Google Git
Sign in
apache / mxnet-site / 5d2e45b9c41410156d7d50982069a8e0f3991694 / . / versions / 0.12.1 / _modules / mxnet / gluon / model_zoo / vision / resnet.html
blob: cd9e8758374e574985f71085b1c59afbea4536f9 [file] [log] [blame]
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8"/>
<meta content="IE=edge" http-equiv="X-UA-Compatible"/>
<meta content="width=device-width, initial-scale=1" name="viewport"/>
<meta content="mxnet.gluon.model_zoo.vision.resnet" property="og:title">
<meta content="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/og-logo.png" property="og:image">
<meta content="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/og-logo.png" property="og:image:secure_url">
<meta content="mxnet.gluon.model_zoo.vision.resnet" property="og:description"/>
<title>mxnet.gluon.model_zoo.vision.resnet — mxnet documentation</title>
<link crossorigin="anonymous" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" rel="stylesheet"/>
<link href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet"/>
<link href="../../../../../_static/basic.css" rel="stylesheet" type="text/css">
<link href="../../../../../_static/pygments.css" rel="stylesheet" type="text/css">
<link href="../../../../../_static/mxnet.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../../../../',
VERSION: '',
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
SOURCELINK_SUFFIX: '.txt'
};
</script>
<script src="https://code.jquery.com/jquery-1.11.1.min.js" type="text/javascript"></script>
<script src="../../../../../_static/underscore.js" type="text/javascript"></script>
<script src="../../../../../_static/searchtools_custom.js" type="text/javascript"></script>
<script src="../../../../../_static/doctools.js" type="text/javascript"></script>
<script src="../../../../../_static/selectlang.js" type="text/javascript"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML" type="text/javascript"></script>
<script type="text/javascript"> jQuery(function() { Search.loadIndex("/versions/0.12.1/searchindex.js"); Search.init();}); </script>
<script>
(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new
Date();a=s.createElement(o),
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
})(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
ga('create', 'UA-96378503-1', 'auto');
ga('send', 'pageview');
</script>
<!-- -->
<!-- <script type="text/javascript" src="../../../../../_static/jquery.js"></script> -->
<!-- -->
<!-- <script type="text/javascript" src="../../../../../_static/underscore.js"></script> -->
<!-- -->
<!-- <script type="text/javascript" src="../../../../../_static/doctools.js"></script> -->
<!-- -->
<!-- <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> -->
<!-- -->
<link href="../../../../../genindex.html" rel="index" title="Index">
<link href="../../../../../search.html" rel="search" title="Search"/>
<link href="../vision.html" rel="up" title="mxnet.gluon.model_zoo.vision"/>
<link href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png" rel="icon" type="image/png"/>
</link></link></link></meta></meta></meta></head>
<body background="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-background-compressed.jpeg" role="document">
<div class="content-block"><div class="navbar navbar-fixed-top">
<div class="container" id="navContainer">
<div class="innder" id="header-inner">
<h1 id="logo-wrap">
<a href="../../../../../" id="logo"><img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet_logo.png"/></a>
</h1>
<nav class="nav-bar" id="main-nav">
<a class="main-nav-link" href="/versions/0.12.1/install/index.html">Install</a>
<span id="dropdown-menu-position-anchor">
<a aria-expanded="true" aria-haspopup="true" class="main-nav-link dropdown-toggle" data-toggle="dropdown" href="#" role="button">Gluon <span class="caret"></span></a>
<ul class="dropdown-menu navbar-menu" id="package-dropdown-menu">
<li><a class="main-nav-link" href="/versions/0.12.1/tutorials/gluon/gluon.html">About</a></li>
<li><a class="main-nav-link" href="https://www.d2l.ai/">Dive into Deep Learning</a></li>
<li><a class="main-nav-link" href="https://gluon-cv.mxnet.io">GluonCV Toolkit</a></li>
<li><a class="main-nav-link" href="https://gluon-nlp.mxnet.io/">GluonNLP Toolkit</a></li>
</ul>
</span>
<span id="dropdown-menu-position-anchor">
<a aria-expanded="true" aria-haspopup="true" class="main-nav-link dropdown-toggle" data-toggle="dropdown" href="#" role="button">API <span class="caret"></span></a>
<ul class="dropdown-menu navbar-menu" id="package-dropdown-menu">
<li><a class="main-nav-link" href="/versions/0.12.1/api/python/index.html">Python</a></li>
<li><a class="main-nav-link" href="/versions/0.12.1/api/c++/index.html">C++</a></li>
<li><a class="main-nav-link" href="/versions/0.12.1/api/julia/index.html">Julia</a></li>
<li><a class="main-nav-link" href="/versions/0.12.1/api/perl/index.html">Perl</a></li>
<li><a class="main-nav-link" href="/versions/0.12.1/api/r/index.html">R</a></li>
<li><a class="main-nav-link" href="/versions/0.12.1/api/scala/index.html">Scala</a></li>
</ul>
</span>
<span id="dropdown-menu-position-anchor-docs">
<a aria-expanded="true" aria-haspopup="true" class="main-nav-link dropdown-toggle" data-toggle="dropdown" href="#" role="button">Docs <span class="caret"></span></a>
<ul class="dropdown-menu navbar-menu" id="package-dropdown-menu-docs">
<li><a class="main-nav-link" href="/versions/0.12.1/faq/index.html">FAQ</a></li>
<li><a class="main-nav-link" href="/versions/0.12.1/tutorials/index.html">Tutorials</a>
<li><a class="main-nav-link" href="https://github.com/apache/incubator-mxnet/tree/0.12.1/example">Examples</a></li>
<li><a class="main-nav-link" href="/versions/0.12.1/architecture/index.html">Architecture</a></li>
<li><a class="main-nav-link" href="https://cwiki.apache.org/confluence/display/MXNET/Apache+MXNet+Home">Developer Wiki</a></li>
<li><a class="main-nav-link" href="/versions/0.12.1/model_zoo/index.html">Model Zoo</a></li>
<li><a class="main-nav-link" href="https://github.com/onnx/onnx-mxnet">ONNX</a></li>
</li></ul>
</span>
<span id="dropdown-menu-position-anchor-community">
<a aria-expanded="true" aria-haspopup="true" class="main-nav-link dropdown-toggle" data-toggle="dropdown" href="#" role="button">Community <span class="caret"></span></a>
<ul class="dropdown-menu navbar-menu" id="package-dropdown-menu-community">
<li><a class="main-nav-link" href="http://discuss.mxnet.io">Forum</a></li>
<li><a class="main-nav-link" href="https://github.com/apache/incubator-mxnet/tree/0.12.1">Github</a></li>
<li><a class="main-nav-link" href="/versions/0.12.1/community/contribute.html">Contribute</a></li>
<li><a class="main-nav-link" href="/versions/0.12.1/community/powered_by.html">Powered By</a></li>
</ul>
</span>
<span id="dropdown-menu-position-anchor-version" style="position: relative"><a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">0.12.1<span class="caret"></span></a><ul id="package-dropdown-menu" class="dropdown-menu"><li><a href="/">master</a></li><li><a href="/versions/1.7.0/">1.7.0</a></li><li><a href=/versions/1.6.0/>1.6.0</a></li><li><a href=/versions/1.5.0/index.html>1.5.0</a></li><li><a href=/versions/1.4.1/index.html>1.4.1</a></li><li><a href=/versions/1.3.1/index.html>1.3.1</a></li><li><a href=/versions/1.2.1/index.html>1.2.1</a></li><li><a href=/versions/1.1.0/index.html>1.1.0</a></li><li><a href=/versions/1.0.0/index.html>1.0.0</a></li><li><a href=/versions/0.12.1/index.html>0.12.1</a></li><li><a href=/versions/0.11.0/index.html>0.11.0</a></li></ul></span></nav>
<script> function getRootPath(){ return "../../../../../" } </script>
<div class="burgerIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button">☰</a>
<ul class="dropdown-menu" id="burgerMenu">
<li><a href="/versions/0.12.1/install/index.html">Install</a></li>
<li><a class="main-nav-link" href="/versions/0.12.1/tutorials/index.html">Tutorials</a></li>
<li class="dropdown-submenu dropdown">
<a aria-expanded="true" aria-haspopup="true" class="dropdown-toggle burger-link" data-toggle="dropdown" href="#" tabindex="-1">Gluon</a>
<ul class="dropdown-menu navbar-menu" id="package-dropdown-menu">
<li><a class="main-nav-link" href="/versions/0.12.1/tutorials/gluon/gluon.html">About</a></li>
<li><a class="main-nav-link" href="http://gluon.mxnet.io">The Straight Dope (Tutorials)</a></li>
<li><a class="main-nav-link" href="https://gluon-cv.mxnet.io">GluonCV Toolkit</a></li>
<li><a class="main-nav-link" href="https://gluon-nlp.mxnet.io/">GluonNLP Toolkit</a></li>
</ul>
</li>
<li class="dropdown-submenu">
<a aria-expanded="true" aria-haspopup="true" class="dropdown-toggle burger-link" data-toggle="dropdown" href="#" tabindex="-1">API</a>
<ul class="dropdown-menu">
<li><a class="main-nav-link" href="/versions/0.12.1/api/python/index.html">Python</a></li>
<li><a class="main-nav-link" href="/versions/0.12.1/api/c++/index.html">C++</a></li>
<li><a class="main-nav-link" href="/versions/0.12.1/api/julia/index.html">Julia</a></li>
<li><a class="main-nav-link" href="/versions/0.12.1/api/perl/index.html">Perl</a></li>
<li><a class="main-nav-link" href="/versions/0.12.1/api/r/index.html">R</a></li>
<li><a class="main-nav-link" href="/versions/0.12.1/api/scala/index.html">Scala</a></li>
</ul>
</li>
<li class="dropdown-submenu">
<a aria-expanded="true" aria-haspopup="true" class="dropdown-toggle burger-link" data-toggle="dropdown" href="#" tabindex="-1">Docs</a>
<ul class="dropdown-menu">
<li><a href="/versions/0.12.1/faq/index.html" tabindex="-1">FAQ</a></li>
<li><a href="/versions/0.12.1/tutorials/index.html" tabindex="-1">Tutorials</a></li>
<li><a href="https://github.com/apache/incubator-mxnet/tree/0.12.1/example" tabindex="-1">Examples</a></li>
<li><a href="/versions/0.12.1/architecture/index.html" tabindex="-1">Architecture</a></li>
<li><a href="https://cwiki.apache.org/confluence/display/MXNET/Apache+MXNet+Home" tabindex="-1">Developer Wiki</a></li>
<li><a href="/versions/0.12.1/model_zoo/index.html" tabindex="-1">Gluon Model Zoo</a></li>
<li><a href="https://github.com/onnx/onnx-mxnet" tabindex="-1">ONNX</a></li>
</ul>
</li>
<li class="dropdown-submenu dropdown">
<a aria-haspopup="true" class="dropdown-toggle burger-link" data-toggle="dropdown" href="#" role="button" tabindex="-1">Community</a>
<ul class="dropdown-menu">
<li><a href="http://discuss.mxnet.io" tabindex="-1">Forum</a></li>
<li><a href="https://github.com/apache/incubator-mxnet/tree/0.12.1" tabindex="-1">Github</a></li>
<li><a href="/versions/0.12.1/community/contribute.html" tabindex="-1">Contribute</a></li>
<li><a href="/versions/0.12.1/community/powered_by.html" tabindex="-1">Powered By</a></li>
</ul>
</li>
<li id="dropdown-menu-position-anchor-version-mobile" class="dropdown-submenu" style="position: relative"><a href="#" tabindex="-1">0.12.1</a><ul class="dropdown-menu"><li><a tabindex="-1" href=/>master</a></li><li><a tabindex="-1" href=/versions/1.6.0/>1.6.0</a></li><li><a tabindex="-1" href=/versions/1.5.0/index.html>1.5.0</a></li><li><a tabindex="-1" href=/versions/1.4.1/index.html>1.4.1</a></li><li><a tabindex="-1" href=/versions/1.3.1/index.html>1.3.1</a></li><li><a tabindex="-1" href=/versions/1.2.1/index.html>1.2.1</a></li><li><a tabindex="-1" href=/versions/1.1.0/index.html>1.1.0</a></li><li><a tabindex="-1" href=/versions/1.0.0/index.html>1.0.0</a></li><li><a tabindex="-1" href=/versions/0.12.1/index.html>0.12.1</a></li><li><a tabindex="-1" href=/versions/0.11.0/index.html>0.11.0</a></li></ul></li></ul>
</div>
<div class="plusIcon dropdown">
<a class="dropdown-toggle" data-toggle="dropdown" href="#" role="button"><span aria-hidden="true" class="glyphicon glyphicon-plus"></span></a>
<ul class="dropdown-menu dropdown-menu-right" id="plusMenu"></ul>
</div>
<div id="search-input-wrap">
<form action="../../../../../search.html" autocomplete="off" class="" method="get" role="search">
<div class="form-group inner-addon left-addon">
<i class="glyphicon glyphicon-search"></i>
<input class="form-control" name="q" placeholder="Search" type="text"/>
</div>
<input name="check_keywords" type="hidden" value="yes">
<input name="area" type="hidden" value="default"/>
</input></form>
<div id="search-preview"></div>
</div>
<div id="searchIcon">
<span aria-hidden="true" class="glyphicon glyphicon-search"></span>
</div>
<!-- <div id="lang-select-wrap"> -->
<!-- <label id="lang-select-label"> -->
<!-- <\!-- <i class="fa fa-globe"></i> -\-> -->
<!-- <span></span> -->
<!-- </label> -->
<!-- <select id="lang-select"> -->
<!-- <option value="en">Eng</option> -->
<!-- <option value="zh">中文</option> -->
<!-- </select> -->
<!-- </div> -->
<!-- <a id="mobile-nav-toggle">
<span class="mobile-nav-toggle-bar"></span>
<span class="mobile-nav-toggle-bar"></span>
<span class="mobile-nav-toggle-bar"></span>
</a> -->
</div>
</div>
</div>
<script type="text/javascript">
$('body').css('background', 'white');
</script>
<div class="container">
<div class="row">
<div aria-label="main navigation" class="sphinxsidebar leftsidebar" role="navigation">
<div class="sphinxsidebarwrapper">
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../../api/python/index.html">Python Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../api/r/index.html">R Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../api/julia/index.html">Julia Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../api/c++/index.html">C++ Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../api/scala/index.html">Scala Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../api/perl/index.html">Perl Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../faq/index.html">HowTo Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../architecture/index.html">System Documents</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Tutorials</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../../community/index.html">Community</a></li>
</ul>
</div>
</div>
<div class="content">
<div class="page-tracker"></div>
<h1>Source code for mxnet.gluon.model_zoo.vision.resnet</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">"""ResNets, implemented in Gluon."""</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">division</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'ResNetV1'</span><span class="p">,</span> <span class="s1">'ResNetV2'</span><span class="p">,</span>
<span class="s1">'BasicBlockV1'</span><span class="p">,</span> <span class="s1">'BasicBlockV2'</span><span class="p">,</span>
<span class="s1">'BottleneckV1'</span><span class="p">,</span> <span class="s1">'BottleneckV2'</span><span class="p">,</span>
<span class="s1">'resnet18_v1'</span><span class="p">,</span> <span class="s1">'resnet34_v1'</span><span class="p">,</span> <span class="s1">'resnet50_v1'</span><span class="p">,</span> <span class="s1">'resnet101_v1'</span><span class="p">,</span> <span class="s1">'resnet152_v1'</span><span class="p">,</span>
<span class="s1">'resnet18_v2'</span><span class="p">,</span> <span class="s1">'resnet34_v2'</span><span class="p">,</span> <span class="s1">'resnet50_v2'</span><span class="p">,</span> <span class="s1">'resnet101_v2'</span><span class="p">,</span> <span class="s1">'resnet152_v2'</span><span class="p">,</span>
<span class="s1">'get_resnet'</span><span class="p">]</span>
<span class="kn">from</span> <span class="nn">....context</span> <span class="k">import</span> <span class="n">cpu</span>
<span class="kn">from</span> <span class="nn">...block</span> <span class="k">import</span> <span class="n">HybridBlock</span>
<span class="kn">from</span> <span class="nn">...</span> <span class="k">import</span> <span class="n">nn</span>
<span class="c1"># Helpers</span>
<span class="k">def</span> <span class="nf">_conv3x3</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">in_channels</span><span class="p">):</span>
<span class="k">return</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_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">stride</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">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">)</span>
<span class="c1"># Blocks</span>
<div class="viewcode-block" id="BasicBlockV1"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.BasicBlockV1">[docs]</a><span class="k">class</span> <span class="nc">BasicBlockV1</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""BasicBlock V1 from `"Deep Residual Learning for Image Recognition"</span>
<span class="sd"> <http://arxiv.org/abs/1512.03385>`_ paper.</span>
<span class="sd"> This is used for ResNet V1 for 18, 34 layers.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> channels : int</span>
<span class="sd"> Number of output channels.</span>
<span class="sd"> stride : int</span>
<span class="sd"> Stride size.</span>
<span class="sd"> downsample : bool, default False</span>
<span class="sd"> Whether to downsample the input.</span>
<span class="sd"> in_channels : int, default 0</span>
<span class="sd"> Number of input channels. Default is 0, to infer from the graph.</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">channels</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">downsample</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</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">BasicBlockV1</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="bp">self</span><span class="o">.</span><span class="n">body</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="bp">self</span><span class="o">.</span><span class="n">body</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">_conv3x3</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">in_channels</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">body</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="bp">self</span><span class="o">.</span><span class="n">body</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="bp">self</span><span class="o">.</span><span class="n">body</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">_conv3x3</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">channels</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">body</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="k">if</span> <span class="n">downsample</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">downsample</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="bp">self</span><span class="o">.</span><span class="n">downsample</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_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span>
<span class="n">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">downsample</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="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">downsample</span> <span class="o">=</span> <span class="kc">None</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">residual</span> <span class="o">=</span> <span class="n">x</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">body</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">downsample</span><span class="p">:</span>
<span class="n">residual</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="p">(</span><span class="n">residual</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="n">residual</span><span class="o">+</span><span class="n">x</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span></div>
<div class="viewcode-block" id="BottleneckV1"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.BottleneckV1">[docs]</a><span class="k">class</span> <span class="nc">BottleneckV1</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""Bottleneck V1 from `"Deep Residual Learning for Image Recognition"</span>
<span class="sd"> <http://arxiv.org/abs/1512.03385>`_ paper.</span>
<span class="sd"> This is used for ResNet V1 for 50, 101, 152 layers.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> channels : int</span>
<span class="sd"> Number of output channels.</span>
<span class="sd"> stride : int</span>
<span class="sd"> Stride size.</span>
<span class="sd"> downsample : bool, default False</span>
<span class="sd"> Whether to downsample the input.</span>
<span class="sd"> in_channels : int, default 0</span>
<span class="sd"> Number of input channels. Default is 0, to infer from the graph.</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">channels</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">downsample</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</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">BottleneckV1</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="bp">self</span><span class="o">.</span><span class="n">body</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="bp">self</span><span class="o">.</span><span class="n">body</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="o">//</span><span class="mi">4</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">strides</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">body</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="bp">self</span><span class="o">.</span><span class="n">body</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="bp">self</span><span class="o">.</span><span class="n">body</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">_conv3x3</span><span class="p">(</span><span class="n">channels</span><span class="o">//</span><span class="mi">4</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">channels</span><span class="o">//</span><span class="mi">4</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">body</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="bp">self</span><span class="o">.</span><span class="n">body</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="bp">self</span><span class="o">.</span><span class="n">body</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_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">strides</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">body</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="k">if</span> <span class="n">downsample</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">downsample</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="bp">self</span><span class="o">.</span><span class="n">downsample</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_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span>
<span class="n">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">downsample</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="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">downsample</span> <span class="o">=</span> <span class="kc">None</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">residual</span> <span class="o">=</span> <span class="n">x</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">body</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">downsample</span><span class="p">:</span>
<span class="n">residual</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="p">(</span><span class="n">residual</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">residual</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span></div>
<div class="viewcode-block" id="BasicBlockV2"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.BasicBlockV2">[docs]</a><span class="k">class</span> <span class="nc">BasicBlockV2</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""BasicBlock V2 from</span>
<span class="sd"> `"Identity Mappings in Deep Residual Networks"</span>
<span class="sd"> <https://arxiv.org/abs/1603.05027>`_ paper.</span>
<span class="sd"> This is used for ResNet V2 for 18, 34 layers.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> channels : int</span>
<span class="sd"> Number of output channels.</span>
<span class="sd"> stride : int</span>
<span class="sd"> Stride size.</span>
<span class="sd"> downsample : bool, default False</span>
<span class="sd"> Whether to downsample the input.</span>
<span class="sd"> in_channels : int, default 0</span>
<span class="sd"> Number of input channels. Default is 0, to infer from the graph.</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">channels</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">downsample</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</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">BasicBlockV2</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="bp">self</span><span class="o">.</span><span class="n">bn1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">_conv3x3</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">in_channels</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bn2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">_conv3x3</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">channels</span><span class="p">)</span>
<span class="k">if</span> <span class="n">downsample</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">downsample</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">channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">downsample</span> <span class="o">=</span> <span class="kc">None</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">residual</span> <span class="o">=</span> <span class="n">x</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn1</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">F</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="p">:</span>
<span class="n">residual</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</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">conv1</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">bn2</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">F</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s1">'relu'</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">conv2</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">residual</span></div>
<div class="viewcode-block" id="BottleneckV2"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.BottleneckV2">[docs]</a><span class="k">class</span> <span class="nc">BottleneckV2</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""Bottleneck V2 from</span>
<span class="sd"> `"Identity Mappings in Deep Residual Networks"</span>
<span class="sd"> <https://arxiv.org/abs/1603.05027>`_ paper.</span>
<span class="sd"> This is used for ResNet V2 for 50, 101, 152 layers.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> channels : int</span>
<span class="sd"> Number of output channels.</span>
<span class="sd"> stride : int</span>
<span class="sd"> Stride size.</span>
<span class="sd"> downsample : bool, default False</span>
<span class="sd"> Whether to downsample the input.</span>
<span class="sd"> in_channels : int, default 0</span>
<span class="sd"> Number of input channels. Default is 0, to infer from the graph.</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">channels</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">downsample</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</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">BottleneckV2</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="bp">self</span><span class="o">.</span><span class="n">bn1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv1</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">channels</span><span class="o">//</span><span class="mi">4</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">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bn2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">_conv3x3</span><span class="p">(</span><span class="n">channels</span><span class="o">//</span><span class="mi">4</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">channels</span><span class="o">//</span><span class="mi">4</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bn3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv3</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">channels</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">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="k">if</span> <span class="n">downsample</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">downsample</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">channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">downsample</span> <span class="o">=</span> <span class="kc">None</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">residual</span> <span class="o">=</span> <span class="n">x</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn1</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">F</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="p">:</span>
<span class="n">residual</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</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">conv1</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">bn2</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">F</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s1">'relu'</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">conv2</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">bn3</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">F</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s1">'relu'</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">conv3</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">residual</span></div>
<span class="c1"># Nets</span>
<div class="viewcode-block" id="ResNetV1"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.ResNetV1">[docs]</a><span class="k">class</span> <span class="nc">ResNetV1</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""ResNet V1 model from</span>
<span class="sd"> `"Deep Residual Learning for Image Recognition"</span>
<span class="sd"> <http://arxiv.org/abs/1512.03385>`_ paper.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> block : HybridBlock</span>
<span class="sd"> Class for the residual block. Options are BasicBlockV1, BottleneckV1.</span>
<span class="sd"> layers : list of int</span>
<span class="sd"> Numbers of layers in each block</span>
<span class="sd"> channels : list of int</span>
<span class="sd"> Numbers of channels in each block. Length should be one larger than layers list.</span>
<span class="sd"> classes : int, default 1000</span>
<span class="sd"> Number of classification classes.</span>
<span class="sd"> thumbnail : bool, default False</span>
<span class="sd"> Enable thumbnail.</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">block</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">channels</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="n">thumbnail</span><span class="o">=</span><span class="kc">False</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">ResNetV1</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">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">layers</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">channels</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</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">if</span> <span class="n">thumbnail</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">_conv3x3</span><span class="p">(</span><span class="n">channels</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="k">else</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">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">7</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">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">in_channels</span><span class="o">=</span><span class="mi">3</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">BatchNorm</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">Activation</span><span class="p">(</span><span class="s1">'relu'</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">MaxPool2D</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">1</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">num_layer</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">layers</span><span class="p">):</span>
<span class="n">stride</span> <span class="o">=</span> <span class="mi">1</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">2</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="bp">self</span><span class="o">.</span><span class="n">_make_layer</span><span class="p">(</span><span class="n">block</span><span class="p">,</span> <span class="n">num_layer</span><span class="p">,</span> <span class="n">channels</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">],</span>
<span class="n">stride</span><span class="p">,</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="n">i</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">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="bp">self</span><span class="o">.</span><span class="n">classifier</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">classifier</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">Dense</span><span class="p">(</span><span class="n">classes</span><span class="p">,</span> <span class="n">in_units</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
<span class="k">def</span> <span class="nf">_make_layer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">block</span><span class="p">,</span> <span class="n">layers</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">stage_index</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="n">layer</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">'stage</span><span class="si">%d</span><span class="s1">_'</span><span class="o">%</span><span class="n">stage_index</span><span class="p">)</span>
<span class="k">with</span> <span class="n">layer</span><span class="o">.</span><span class="n">name_scope</span><span class="p">():</span>
<span class="n">layer</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">block</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">channels</span> <span class="o">!=</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</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">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">layers</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
<span class="n">layer</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">block</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="n">channels</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">return</span> <span class="n">layer</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">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>
<div class="viewcode-block" id="ResNetV2"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.ResNetV2">[docs]</a><span class="k">class</span> <span class="nc">ResNetV2</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""ResNet V2 model from</span>
<span class="sd"> `"Identity Mappings in Deep Residual Networks"</span>
<span class="sd"> <https://arxiv.org/abs/1603.05027>`_ paper.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> block : HybridBlock</span>
<span class="sd"> Class for the residual block. Options are BasicBlockV1, BottleneckV1.</span>
<span class="sd"> layers : list of int</span>
<span class="sd"> Numbers of layers in each block</span>
<span class="sd"> channels : list of int</span>
<span class="sd"> Numbers of channels in each block. Length should be one larger than layers list.</span>
<span class="sd"> classes : int, default 1000</span>
<span class="sd"> Number of classification classes.</span>
<span class="sd"> thumbnail : bool, default False</span>
<span class="sd"> Enable thumbnail.</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">block</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">channels</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="n">thumbnail</span><span class="o">=</span><span class="kc">False</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">ResNetV2</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">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">layers</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">channels</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</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="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">BatchNorm</span><span class="p">(</span><span class="n">scale</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
<span class="k">if</span> <span class="n">thumbnail</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">_conv3x3</span><span class="p">(</span><span class="n">channels</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="k">else</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">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">7</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">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">in_channels</span><span class="o">=</span><span class="mi">3</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">BatchNorm</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">Activation</span><span class="p">(</span><span class="s1">'relu'</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">MaxPool2D</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">1</span><span class="p">))</span>
<span class="n">in_channels</span> <span class="o">=</span> <span class="n">channels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">num_layer</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">layers</span><span class="p">):</span>
<span class="n">stride</span> <span class="o">=</span> <span class="mi">1</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">2</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="bp">self</span><span class="o">.</span><span class="n">_make_layer</span><span class="p">(</span><span class="n">block</span><span class="p">,</span> <span class="n">num_layer</span><span class="p">,</span> <span class="n">channels</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">],</span>
<span class="n">stride</span><span class="p">,</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">))</span>
<span class="n">in_channels</span> <span class="o">=</span> <span class="n">channels</span><span class="p">[</span><span class="n">i</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">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="bp">self</span><span class="o">.</span><span class="n">classifier</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="bp">self</span><span class="o">.</span><span class="n">classifier</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="bp">self</span><span class="o">.</span><span class="n">classifier</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">classifier</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">classifier</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">Dense</span><span class="p">(</span><span class="n">classes</span><span class="p">,</span> <span class="n">in_units</span><span class="o">=</span><span class="n">in_channels</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">_make_layer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">block</span><span class="p">,</span> <span class="n">layers</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">stage_index</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="n">layer</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">'stage</span><span class="si">%d</span><span class="s1">_'</span><span class="o">%</span><span class="n">stage_index</span><span class="p">)</span>
<span class="k">with</span> <span class="n">layer</span><span class="o">.</span><span class="n">name_scope</span><span class="p">():</span>
<span class="n">layer</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">block</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">channels</span> <span class="o">!=</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</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">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">layers</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
<span class="n">layer</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">block</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="n">channels</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">return</span> <span class="n">layer</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">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>
<span class="c1"># Specification</span>
<span class="n">resnet_spec</span> <span class="o">=</span> <span class="p">{</span><span class="mi">18</span><span class="p">:</span> <span class="p">(</span><span class="s1">'basic_block'</span><span class="p">,</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="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</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="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">]),</span>
<span class="mi">34</span><span class="p">:</span> <span class="p">(</span><span class="s1">'basic_block'</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</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="p">[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">]),</span>
<span class="mi">50</span><span class="p">:</span> <span class="p">(</span><span class="s1">'bottle_neck'</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</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="p">[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">2048</span><span class="p">]),</span>
<span class="mi">101</span><span class="p">:</span> <span class="p">(</span><span class="s1">'bottle_neck'</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">23</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</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">512</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">2048</span><span class="p">]),</span>
<span class="mi">152</span><span class="p">:</span> <span class="p">(</span><span class="s1">'bottle_neck'</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">36</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</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">512</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">2048</span><span class="p">])}</span>
<span class="n">resnet_net_versions</span> <span class="o">=</span> <span class="p">[</span><span class="n">ResNetV1</span><span class="p">,</span> <span class="n">ResNetV2</span><span class="p">]</span>
<span class="n">resnet_block_versions</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">'basic_block'</span><span class="p">:</span> <span class="n">BasicBlockV1</span><span class="p">,</span> <span class="s1">'bottle_neck'</span><span class="p">:</span> <span class="n">BottleneckV1</span><span class="p">},</span>
<span class="p">{</span><span class="s1">'basic_block'</span><span class="p">:</span> <span class="n">BasicBlockV2</span><span class="p">,</span> <span class="s1">'bottle_neck'</span><span class="p">:</span> <span class="n">BottleneckV2</span><span class="p">}]</span>
<span class="c1"># Constructor</span>
<div class="viewcode-block" id="get_resnet"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.get_resnet">[docs]</a><span class="k">def</span> <span class="nf">get_resnet</span><span class="p">(</span><span class="n">version</span><span class="p">,</span> <span class="n">num_layers</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">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">"""ResNet V1 model from `"Deep Residual Learning for Image Recognition"</span>
<span class="sd"> <http://arxiv.org/abs/1512.03385>`_ paper.</span>
<span class="sd"> ResNet V2 model from `"Identity Mappings in Deep Residual Networks"</span>
<span class="sd"> <https://arxiv.org/abs/1603.05027>`_ paper.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> version : int</span>
<span class="sd"> Version of ResNet. Options are 1, 2.</span>
<span class="sd"> num_layers : int</span>
<span class="sd"> Numbers of layers. Options are 18, 34, 50, 101, 152.</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">block_type</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">channels</span> <span class="o">=</span> <span class="n">resnet_spec</span><span class="p">[</span><span class="n">num_layers</span><span class="p">]</span>
<span class="n">resnet_class</span> <span class="o">=</span> <span class="n">resnet_net_versions</span><span class="p">[</span><span class="n">version</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">block_class</span> <span class="o">=</span> <span class="n">resnet_block_versions</span><span class="p">[</span><span class="n">version</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="n">block_type</span><span class="p">]</span>
<span class="n">net</span> <span class="o">=</span> <span class="n">resnet_class</span><span class="p">(</span><span class="n">block_class</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">channels</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="k">import</span> <span class="n">get_model_file</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">'resnet</span><span class="si">%d</span><span class="s1">_v</span><span class="si">%d</span><span class="s1">'</span><span class="o">%</span><span class="p">(</span><span class="n">num_layers</span><span class="p">,</span> <span class="n">version</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="resnet18_v1"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.resnet18_v1">[docs]</a><span class="k">def</span> <span class="nf">resnet18_v1</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">"""ResNet-18 V1 model from `"Deep Residual Learning for Image Recognition"</span>
<span class="sd"> <http://arxiv.org/abs/1512.03385>`_ paper.</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"> root : str, default '~/.mxnet/models'</span>
<span class="sd"> Location for keeping the model parameters.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">get_resnet</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">18</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="resnet34_v1"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.resnet34_v1">[docs]</a><span class="k">def</span> <span class="nf">resnet34_v1</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">"""ResNet-34 V1 model from `"Deep Residual Learning for Image Recognition"</span>
<span class="sd"> <http://arxiv.org/abs/1512.03385>`_ paper.</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"> root : str, default '~/.mxnet/models'</span>
<span class="sd"> Location for keeping the model parameters.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">get_resnet</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">34</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="resnet50_v1"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.resnet50_v1">[docs]</a><span class="k">def</span> <span class="nf">resnet50_v1</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">"""ResNet-50 V1 model from `"Deep Residual Learning for Image Recognition"</span>
<span class="sd"> <http://arxiv.org/abs/1512.03385>`_ paper.</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"> root : str, default '~/.mxnet/models'</span>
<span class="sd"> Location for keeping the model parameters.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">get_resnet</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="resnet101_v1"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.resnet101_v1">[docs]</a><span class="k">def</span> <span class="nf">resnet101_v1</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">"""ResNet-101 V1 model from `"Deep Residual Learning for Image Recognition"</span>
<span class="sd"> <http://arxiv.org/abs/1512.03385>`_ paper.</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"> root : str, default '~/.mxnet/models'</span>
<span class="sd"> Location for keeping the model parameters.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">get_resnet</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">101</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="resnet152_v1"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.resnet152_v1">[docs]</a><span class="k">def</span> <span class="nf">resnet152_v1</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">"""ResNet-152 V1 model from `"Deep Residual Learning for Image Recognition"</span>
<span class="sd"> <http://arxiv.org/abs/1512.03385>`_ paper.</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"> root : str, default '~/.mxnet/models'</span>
<span class="sd"> Location for keeping the model parameters.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">get_resnet</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">152</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="resnet18_v2"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.resnet18_v2">[docs]</a><span class="k">def</span> <span class="nf">resnet18_v2</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">"""ResNet-18 V2 model from `"Identity Mappings in Deep Residual Networks"</span>
<span class="sd"> <https://arxiv.org/abs/1603.05027>`_ paper.</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"> root : str, default '~/.mxnet/models'</span>
<span class="sd"> Location for keeping the model parameters.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">get_resnet</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">18</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="resnet34_v2"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.resnet34_v2">[docs]</a><span class="k">def</span> <span class="nf">resnet34_v2</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">"""ResNet-34 V2 model from `"Identity Mappings in Deep Residual Networks"</span>
<span class="sd"> <https://arxiv.org/abs/1603.05027>`_ paper.</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"> root : str, default '~/.mxnet/models'</span>
<span class="sd"> Location for keeping the model parameters.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">get_resnet</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">34</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="resnet50_v2"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.resnet50_v2">[docs]</a><span class="k">def</span> <span class="nf">resnet50_v2</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">"""ResNet-50 V2 model from `"Identity Mappings in Deep Residual Networks"</span>
<span class="sd"> <https://arxiv.org/abs/1603.05027>`_ paper.</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"> root : str, default '~/.mxnet/models'</span>
<span class="sd"> Location for keeping the model parameters.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">get_resnet</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="resnet101_v2"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.resnet101_v2">[docs]</a><span class="k">def</span> <span class="nf">resnet101_v2</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">"""ResNet-101 V2 model from `"Identity Mappings in Deep Residual Networks"</span>
<span class="sd"> <https://arxiv.org/abs/1603.05027>`_ paper.</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"> root : str, default '~/.mxnet/models'</span>
<span class="sd"> Location for keeping the model parameters.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">get_resnet</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">101</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="resnet152_v2"><a class="viewcode-back" href="../../../../../api/python/gluon/model_zoo.html#mxnet.gluon.model_zoo.vision.resnet152_v2">[docs]</a><span class="k">def</span> <span class="nf">resnet152_v2</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">"""ResNet-152 V2 model from `"Identity Mappings in Deep Residual Networks"</span>
<span class="sd"> <https://arxiv.org/abs/1603.05027>`_ paper.</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"> root : str, default '~/.mxnet/models'</span>
<span class="sd"> Location for keeping the model parameters.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">get_resnet</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">152</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
</pre></div>
</div>
</div>
<div aria-label="main navigation" class="sphinxsidebar rightsidebar" role="navigation">
<div class="sphinxsidebarwrapper">
</div>
</div>
</div><div class="footer">
<div class="section-disclaimer">
<div class="container">
<div>
<img height="60" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/apache_incubator_logo.png"/>
<p>
Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), <strong>sponsored by the <i>Apache Incubator</i></strong>. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.
</p>
<p>
"Copyright © 2017-2018, The Apache Software Foundation
Apache MXNet, MXNet, Apache, the Apache feather, and the Apache MXNet project logo are either registered trademarks or trademarks of the Apache Software Foundation."
</p>
</div>
</div>
</div>
</div> <!-- pagename != index -->
</div>
<script crossorigin="anonymous" integrity="sha384-0mSbJDEHialfmuBBQP6A4Qrprq5OVfW37PRR3j5ELqxss1yVqOtnepnHVP9aJ7xS" src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/js/bootstrap.min.js"></script>
<script src="../../../../../_static/js/sidebar.js" type="text/javascript"></script>
<script src="../../../../../_static/js/search.js" type="text/javascript"></script>
<script src="../../../../../_static/js/navbar.js" type="text/javascript"></script>
<script src="../../../../../_static/js/clipboard.min.js" type="text/javascript"></script>
<script src="../../../../../_static/js/copycode.js" type="text/javascript"></script>
<script src="../../../../../_static/js/page.js" type="text/javascript"></script>
<script src="../../../../../_static/js/docversion.js" type="text/javascript"></script>
<script type="text/javascript">
$('body').ready(function () {
$('body').css('visibility', 'visible');
});
</script>
</body>
</html>