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<h1>Source code for torchvision.models.mnasnet</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">math</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">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">'MNASNet'</span><span class="p">,</span> <span class="s1">'mnasnet0_5'</span><span class="p">,</span> <span class="s1">'mnasnet0_75'</span><span class="p">,</span> <span class="s1">'mnasnet1_0'</span><span class="p">,</span> <span class="s1">'mnasnet1_3'</span><span class="p">]</span>
<span class="n">_MODEL_URLS</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"mnasnet0_5"</span><span class="p">:</span>
<span class="s2">"https://download.pytorch.org/models/mnasnet0.5_top1_67.592-7c6cb539b9.pth"</span><span class="p">,</span>
<span class="s2">"mnasnet0_75"</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
<span class="s2">"mnasnet1_0"</span><span class="p">:</span>
<span class="s2">"https://download.pytorch.org/models/mnasnet1.0_top1_73.512-f206786ef8.pth"</span><span class="p">,</span>
<span class="s2">"mnasnet1_3"</span><span class="p">:</span> <span class="kc">None</span>
<span class="p">}</span>
<span class="c1"># Paper suggests 0.9997 momentum, for TensorFlow. Equivalent PyTorch momentum is</span>
<span class="c1"># 1.0 - tensorflow.</span>
<span class="n">_BN_MOMENTUM</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="mf">0.9997</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">in_ch</span><span class="p">,</span> <span class="n">out_ch</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">expansion_factor</span><span class="p">,</span>
<span class="n">bn_momentum</span><span class="o">=</span><span class="mf">0.1</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">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="k">assert</span> <span class="n">kernel_size</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">]</span>
<span class="n">mid_ch</span> <span class="o">=</span> <span class="n">in_ch</span> <span class="o">*</span> <span class="n">expansion_factor</span>
<span class="bp">self</span><span class="o">.</span><span class="n">apply_residual</span> <span class="o">=</span> <span class="p">(</span><span class="n">in_ch</span> <span class="o">==</span> <span class="n">out_ch</span> <span class="ow">and</span> <span class="n">stride</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">layers</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="c1"># Pointwise</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_ch</span><span class="p">,</span> <span class="n">mid_ch</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">mid_ch</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="n">bn_momentum</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="c1"># Depthwise</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">mid_ch</span><span class="p">,</span> <span class="n">mid_ch</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="n">kernel_size</span> <span class="o">//</span> <span class="mi">2</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">mid_ch</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">mid_ch</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="n">bn_momentum</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="c1"># Linear pointwise. Note that there's no activation.</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">mid_ch</span><span class="p">,</span> <span class="n">out_ch</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">out_ch</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="n">bn_momentum</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="nb">input</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_residual</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span> <span class="o">+</span> <span class="nb">input</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">layers</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_stack</span><span class="p">(</span><span class="n">in_ch</span><span class="p">,</span> <span class="n">out_ch</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">exp_factor</span><span class="p">,</span> <span class="n">repeats</span><span class="p">,</span>
<span class="n">bn_momentum</span><span class="p">):</span>
<span class="sd">""" Creates a stack of inverted residuals. """</span>
<span class="k">assert</span> <span class="n">repeats</span> <span class="o">>=</span> <span class="mi">1</span>
<span class="c1"># First one has no skip, because feature map size changes.</span>
<span class="n">first</span> <span class="o">=</span> <span class="n">_InvertedResidual</span><span class="p">(</span><span class="n">in_ch</span><span class="p">,</span> <span class="n">out_ch</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">exp_factor</span><span class="p">,</span>
<span class="n">bn_momentum</span><span class="o">=</span><span class="n">bn_momentum</span><span class="p">)</span>
<span class="n">remaining</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">repeats</span><span class="p">):</span>
<span class="n">remaining</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">out_ch</span><span class="p">,</span> <span class="n">out_ch</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">exp_factor</span><span class="p">,</span>
<span class="n">bn_momentum</span><span class="o">=</span><span class="n">bn_momentum</span><span class="p">))</span>
<span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">first</span><span class="p">,</span> <span class="o">*</span><span class="n">remaining</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_round_to_multiple_of</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">divisor</span><span class="p">,</span> <span class="n">round_up_bias</span><span class="o">=</span><span class="mf">0.9</span><span class="p">):</span>
<span class="sd">""" Asymmetric rounding to make `val` divisible by `divisor`. With default</span>
<span class="sd"> bias, will round up, unless the number is no more than 10% greater than the</span>
<span class="sd"> smaller divisible value, i.e. (83, 8) -> 80, but (84, 8) -> 88. """</span>
<span class="k">assert</span> <span class="mf">0.0</span> <span class="o"><</span> <span class="n">round_up_bias</span> <span class="o"><</span> <span class="mf">1.0</span>
<span class="n">new_val</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">divisor</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">val</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="k">return</span> <span class="n">new_val</span> <span class="k">if</span> <span class="n">new_val</span> <span class="o">>=</span> <span class="n">round_up_bias</span> <span class="o">*</span> <span class="n">val</span> <span class="k">else</span> <span class="n">new_val</span> <span class="o">+</span> <span class="n">divisor</span>
<span class="k">def</span> <span class="nf">_scale_depths</span><span class="p">(</span><span class="n">depths</span><span class="p">,</span> <span class="n">alpha</span><span class="p">):</span>
<span class="sd">""" Scales tensor depths as in reference MobileNet code, prefers rouding up</span>
<span class="sd"> rather than down. """</span>
<span class="k">return</span> <span class="p">[</span><span class="n">_round_to_multiple_of</span><span class="p">(</span><span class="n">depth</span> <span class="o">*</span> <span class="n">alpha</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span> <span class="k">for</span> <span class="n">depth</span> <span class="ow">in</span> <span class="n">depths</span><span class="p">]</span>
<span class="k">class</span> <span class="nc">MNASNet</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="sd">""" MNASNet, as described in https://arxiv.org/pdf/1807.11626.pdf.</span>
<span class="sd"> >>> model = MNASNet(1000, 1.0)</span>
<span class="sd"> >>> x = torch.rand(1, 3, 224, 224)</span>
<span class="sd"> >>> y = model(x)</span>
<span class="sd"> >>> y.dim()</span>
<span class="sd"> 1</span>
<span class="sd"> >>> y.nelement()</span>
<span class="sd"> 1000</span>
<span class="sd"> """</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">alpha</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">dropout</span><span class="o">=</span><span class="mf">0.2</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MNASNet</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">depths</span> <span class="o">=</span> <span class="n">_scale_depths</span><span class="p">([</span><span class="mi">24</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">80</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">320</span><span class="p">],</span> <span class="n">alpha</span><span class="p">)</span>
<span class="n">layers</span> <span class="o">=</span> <span class="p">[</span>
<span class="c1"># First layer: regular conv.</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">32</span><span class="p">,</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="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="n">_BN_MOMENTUM</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="c1"># Depthwise separable, no skip.</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</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="n">padding</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">groups</span><span class="o">=</span><span class="mi">32</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="mi">32</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="n">_BN_MOMENTUM</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">Conv2d</span><span class="p">(</span><span class="mi">32</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="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">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="mi">16</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="n">_BN_MOMENTUM</span><span class="p">),</span>
<span class="c1"># MNASNet blocks: stacks of inverted residuals.</span>
<span class="n">_stack</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="n">depths</span><span class="p">[</span><span class="mi">0</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">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">_BN_MOMENTUM</span><span class="p">),</span>
<span class="n">_stack</span><span class="p">(</span><span class="n">depths</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">depths</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">_BN_MOMENTUM</span><span class="p">),</span>
<span class="n">_stack</span><span class="p">(</span><span class="n">depths</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">depths</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">_BN_MOMENTUM</span><span class="p">),</span>
<span class="n">_stack</span><span class="p">(</span><span class="n">depths</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">depths</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">_BN_MOMENTUM</span><span class="p">),</span>
<span class="n">_stack</span><span class="p">(</span><span class="n">depths</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">depths</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">_BN_MOMENTUM</span><span class="p">),</span>
<span class="n">_stack</span><span class="p">(</span><span class="n">depths</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span> <span class="n">depths</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">_BN_MOMENTUM</span><span class="p">),</span>
<span class="c1"># Final mapping to classifier input.</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">depths</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="mi">1280</span><span class="p">,</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">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">1280</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="n">_BN_MOMENTUM</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">layers</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="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="n">dropout</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">Linear</span><span class="p">(</span><span class="mi">1280</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_initialize_weights</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">layers</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="c1"># Equivalent to global avgpool and removing H and W dimensions.</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="k">return</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">def</span> <span class="nf">_initialize_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">modules</span><span class="p">():</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">):</span>
<span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">kaiming_normal_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"fan_out"</span><span class="p">,</span>
<span class="n">nonlinearity</span><span class="o">=</span><span class="s2">"relu"</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="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">_load_pretrained</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">progress</span><span class="p">):</span>
<span class="k">if</span> <span class="n">model_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">_MODEL_URLS</span> <span class="ow">or</span> <span class="n">_MODEL_URLS</span><span class="p">[</span><span class="n">model_name</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s2">"No checkpoint is available for model type </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">model_name</span><span class="p">))</span>
<span class="n">checkpoint_url</span> <span class="o">=</span> <span class="n">_MODEL_URLS</span><span class="p">[</span><span class="n">model_name</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">load_state_dict_from_url</span><span class="p">(</span><span class="n">checkpoint_url</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="n">progress</span><span class="p">))</span>
<div class="viewcode-block" id="mnasnet0_5"><a class="viewcode-back" href="../../../torchvision/models.html#torchvision.models.mnasnet0_5">[docs]</a><span class="k">def</span> <span class="nf">mnasnet0_5</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">"""MNASNet with depth multiplier of 0.5 from</span>
<span class="sd"> `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"</span>
<span class="sd"> <https://arxiv.org/pdf/1807.11626.pdf>`_.</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">MNASNet</span><span class="p">(</span><span class="mf">0.5</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">_load_pretrained</span><span class="p">(</span><span class="s2">"mnasnet0_5"</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">progress</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span></div>
<div class="viewcode-block" id="mnasnet0_75"><a class="viewcode-back" href="../../../torchvision/models.html#torchvision.models.mnasnet0_75">[docs]</a><span class="k">def</span> <span class="nf">mnasnet0_75</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">"""MNASNet with depth multiplier of 0.75 from</span>
<span class="sd"> `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"</span>
<span class="sd"> <https://arxiv.org/pdf/1807.11626.pdf>`_.</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">MNASNet</span><span class="p">(</span><span class="mf">0.75</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">_load_pretrained</span><span class="p">(</span><span class="s2">"mnasnet0_75"</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">progress</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span></div>
<div class="viewcode-block" id="mnasnet1_0"><a class="viewcode-back" href="../../../torchvision/models.html#torchvision.models.mnasnet1_0">[docs]</a><span class="k">def</span> <span class="nf">mnasnet1_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="sd">"""MNASNet with depth multiplier of 1.0 from</span>
<span class="sd"> `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"</span>
<span class="sd"> <https://arxiv.org/pdf/1807.11626.pdf>`_.</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">MNASNet</span><span class="p">(</span><span class="mf">1.0</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">_load_pretrained</span><span class="p">(</span><span class="s2">"mnasnet1_0"</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">progress</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span></div>
<div class="viewcode-block" id="mnasnet1_3"><a class="viewcode-back" href="../../../torchvision/models.html#torchvision.models.mnasnet1_3">[docs]</a><span class="k">def</span> <span class="nf">mnasnet1_3</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">"""MNASNet with depth multiplier of 1.3 from</span>
<span class="sd"> `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"</span>
<span class="sd"> <https://arxiv.org/pdf/1807.11626.pdf>`_.</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">MNASNet</span><span class="p">(</span><span class="mf">1.3</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">_load_pretrained</span><span class="p">(</span><span class="s2">"mnasnet1_3"</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">progress</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span></div>
</pre></div>
</article>
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