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<h1>Source code for torchvision.models.mobilenet</h1><div class="highlight"><pre>
<span></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">.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">'MobileNetV2'</span><span class="p">,</span> <span class="s1">'mobilenet_v2'</span><span class="p">]</span>
<span class="n">model_urls</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'mobilenet_v2'</span><span class="p">:</span> <span class="s1">'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth'</span><span class="p">,</span>
<span class="p">}</span>
<span class="k">def</span> <span class="nf">_make_divisible</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">divisor</span><span class="p">,</span> <span class="n">min_value</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> This function is taken from the original tf repo.</span>
<span class="sd"> It ensures that all layers have a channel number that is divisible by 8</span>
<span class="sd"> It can be seen here:</span>
<span class="sd"> https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py</span>
<span class="sd"> :param v:</span>
<span class="sd"> :param divisor:</span>
<span class="sd"> :param min_value:</span>
<span class="sd"> :return:</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">min_value</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">min_value</span> <span class="o">=</span> <span class="n">divisor</span>
<span class="n">new_v</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">min_value</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">v</span> <span class="o">+</span> <span class="n">divisor</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> <span class="o">//</span> <span class="n">divisor</span> <span class="o">*</span> <span class="n">divisor</span><span class="p">)</span>
<span class="c1"># Make sure that round down does not go down by more than 10%.</span>
<span class="k">if</span> <span class="n">new_v</span> <span class="o"><</span> <span class="mf">0.9</span> <span class="o">*</span> <span class="n">v</span><span class="p">:</span>
<span class="n">new_v</span> <span class="o">+=</span> <span class="n">divisor</span>
<span class="k">return</span> <span class="n">new_v</span>
<span class="k">class</span> <span class="nc">ConvBNReLU</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="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_planes</span><span class="p">,</span> <span class="n">out_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">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">groups</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="p">(</span><span class="n">kernel_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span>
<span class="nb">super</span><span class="p">(</span><span class="n">ConvBNReLU</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="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_planes</span><span class="p">,</span> <span class="n">out_planes</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">groups</span><span class="o">=</span><span class="n">groups</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">out_planes</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">ReLU6</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="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="n">expand_ratio</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="bp">self</span><span class="o">.</span><span class="n">stride</span> <span class="o">=</span> <span class="n">stride</span>
<span class="k">assert</span> <span class="n">stride</span> <span class="ow">in</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">hidden_dim</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">inp</span> <span class="o">*</span> <span class="n">expand_ratio</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">use_res_connect</span> <span class="o">=</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="ow">and</span> <span class="n">inp</span> <span class="o">==</span> <span class="n">oup</span>
<span class="n">layers</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">if</span> <span class="n">expand_ratio</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
<span class="c1"># pw</span>
<span class="n">layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ConvBNReLU</span><span class="p">(</span><span class="n">inp</span><span class="p">,</span> <span class="n">hidden_dim</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">layers</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span>
<span class="c1"># dw</span>
<span class="n">ConvBNReLU</span><span class="p">(</span><span class="n">hidden_dim</span><span class="p">,</span> <span class="n">hidden_dim</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">groups</span><span class="o">=</span><span class="n">hidden_dim</span><span class="p">),</span>
<span class="c1"># pw-linear</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">hidden_dim</span><span class="p">,</span> <span class="n">oup</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">oup</span><span class="p">),</span>
<span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv</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="o">*</span><span class="n">layers</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">use_res_connect</span><span class="p">:</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">MobileNetV2</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">num_classes</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">width_mult</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">inverted_residual_setting</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">round_nearest</span><span class="o">=</span><span class="mi">8</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> MobileNet V2 main class</span>
<span class="sd"> Args:</span>
<span class="sd"> num_classes (int): Number of classes</span>
<span class="sd"> width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount</span>
<span class="sd"> inverted_residual_setting: Network structure</span>
<span class="sd"> round_nearest (int): Round the number of channels in each layer to be a multiple of this number</span>
<span class="sd"> Set to 1 to turn off rounding</span>
<span class="sd"> """</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MobileNetV2</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="n">block</span> <span class="o">=</span> <span class="n">InvertedResidual</span>
<span class="n">input_channel</span> <span class="o">=</span> <span class="mi">32</span>
<span class="n">last_channel</span> <span class="o">=</span> <span class="mi">1280</span>
<span class="k">if</span> <span class="n">inverted_residual_setting</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">inverted_residual_setting</span> <span class="o">=</span> <span class="p">[</span>
<span class="c1"># t, c, n, s</span>
<span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">16</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="mi">6</span><span class="p">,</span> <span class="mi">24</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="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">32</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="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
<span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">96</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="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">160</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="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">320</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="c1"># only check the first element, assuming user knows t,c,n,s are required</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">inverted_residual_setting</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">inverted_residual_setting</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">4</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"inverted_residual_setting should be non-empty "</span>
<span class="s2">"or a 4-element list, got </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">inverted_residual_setting</span><span class="p">))</span>
<span class="c1"># building first layer</span>
<span class="n">input_channel</span> <span class="o">=</span> <span class="n">_make_divisible</span><span class="p">(</span><span class="n">input_channel</span> <span class="o">*</span> <span class="n">width_mult</span><span class="p">,</span> <span class="n">round_nearest</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">last_channel</span> <span class="o">=</span> <span class="n">_make_divisible</span><span class="p">(</span><span class="n">last_channel</span> <span class="o">*</span> <span class="nb">max</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">width_mult</span><span class="p">),</span> <span class="n">round_nearest</span><span class="p">)</span>
<span class="n">features</span> <span class="o">=</span> <span class="p">[</span><span class="n">ConvBNReLU</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">input_channel</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="c1"># building inverted residual blocks</span>
<span class="k">for</span> <span class="n">t</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">inverted_residual_setting</span><span class="p">:</span>
<span class="n">output_channel</span> <span class="o">=</span> <span class="n">_make_divisible</span><span class="p">(</span><span class="n">c</span> <span class="o">*</span> <span class="n">width_mult</span><span class="p">,</span> <span class="n">round_nearest</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">n</span><span class="p">):</span>
<span class="n">stride</span> <span class="o">=</span> <span class="n">s</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">1</span>
<span class="n">features</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">block</span><span class="p">(</span><span class="n">input_channel</span><span class="p">,</span> <span class="n">output_channel</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">expand_ratio</span><span class="o">=</span><span class="n">t</span><span class="p">))</span>
<span class="n">input_channel</span> <span class="o">=</span> <span class="n">output_channel</span>
<span class="c1"># building last several layers</span>
<span class="n">features</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ConvBNReLU</span><span class="p">(</span><span class="n">input_channel</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_channel</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="c1"># make it nn.Sequential</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="o">*</span><span class="n">features</span><span class="p">)</span>
<span class="c1"># building classifier</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="mf">0.2</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="bp">self</span><span class="o">.</span><span class="n">last_channel</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">),</span>
<span class="p">)</span>
<span class="c1"># weight initialization</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">'fan_out'</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">zeros_</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="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">ones_</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">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">zeros_</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="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="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">zeros_</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="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="n">x</span><span class="o">.</span><span class="n">mean</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">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>
<div class="viewcode-block" id="mobilenet_v2"><a class="viewcode-back" href="../../../torchvision/models.html#torchvision.models.mobilenet_v2">[docs]</a><span class="k">def</span> <span class="nf">mobilenet_v2</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="sd">"""</span>
<span class="sd"> Constructs a MobileNetV2 architecture from</span>
<span class="sd"> `"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_.</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="n">model</span> <span class="o">=</span> <span class="n">MobileNetV2</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">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="s1">'mobilenet_v2'</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>
</pre></div>
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