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<h1>Source code for easygraph.model.hypergraphs.hwnn</h1><div class="highlight"><pre>
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<span></span><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">easygraph.nn</span> <span class="kn">import</span> <span class="n">HWNNConv</span>
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<div class="viewcode-block" id="HWNN"><a class="viewcode-back" href="../../../../reference/easygraph.model.hypergraphs.hwnn.html#easygraph.model.hypergraphs.hwnn.HWNN">[docs]</a><span class="k">class</span> <span class="nc">HWNN</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="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;The HGNN model proposed in `Hypergraph Neural Networks &lt;https://arxiv.org/pdf/1809.09401&gt;`_ paper (AAAI 2019).</span>
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<span class="sd"> Parameters:</span>
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<span class="sd"> ``in_channels`` (``int``): :math:`C_{in}` is the number of input channels.</span>
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<span class="sd"> ``hid_channels`` (``int``): :math:`C_{hid}` is the number of hidden channels.</span>
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<span class="sd"> ``num_classes`` (``int``): The Number of class of the classification task.</span>
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<span class="sd"> ``ncount`` (``int``): The Number of node in the hypergraph.</span>
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<span class="sd"> ``hyper_snapshot_num`` (``int``): The Number of snapshots splited from hypergraph.</span>
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<span class="sd"> ``drop_rate`` (``float``, optional): Dropout ratio. Defaults to 0.5.</span>
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<span class="sd"> &quot;&quot;&quot;</span>
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<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
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<span class="bp">self</span><span class="p">,</span>
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<span class="n">in_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
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<span class="n">num_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
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<span class="n">ncount</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
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<span class="n">hyper_snapshot_num</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
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<span class="n">hid_channels</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span>
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<span class="n">drop_rate</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span>
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<span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
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<span class="nb">super</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">drop_rate</span> <span class="o">=</span> <span class="n">drop_rate</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">convolution_1</span> <span class="o">=</span> <span class="n">HWNNConv</span><span class="p">(</span>
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<span class="n">in_channels</span><span class="p">,</span> <span class="n">hid_channels</span><span class="p">,</span> <span class="n">ncount</span><span class="p">,</span> <span class="n">K1</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">K2</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">approx</span><span class="o">=</span><span class="kc">True</span>
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<span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">convolution_2</span> <span class="o">=</span> <span class="n">HWNNConv</span><span class="p">(</span>
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<span class="n">hid_channels</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">,</span> <span class="n">ncount</span><span class="p">,</span> <span class="n">K1</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">K2</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">approx</span><span class="o">=</span><span class="kc">True</span>
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<span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">par</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">hyper_snapshot_num</span><span class="p">))</span>
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<span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">uniform_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">par</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">0.99</span><span class="p">)</span>
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<div class="viewcode-block" id="HWNN.forward"><a class="viewcode-back" href="../../../../reference/easygraph.model.hypergraphs.hwnn.html#easygraph.model.hypergraphs.hwnn.HWNN.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> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">hgs</span><span class="p">:</span> <span class="nb">list</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
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<span class="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;The forward function.</span>
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<span class="sd"> Parameters:</span>
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<span class="sd"> ``X`` (``torch.Tensor``): Input vertex feature matrix. Size :math:`(N, C_{in})`.</span>
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<span class="sd"> ``hg`` (``eg.Hypergraph``): The hypergraph structure that contains :math:`N` vertices.</span>
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<span class="sd"> ``hgs`` (``list`` of ``Hypergraph``): A list of hypergraph structures whcih stands for snapshots.</span>
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<span class="sd"> &quot;&quot;&quot;</span>
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<span class="n">channel</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="n">hyper_snapshot_num</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">hgs</span><span class="p">)</span>
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<span class="k">for</span> <span class="n">snap_index</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">hyper_snapshot_num</span><span class="p">):</span>
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<span class="n">hg</span> <span class="o">=</span> <span class="n">hgs</span><span class="p">[</span><span class="n">snap_index</span><span class="p">]</span>
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<span class="n">Y</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">convolution_1</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">hg</span><span class="p">))</span>
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<span class="n">Y</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_rate</span><span class="p">)</span>
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<span class="n">Y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convolution_2</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">hg</span><span class="p">)</span>
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<span class="n">Y</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">log_softmax</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<span class="n">channel</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span>
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<span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">channel</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
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<span class="k">for</span> <span class="n">ind</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">hyper_snapshot_num</span><span class="p">):</span>
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<span class="n">X</span> <span class="o">=</span> <span class="n">X</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">par</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span> <span class="o">*</span> <span class="n">channel</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span>
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<span class="k">return</span> <span class="n">X</span></div></div>
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