</span> |
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<span class="n">h</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</span><span class="o">.</span><span class="n">data</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">hs</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span> |
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<span class="n">z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">proj</span><span class="p">(</span><span class="n">h</span><span class="p">)</span> |
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<span class="n">z</span> <span class="o">=</span> <span class="n">PackedSequence</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">batch_sizes</span><span class="p">)</span> |
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<span class="k">else</span><span class="p">:</span> |
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<span class="n">h</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</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="n">hs</span><span class="p">],</span> <span class="mi">2</span><span class="p">)</span> |
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<span class="n">z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">proj</span><span class="p">(</span><span class="n">h</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">h</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">2</span><span class="p">)))</span> |
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<span class="n">z</span> <span class="o">=</span> <span class="n">z</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> |
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<span class="k">return</span> <span class="n">z</span></div> |
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</pre></div> |
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