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<h1>Source code for torchreid.models.__init__</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">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">.resnet</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.resnetmid</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.senet</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.densenet</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.inceptionresnetv2</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.inceptionv4</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.xception</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.nasnet</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.mobilenetv2</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.shufflenet</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.squeezenet</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.shufflenetv2</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.mudeep</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.hacnn</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.pcb</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.mlfn</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.osnet</span> <span class="k">import</span> <span class="o">*</span>
<span class="n">__model_factory</span> <span class="o">=</span> <span class="p">{</span>
<span class="c1"># image classification models</span>
<span class="s1">&#39;resnet18&#39;</span><span class="p">:</span> <span class="n">resnet18</span><span class="p">,</span>
<span class="s1">&#39;resnet34&#39;</span><span class="p">:</span> <span class="n">resnet34</span><span class="p">,</span>
<span class="s1">&#39;resnet50&#39;</span><span class="p">:</span> <span class="n">resnet50</span><span class="p">,</span>
<span class="s1">&#39;resnet101&#39;</span><span class="p">:</span> <span class="n">resnet101</span><span class="p">,</span>
<span class="s1">&#39;resnet152&#39;</span><span class="p">:</span> <span class="n">resnet152</span><span class="p">,</span>
<span class="s1">&#39;resnext50_32x4d&#39;</span><span class="p">:</span> <span class="n">resnext50_32x4d</span><span class="p">,</span>
<span class="s1">&#39;resnext101_32x8d&#39;</span><span class="p">:</span> <span class="n">resnext101_32x8d</span><span class="p">,</span>
<span class="s1">&#39;resnet50_fc512&#39;</span><span class="p">:</span> <span class="n">resnet50_fc512</span><span class="p">,</span>
<span class="s1">&#39;se_resnet50&#39;</span><span class="p">:</span> <span class="n">se_resnet50</span><span class="p">,</span>
<span class="s1">&#39;se_resnet50_fc512&#39;</span><span class="p">:</span> <span class="n">se_resnet50_fc512</span><span class="p">,</span>
<span class="s1">&#39;se_resnet101&#39;</span><span class="p">:</span> <span class="n">se_resnet101</span><span class="p">,</span>
<span class="s1">&#39;se_resnext50_32x4d&#39;</span><span class="p">:</span> <span class="n">se_resnext50_32x4d</span><span class="p">,</span>
<span class="s1">&#39;se_resnext101_32x4d&#39;</span><span class="p">:</span> <span class="n">se_resnext101_32x4d</span><span class="p">,</span>
<span class="s1">&#39;densenet121&#39;</span><span class="p">:</span> <span class="n">densenet121</span><span class="p">,</span>
<span class="s1">&#39;densenet169&#39;</span><span class="p">:</span> <span class="n">densenet169</span><span class="p">,</span>
<span class="s1">&#39;densenet201&#39;</span><span class="p">:</span> <span class="n">densenet201</span><span class="p">,</span>
<span class="s1">&#39;densenet161&#39;</span><span class="p">:</span> <span class="n">densenet161</span><span class="p">,</span>
<span class="s1">&#39;densenet121_fc512&#39;</span><span class="p">:</span> <span class="n">densenet121_fc512</span><span class="p">,</span>
<span class="s1">&#39;inceptionresnetv2&#39;</span><span class="p">:</span> <span class="n">inceptionresnetv2</span><span class="p">,</span>
<span class="s1">&#39;inceptionv4&#39;</span><span class="p">:</span> <span class="n">inceptionv4</span><span class="p">,</span>
<span class="s1">&#39;xception&#39;</span><span class="p">:</span> <span class="n">xception</span><span class="p">,</span>
<span class="c1"># lightweight models</span>
<span class="s1">&#39;nasnsetmobile&#39;</span><span class="p">:</span> <span class="n">nasnetamobile</span><span class="p">,</span>
<span class="s1">&#39;mobilenetv2_x1_0&#39;</span><span class="p">:</span> <span class="n">mobilenetv2_x1_0</span><span class="p">,</span>
<span class="s1">&#39;mobilenetv2_x1_4&#39;</span><span class="p">:</span> <span class="n">mobilenetv2_x1_4</span><span class="p">,</span>
<span class="s1">&#39;shufflenet&#39;</span><span class="p">:</span> <span class="n">shufflenet</span><span class="p">,</span>
<span class="s1">&#39;squeezenet1_0&#39;</span><span class="p">:</span> <span class="n">squeezenet1_0</span><span class="p">,</span>
<span class="s1">&#39;squeezenet1_0_fc512&#39;</span><span class="p">:</span> <span class="n">squeezenet1_0_fc512</span><span class="p">,</span>
<span class="s1">&#39;squeezenet1_1&#39;</span><span class="p">:</span> <span class="n">squeezenet1_1</span><span class="p">,</span>
<span class="s1">&#39;shufflenet_v2_x0_5&#39;</span><span class="p">:</span> <span class="n">shufflenet_v2_x0_5</span><span class="p">,</span>
<span class="s1">&#39;shufflenet_v2_x1_0&#39;</span><span class="p">:</span> <span class="n">shufflenet_v2_x1_0</span><span class="p">,</span>
<span class="s1">&#39;shufflenet_v2_x1_5&#39;</span><span class="p">:</span> <span class="n">shufflenet_v2_x1_5</span><span class="p">,</span>
<span class="s1">&#39;shufflenet_v2_x2_0&#39;</span><span class="p">:</span> <span class="n">shufflenet_v2_x2_0</span><span class="p">,</span>
<span class="c1"># reid-specific models</span>
<span class="s1">&#39;mudeep&#39;</span><span class="p">:</span> <span class="n">MuDeep</span><span class="p">,</span>
<span class="s1">&#39;resnet50mid&#39;</span><span class="p">:</span> <span class="n">resnet50mid</span><span class="p">,</span>
<span class="s1">&#39;hacnn&#39;</span><span class="p">:</span> <span class="n">HACNN</span><span class="p">,</span>
<span class="s1">&#39;pcb_p6&#39;</span><span class="p">:</span> <span class="n">pcb_p6</span><span class="p">,</span>
<span class="s1">&#39;pcb_p4&#39;</span><span class="p">:</span> <span class="n">pcb_p4</span><span class="p">,</span>
<span class="s1">&#39;mlfn&#39;</span><span class="p">:</span> <span class="n">mlfn</span><span class="p">,</span>
<span class="s1">&#39;osnet_x1_0&#39;</span><span class="p">:</span> <span class="n">osnet_x1_0</span><span class="p">,</span>
<span class="s1">&#39;osnet_x0_75&#39;</span><span class="p">:</span> <span class="n">osnet_x0_75</span><span class="p">,</span>
<span class="s1">&#39;osnet_x0_5&#39;</span><span class="p">:</span> <span class="n">osnet_x0_5</span><span class="p">,</span>
<span class="s1">&#39;osnet_x0_25&#39;</span><span class="p">:</span> <span class="n">osnet_x0_25</span><span class="p">,</span>
<span class="s1">&#39;osnet_ibn_x1_0&#39;</span><span class="p">:</span> <span class="n">osnet_ibn_x1_0</span>
<span class="p">}</span>
<div class="viewcode-block" id="show_avai_models"><a class="viewcode-back" href="../../../pkg/models.html#torchreid.models.__init__.show_avai_models">[docs]</a><span class="k">def</span> <span class="nf">show_avai_models</span><span class="p">():</span>
<span class="sd">&quot;&quot;&quot;Displays available models.</span>
<span class="sd"> Examples::</span>
<span class="sd"> &gt;&gt;&gt; from torchreid import models</span>
<span class="sd"> &gt;&gt;&gt; models.show_avai_models()</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">__model_factory</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span></div>
<div class="viewcode-block" id="build_model"><a class="viewcode-back" href="../../../pkg/models.html#torchreid.models.__init__.build_model">[docs]</a><span class="k">def</span> <span class="nf">build_model</span><span class="p">(</span><span class="n">name</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="n">use_gpu</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;A function wrapper for building a model.</span>
<span class="sd"> Args:</span>
<span class="sd"> name (str): model name.</span>
<span class="sd"> num_classes (int): number of training identities.</span>
<span class="sd"> loss (str, optional): loss function to optimize the model. Currently</span>
<span class="sd"> supports &quot;softmax&quot; and &quot;triplet&quot;. Default is &quot;softmax&quot;.</span>
<span class="sd"> pretrained (bool, optional): whether to load ImageNet-pretrained weights.</span>
<span class="sd"> Default is True.</span>
<span class="sd"> use_gpu (bool, optional): whether to use gpu. Default is True.</span>
<span class="sd"> Returns:</span>
<span class="sd"> nn.Module</span>
<span class="sd"> Examples::</span>
<span class="sd"> &gt;&gt;&gt; from torchreid import models</span>
<span class="sd"> &gt;&gt;&gt; model = models.build_model(&#39;resnet50&#39;, 751, loss=&#39;softmax&#39;)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">avai_models</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">__model_factory</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="k">if</span> <span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">avai_models</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="s1">&#39;Unknown model: </span><span class="si">{}</span><span class="s1">. Must be one of </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">name</span><span class="p">,</span> <span class="n">avai_models</span><span class="p">))</span>
<span class="k">return</span> <span class="n">__model_factory</span><span class="p">[</span><span class="n">name</span><span class="p">](</span>
<span class="n">num_classes</span><span class="o">=</span><span class="n">num_classes</span><span class="p">,</span>
<span class="n">loss</span><span class="o">=</span><span class="n">loss</span><span class="p">,</span>
<span class="n">pretrained</span><span class="o">=</span><span class="n">pretrained</span><span class="p">,</span>
<span class="n">use_gpu</span><span class="o">=</span><span class="n">use_gpu</span>
<span class="p">)</span></div>
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