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<h1>Source code for torchreid.models.nasnet</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">absolute_import</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">division</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;nasnetamobile&#39;</span><span class="p">]</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.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="kn">import</span> <span class="nn">torch.utils.model_zoo</span> <span class="k">as</span> <span class="nn">model_zoo</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">NASNet Mobile</span>
<span class="sd">Thanks to Anastasiia (https://github.com/DagnyT) for the great help, support and motivation!</span>
<span class="sd">------------------------------------------------------------------------------------</span>
<span class="sd"> Architecture | Top-1 Acc | Top-5 Acc | Multiply-Adds | Params (M)</span>
<span class="sd">------------------------------------------------------------------------------------</span>
<span class="sd">| NASNet-A (4 @ 1056) | 74.08% | 91.74% | 564 M | 5.3 |</span>
<span class="sd">------------------------------------------------------------------------------------</span>
<span class="sd"># References:</span>
<span class="sd"> - [Learning Transferable Architectures for Scalable Image Recognition]</span>
<span class="sd"> (https://arxiv.org/abs/1707.07012)</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Code imported from https://github.com/Cadene/pretrained-models.pytorch</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="n">pretrained_settings</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">&#39;nasnetamobile&#39;</span><span class="p">:</span> <span class="p">{</span>
<span class="s1">&#39;imagenet&#39;</span><span class="p">:</span> <span class="p">{</span>
<span class="c1">#&#39;url&#39;: &#39;https://github.com/veronikayurchuk/pretrained-models.pytorch/releases/download/v1.0/nasnetmobile-7e03cead.pth.tar&#39;,</span>
<span class="s1">&#39;url&#39;</span><span class="p">:</span> <span class="s1">&#39;http://data.lip6.fr/cadene/pretrainedmodels/nasnetamobile-7e03cead.pth&#39;</span><span class="p">,</span>
<span class="s1">&#39;input_space&#39;</span><span class="p">:</span> <span class="s1">&#39;RGB&#39;</span><span class="p">,</span>
<span class="s1">&#39;input_size&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">],</span> <span class="c1"># resize 256</span>
<span class="s1">&#39;input_range&#39;</span><span class="p">:</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="s1">&#39;mean&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">],</span>
<span class="s1">&#39;std&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">],</span>
<span class="s1">&#39;num_classes&#39;</span><span class="p">:</span> <span class="mi">1000</span>
<span class="p">},</span>
<span class="c1"># &#39;imagenet+background&#39;: {</span>
<span class="c1"># # &#39;url&#39;: &#39;http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth&#39;,</span>
<span class="c1"># &#39;input_space&#39;: &#39;RGB&#39;,</span>
<span class="c1"># &#39;input_size&#39;: [3, 224, 224], # resize 256</span>
<span class="c1"># &#39;input_range&#39;: [0, 1],</span>
<span class="c1"># &#39;mean&#39;: [0.5, 0.5, 0.5],</span>
<span class="c1"># &#39;std&#39;: [0.5, 0.5, 0.5],</span>
<span class="c1"># &#39;num_classes&#39;: 1001</span>
<span class="c1"># }</span>
<span class="p">}</span>
<span class="p">}</span>
<span class="k">class</span> <span class="nc">MaxPoolPad</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="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MaxPoolPad</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">pad</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ZeroPad2d</span><span class="p">((</span><span class="mi">1</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="mi">0</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<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">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pad</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">pool</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="n">x</span><span class="p">[:,</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="o">.</span><span class="n">contiguous</span><span class="p">()</span>
<span class="k">return</span> <span class="n">x</span>
<span class="k">class</span> <span class="nc">AvgPoolPad</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="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">AvgPoolPad</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">pad</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ZeroPad2d</span><span class="p">((</span><span class="mi">1</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="mi">0</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="n">padding</span><span class="p">,</span> <span class="n">count_include_pad</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<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">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pad</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">pool</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="n">x</span><span class="p">[:,</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="o">.</span><span class="n">contiguous</span><span class="p">()</span>
<span class="k">return</span> <span class="n">x</span>
<span class="k">class</span> <span class="nc">SeparableConv2d</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="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">dw_kernel</span><span class="p">,</span> <span class="n">dw_stride</span><span class="p">,</span> <span class="n">dw_padding</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">SeparableConv2d</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">depthwise_conv2d</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">in_channels</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">dw_kernel</span><span class="p">,</span>
<span class="n">stride</span><span class="o">=</span><span class="n">dw_stride</span><span class="p">,</span>
<span class="n">padding</span><span class="o">=</span><span class="n">dw_padding</span><span class="p">,</span>
<span class="n">bias</span><span class="o">=</span><span class="n">bias</span><span class="p">,</span>
<span class="n">groups</span><span class="o">=</span><span class="n">in_channels</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">pointwise_conv2d</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">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="n">bias</span><span class="p">)</span>
<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">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">depthwise_conv2d</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">pointwise_conv2d</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="k">class</span> <span class="nc">BranchSeparables</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="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">BranchSeparables</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">relu</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="bp">self</span><span class="o">.</span><span class="n">separable_1</span> <span class="o">=</span> <span class="n">SeparableConv2d</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="n">bias</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bn_sep_1</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="n">in_channels</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">relu1</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="bp">self</span><span class="o">.</span><span class="n">separable_2</span> <span class="o">=</span> <span class="n">SeparableConv2d</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="n">bias</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bn_sep_2</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="n">out_channels</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
<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">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">==</span> <span class="s1">&#39;specific&#39;</span><span class="p">:</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ZeroPad2d</span><span class="p">((</span><span class="mi">1</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="mi">0</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">separable_1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">==</span> <span class="s1">&#39;specific&#39;</span><span class="p">:</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="p">[:,</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="o">.</span><span class="n">contiguous</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">bn_sep_1</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">relu1</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">separable_2</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">bn_sep_2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="k">class</span> <span class="nc">BranchSeparablesStem</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="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">BranchSeparablesStem</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">relu</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="bp">self</span><span class="o">.</span><span class="n">separable_1</span> <span class="o">=</span> <span class="n">SeparableConv2d</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="n">bias</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bn_sep_1</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="n">out_channels</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">relu1</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="bp">self</span><span class="o">.</span><span class="n">separable_2</span> <span class="o">=</span> <span class="n">SeparableConv2d</span><span class="p">(</span><span class="n">out_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="n">bias</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bn_sep_2</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="n">out_channels</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<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">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</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">separable_1</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">bn_sep_1</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">relu1</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">separable_2</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">bn_sep_2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="k">class</span> <span class="nc">BranchSeparablesReduction</span><span class="p">(</span><span class="n">BranchSeparables</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">z_padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="n">BranchSeparables</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">bias</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">padding</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ZeroPad2d</span><span class="p">((</span><span class="n">z_padding</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">z_padding</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
<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">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</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">padding</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">separable_1</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="n">x</span><span class="p">[:,</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="o">.</span><span class="n">contiguous</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">bn_sep_1</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">relu1</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">separable_2</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">bn_sep_2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="k">class</span> <span class="nc">CellStem0</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="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">stem_filters</span><span class="p">,</span> <span class="n">num_filters</span><span class="o">=</span><span class="mi">42</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">CellStem0</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">num_filters</span> <span class="o">=</span> <span class="n">num_filters</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stem_filters</span> <span class="o">=</span> <span class="n">stem_filters</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv_1x1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">nn</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">conv_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stem_filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">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">conv_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;bn&#39;</span><span class="p">,</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">num_filters</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_0_left</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_0_right</span> <span class="o">=</span> <span class="n">BranchSeparablesStem</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stem_filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">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">comb_iter_1_left</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_1_right</span> <span class="o">=</span> <span class="n">BranchSeparablesStem</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stem_filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">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">comb_iter_2_left</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">count_include_pad</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">comb_iter_2_right</span> <span class="o">=</span> <span class="n">BranchSeparablesStem</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stem_filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">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">comb_iter_3_right</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">count_include_pad</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">comb_iter_4_left</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="p">,</span> <span class="mi">3</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">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">comb_iter_4_right</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<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">x1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv_1x1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x_comb_iter_0_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_0_left</span><span class="p">(</span><span class="n">x1</span><span class="p">)</span>
<span class="n">x_comb_iter_0_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_0_right</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x_comb_iter_0</span> <span class="o">=</span> <span class="n">x_comb_iter_0_left</span> <span class="o">+</span> <span class="n">x_comb_iter_0_right</span>
<span class="n">x_comb_iter_1_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_1_left</span><span class="p">(</span><span class="n">x1</span><span class="p">)</span>
<span class="n">x_comb_iter_1_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_1_right</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x_comb_iter_1</span> <span class="o">=</span> <span class="n">x_comb_iter_1_left</span> <span class="o">+</span> <span class="n">x_comb_iter_1_right</span>
<span class="n">x_comb_iter_2_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_2_left</span><span class="p">(</span><span class="n">x1</span><span class="p">)</span>
<span class="n">x_comb_iter_2_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_2_right</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x_comb_iter_2</span> <span class="o">=</span> <span class="n">x_comb_iter_2_left</span> <span class="o">+</span> <span class="n">x_comb_iter_2_right</span>
<span class="n">x_comb_iter_3_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_3_right</span><span class="p">(</span><span class="n">x_comb_iter_0</span><span class="p">)</span>
<span class="n">x_comb_iter_3</span> <span class="o">=</span> <span class="n">x_comb_iter_3_right</span> <span class="o">+</span> <span class="n">x_comb_iter_1</span>
<span class="n">x_comb_iter_4_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_4_left</span><span class="p">(</span><span class="n">x_comb_iter_0</span><span class="p">)</span>
<span class="n">x_comb_iter_4_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_4_right</span><span class="p">(</span><span class="n">x1</span><span class="p">)</span>
<span class="n">x_comb_iter_4</span> <span class="o">=</span> <span class="n">x_comb_iter_4_left</span> <span class="o">+</span> <span class="n">x_comb_iter_4_right</span>
<span class="n">x_out</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">x_comb_iter_1</span><span class="p">,</span> <span class="n">x_comb_iter_2</span><span class="p">,</span> <span class="n">x_comb_iter_3</span><span class="p">,</span> <span class="n">x_comb_iter_4</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x_out</span>
<span class="k">class</span> <span class="nc">CellStem1</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="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">stem_filters</span><span class="p">,</span> <span class="n">num_filters</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">CellStem1</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">num_filters</span> <span class="o">=</span> <span class="n">num_filters</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stem_filters</span> <span class="o">=</span> <span class="n">stem_filters</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv_1x1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">nn</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">conv_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv&#39;</span><span class="p">,</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">num_filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">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">conv_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;bn&#39;</span><span class="p">,</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">num_filters</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">relu</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="bp">self</span><span class="o">.</span><span class="n">path_1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">path_1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;avgpool&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">count_include_pad</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">path_1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stem_filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="o">//</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">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">path_2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">path_2</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;pad&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">ZeroPad2d</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="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">path_2</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;avgpool&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">count_include_pad</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">path_2</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stem_filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="o">//</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">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">final_path_bn</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">num_filters</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_0_left</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;specific&#39;</span><span class="p">,</span> <span class="n">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">comb_iter_0_right</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;specific&#39;</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="c1"># self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_1_left</span> <span class="o">=</span> <span class="n">MaxPoolPad</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_1_right</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;specific&#39;</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="c1"># self.comb_iter_2_left = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_2_left</span> <span class="o">=</span> <span class="n">AvgPoolPad</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_2_right</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;specific&#39;</span><span class="p">,</span> <span class="n">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">comb_iter_3_right</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">count_include_pad</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">comb_iter_4_left</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_filters</span><span class="p">,</span> <span class="mi">3</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">name</span><span class="o">=</span><span class="s1">&#39;specific&#39;</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="c1"># self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_4_right</span> <span class="o">=</span> <span class="n">MaxPoolPad</span><span class="p">()</span>
<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_conv0</span><span class="p">,</span> <span class="n">x_stem_0</span><span class="p">):</span>
<span class="n">x_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv_1x1</span><span class="p">(</span><span class="n">x_stem_0</span><span class="p">)</span>
<span class="n">x_relu</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">x_conv0</span><span class="p">)</span>
<span class="c1"># path 1</span>
<span class="n">x_path1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">path_1</span><span class="p">(</span><span class="n">x_relu</span><span class="p">)</span>
<span class="c1"># path 2</span>
<span class="n">x_path2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">path_2</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">x_relu</span><span class="p">)</span>
<span class="n">x_path2</span> <span class="o">=</span> <span class="n">x_path2</span><span class="p">[:,</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">x_path2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">path_2</span><span class="o">.</span><span class="n">avgpool</span><span class="p">(</span><span class="n">x_path2</span><span class="p">)</span>
<span class="n">x_path2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">path_2</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x_path2</span><span class="p">)</span>
<span class="c1"># final path</span>
<span class="n">x_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">final_path_bn</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">x_path1</span><span class="p">,</span> <span class="n">x_path2</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span>
<span class="n">x_comb_iter_0_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_0_left</span><span class="p">(</span><span class="n">x_left</span><span class="p">)</span>
<span class="n">x_comb_iter_0_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_0_right</span><span class="p">(</span><span class="n">x_right</span><span class="p">)</span>
<span class="n">x_comb_iter_0</span> <span class="o">=</span> <span class="n">x_comb_iter_0_left</span> <span class="o">+</span> <span class="n">x_comb_iter_0_right</span>
<span class="n">x_comb_iter_1_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_1_left</span><span class="p">(</span><span class="n">x_left</span><span class="p">)</span>
<span class="n">x_comb_iter_1_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_1_right</span><span class="p">(</span><span class="n">x_right</span><span class="p">)</span>
<span class="n">x_comb_iter_1</span> <span class="o">=</span> <span class="n">x_comb_iter_1_left</span> <span class="o">+</span> <span class="n">x_comb_iter_1_right</span>
<span class="n">x_comb_iter_2_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_2_left</span><span class="p">(</span><span class="n">x_left</span><span class="p">)</span>
<span class="n">x_comb_iter_2_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_2_right</span><span class="p">(</span><span class="n">x_right</span><span class="p">)</span>
<span class="n">x_comb_iter_2</span> <span class="o">=</span> <span class="n">x_comb_iter_2_left</span> <span class="o">+</span> <span class="n">x_comb_iter_2_right</span>
<span class="n">x_comb_iter_3_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_3_right</span><span class="p">(</span><span class="n">x_comb_iter_0</span><span class="p">)</span>
<span class="n">x_comb_iter_3</span> <span class="o">=</span> <span class="n">x_comb_iter_3_right</span> <span class="o">+</span> <span class="n">x_comb_iter_1</span>
<span class="n">x_comb_iter_4_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_4_left</span><span class="p">(</span><span class="n">x_comb_iter_0</span><span class="p">)</span>
<span class="n">x_comb_iter_4_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_4_right</span><span class="p">(</span><span class="n">x_left</span><span class="p">)</span>
<span class="n">x_comb_iter_4</span> <span class="o">=</span> <span class="n">x_comb_iter_4_left</span> <span class="o">+</span> <span class="n">x_comb_iter_4_right</span>
<span class="n">x_out</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">x_comb_iter_1</span><span class="p">,</span> <span class="n">x_comb_iter_2</span><span class="p">,</span> <span class="n">x_comb_iter_3</span><span class="p">,</span> <span class="n">x_comb_iter_4</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x_out</span>
<span class="k">class</span> <span class="nc">FirstCell</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="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_channels_left</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="p">,</span> <span class="n">in_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">FirstCell</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">conv_1x1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">nn</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">conv_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">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">conv_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;bn&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">relu</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="bp">self</span><span class="o">.</span><span class="n">path_1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">path_1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;avgpool&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">count_include_pad</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">path_1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels_left</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">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">path_2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">path_2</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;pad&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">ZeroPad2d</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="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">path_2</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;avgpool&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">count_include_pad</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">path_2</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels_left</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">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">final_path_bn</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="n">out_channels_left</span> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_0_left</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">5</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">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">comb_iter_0_right</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">3</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">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">comb_iter_1_left</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">5</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">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">comb_iter_1_right</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">3</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">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">comb_iter_2_left</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">count_include_pad</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">comb_iter_3_left</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">count_include_pad</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">comb_iter_3_right</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">count_include_pad</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">comb_iter_4_left</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">3</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">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<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">x_prev</span><span class="p">):</span>
<span class="n">x_relu</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">x_prev</span><span class="p">)</span>
<span class="c1"># path 1</span>
<span class="n">x_path1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">path_1</span><span class="p">(</span><span class="n">x_relu</span><span class="p">)</span>
<span class="c1"># path 2</span>
<span class="n">x_path2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">path_2</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">x_relu</span><span class="p">)</span>
<span class="n">x_path2</span> <span class="o">=</span> <span class="n">x_path2</span><span class="p">[:,</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">x_path2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">path_2</span><span class="o">.</span><span class="n">avgpool</span><span class="p">(</span><span class="n">x_path2</span><span class="p">)</span>
<span class="n">x_path2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">path_2</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x_path2</span><span class="p">)</span>
<span class="c1"># final path</span>
<span class="n">x_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">final_path_bn</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">x_path1</span><span class="p">,</span> <span class="n">x_path2</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span>
<span class="n">x_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv_1x1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x_comb_iter_0_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_0_left</span><span class="p">(</span><span class="n">x_right</span><span class="p">)</span>
<span class="n">x_comb_iter_0_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_0_right</span><span class="p">(</span><span class="n">x_left</span><span class="p">)</span>
<span class="n">x_comb_iter_0</span> <span class="o">=</span> <span class="n">x_comb_iter_0_left</span> <span class="o">+</span> <span class="n">x_comb_iter_0_right</span>
<span class="n">x_comb_iter_1_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_1_left</span><span class="p">(</span><span class="n">x_left</span><span class="p">)</span>
<span class="n">x_comb_iter_1_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_1_right</span><span class="p">(</span><span class="n">x_left</span><span class="p">)</span>
<span class="n">x_comb_iter_1</span> <span class="o">=</span> <span class="n">x_comb_iter_1_left</span> <span class="o">+</span> <span class="n">x_comb_iter_1_right</span>
<span class="n">x_comb_iter_2_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_2_left</span><span class="p">(</span><span class="n">x_right</span><span class="p">)</span>
<span class="n">x_comb_iter_2</span> <span class="o">=</span> <span class="n">x_comb_iter_2_left</span> <span class="o">+</span> <span class="n">x_left</span>
<span class="n">x_comb_iter_3_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_3_left</span><span class="p">(</span><span class="n">x_left</span><span class="p">)</span>
<span class="n">x_comb_iter_3_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_3_right</span><span class="p">(</span><span class="n">x_left</span><span class="p">)</span>
<span class="n">x_comb_iter_3</span> <span class="o">=</span> <span class="n">x_comb_iter_3_left</span> <span class="o">+</span> <span class="n">x_comb_iter_3_right</span>
<span class="n">x_comb_iter_4_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_4_left</span><span class="p">(</span><span class="n">x_right</span><span class="p">)</span>
<span class="n">x_comb_iter_4</span> <span class="o">=</span> <span class="n">x_comb_iter_4_left</span> <span class="o">+</span> <span class="n">x_right</span>
<span class="n">x_out</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">x_left</span><span class="p">,</span> <span class="n">x_comb_iter_0</span><span class="p">,</span> <span class="n">x_comb_iter_1</span><span class="p">,</span> <span class="n">x_comb_iter_2</span><span class="p">,</span> <span class="n">x_comb_iter_3</span><span class="p">,</span> <span class="n">x_comb_iter_4</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x_out</span>
<span class="k">class</span> <span class="nc">NormalCell</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="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_channels_left</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="p">,</span> <span class="n">in_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">NormalCell</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">conv_prev_1x1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv_prev_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">nn</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">conv_prev_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels_left</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">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">conv_prev_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;bn&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">out_channels_left</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv_1x1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">nn</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">conv_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">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">conv_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;bn&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_0_left</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">5</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">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">comb_iter_0_right</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="n">out_channels_left</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="p">,</span> <span class="mi">3</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">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">comb_iter_1_left</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="n">out_channels_left</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="p">,</span> <span class="mi">5</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">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">comb_iter_1_right</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="n">out_channels_left</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="p">,</span> <span class="mi">3</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">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">comb_iter_2_left</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">count_include_pad</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">comb_iter_3_left</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">count_include_pad</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">comb_iter_3_right</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">count_include_pad</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">comb_iter_4_left</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">3</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">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<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">x_prev</span><span class="p">):</span>
<span class="n">x_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv_prev_1x1</span><span class="p">(</span><span class="n">x_prev</span><span class="p">)</span>
<span class="n">x_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv_1x1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x_comb_iter_0_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_0_left</span><span class="p">(</span><span class="n">x_right</span><span class="p">)</span>
<span class="n">x_comb_iter_0_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_0_right</span><span class="p">(</span><span class="n">x_left</span><span class="p">)</span>
<span class="n">x_comb_iter_0</span> <span class="o">=</span> <span class="n">x_comb_iter_0_left</span> <span class="o">+</span> <span class="n">x_comb_iter_0_right</span>
<span class="n">x_comb_iter_1_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_1_left</span><span class="p">(</span><span class="n">x_left</span><span class="p">)</span>
<span class="n">x_comb_iter_1_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_1_right</span><span class="p">(</span><span class="n">x_left</span><span class="p">)</span>
<span class="n">x_comb_iter_1</span> <span class="o">=</span> <span class="n">x_comb_iter_1_left</span> <span class="o">+</span> <span class="n">x_comb_iter_1_right</span>
<span class="n">x_comb_iter_2_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_2_left</span><span class="p">(</span><span class="n">x_right</span><span class="p">)</span>
<span class="n">x_comb_iter_2</span> <span class="o">=</span> <span class="n">x_comb_iter_2_left</span> <span class="o">+</span> <span class="n">x_left</span>
<span class="n">x_comb_iter_3_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_3_left</span><span class="p">(</span><span class="n">x_left</span><span class="p">)</span>
<span class="n">x_comb_iter_3_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_3_right</span><span class="p">(</span><span class="n">x_left</span><span class="p">)</span>
<span class="n">x_comb_iter_3</span> <span class="o">=</span> <span class="n">x_comb_iter_3_left</span> <span class="o">+</span> <span class="n">x_comb_iter_3_right</span>
<span class="n">x_comb_iter_4_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_4_left</span><span class="p">(</span><span class="n">x_right</span><span class="p">)</span>
<span class="n">x_comb_iter_4</span> <span class="o">=</span> <span class="n">x_comb_iter_4_left</span> <span class="o">+</span> <span class="n">x_right</span>
<span class="n">x_out</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">x_left</span><span class="p">,</span> <span class="n">x_comb_iter_0</span><span class="p">,</span> <span class="n">x_comb_iter_1</span><span class="p">,</span> <span class="n">x_comb_iter_2</span><span class="p">,</span> <span class="n">x_comb_iter_3</span><span class="p">,</span> <span class="n">x_comb_iter_4</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x_out</span>
<span class="k">class</span> <span class="nc">ReductionCell0</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="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_channels_left</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="p">,</span> <span class="n">in_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">ReductionCell0</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">conv_prev_1x1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv_prev_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">nn</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">conv_prev_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels_left</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">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">conv_prev_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;bn&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">out_channels_left</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv_1x1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">nn</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">conv_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">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">conv_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;bn&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_0_left</span> <span class="o">=</span> <span class="n">BranchSeparablesReduction</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">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">comb_iter_0_right</span> <span class="o">=</span> <span class="n">BranchSeparablesReduction</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">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">comb_iter_1_left</span> <span class="o">=</span> <span class="n">MaxPoolPad</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_1_right</span> <span class="o">=</span> <span class="n">BranchSeparablesReduction</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">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">comb_iter_2_left</span> <span class="o">=</span> <span class="n">AvgPoolPad</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_2_right</span> <span class="o">=</span> <span class="n">BranchSeparablesReduction</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">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">comb_iter_3_right</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">count_include_pad</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">comb_iter_4_left</span> <span class="o">=</span> <span class="n">BranchSeparablesReduction</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">3</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">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">comb_iter_4_right</span> <span class="o">=</span> <span class="n">MaxPoolPad</span><span class="p">()</span>
<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">x_prev</span><span class="p">):</span>
<span class="n">x_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv_prev_1x1</span><span class="p">(</span><span class="n">x_prev</span><span class="p">)</span>
<span class="n">x_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv_1x1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x_comb_iter_0_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_0_left</span><span class="p">(</span><span class="n">x_right</span><span class="p">)</span>
<span class="n">x_comb_iter_0_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_0_right</span><span class="p">(</span><span class="n">x_left</span><span class="p">)</span>
<span class="n">x_comb_iter_0</span> <span class="o">=</span> <span class="n">x_comb_iter_0_left</span> <span class="o">+</span> <span class="n">x_comb_iter_0_right</span>
<span class="n">x_comb_iter_1_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_1_left</span><span class="p">(</span><span class="n">x_right</span><span class="p">)</span>
<span class="n">x_comb_iter_1_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_1_right</span><span class="p">(</span><span class="n">x_left</span><span class="p">)</span>
<span class="n">x_comb_iter_1</span> <span class="o">=</span> <span class="n">x_comb_iter_1_left</span> <span class="o">+</span> <span class="n">x_comb_iter_1_right</span>
<span class="n">x_comb_iter_2_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_2_left</span><span class="p">(</span><span class="n">x_right</span><span class="p">)</span>
<span class="n">x_comb_iter_2_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_2_right</span><span class="p">(</span><span class="n">x_left</span><span class="p">)</span>
<span class="n">x_comb_iter_2</span> <span class="o">=</span> <span class="n">x_comb_iter_2_left</span> <span class="o">+</span> <span class="n">x_comb_iter_2_right</span>
<span class="n">x_comb_iter_3_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_3_right</span><span class="p">(</span><span class="n">x_comb_iter_0</span><span class="p">)</span>
<span class="n">x_comb_iter_3</span> <span class="o">=</span> <span class="n">x_comb_iter_3_right</span> <span class="o">+</span> <span class="n">x_comb_iter_1</span>
<span class="n">x_comb_iter_4_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_4_left</span><span class="p">(</span><span class="n">x_comb_iter_0</span><span class="p">)</span>
<span class="n">x_comb_iter_4_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_4_right</span><span class="p">(</span><span class="n">x_right</span><span class="p">)</span>
<span class="n">x_comb_iter_4</span> <span class="o">=</span> <span class="n">x_comb_iter_4_left</span> <span class="o">+</span> <span class="n">x_comb_iter_4_right</span>
<span class="n">x_out</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">x_comb_iter_1</span><span class="p">,</span> <span class="n">x_comb_iter_2</span><span class="p">,</span> <span class="n">x_comb_iter_3</span><span class="p">,</span> <span class="n">x_comb_iter_4</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x_out</span>
<span class="k">class</span> <span class="nc">ReductionCell1</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="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_channels_left</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="p">,</span> <span class="n">in_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">ReductionCell1</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">conv_prev_1x1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv_prev_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">nn</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">conv_prev_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels_left</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">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">conv_prev_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;bn&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">out_channels_left</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv_1x1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">nn</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">conv_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">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">conv_1x1</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;bn&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_0_left</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;specific&#39;</span><span class="p">,</span> <span class="n">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">comb_iter_0_right</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;specific&#39;</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="c1"># self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_1_left</span> <span class="o">=</span> <span class="n">MaxPoolPad</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_1_right</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;specific&#39;</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="c1"># self.comb_iter_2_left = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_2_left</span> <span class="o">=</span> <span class="n">AvgPoolPad</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_2_right</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;specific&#39;</span><span class="p">,</span> <span class="n">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">comb_iter_3_right</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">count_include_pad</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">comb_iter_4_left</span> <span class="o">=</span> <span class="n">BranchSeparables</span><span class="p">(</span><span class="n">out_channels_right</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="p">,</span> <span class="mi">3</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">name</span><span class="o">=</span><span class="s1">&#39;specific&#39;</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="c1"># self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_4_right</span> <span class="o">=</span><span class="n">MaxPoolPad</span><span class="p">()</span>
<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">x_prev</span><span class="p">):</span>
<span class="n">x_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv_prev_1x1</span><span class="p">(</span><span class="n">x_prev</span><span class="p">)</span>
<span class="n">x_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv_1x1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x_comb_iter_0_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_0_left</span><span class="p">(</span><span class="n">x_right</span><span class="p">)</span>
<span class="n">x_comb_iter_0_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_0_right</span><span class="p">(</span><span class="n">x_left</span><span class="p">)</span>
<span class="n">x_comb_iter_0</span> <span class="o">=</span> <span class="n">x_comb_iter_0_left</span> <span class="o">+</span> <span class="n">x_comb_iter_0_right</span>
<span class="n">x_comb_iter_1_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_1_left</span><span class="p">(</span><span class="n">x_right</span><span class="p">)</span>
<span class="n">x_comb_iter_1_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_1_right</span><span class="p">(</span><span class="n">x_left</span><span class="p">)</span>
<span class="n">x_comb_iter_1</span> <span class="o">=</span> <span class="n">x_comb_iter_1_left</span> <span class="o">+</span> <span class="n">x_comb_iter_1_right</span>
<span class="n">x_comb_iter_2_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_2_left</span><span class="p">(</span><span class="n">x_right</span><span class="p">)</span>
<span class="n">x_comb_iter_2_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_2_right</span><span class="p">(</span><span class="n">x_left</span><span class="p">)</span>
<span class="n">x_comb_iter_2</span> <span class="o">=</span> <span class="n">x_comb_iter_2_left</span> <span class="o">+</span> <span class="n">x_comb_iter_2_right</span>
<span class="n">x_comb_iter_3_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_3_right</span><span class="p">(</span><span class="n">x_comb_iter_0</span><span class="p">)</span>
<span class="n">x_comb_iter_3</span> <span class="o">=</span> <span class="n">x_comb_iter_3_right</span> <span class="o">+</span> <span class="n">x_comb_iter_1</span>
<span class="n">x_comb_iter_4_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_4_left</span><span class="p">(</span><span class="n">x_comb_iter_0</span><span class="p">)</span>
<span class="n">x_comb_iter_4_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comb_iter_4_right</span><span class="p">(</span><span class="n">x_right</span><span class="p">)</span>
<span class="n">x_comb_iter_4</span> <span class="o">=</span> <span class="n">x_comb_iter_4_left</span> <span class="o">+</span> <span class="n">x_comb_iter_4_right</span>
<span class="n">x_out</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">x_comb_iter_1</span><span class="p">,</span> <span class="n">x_comb_iter_2</span><span class="p">,</span> <span class="n">x_comb_iter_3</span><span class="p">,</span> <span class="n">x_comb_iter_4</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x_out</span>
<div class="viewcode-block" id="NASNetAMobile"><a class="viewcode-back" href="../../../pkg/models.html#torchreid.models.nasnet.NASNetAMobile">[docs]</a><span class="k">class</span> <span class="nc">NASNetAMobile</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">&quot;&quot;&quot;Neural Architecture Search (NAS).</span>
<span class="sd"> Reference:</span>
<span class="sd"> Zoph et al. Learning Transferable Architectures</span>
<span class="sd"> for Scalable Image Recognition. CVPR 2018.</span>
<span class="sd"> Public keys:</span>
<span class="sd"> - ``nasnetamobile``: NASNet-A Mobile.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">,</span> <span class="n">loss</span><span class="p">,</span> <span class="n">stem_filters</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">penultimate_filters</span><span class="o">=</span><span class="mi">1056</span><span class="p">,</span> <span class="n">filters_multiplier</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">NASNetAMobile</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">stem_filters</span> <span class="o">=</span> <span class="n">stem_filters</span>
<span class="bp">self</span><span class="o">.</span><span class="n">penultimate_filters</span> <span class="o">=</span> <span class="n">penultimate_filters</span>
<span class="bp">self</span><span class="o">.</span><span class="n">filters_multiplier</span> <span class="o">=</span> <span class="n">filters_multiplier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span>
<span class="n">filters</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">penultimate_filters</span> <span class="o">//</span> <span class="mi">24</span>
<span class="c1"># 24 is default value for the architecture</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv0</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv0</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">stem_filters</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">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">conv0</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;bn&#39;</span><span class="p">,</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">stem_filters</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cell_stem_0</span> <span class="o">=</span> <span class="n">CellStem0</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stem_filters</span><span class="p">,</span> <span class="n">num_filters</span><span class="o">=</span><span class="n">filters</span> <span class="o">//</span> <span class="p">(</span><span class="n">filters_multiplier</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">cell_stem_1</span> <span class="o">=</span> <span class="n">CellStem1</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stem_filters</span><span class="p">,</span> <span class="n">num_filters</span><span class="o">=</span><span class="n">filters</span> <span class="o">//</span> <span class="n">filters_multiplier</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cell_0</span> <span class="o">=</span> <span class="n">FirstCell</span><span class="p">(</span><span class="n">in_channels_left</span><span class="o">=</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="o">=</span><span class="n">filters</span><span class="o">//</span><span class="mi">2</span><span class="p">,</span> <span class="c1"># 1, 0.5</span>
<span class="n">in_channels_right</span><span class="o">=</span><span class="mi">2</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="o">=</span><span class="n">filters</span><span class="p">)</span> <span class="c1"># 2, 1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cell_1</span> <span class="o">=</span> <span class="n">NormalCell</span><span class="p">(</span><span class="n">in_channels_left</span><span class="o">=</span><span class="mi">2</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="o">=</span><span class="n">filters</span><span class="p">,</span> <span class="c1"># 2, 1</span>
<span class="n">in_channels_right</span><span class="o">=</span><span class="mi">6</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="o">=</span><span class="n">filters</span><span class="p">)</span> <span class="c1"># 6, 1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cell_2</span> <span class="o">=</span> <span class="n">NormalCell</span><span class="p">(</span><span class="n">in_channels_left</span><span class="o">=</span><span class="mi">6</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="o">=</span><span class="n">filters</span><span class="p">,</span> <span class="c1"># 6, 1</span>
<span class="n">in_channels_right</span><span class="o">=</span><span class="mi">6</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="o">=</span><span class="n">filters</span><span class="p">)</span> <span class="c1"># 6, 1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cell_3</span> <span class="o">=</span> <span class="n">NormalCell</span><span class="p">(</span><span class="n">in_channels_left</span><span class="o">=</span><span class="mi">6</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="o">=</span><span class="n">filters</span><span class="p">,</span> <span class="c1"># 6, 1</span>
<span class="n">in_channels_right</span><span class="o">=</span><span class="mi">6</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="o">=</span><span class="n">filters</span><span class="p">)</span> <span class="c1"># 6, 1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reduction_cell_0</span> <span class="o">=</span> <span class="n">ReductionCell0</span><span class="p">(</span><span class="n">in_channels_left</span><span class="o">=</span><span class="mi">6</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="o">=</span><span class="mi">2</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="c1"># 6, 2</span>
<span class="n">in_channels_right</span><span class="o">=</span><span class="mi">6</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="o">=</span><span class="mi">2</span><span class="o">*</span><span class="n">filters</span><span class="p">)</span> <span class="c1"># 6, 2</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cell_6</span> <span class="o">=</span> <span class="n">FirstCell</span><span class="p">(</span><span class="n">in_channels_left</span><span class="o">=</span><span class="mi">6</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="o">=</span><span class="n">filters</span><span class="p">,</span> <span class="c1"># 6, 1</span>
<span class="n">in_channels_right</span><span class="o">=</span><span class="mi">8</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="o">=</span><span class="mi">2</span><span class="o">*</span><span class="n">filters</span><span class="p">)</span> <span class="c1"># 8, 2</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cell_7</span> <span class="o">=</span> <span class="n">NormalCell</span><span class="p">(</span><span class="n">in_channels_left</span><span class="o">=</span><span class="mi">8</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="o">=</span><span class="mi">2</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="c1"># 8, 2</span>
<span class="n">in_channels_right</span><span class="o">=</span><span class="mi">12</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="o">=</span><span class="mi">2</span><span class="o">*</span><span class="n">filters</span><span class="p">)</span> <span class="c1"># 12, 2</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cell_8</span> <span class="o">=</span> <span class="n">NormalCell</span><span class="p">(</span><span class="n">in_channels_left</span><span class="o">=</span><span class="mi">12</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="o">=</span><span class="mi">2</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="c1"># 12, 2</span>
<span class="n">in_channels_right</span><span class="o">=</span><span class="mi">12</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="o">=</span><span class="mi">2</span><span class="o">*</span><span class="n">filters</span><span class="p">)</span> <span class="c1"># 12, 2</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cell_9</span> <span class="o">=</span> <span class="n">NormalCell</span><span class="p">(</span><span class="n">in_channels_left</span><span class="o">=</span><span class="mi">12</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="o">=</span><span class="mi">2</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="c1"># 12, 2</span>
<span class="n">in_channels_right</span><span class="o">=</span><span class="mi">12</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="o">=</span><span class="mi">2</span><span class="o">*</span><span class="n">filters</span><span class="p">)</span> <span class="c1"># 12, 2</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reduction_cell_1</span> <span class="o">=</span> <span class="n">ReductionCell1</span><span class="p">(</span><span class="n">in_channels_left</span><span class="o">=</span><span class="mi">12</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="o">=</span><span class="mi">4</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="c1"># 12, 4</span>
<span class="n">in_channels_right</span><span class="o">=</span><span class="mi">12</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="o">=</span><span class="mi">4</span><span class="o">*</span><span class="n">filters</span><span class="p">)</span> <span class="c1"># 12, 4</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cell_12</span> <span class="o">=</span> <span class="n">FirstCell</span><span class="p">(</span><span class="n">in_channels_left</span><span class="o">=</span><span class="mi">12</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="o">=</span><span class="mi">2</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="c1"># 12, 2</span>
<span class="n">in_channels_right</span><span class="o">=</span><span class="mi">16</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="o">=</span><span class="mi">4</span><span class="o">*</span><span class="n">filters</span><span class="p">)</span> <span class="c1"># 16, 4</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cell_13</span> <span class="o">=</span> <span class="n">NormalCell</span><span class="p">(</span><span class="n">in_channels_left</span><span class="o">=</span><span class="mi">16</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="o">=</span><span class="mi">4</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="c1"># 16, 4</span>
<span class="n">in_channels_right</span><span class="o">=</span><span class="mi">24</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="o">=</span><span class="mi">4</span><span class="o">*</span><span class="n">filters</span><span class="p">)</span> <span class="c1"># 24, 4</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cell_14</span> <span class="o">=</span> <span class="n">NormalCell</span><span class="p">(</span><span class="n">in_channels_left</span><span class="o">=</span><span class="mi">24</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="o">=</span><span class="mi">4</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="c1"># 24, 4</span>
<span class="n">in_channels_right</span><span class="o">=</span><span class="mi">24</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="o">=</span><span class="mi">4</span><span class="o">*</span><span class="n">filters</span><span class="p">)</span> <span class="c1"># 24, 4</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cell_15</span> <span class="o">=</span> <span class="n">NormalCell</span><span class="p">(</span><span class="n">in_channels_left</span><span class="o">=</span><span class="mi">24</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_left</span><span class="o">=</span><span class="mi">4</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="c1"># 24, 4</span>
<span class="n">in_channels_right</span><span class="o">=</span><span class="mi">24</span><span class="o">*</span><span class="n">filters</span><span class="p">,</span> <span class="n">out_channels_right</span><span class="o">=</span><span class="mi">4</span><span class="o">*</span><span class="n">filters</span><span class="p">)</span> <span class="c1"># 24, 4</span>
<span class="bp">self</span><span class="o">.</span><span class="n">relu</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="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="bp">self</span><span class="o">.</span><span class="n">classifier</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="mi">24</span> <span class="o">*</span> <span class="n">filters</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_init_params</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">_init_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">modules</span><span class="p">():</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">):</span>
<span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">kaiming_normal_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;fan_out&#39;</span><span class="p">,</span> <span class="n">nonlinearity</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">m</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">):</span>
<span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm1d</span><span class="p">):</span>
<span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">):</span>
<span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
<span class="k">if</span> <span class="n">m</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">features</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="n">x_conv0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv0</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="n">x_stem_0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cell_stem_0</span><span class="p">(</span><span class="n">x_conv0</span><span class="p">)</span>
<span class="n">x_stem_1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cell_stem_1</span><span class="p">(</span><span class="n">x_conv0</span><span class="p">,</span> <span class="n">x_stem_0</span><span class="p">)</span>
<span class="n">x_cell_0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cell_0</span><span class="p">(</span><span class="n">x_stem_1</span><span class="p">,</span> <span class="n">x_stem_0</span><span class="p">)</span>
<span class="n">x_cell_1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cell_1</span><span class="p">(</span><span class="n">x_cell_0</span><span class="p">,</span> <span class="n">x_stem_1</span><span class="p">)</span>
<span class="n">x_cell_2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cell_2</span><span class="p">(</span><span class="n">x_cell_1</span><span class="p">,</span> <span class="n">x_cell_0</span><span class="p">)</span>
<span class="n">x_cell_3</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cell_3</span><span class="p">(</span><span class="n">x_cell_2</span><span class="p">,</span> <span class="n">x_cell_1</span><span class="p">)</span>
<span class="n">x_reduction_cell_0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction_cell_0</span><span class="p">(</span><span class="n">x_cell_3</span><span class="p">,</span> <span class="n">x_cell_2</span><span class="p">)</span>
<span class="n">x_cell_6</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cell_6</span><span class="p">(</span><span class="n">x_reduction_cell_0</span><span class="p">,</span> <span class="n">x_cell_3</span><span class="p">)</span>
<span class="n">x_cell_7</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cell_7</span><span class="p">(</span><span class="n">x_cell_6</span><span class="p">,</span> <span class="n">x_reduction_cell_0</span><span class="p">)</span>
<span class="n">x_cell_8</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cell_8</span><span class="p">(</span><span class="n">x_cell_7</span><span class="p">,</span> <span class="n">x_cell_6</span><span class="p">)</span>
<span class="n">x_cell_9</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cell_9</span><span class="p">(</span><span class="n">x_cell_8</span><span class="p">,</span> <span class="n">x_cell_7</span><span class="p">)</span>
<span class="n">x_reduction_cell_1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction_cell_1</span><span class="p">(</span><span class="n">x_cell_9</span><span class="p">,</span> <span class="n">x_cell_8</span><span class="p">)</span>
<span class="n">x_cell_12</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cell_12</span><span class="p">(</span><span class="n">x_reduction_cell_1</span><span class="p">,</span> <span class="n">x_cell_9</span><span class="p">)</span>
<span class="n">x_cell_13</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cell_13</span><span class="p">(</span><span class="n">x_cell_12</span><span class="p">,</span> <span class="n">x_reduction_cell_1</span><span class="p">)</span>
<span class="n">x_cell_14</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cell_14</span><span class="p">(</span><span class="n">x_cell_13</span><span class="p">,</span> <span class="n">x_cell_12</span><span class="p">)</span>
<span class="n">x_cell_15</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cell_15</span><span class="p">(</span><span class="n">x_cell_14</span><span class="p">,</span> <span class="n">x_cell_13</span><span class="p">)</span>
<span class="n">x_cell_15</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">x_cell_15</span><span class="p">)</span>
<span class="n">x_cell_15</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">avg_pool2d</span><span class="p">(</span><span class="n">x_cell_15</span><span class="p">,</span> <span class="n">x_cell_15</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">2</span><span class="p">:])</span> <span class="c1"># global average pool</span>
<span class="n">x_cell_15</span> <span class="o">=</span> <span class="n">x_cell_15</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">x_cell_15</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="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">x_cell_15</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">x_cell_15</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x_cell_15</span>
<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="nb">input</span><span class="p">):</span>
<span class="n">v</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="p">:</span>
<span class="k">return</span> <span class="n">v</span>
<span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss</span> <span class="o">==</span> <span class="s1">&#39;softmax&#39;</span><span class="p">:</span>
<span class="k">return</span> <span class="n">y</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss</span> <span class="o">==</span> <span class="s1">&#39;triplet&#39;</span><span class="p">:</span>
<span class="k">return</span> <span class="n">y</span><span class="p">,</span> <span class="n">v</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="s1">&#39;Unsupported loss: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="p">))</span></div>
<span class="k">def</span> <span class="nf">init_pretrained_weights</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">model_url</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Initializes model with pretrained weights.</span>
<span class="sd"> </span>
<span class="sd"> Layers that don&#39;t match with pretrained layers in name or size are kept unchanged.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">pretrain_dict</span> <span class="o">=</span> <span class="n">model_zoo</span><span class="o">.</span><span class="n">load_url</span><span class="p">(</span><span class="n">model_url</span><span class="p">)</span>
<span class="n">model_dict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span>
<span class="n">pretrain_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">pretrain_dict</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">model_dict</span> <span class="ow">and</span> <span class="n">model_dict</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">()</span> <span class="o">==</span> <span class="n">v</span><span class="o">.</span><span class="n">size</span><span class="p">()}</span>
<span class="n">model_dict</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">pretrain_dict</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">model_dict</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">nasnetamobile</span><span class="p">(</span><span class="n">num_classes</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">True</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">NASNetAMobile</span><span class="p">(</span><span class="n">num_classes</span><span class="p">,</span> <span class="n">loss</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">if</span> <span class="n">pretrained</span><span class="p">:</span>
<span class="n">model_url</span> <span class="o">=</span> <span class="n">pretrained_settings</span><span class="p">[</span><span class="s1">&#39;nasnetamobile&#39;</span><span class="p">][</span><span class="s1">&#39;imagenet&#39;</span><span class="p">][</span><span class="s1">&#39;url&#39;</span><span class="p">]</span>
<span class="n">init_pretrained_weights</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">model_url</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span>
</pre></div>
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