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<h1>Source code for torchreid.models.shufflenetv2</h1><div class="highlight"><pre>
<span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Code source: https://github.com/pytorch/vision</span>
<span class="sd">&quot;&quot;&quot;</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;shufflenet_v2_x0_5&#39;</span><span class="p">,</span> <span class="s1">&#39;shufflenet_v2_x1_0&#39;</span><span class="p">,</span> <span class="s1">&#39;shufflenet_v2_x1_5&#39;</span><span class="p">,</span> <span class="s1">&#39;shufflenet_v2_x2_0&#39;</span><span class="p">]</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="k">import</span> <span class="n">nn</span>
<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="k">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>
<span class="kn">import</span> <span class="nn">torchvision</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="n">model_urls</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">&#39;shufflenetv2_x0.5&#39;</span><span class="p">:</span> <span class="s1">&#39;https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth&#39;</span><span class="p">,</span>
<span class="s1">&#39;shufflenetv2_x1.0&#39;</span><span class="p">:</span> <span class="s1">&#39;https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth&#39;</span><span class="p">,</span>
<span class="s1">&#39;shufflenetv2_x1.5&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
<span class="s1">&#39;shufflenetv2_x2.0&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
<span class="p">}</span>
<span class="k">def</span> <span class="nf">channel_shuffle</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">groups</span><span class="p">):</span>
<span class="n">batchsize</span><span class="p">,</span> <span class="n">num_channels</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">width</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
<span class="n">channels_per_group</span> <span class="o">=</span> <span class="n">num_channels</span> <span class="o">//</span> <span class="n">groups</span>
<span class="c1"># reshape</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">batchsize</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span>
<span class="n">channels_per_group</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">width</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">x</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="o">.</span><span class="n">contiguous</span><span class="p">()</span>
<span class="c1"># flatten</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">batchsize</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">width</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="k">class</span> <span class="nc">InvertedResidual</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">inp</span><span class="p">,</span> <span class="n">oup</span><span class="p">,</span> <span class="n">stride</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">InvertedResidual</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="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="mi">1</span> <span class="o">&lt;=</span> <span class="n">stride</span> <span class="o">&lt;=</span> <span class="mi">3</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;illegal stride value&#39;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stride</span> <span class="o">=</span> <span class="n">stride</span>
<span class="n">branch_features</span> <span class="o">=</span> <span class="n">oup</span> <span class="o">//</span> <span class="mi">2</span>
<span class="k">assert</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stride</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">inp</span> <span class="o">==</span> <span class="n">branch_features</span> <span class="o">&lt;&lt;</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">stride</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">branch1</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">depthwise_conv</span><span class="p">(</span><span class="n">inp</span><span class="p">,</span> <span class="n">inp</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">stride</span><span class="o">=</span><span class="bp">self</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="mi">1</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">inp</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">inp</span><span class="p">,</span> <span class="n">branch_features</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</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">padding</span><span class="o">=</span><span class="mi">0</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">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">branch_features</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="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">branch2</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="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">inp</span> <span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stride</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">)</span> <span class="k">else</span> <span class="n">branch_features</span><span class="p">,</span>
<span class="n">branch_features</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</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">padding</span><span class="o">=</span><span class="mi">0</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">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">branch_features</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="n">inplace</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">depthwise_conv</span><span class="p">(</span><span class="n">branch_features</span><span class="p">,</span> <span class="n">branch_features</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">stride</span><span class="o">=</span><span class="bp">self</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="mi">1</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">branch_features</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">branch_features</span><span class="p">,</span> <span class="n">branch_features</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</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">padding</span><span class="o">=</span><span class="mi">0</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">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">branch_features</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="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
<span class="p">)</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">depthwise_conv</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">kernel_size</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">0</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">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">o</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="n">groups</span><span class="o">=</span><span class="n">i</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="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">stride</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">x1</span><span class="p">,</span> <span class="n">x2</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">chunk</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">x1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">branch2</span><span class="p">(</span><span class="n">x2</span><span class="p">)),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">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="bp">self</span><span class="o">.</span><span class="n">branch1</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">branch2</span><span class="p">(</span><span class="n">x</span><span class="p">)),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">channel_shuffle</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="k">return</span> <span class="n">out</span>
<div class="viewcode-block" id="ShuffleNetV2"><a class="viewcode-back" href="../../../pkg/models.html#torchreid.models.shufflenetv2.ShuffleNetV2">[docs]</a><span class="k">class</span> <span class="nc">ShuffleNetV2</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;ShuffleNetV2.</span>
<span class="sd"> </span>
<span class="sd"> Reference:</span>
<span class="sd"> Ma et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. ECCV 2018.</span>
<span class="sd"> Public keys:</span>
<span class="sd"> - ``shufflenet_v2_x0_5``: ShuffleNetV2 x0.5.</span>
<span class="sd"> - ``shufflenet_v2_x1_0``: ShuffleNetV2 x1.0.</span>
<span class="sd"> - ``shufflenet_v2_x1_5``: ShuffleNetV2 x1.5.</span>
<span class="sd"> - ``shufflenet_v2_x2_0``: ShuffleNetV2 x2.0.</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">stages_repeats</span><span class="p">,</span> <span class="n">stages_out_channels</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">ShuffleNetV2</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">loss</span> <span class="o">=</span> <span class="n">loss</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">stages_repeats</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;expected stages_repeats as list of 3 positive ints&#39;</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">stages_out_channels</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">5</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;expected stages_out_channels as list of 5 positive ints&#39;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_stage_out_channels</span> <span class="o">=</span> <span class="n">stages_out_channels</span>
<span class="n">input_channels</span> <span class="o">=</span> <span class="mi">3</span>
<span class="n">output_channels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stage_out_channels</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">conv1</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="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">input_channels</span><span class="p">,</span> <span class="n">output_channels</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</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="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">output_channels</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="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
<span class="p">)</span>
<span class="n">input_channels</span> <span class="o">=</span> <span class="n">output_channels</span>
<span class="bp">self</span><span class="o">.</span><span class="n">maxpool</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="n">kernel_size</span><span class="o">=</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">stage_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;stage</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="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</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="mi">4</span><span class="p">]]</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">repeats</span><span class="p">,</span> <span class="n">output_channels</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
<span class="n">stage_names</span><span class="p">,</span> <span class="n">stages_repeats</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stage_out_channels</span><span class="p">[</span><span class="mi">1</span><span class="p">:]):</span>
<span class="n">seq</span> <span class="o">=</span> <span class="p">[</span><span class="n">InvertedResidual</span><span class="p">(</span><span class="n">input_channels</span><span class="p">,</span> <span class="n">output_channels</span><span class="p">,</span> <span class="mi">2</span><span class="p">)]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">repeats</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
<span class="n">seq</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">InvertedResidual</span><span class="p">(</span><span class="n">output_channels</span><span class="p">,</span> <span class="n">output_channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="nb">setattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="n">seq</span><span class="p">))</span>
<span class="n">input_channels</span> <span class="o">=</span> <span class="n">output_channels</span>
<span class="n">output_channels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stage_out_channels</span><span class="p">[</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">conv5</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="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">input_channels</span><span class="p">,</span> <span class="n">output_channels</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="mi">0</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">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">output_channels</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="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">global_avgpool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AdaptiveAvgPool2d</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="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="n">output_channels</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">featuremaps</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">conv1</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">maxpool</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">stage2</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">stage3</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">stage4</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">conv5</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">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">f</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">featuremaps</span><span class="p">(</span><span class="n">x</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">global_avgpool</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="n">v</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">v</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="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="s2">&quot;Unsupported loss: </span><span class="si">{}</span><span class="s2">&quot;</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="k">if</span> <span class="n">model_url</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;ImageNet pretrained weights are unavailable for this model&#39;</span><span class="p">)</span>
<span class="k">return</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">shufflenet_v2_x0_5</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">ShuffleNetV2</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="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">24</span><span class="p">,</span> <span class="mi">48</span><span class="p">,</span> <span class="mi">96</span><span class="p">,</span> <span class="mi">192</span><span class="p">,</span> <span class="mi">1024</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">init_pretrained_weights</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">model_urls</span><span class="p">[</span><span class="s1">&#39;shufflenetv2_x0.5&#39;</span><span class="p">])</span>
<span class="k">return</span> <span class="n">model</span>
<span class="k">def</span> <span class="nf">shufflenet_v2_x1_0</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">ShuffleNetV2</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="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">24</span><span class="p">,</span> <span class="mi">116</span><span class="p">,</span> <span class="mi">232</span><span class="p">,</span> <span class="mi">464</span><span class="p">,</span> <span class="mi">1024</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">init_pretrained_weights</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">model_urls</span><span class="p">[</span><span class="s1">&#39;shufflenetv2_x1.0&#39;</span><span class="p">])</span>
<span class="k">return</span> <span class="n">model</span>
<span class="k">def</span> <span class="nf">shufflenet_v2_x1_5</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">ShuffleNetV2</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="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">24</span><span class="p">,</span> <span class="mi">176</span><span class="p">,</span> <span class="mi">352</span><span class="p">,</span> <span class="mi">704</span><span class="p">,</span> <span class="mi">1024</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">init_pretrained_weights</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">model_urls</span><span class="p">[</span><span class="s1">&#39;shufflenetv2_x1.5&#39;</span><span class="p">])</span>
<span class="k">return</span> <span class="n">model</span>
<span class="k">def</span> <span class="nf">shufflenet_v2_x2_0</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">ShuffleNetV2</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="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">24</span><span class="p">,</span> <span class="mi">244</span><span class="p">,</span> <span class="mi">488</span><span class="p">,</span> <span class="mi">976</span><span class="p">,</span> <span class="mi">2048</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">init_pretrained_weights</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">model_urls</span><span class="p">[</span><span class="s1">&#39;shufflenetv2_x2.0&#39;</span><span class="p">])</span>
<span class="k">return</span> <span class="n">model</span>
</pre></div>
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