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<h1>Source code for torchvision.models.squeezenet</h1><div class="highlight"><pre>
<span></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.init</span> <span class="k">as</span> <span class="nn">init</span>
<span class="kn">from</span> <span class="nn">.utils</span> <span class="k">import</span> <span class="n">load_state_dict_from_url</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'SqueezeNet'</span><span class="p">,</span> <span class="s1">'squeezenet1_0'</span><span class="p">,</span> <span class="s1">'squeezenet1_1'</span><span class="p">]</span>
<span class="n">model_urls</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'squeezenet1_0'</span><span class="p">:</span> <span class="s1">'https://download.pytorch.org/models/squeezenet1_0-a815701f.pth'</span><span class="p">,</span>
<span class="s1">'squeezenet1_1'</span><span class="p">:</span> <span class="s1">'https://download.pytorch.org/models/squeezenet1_1-f364aa15.pth'</span><span class="p">,</span>
<span class="p">}</span>
<span class="k">class</span> <span class="nc">Fire</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">inplanes</span><span class="p">,</span> <span class="n">squeeze_planes</span><span class="p">,</span>
<span class="n">expand1x1_planes</span><span class="p">,</span> <span class="n">expand3x3_planes</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Fire</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">inplanes</span> <span class="o">=</span> <span class="n">inplanes</span>
<span class="bp">self</span><span class="o">.</span><span class="n">squeeze</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">inplanes</span><span class="p">,</span> <span class="n">squeeze_planes</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="bp">self</span><span class="o">.</span><span class="n">squeeze_activation</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="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">expand1x1</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">squeeze_planes</span><span class="p">,</span> <span class="n">expand1x1_planes</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="bp">self</span><span class="o">.</span><span class="n">expand1x1_activation</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="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">expand3x3</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">squeeze_planes</span><span class="p">,</span> <span class="n">expand3x3_planes</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">1</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">expand3x3_activation</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="n">inplace</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">squeeze_activation</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="k">return</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">expand1x1_activation</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">expand1x1</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">expand3x3_activation</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">expand3x3</span><span class="p">(</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="k">class</span> <span class="nc">SqueezeNet</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">version</span><span class="o">=</span><span class="s1">'1_0'</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">SqueezeNet</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_classes</span> <span class="o">=</span> <span class="n">num_classes</span>
<span class="k">if</span> <span class="n">version</span> <span class="o">==</span> <span class="s1">'1_0'</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">features</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="mi">3</span><span class="p">,</span> <span class="mi">96</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">7</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">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="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">ceil_mode</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
<span class="n">Fire</span><span class="p">(</span><span class="mi">96</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">),</span>
<span class="n">Fire</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">),</span>
<span class="n">Fire</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">),</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">ceil_mode</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
<span class="n">Fire</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">),</span>
<span class="n">Fire</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">48</span><span class="p">,</span> <span class="mi">192</span><span class="p">,</span> <span class="mi">192</span><span class="p">),</span>
<span class="n">Fire</span><span class="p">(</span><span class="mi">384</span><span class="p">,</span> <span class="mi">48</span><span class="p">,</span> <span class="mi">192</span><span class="p">,</span> <span class="mi">192</span><span class="p">),</span>
<span class="n">Fire</span><span class="p">(</span><span class="mi">384</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">),</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">ceil_mode</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
<span class="n">Fire</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">),</span>
<span class="p">)</span>
<span class="k">elif</span> <span class="n">version</span> <span class="o">==</span> <span class="s1">'1_1'</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">features</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="mi">3</span><span class="p">,</span> <span class="mi">64</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">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="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">ceil_mode</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
<span class="n">Fire</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">),</span>
<span class="n">Fire</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">),</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">ceil_mode</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
<span class="n">Fire</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">),</span>
<span class="n">Fire</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">),</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">ceil_mode</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
<span class="n">Fire</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">48</span><span class="p">,</span> <span class="mi">192</span><span class="p">,</span> <span class="mi">192</span><span class="p">),</span>
<span class="n">Fire</span><span class="p">(</span><span class="mi">384</span><span class="p">,</span> <span class="mi">48</span><span class="p">,</span> <span class="mi">192</span><span class="p">,</span> <span class="mi">192</span><span class="p">),</span>
<span class="n">Fire</span><span class="p">(</span><span class="mi">384</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">),</span>
<span class="n">Fire</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">),</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># FIXME: Is this needed? SqueezeNet should only be called from the</span>
<span class="c1"># FIXME: squeezenet1_x() functions</span>
<span class="c1"># FIXME: This checking is not done for the other models</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Unsupported SqueezeNet version </span><span class="si">{version}</span><span class="s2">:"</span>
<span class="s2">"1_0 or 1_1 expected"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">version</span><span class="o">=</span><span class="n">version</span><span class="p">))</span>
<span class="c1"># Final convolution is initialized differently from the rest</span>
<span class="n">final_conv</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="mi">512</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</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="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">Sequential</span><span class="p">(</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">),</span>
<span class="n">final_conv</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="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="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="k">if</span> <span class="n">m</span> <span class="ow">is</span> <span class="n">final_conv</span><span class="p">:</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="n">mean</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">init</span><span class="o">.</span><span class="n">kaiming_uniform_</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="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">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">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">features</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">classifier</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="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_squeezenet</span><span class="p">(</span><span class="n">version</span><span class="p">,</span> <span class="n">pretrained</span><span class="p">,</span> <span class="n">progress</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">SqueezeNet</span><span class="p">(</span><span class="n">version</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">arch</span> <span class="o">=</span> <span class="s1">'squeezenet'</span> <span class="o">+</span> <span class="n">version</span>
<span class="n">state_dict</span> <span class="o">=</span> <span class="n">load_state_dict_from_url</span><span class="p">(</span><span class="n">model_urls</span><span class="p">[</span><span class="n">arch</span><span class="p">],</span>
<span class="n">progress</span><span class="o">=</span><span class="n">progress</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">state_dict</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span>
<div class="viewcode-block" id="squeezenet1_0"><a class="viewcode-back" href="../../../torchvision/models.html#torchvision.models.squeezenet1_0">[docs]</a><span class="k">def</span> <span class="nf">squeezenet1_0</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">progress</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="sa">r</span><span class="sd">"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level</span>
<span class="sd"> accuracy with 50x fewer parameters and <0.5MB model size"</span>
<span class="sd"> <https://arxiv.org/abs/1602.07360>`_ paper.</span>
<span class="sd"> Args:</span>
<span class="sd"> pretrained (bool): If True, returns a model pre-trained on ImageNet</span>
<span class="sd"> progress (bool): If True, displays a progress bar of the download to stderr</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_squeezenet</span><span class="p">(</span><span class="s1">'1_0'</span><span class="p">,</span> <span class="n">pretrained</span><span class="p">,</span> <span class="n">progress</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="squeezenet1_1"><a class="viewcode-back" href="../../../torchvision/models.html#torchvision.models.squeezenet1_1">[docs]</a><span class="k">def</span> <span class="nf">squeezenet1_1</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">progress</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="sa">r</span><span class="sd">"""SqueezeNet 1.1 model from the `official SqueezeNet repo</span>
<span class="sd"> <https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_.</span>
<span class="sd"> SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters</span>
<span class="sd"> than SqueezeNet 1.0, without sacrificing accuracy.</span>
<span class="sd"> Args:</span>
<span class="sd"> pretrained (bool): If True, returns a model pre-trained on ImageNet</span>
<span class="sd"> progress (bool): If True, displays a progress bar of the download to stderr</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_squeezenet</span><span class="p">(</span><span class="s1">'1_1'</span><span class="p">,</span> <span class="n">pretrained</span><span class="p">,</span> <span class="n">progress</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
</pre></div>
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