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<h1>Source code for mxnet.gluon.model_zoo.vision.mobilenet</h1><div class="highlight"><pre>
<span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span>
<span class="c1"># or more contributor license agreements. See the NOTICE file</span>
<span class="c1"># distributed with this work for additional information</span>
<span class="c1"># regarding copyright ownership. The ASF licenses this file</span>
<span class="c1"># to you under the Apache License, Version 2.0 (the</span>
<span class="c1"># "License"); you may not use this file except in compliance</span>
<span class="c1"># with the License. You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing,</span>
<span class="c1"># software distributed under the License is distributed on an</span>
<span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span>
<span class="c1"># KIND, either express or implied. See the License for the</span>
<span class="c1"># specific language governing permissions and limitations</span>
<span class="c1"># under the License.</span>
<span class="c1"># coding: utf-8</span>
<span class="c1"># pylint: disable= arguments-differ</span>
<span class="sd">"""MobileNet, implemented in Gluon."""</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'MobileNet'</span><span class="p">,</span> <span class="s1">'mobilenet1_0'</span><span class="p">,</span> <span class="s1">'mobilenet0_75'</span><span class="p">,</span> <span class="s1">'mobilenet0_5'</span><span class="p">,</span> <span class="s1">'mobilenet0_25'</span><span class="p">,</span>
<span class="s1">'get_mobilenet'</span><span class="p">]</span>
<span class="kn">from</span> <span class="nn">....context</span> <span class="kn">import</span> <span class="n">cpu</span>
<span class="kn">from</span> <span class="nn">...block</span> <span class="kn">import</span> <span class="n">HybridBlock</span>
<span class="kn">from</span> <span class="nn">...</span> <span class="kn">import</span> <span class="n">nn</span>
<span class="c1"># Helpers</span>
<span class="k">def</span> <span class="nf">_add_conv</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">channels</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">kernel</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">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_group</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="n">out</span><span class="o">.</span><span class="n">add</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">channels</span><span class="p">,</span> <span class="n">kernel</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">pad</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="n">num_group</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="bp">False</span><span class="p">))</span>
<span class="n">out</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">(</span><span class="n">scale</span><span class="o">=</span><span class="bp">False</span><span class="p">))</span>
<span class="n">out</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="s1">'relu'</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">_add_conv_dw</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">dw_channels</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">stride</span><span class="p">):</span>
<span class="n">_add_conv</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">channels</span><span class="o">=</span><span class="n">dw_channels</span><span class="p">,</span> <span class="n">kernel</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="n">stride</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_group</span><span class="o">=</span><span class="n">dw_channels</span><span class="p">)</span>
<span class="n">_add_conv</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">channels</span><span class="o">=</span><span class="n">channels</span><span class="p">)</span>
<span class="c1"># Net</span>
<div class="viewcode-block" id="MobileNet"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.MobileNet">[docs]</a><span class="k">class</span> <span class="nc">MobileNet</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""MobileNet model from the</span>
<span class="sd"> `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"</span>
<span class="sd"> <https: 1704.04861="" abs="" arxiv.org="">`_ paper.</https:></span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> multiplier : float, default 1.0</span>
<span class="sd"> The width multiplier for controling the model size. Only multipliers that are no</span>
<span class="sd"> less than 0.25 are supported. The actual number of channels is equal to the original</span>
<span class="sd"> channel size multiplied by this multiplier.</span>
<span class="sd"> classes : int, default 1000</span>
<span class="sd"> Number of classes for the output layer.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">multiplier</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MobileNet</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="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">name_scope</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">HybridSequential</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="s1">''</span><span class="p">)</span>
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">name_scope</span><span class="p">():</span>
<span class="n">_add_conv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="p">,</span> <span class="n">channels</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="mi">32</span><span class="o">*</span><span class="n">multiplier</span><span class="p">),</span> <span class="n">kernel</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">pad</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">dw_channels</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="o">*</span><span class="n">multiplier</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">32</span><span class="p">,</span> <span class="mi">64</span><span class="p">]</span><span class="o">+</span><span class="p">[</span><span class="mi">128</span><span class="p">]</span><span class="o">*</span><span class="mi">2</span><span class="o">+</span><span class="p">[</span><span class="mi">256</span><span class="p">]</span><span class="o">*</span><span class="mi">2</span><span class="o">+</span><span class="p">[</span><span class="mi">512</span><span class="p">]</span><span class="o">*</span><span class="mi">6</span><span class="o">+</span><span class="p">[</span><span class="mi">1024</span><span class="p">]]</span>
<span class="n">channels</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="o">*</span><span class="n">multiplier</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">64</span><span class="p">]</span><span class="o">+</span><span class="p">[</span><span class="mi">128</span><span class="p">]</span><span class="o">*</span><span class="mi">2</span><span class="o">+</span><span class="p">[</span><span class="mi">256</span><span class="p">]</span><span class="o">*</span><span class="mi">2</span><span class="o">+</span><span class="p">[</span><span class="mi">512</span><span class="p">]</span><span class="o">*</span><span class="mi">6</span><span class="o">+</span><span class="p">[</span><span class="mi">1024</span><span class="p">]</span><span class="o">*</span><span class="mi">2</span><span class="p">]</span>
<span class="n">strides</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="mi">3</span> <span class="o">+</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="mi">5</span> <span class="o">+</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="k">for</span> <span class="n">dwc</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">dw_channels</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">strides</span><span class="p">):</span>
<span class="n">_add_conv_dw</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="p">,</span> <span class="n">dw_channels</span><span class="o">=</span><span class="n">dwc</span><span class="p">,</span> <span class="n">channels</span><span class="o">=</span><span class="n">c</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">s</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">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">GlobalAvgPool2D</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">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span><span class="p">())</span>
<span class="bp">self</span><span class="o">.</span><span class="n">output</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="n">classes</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</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">output</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>
<span class="c1"># Constructor</span>
<div class="viewcode-block" id="get_mobilenet"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.get_mobilenet">[docs]</a><span class="k">def</span> <span class="nf">get_mobilenet</span><span class="p">(</span><span class="n">multiplier</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">cpu</span><span class="p">(),</span> <span class="n">root</span><span class="o">=</span><span class="s1">'~/.mxnet/models'</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">"""MobileNet model from the</span>
<span class="sd"> `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"</span>
<span class="sd"> <https: 1704.04861="" abs="" arxiv.org="">`_ paper.</https:></span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> multiplier : float</span>
<span class="sd"> The width multiplier for controling the model size. Only multipliers that are no</span>
<span class="sd"> less than 0.25 are supported. The actual number of channels is equal to the original</span>
<span class="sd"> channel size multiplied by this multiplier.</span>
<span class="sd"> pretrained : bool, default False</span>
<span class="sd"> Whether to load the pretrained weights for model.</span>
<span class="sd"> ctx : Context, default CPU</span>
<span class="sd"> The context in which to load the pretrained weights.</span>
<span class="sd"> root : str, default '~/.mxnet/models'</span>
<span class="sd"> Location for keeping the model parameters.</span>
<span class="sd"> """</span>
<span class="n">net</span> <span class="o">=</span> <span class="n">MobileNet</span><span class="p">(</span><span class="n">multiplier</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="kn">from</span> <span class="nn">..model_store</span> <span class="kn">import</span> <span class="n">get_model_file</span>
<span class="n">version_suffix</span> <span class="o">=</span> <span class="s1">'{0:.2f}'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">multiplier</span><span class="p">)</span>
<span class="k">if</span> <span class="n">version_suffix</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'1.00'</span><span class="p">,</span> <span class="s1">'0.50'</span><span class="p">):</span>
<span class="n">version_suffix</span> <span class="o">=</span> <span class="n">version_suffix</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">net</span><span class="o">.</span><span class="n">load_params</span><span class="p">(</span><span class="n">get_model_file</span><span class="p">(</span><span class="s1">'mobilenet</span><span class="si">%s</span><span class="s1">'</span><span class="o">%</span><span class="n">version_suffix</span><span class="p">,</span> <span class="n">root</span><span class="o">=</span><span class="n">root</span><span class="p">),</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span>
<span class="k">return</span> <span class="n">net</span></div>
<div class="viewcode-block" id="mobilenet1_0"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.mobilenet1_0">[docs]</a><span class="k">def</span> <span class="nf">mobilenet1_0</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">"""MobileNet model from the</span>
<span class="sd"> `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"</span>
<span class="sd"> <https: 1704.04861="" abs="" arxiv.org="">`_ paper, with width multiplier 1.0.</https:></span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> pretrained : bool, default False</span>
<span class="sd"> Whether to load the pretrained weights for model.</span>
<span class="sd"> ctx : Context, default CPU</span>
<span class="sd"> The context in which to load the pretrained weights.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">get_mobilenet</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></div>
<div class="viewcode-block" id="mobilenet0_75"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.mobilenet0_75">[docs]</a><span class="k">def</span> <span class="nf">mobilenet0_75</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">"""MobileNet model from the</span>
<span class="sd"> `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"</span>
<span class="sd"> <https: 1704.04861="" abs="" arxiv.org="">`_ paper, with width multiplier 0.75.</https:></span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> pretrained : bool, default False</span>
<span class="sd"> Whether to load the pretrained weights for model.</span>
<span class="sd"> ctx : Context, default CPU</span>
<span class="sd"> The context in which to load the pretrained weights.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">get_mobilenet</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></div>
<div class="viewcode-block" id="mobilenet0_5"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.mobilenet0_5">[docs]</a><span class="k">def</span> <span class="nf">mobilenet0_5</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">"""MobileNet model from the</span>
<span class="sd"> `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"</span>
<span class="sd"> <https: 1704.04861="" abs="" arxiv.org="">`_ paper, with width multiplier 0.5.</https:></span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> pretrained : bool, default False</span>
<span class="sd"> Whether to load the pretrained weights for model.</span>
<span class="sd"> ctx : Context, default CPU</span>
<span class="sd"> The context in which to load the pretrained weights.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">get_mobilenet</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></div>
<div class="viewcode-block" id="mobilenet0_25"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.mobilenet0_25">[docs]</a><span class="k">def</span> <span class="nf">mobilenet0_25</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">"""MobileNet model from the</span>
<span class="sd"> `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"</span>
<span class="sd"> <https: 1704.04861="" abs="" arxiv.org="">`_ paper, with width multiplier 0.25.</https:></span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> pretrained : bool, default False</span>
<span class="sd"> Whether to load the pretrained weights for model.</span>
<span class="sd"> ctx : Context, default CPU</span>
<span class="sd"> The context in which to load the pretrained weights.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">get_mobilenet</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
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