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<li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_classification_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ๋ถ๋ฅํ๊ธฐ</a></li>
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<div class="sphx-glr-example-title section" id="experimental-static-quantization-with-eager-mode-in-pytorch">
<span id="sphx-glr-advanced-static-quantization-tutorial-py"></span><h1>(experimental) Static Quantization with Eager Mode in PyTorch<a class="headerlink" href="#experimental-static-quantization-with-eager-mode-in-pytorch" title="Permalink to this headline">ยถ</a></h1>
<p><strong>Author</strong>: <a class="reference external" href="https://github.com/raghuramank100">Raghuraman Krishnamoorthi</a></p>
<p><strong>Edited by</strong>: <a class="reference external" href="https://github.com/SethHWeidman/">Seth Weidman</a></p>
<p>This tutorial shows how to do post-training static quantization, as well as illustrating
two more advanced techniques - per-channel quantization and quantization-aware training -
to further improve the modelโs accuracy. Note that quantization is currently only supported
for CPUs, so we will not be utilizing GPUs / CUDA in this tutorial.</p>
<p>By the end of this tutorial, you will see how quantization in PyTorch can result in
significant decreases in model size while increasing speed. Furthermore, youโll see how
to easily apply some advanced quantization techniques shown
<a class="reference external" href="https://arxiv.org/abs/1806.08342">here</a> so that your quantized models take much less
of an accuracy hit than they would otherwise.</p>
<p>Warning: we use a lot of boilerplate code from other PyTorch repos to, for example,
define the <code class="docutils literal notranslate"><span class="pre">MobileNetV2</span></code> model archtecture, define data loaders, and so on. We of course
encourage you to read it; but if you want to get to the quantization features, feel free
to skip to the โ4. Post-training static quantizationโ section.</p>
<p>Weโll start by doing the necessary imports:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torchvision</span>
<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="k">import</span> <span class="n">DataLoader</span>
<span class="kn">from</span> <span class="nn">torchvision</span> <span class="k">import</span> <span class="n">datasets</span>
<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">torch.quantization</span>
<span class="c1"># # Setup warnings</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span>
<span class="n">action</span><span class="o">=</span><span class="s1">'ignore'</span><span class="p">,</span>
<span class="n">category</span><span class="o">=</span><span class="ne">DeprecationWarning</span><span class="p">,</span>
<span class="n">module</span><span class="o">=</span><span class="sa">r</span><span class="s1">'.*'</span>
<span class="p">)</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span>
<span class="n">action</span><span class="o">=</span><span class="s1">'default'</span><span class="p">,</span>
<span class="n">module</span><span class="o">=</span><span class="sa">r</span><span class="s1">'torch.quantization'</span>
<span class="p">)</span>
<span class="c1"># Specify random seed for repeatable results</span>
<span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">191009</span><span class="p">)</span>
</pre></div>
</div>
<div class="section" id="model-architecture">
<h2>1. Model architecture<a class="headerlink" href="#model-architecture" title="Permalink to this headline">ยถ</a></h2>
<p>We first define the MobileNetV2 model architecture, with several notable modifications
to enable quantization:</p>
<ul class="simple">
<li><p>Replacing addition with <code class="docutils literal notranslate"><span class="pre">nn.quantized.FloatFunctional</span></code></p></li>
<li><p>Insert <code class="docutils literal notranslate"><span class="pre">QuantStub</span></code> and <code class="docutils literal notranslate"><span class="pre">DeQuantStub</span></code> at the beginning and end of the network.</p></li>
<li><p>Replace ReLU6 with ReLU</p></li>
</ul>
<p>Note: this code is taken from
<a class="reference external" href="https://github.com/pytorch/vision/blob/master/torchvision/models/mobilenet.py">here</a>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.quantization</span> <span class="k">import</span> <span class="n">QuantStub</span><span class="p">,</span> <span class="n">DeQuantStub</span>
<span class="k">def</span> <span class="nf">_make_divisible</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">divisor</span><span class="p">,</span> <span class="n">min_value</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> This function is taken from the original tf repo.</span>
<span class="sd"> It ensures that all layers have a channel number that is divisible by 8</span>
<span class="sd"> It can be seen here:</span>
<span class="sd"> https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py</span>
<span class="sd"> :param v:</span>
<span class="sd"> :param divisor:</span>
<span class="sd"> :param min_value:</span>
<span class="sd"> :return:</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">min_value</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">min_value</span> <span class="o">=</span> <span class="n">divisor</span>
<span class="n">new_v</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">min_value</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">v</span> <span class="o">+</span> <span class="n">divisor</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> <span class="o">//</span> <span class="n">divisor</span> <span class="o">*</span> <span class="n">divisor</span><span class="p">)</span>
<span class="c1"># Make sure that round down does not go down by more than 10%.</span>
<span class="k">if</span> <span class="n">new_v</span> <span class="o"><</span> <span class="mf">0.9</span> <span class="o">*</span> <span class="n">v</span><span class="p">:</span>
<span class="n">new_v</span> <span class="o">+=</span> <span class="n">divisor</span>
<span class="k">return</span> <span class="n">new_v</span>
<span class="k">class</span> <span class="nc">ConvBNReLU</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_planes</span><span class="p">,</span> <span class="n">out_planes</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="n">padding</span> <span class="o">=</span> <span class="p">(</span><span class="n">kernel_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span>
<span class="nb">super</span><span class="p">(</span><span class="n">ConvBNReLU</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_planes</span><span class="p">,</span> <span class="n">out_planes</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="n">groups</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">out_planes</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">),</span>
<span class="c1"># Replace with ReLU</span>
<span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="p">)</span>
<span class="k">class</span> <span class="nc">InvertedResidual</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inp</span><span class="p">,</span> <span class="n">oup</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">expand_ratio</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">InvertedResidual</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stride</span> <span class="o">=</span> <span class="n">stride</span>
<span class="k">assert</span> <span class="n">stride</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
<span class="n">hidden_dim</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">inp</span> <span class="o">*</span> <span class="n">expand_ratio</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">use_res_connect</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stride</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">inp</span> <span class="o">==</span> <span class="n">oup</span>
<span class="n">layers</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">if</span> <span class="n">expand_ratio</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
<span class="c1"># pw</span>
<span class="n">layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ConvBNReLU</span><span class="p">(</span><span class="n">inp</span><span class="p">,</span> <span class="n">hidden_dim</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
<span class="n">layers</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span>
<span class="c1"># dw</span>
<span class="n">ConvBNReLU</span><span class="p">(</span><span class="n">hidden_dim</span><span class="p">,</span> <span class="n">hidden_dim</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="n">hidden_dim</span><span class="p">),</span>
<span class="c1"># pw-linear</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">hidden_dim</span><span class="p">,</span> <span class="n">oup</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">oup</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">),</span>
<span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="n">layers</span><span class="p">)</span>
<span class="c1"># Replace torch.add with floatfunctional</span>
<span class="bp">self</span><span class="o">.</span><span class="n">skip_add</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">quantized</span><span class="o">.</span><span class="n">FloatFunctional</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_res_connect</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">skip_add</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">MobileNetV2</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">width_mult</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">inverted_residual_setting</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">round_nearest</span><span class="o">=</span><span class="mi">8</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> MobileNet V2 main class</span>
<span class="sd"> Args:</span>
<span class="sd"> num_classes (int): Number of classes</span>
<span class="sd"> width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount</span>
<span class="sd"> inverted_residual_setting: Network structure</span>
<span class="sd"> round_nearest (int): Round the number of channels in each layer to be a multiple of this number</span>
<span class="sd"> Set to 1 to turn off rounding</span>
<span class="sd"> """</span>
<span class="nb">super</span><span class="p">(</span><span class="n">MobileNetV2</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="n">block</span> <span class="o">=</span> <span class="n">InvertedResidual</span>
<span class="n">input_channel</span> <span class="o">=</span> <span class="mi">32</span>
<span class="n">last_channel</span> <span class="o">=</span> <span class="mi">1280</span>
<span class="k">if</span> <span class="n">inverted_residual_setting</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">inverted_residual_setting</span> <span class="o">=</span> <span class="p">[</span>
<span class="c1"># t, c, n, s</span>
<span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">24</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
<span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
<span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
<span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">96</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">160</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
<span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">320</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="p">]</span>
<span class="c1"># only check the first element, assuming user knows t,c,n,s are required</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">inverted_residual_setting</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">inverted_residual_setting</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">4</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"inverted_residual_setting should be non-empty "</span>
<span class="s2">"or a 4-element list, got </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">inverted_residual_setting</span><span class="p">))</span>
<span class="c1"># building first layer</span>
<span class="n">input_channel</span> <span class="o">=</span> <span class="n">_make_divisible</span><span class="p">(</span><span class="n">input_channel</span> <span class="o">*</span> <span class="n">width_mult</span><span class="p">,</span> <span class="n">round_nearest</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">last_channel</span> <span class="o">=</span> <span class="n">_make_divisible</span><span class="p">(</span><span class="n">last_channel</span> <span class="o">*</span> <span class="nb">max</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">width_mult</span><span class="p">),</span> <span class="n">round_nearest</span><span class="p">)</span>
<span class="n">features</span> <span class="o">=</span> <span class="p">[</span><span class="n">ConvBNReLU</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">input_channel</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)]</span>
<span class="c1"># building inverted residual blocks</span>
<span class="k">for</span> <span class="n">t</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">inverted_residual_setting</span><span class="p">:</span>
<span class="n">output_channel</span> <span class="o">=</span> <span class="n">_make_divisible</span><span class="p">(</span><span class="n">c</span> <span class="o">*</span> <span class="n">width_mult</span><span class="p">,</span> <span class="n">round_nearest</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="n">stride</span> <span class="o">=</span> <span class="n">s</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">1</span>
<span class="n">features</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">block</span><span class="p">(</span><span class="n">input_channel</span><span class="p">,</span> <span class="n">output_channel</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">expand_ratio</span><span class="o">=</span><span class="n">t</span><span class="p">))</span>
<span class="n">input_channel</span> <span class="o">=</span> <span class="n">output_channel</span>
<span class="c1"># building last several layers</span>
<span class="n">features</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ConvBNReLU</span><span class="p">(</span><span class="n">input_channel</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_channel</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
<span class="c1"># make it nn.Sequential</span>
<span class="bp">self</span><span class="o">.</span><span class="n">features</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="n">features</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">quant</span> <span class="o">=</span> <span class="n">QuantStub</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dequant</span> <span class="o">=</span> <span class="n">DeQuantStub</span><span class="p">()</span>
<span class="c1"># building classifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">last_channel</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">),</span>
<span class="p">)</span>
<span class="c1"># weight initialization</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">modules</span><span class="p">():</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">):</span>
<span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">kaiming_normal_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'fan_out'</span><span class="p">)</span>
<span class="k">if</span> <span class="n">m</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">zeros_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">):</span>
<span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">ones_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
<span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">zeros_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">):</span>
<span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
<span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">zeros_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">quant</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">mean</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dequant</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="c1"># Fuse Conv+BN and Conv+BN+Relu modules prior to quantization</span>
<span class="c1"># This operation does not change the numerics</span>
<span class="k">def</span> <span class="nf">fuse_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">modules</span><span class="p">():</span>
<span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">m</span><span class="p">)</span> <span class="o">==</span> <span class="n">ConvBNReLU</span><span class="p">:</span>
<span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">fuse_modules</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="p">[</span><span class="s1">'0'</span><span class="p">,</span> <span class="s1">'1'</span><span class="p">,</span> <span class="s1">'2'</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">m</span><span class="p">)</span> <span class="o">==</span> <span class="n">InvertedResidual</span><span class="p">:</span>
<span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">conv</span><span class="p">)):</span>
<span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">conv</span><span class="p">[</span><span class="n">idx</span><span class="p">])</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">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">fuse_modules</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">conv</span><span class="p">,</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">idx</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="helper-functions">
<h2>2. Helper functions<a class="headerlink" href="#helper-functions" title="Permalink to this headline">ยถ</a></h2>
<p>We next define several helper functions to help with model evaluation. These mostly come from
<a class="reference external" href="https://github.com/pytorch/examples/blob/master/imagenet/main.py">here</a>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">AverageMeter</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">"""Computes and stores the average and current value"""</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">name</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">':f'</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fmt</span> <span class="o">=</span> <span class="n">fmt</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">val</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">avg</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">n</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">val</span> <span class="o">=</span> <span class="n">val</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sum</span> <span class="o">+=</span> <span class="n">val</span> <span class="o">*</span> <span class="n">n</span>
<span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="n">n</span>
<span class="bp">self</span><span class="o">.</span><span class="n">avg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span>
<span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">fmtstr</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{name}</span><span class="s1"> {val'</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">fmt</span> <span class="o">+</span> <span class="s1">'} ({avg'</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">fmt</span> <span class="o">+</span> <span class="s1">'})'</span>
<span class="k">return</span> <span class="n">fmtstr</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">accuracy</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">topk</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,)):</span>
<span class="sd">"""Computes the accuracy over the k top predictions for the specified values of k"""</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="n">maxk</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">topk</span><span class="p">)</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">_</span><span class="p">,</span> <span class="n">pred</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="n">maxk</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">t</span><span class="p">()</span>
<span class="n">correct</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">target</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">expand_as</span><span class="p">(</span><span class="n">pred</span><span class="p">))</span>
<span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">topk</span><span class="p">:</span>
<span class="n">correct_k</span> <span class="o">=</span> <span class="n">correct</span><span class="p">[:</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">res</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">correct_k</span><span class="o">.</span><span class="n">mul_</span><span class="p">(</span><span class="mf">100.0</span> <span class="o">/</span> <span class="n">batch_size</span><span class="p">))</span>
<span class="k">return</span> <span class="n">res</span>
<span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">data_loader</span><span class="p">,</span> <span class="n">neval_batches</span><span class="p">):</span>
<span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="n">top1</span> <span class="o">=</span> <span class="n">AverageMeter</span><span class="p">(</span><span class="s1">'Acc@1'</span><span class="p">,</span> <span class="s1">':6.2f'</span><span class="p">)</span>
<span class="n">top5</span> <span class="o">=</span> <span class="n">AverageMeter</span><span class="p">(</span><span class="s1">'Acc@5'</span><span class="p">,</span> <span class="s1">':6.2f'</span><span class="p">)</span>
<span class="n">cnt</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="k">for</span> <span class="n">image</span><span class="p">,</span> <span class="n">target</span> <span class="ow">in</span> <span class="n">data_loader</span><span class="p">:</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
<span class="n">cnt</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">acc1</span><span class="p">,</span> <span class="n">acc5</span> <span class="o">=</span> <span class="n">accuracy</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">topk</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'.'</span><span class="p">,</span> <span class="n">end</span> <span class="o">=</span> <span class="s1">''</span><span class="p">)</span>
<span class="n">top1</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">acc1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">image</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
<span class="n">top5</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">acc5</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">image</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
<span class="k">if</span> <span class="n">cnt</span> <span class="o">>=</span> <span class="n">neval_batches</span><span class="p">:</span>
<span class="k">return</span> <span class="n">top1</span><span class="p">,</span> <span class="n">top5</span>
<span class="k">return</span> <span class="n">top1</span><span class="p">,</span> <span class="n">top5</span>
<span class="k">def</span> <span class="nf">load_model</span><span class="p">(</span><span class="n">model_file</span><span class="p">):</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">MobileNetV2</span><span class="p">()</span>
<span class="n">state_dict</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">model_file</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">state_dict</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">'cpu'</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span>
<span class="k">def</span> <span class="nf">print_size_of_model</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
<span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="s2">"temp.p"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Size (MB):'</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">getsize</span><span class="p">(</span><span class="s2">"temp.p"</span><span class="p">)</span><span class="o">/</span><span class="mf">1e6</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">'temp.p'</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="define-dataset-and-data-loaders">
<h2>3. Define dataset and data loaders<a class="headerlink" href="#define-dataset-and-data-loaders" title="Permalink to this headline">ยถ</a></h2>
<p>As our last major setup step, we define our dataloaders for our training and testing set.</p>
<div class="section" id="imagenet-data">
<h3>ImageNet Data<a class="headerlink" href="#imagenet-data" title="Permalink to this headline">ยถ</a></h3>
<p>The specific dataset weโve created for this tutorial contains just 1000 images from the ImageNet data, one from
each class (this dataset, at just over 250 MB, is small enough that it can be downloaded
relatively easily). The URL for this custom dataset is:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">s3</span><span class="o">.</span><span class="n">amazonaws</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">pytorch</span><span class="o">-</span><span class="n">tutorial</span><span class="o">-</span><span class="n">assets</span><span class="o">/</span><span class="n">imagenet_1k</span><span class="o">.</span><span class="n">zip</span>
</pre></div>
</div>
<p>To download this data locally using Python, you could use:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">requests</span>
<span class="n">url</span> <span class="o">=</span> <span class="s1">'https://s3.amazonaws.com/pytorch-tutorial-assets/imagenet_1k.zip`</span>
<span class="n">filename</span> <span class="o">=</span> <span class="s1">'~/Downloads/imagenet_1k_data.zip'</span>
<span class="n">r</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">url</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">r</span><span class="o">.</span><span class="n">content</span><span class="p">)</span>
</pre></div>
</div>
<p>For this tutorial to run, we download this data and move it to the right place using
<a class="reference external" href="https://github.com/pytorch/tutorials/blob/master/Makefile#L97-L98">these lines</a>
from the <a class="reference external" href="https://github.com/pytorch/tutorials/blob/master/Makefile">Makefile</a>.</p>
<p>To run the code in this tutorial using the entire ImageNet dataset, on the other hand, you could download
the data using <code class="docutils literal notranslate"><span class="pre">torchvision</span></code> following
<a class="reference external" href="https://pytorch.org/docs/stable/torchvision/datasets.html#imagenet">here</a>. For example,
to download the training set and apply some standard transformations to it, you could use:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision</span>
<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="kn">as</span> <span class="nn">transforms</span>
<span class="n">imagenet_dataset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">ImageNet</span><span class="p">(</span>
<span class="s1">'~/.data/imagenet'</span><span class="p">,</span>
<span class="n">split</span><span class="o">=</span><span class="s1">'train'</span><span class="p">,</span>
<span class="n">download</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">RandomResizedCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">RandomHorizontalFlip</span><span class="p">(),</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],</span>
<span class="n">std</span><span class="o">=</span><span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">]),</span>
<span class="p">])</span>
</pre></div>
</div>
<p>With the data downloaded, we show functions below that define dataloaders weโll use to read
in this data. These functions mostly come from
<a class="reference external" href="https://github.com/pytorch/vision/blob/master/references/detection/train.py">here</a>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">prepare_data_loaders</span><span class="p">(</span><span class="n">data_path</span><span class="p">):</span>
<span class="n">traindir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="s1">'train'</span><span class="p">)</span>
<span class="n">valdir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="s1">'val'</span><span class="p">)</span>
<span class="n">normalize</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],</span>
<span class="n">std</span><span class="o">=</span><span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])</span>
<span class="n">dataset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span>
<span class="n">traindir</span><span class="p">,</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">RandomResizedCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">RandomHorizontalFlip</span><span class="p">(),</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
<span class="n">normalize</span><span class="p">,</span>
<span class="p">]))</span>
<span class="n">dataset_test</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span>
<span class="n">valdir</span><span class="p">,</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
<span class="n">normalize</span><span class="p">,</span>
<span class="p">]))</span>
<span class="n">train_sampler</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">RandomSampler</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
<span class="n">test_sampler</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">SequentialSampler</span><span class="p">(</span><span class="n">dataset_test</span><span class="p">)</span>
<span class="n">data_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
<span class="n">dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">train_batch_size</span><span class="p">,</span>
<span class="n">sampler</span><span class="o">=</span><span class="n">train_sampler</span><span class="p">)</span>
<span class="n">data_loader_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
<span class="n">dataset_test</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">eval_batch_size</span><span class="p">,</span>
<span class="n">sampler</span><span class="o">=</span><span class="n">test_sampler</span><span class="p">)</span>
<span class="k">return</span> <span class="n">data_loader</span><span class="p">,</span> <span class="n">data_loader_test</span>
</pre></div>
</div>
<p>Next, weโll load in the pre-trained MobileNetV2 model. We provide the URL to download the data from in <code class="docutils literal notranslate"><span class="pre">torchvision</span></code>
<a class="reference external" href="https://github.com/pytorch/vision/blob/master/torchvision/models/mobilenet.py#L9">here</a>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">data_path</span> <span class="o">=</span> <span class="s1">'data/imagenet_1k'</span>
<span class="n">saved_model_dir</span> <span class="o">=</span> <span class="s1">'data/'</span>
<span class="n">float_model_file</span> <span class="o">=</span> <span class="s1">'mobilenet_pretrained_float.pth'</span>
<span class="n">scripted_float_model_file</span> <span class="o">=</span> <span class="s1">'mobilenet_quantization_scripted.pth'</span>
<span class="n">scripted_quantized_model_file</span> <span class="o">=</span> <span class="s1">'mobilenet_quantization_scripted_quantized.pth'</span>
<span class="n">train_batch_size</span> <span class="o">=</span> <span class="mi">30</span>
<span class="n">eval_batch_size</span> <span class="o">=</span> <span class="mi">30</span>
<span class="n">data_loader</span><span class="p">,</span> <span class="n">data_loader_test</span> <span class="o">=</span> <span class="n">prepare_data_loaders</span><span class="p">(</span><span class="n">data_path</span><span class="p">)</span>
<span class="n">criterion</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
<span class="n">float_model</span> <span class="o">=</span> <span class="n">load_model</span><span class="p">(</span><span class="n">saved_model_dir</span> <span class="o">+</span> <span class="n">float_model_file</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">'cpu'</span><span class="p">)</span>
</pre></div>
</div>
<p>Next, weโll โfuse modulesโ; this can both make the model faster by saving on memory access
while also improving numerical accuracy. While this can be used with any model, this is
especially common with quantized models.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1"> Inverted Residual Block: Before fusion </span><span class="se">\n\n</span><span class="s1">'</span><span class="p">,</span> <span class="n">float_model</span><span class="o">.</span><span class="n">features</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">conv</span><span class="p">)</span>
<span class="n">float_model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="c1"># Fuses modules</span>
<span class="n">float_model</span><span class="o">.</span><span class="n">fuse_model</span><span class="p">()</span>
<span class="c1"># Note fusion of Conv+BN+Relu and Conv+Relu</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1"> Inverted Residual Block: After fusion</span><span class="se">\n\n</span><span class="s1">'</span><span class="p">,</span><span class="n">float_model</span><span class="o">.</span><span class="n">features</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">conv</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Inverted Residual Block: Before fusion
Sequential(
(0): ConvBNReLU(
(0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
Inverted Residual Block: After fusion
Sequential(
(0): ConvBNReLU(
(0): ConvReLU2d(
(0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32)
(1): ReLU()
)
(1): Identity()
(2): Identity()
)
(1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1))
(2): Identity()
)
</pre></div>
</div>
<p>Finally to get a โbaselineโ accuracy, letโs see the accuracy of our un-quantized model
with fused modules</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">num_eval_batches</span> <span class="o">=</span> <span class="mi">10</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Size of baseline model"</span><span class="p">)</span>
<span class="n">print_size_of_model</span><span class="p">(</span><span class="n">float_model</span><span class="p">)</span>
<span class="n">top1</span><span class="p">,</span> <span class="n">top5</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">float_model</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">data_loader_test</span><span class="p">,</span> <span class="n">neval_batches</span><span class="o">=</span><span class="n">num_eval_batches</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Evaluation accuracy on </span><span class="si">%d</span><span class="s1"> images, </span><span class="si">%2.2f</span><span class="s1">'</span><span class="o">%</span><span class="p">(</span><span class="n">num_eval_batches</span> <span class="o">*</span> <span class="n">eval_batch_size</span><span class="p">,</span> <span class="n">top1</span><span class="o">.</span><span class="n">avg</span><span class="p">))</span>
<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">float_model</span><span class="p">),</span> <span class="n">saved_model_dir</span> <span class="o">+</span> <span class="n">scripted_float_model_file</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Size of baseline model
Size (MB): 13.980272
..........Evaluation accuracy on 300 images, 78.00
</pre></div>
</div>
<p>We see 78% accuracy on 300 images, a solid baseline for ImageNet,
especially considering our model is just 14.0 MB.</p>
<p>This will be our baseline to compare to. Next, letโs try different quantization methods</p>
</div>
</div>
<div class="section" id="post-training-static-quantization">
<h2>4. Post-training static quantization<a class="headerlink" href="#post-training-static-quantization" title="Permalink to this headline">ยถ</a></h2>
<p>Post-training static quantization involves not just converting the weights from float to int,
as in dynamic quantization, but also performing the additional step of first feeding batches
of data through the network and computing the resulting distributions of the different activations
(specifically, this is done by inserting <cite>observer</cite> modules at different points that record this
data). These distributions are then used to determine how the specifically the different activations
should be quantized at inference time (a simple technique would be to simply divide the entire range
of activations into 256 levels, but we support more sophisticated methods as well). Importantly,
this additional step allows us to pass quantized values between operations instead of converting these
values to floats - and then back to ints - between every operation, resulting in a significant speed-up.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">num_calibration_batches</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">myModel</span> <span class="o">=</span> <span class="n">load_model</span><span class="p">(</span><span class="n">saved_model_dir</span> <span class="o">+</span> <span class="n">float_model_file</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">'cpu'</span><span class="p">)</span>
<span class="n">myModel</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="c1"># Fuse Conv, bn and relu</span>
<span class="n">myModel</span><span class="o">.</span><span class="n">fuse_model</span><span class="p">()</span>
<span class="c1"># Specify quantization configuration</span>
<span class="c1"># Start with simple min/max range estimation and per-tensor quantization of weights</span>
<span class="n">myModel</span><span class="o">.</span><span class="n">qconfig</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">default_qconfig</span>
<span class="nb">print</span><span class="p">(</span><span class="n">myModel</span><span class="o">.</span><span class="n">qconfig</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">prepare</span><span class="p">(</span><span class="n">myModel</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># Calibrate first</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Post Training Quantization Prepare: Inserting Observers'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1"> Inverted Residual Block:After observer insertion </span><span class="se">\n\n</span><span class="s1">'</span><span class="p">,</span> <span class="n">myModel</span><span class="o">.</span><span class="n">features</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">conv</span><span class="p">)</span>
<span class="c1"># Calibrate with the training set</span>
<span class="n">evaluate</span><span class="p">(</span><span class="n">myModel</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">data_loader</span><span class="p">,</span> <span class="n">neval_batches</span><span class="o">=</span><span class="n">num_calibration_batches</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Post Training Quantization: Calibration done'</span><span class="p">)</span>
<span class="c1"># Convert to quantized model</span>
<span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="n">myModel</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Post Training Quantization: Convert done'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1"> Inverted Residual Block: After fusion and quantization, note fused modules: </span><span class="se">\n\n</span><span class="s1">'</span><span class="p">,</span><span class="n">myModel</span><span class="o">.</span><span class="n">features</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">conv</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Size of model after quantization"</span><span class="p">)</span>
<span class="n">print_size_of_model</span><span class="p">(</span><span class="n">myModel</span><span class="p">)</span>
<span class="n">top1</span><span class="p">,</span> <span class="n">top5</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">myModel</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">data_loader_test</span><span class="p">,</span> <span class="n">neval_batches</span><span class="o">=</span><span class="n">num_eval_batches</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Evaluation accuracy on </span><span class="si">%d</span><span class="s1"> images, </span><span class="si">%2.2f</span><span class="s1">'</span><span class="o">%</span><span class="p">(</span><span class="n">num_eval_batches</span> <span class="o">*</span> <span class="n">eval_batch_size</span><span class="p">,</span> <span class="n">top1</span><span class="o">.</span><span class="n">avg</span><span class="p">))</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>QConfig(activation=functools.partial(<class 'torch.quantization.observer.MinMaxObserver'>, reduce_range=True), weight=functools.partial(<class 'torch.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric))
Post Training Quantization Prepare: Inserting Observers
Inverted Residual Block:After observer insertion
Sequential(
(0): ConvBNReLU(
(0): ConvReLU2d(
(0): Conv2d(
32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32
(observer): MinMaxObserver(min_val=None, max_val=None)
)
(1): ReLU(
(observer): MinMaxObserver(min_val=None, max_val=None)
)
)
(1): Identity()
(2): Identity()
)
(1): Conv2d(
32, 16, kernel_size=(1, 1), stride=(1, 1)
(observer): MinMaxObserver(min_val=None, max_val=None)
)
(2): Identity()
)
..........Post Training Quantization: Calibration done
Post Training Quantization: Convert done
Inverted Residual Block: After fusion and quantization, note fused modules:
Sequential(
(0): ConvBNReLU(
(0): QuantizedConvReLU2d(32, 32, kernel_size=(3, 3), stride=(1, 1), scale=0.15047618746757507, zero_point=0, padding=(1, 1), groups=32)
(1): Identity()
(2): Identity()
)
(1): QuantizedConv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), scale=0.1900387406349182, zero_point=75)
(2): Identity()
)
Size of model after quantization
Size (MB): 3.586995
..........Evaluation accuracy on 300 images, 62.67
</pre></div>
</div>
<p>For this quantized model, we see a significantly lower accuracy of just ~62% on these same 300
images. Nevertheless, we did reduce the size of our model down to just under 3.6 MB, almost a 4x
decrease.</p>
<p>In addition, we can significantly improve on the accuracy simply by using a different
quantization configuration. We repeat the same exercise with the recommended configuration for
quantizing for x86 architectures. This configuration does the following:</p>
<ul class="simple">
<li><p>Quantizes weights on a per-channel basis</p></li>
<li><p>Uses a histogram observer that collects a histogram of activations and then picks
quantization parameters in an optimal manner.</p></li>
</ul>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">per_channel_quantized_model</span> <span class="o">=</span> <span class="n">load_model</span><span class="p">(</span><span class="n">saved_model_dir</span> <span class="o">+</span> <span class="n">float_model_file</span><span class="p">)</span>
<span class="n">per_channel_quantized_model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="n">per_channel_quantized_model</span><span class="o">.</span><span class="n">fuse_model</span><span class="p">()</span>
<span class="n">per_channel_quantized_model</span><span class="o">.</span><span class="n">qconfig</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">get_default_qconfig</span><span class="p">(</span><span class="s1">'fbgemm'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">per_channel_quantized_model</span><span class="o">.</span><span class="n">qconfig</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">prepare</span><span class="p">(</span><span class="n">per_channel_quantized_model</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">evaluate</span><span class="p">(</span><span class="n">per_channel_quantized_model</span><span class="p">,</span><span class="n">criterion</span><span class="p">,</span> <span class="n">data_loader</span><span class="p">,</span> <span class="n">num_calibration_batches</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="n">per_channel_quantized_model</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">top1</span><span class="p">,</span> <span class="n">top5</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">per_channel_quantized_model</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">data_loader_test</span><span class="p">,</span> <span class="n">neval_batches</span><span class="o">=</span><span class="n">num_eval_batches</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Evaluation accuracy on </span><span class="si">%d</span><span class="s1"> images, </span><span class="si">%2.2f</span><span class="s1">'</span><span class="o">%</span><span class="p">(</span><span class="n">num_eval_batches</span> <span class="o">*</span> <span class="n">eval_batch_size</span><span class="p">,</span> <span class="n">top1</span><span class="o">.</span><span class="n">avg</span><span class="p">))</span>
<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">per_channel_quantized_model</span><span class="p">),</span> <span class="n">saved_model_dir</span> <span class="o">+</span> <span class="n">scripted_quantized_model_file</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>QConfig(activation=functools.partial(<class 'torch.quantization.observer.HistogramObserver'>, reduce_range=True), weight=functools.partial(<class 'torch.quantization.observer.PerChannelMinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_channel_symmetric))
....................Evaluation accuracy on 300 images, 75.00
</pre></div>
</div>
<p>Changing just this quantization configuration method resulted in an increase
of the accuracy to over 76%! Still, this is 1-2% worse than the baseline of 78% achieved above.
So lets try quantization aware training.</p>
</div>
<div class="section" id="quantization-aware-training">
<h2>5. Quantization-aware training<a class="headerlink" href="#quantization-aware-training" title="Permalink to this headline">ยถ</a></h2>
<p>Quantization-aware training (QAT) is the quantization method that typically results in the highest accuracy.
With QAT, all weights and activations are โfake quantizedโ during both the forward and backward passes of
training: that is, float values are rounded to mimic int8 values, but all computations are still done with
floating point numbers. Thus, all the weight adjustments during training are made while โawareโ of the fact
that the model will ultimately be quantized; after quantizing, therefore, this method will usually yield
higher accuracy than either dynamic quantization or post-training static quantization.</p>
<p>The overall workflow for actually performing QAT is very similar to before:</p>
<ul class="simple">
<li><p>We can use the same model as before: there is no additional preparation needed for quantization-aware
training.</p></li>
<li><p>We need to use a <code class="docutils literal notranslate"><span class="pre">qconfig</span></code> specifying what kind of fake-quantization is to be inserted after weights
and activations, instead of specifying observers</p></li>
</ul>
<p>We first define a training function:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">train_one_epoch</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">data_loader</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">ntrain_batches</span><span class="p">):</span>
<span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
<span class="n">top1</span> <span class="o">=</span> <span class="n">AverageMeter</span><span class="p">(</span><span class="s1">'Acc@1'</span><span class="p">,</span> <span class="s1">':6.2f'</span><span class="p">)</span>
<span class="n">top5</span> <span class="o">=</span> <span class="n">AverageMeter</span><span class="p">(</span><span class="s1">'Acc@5'</span><span class="p">,</span> <span class="s1">':6.2f'</span><span class="p">)</span>
<span class="n">avgloss</span> <span class="o">=</span> <span class="n">AverageMeter</span><span class="p">(</span><span class="s1">'Loss'</span><span class="p">,</span> <span class="s1">'1.5f'</span><span class="p">)</span>
<span class="n">cnt</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">image</span><span class="p">,</span> <span class="n">target</span> <span class="ow">in</span> <span class="n">data_loader</span><span class="p">:</span>
<span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'.'</span><span class="p">,</span> <span class="n">end</span> <span class="o">=</span> <span class="s1">''</span><span class="p">)</span>
<span class="n">cnt</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">image</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">target</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="n">acc1</span><span class="p">,</span> <span class="n">acc5</span> <span class="o">=</span> <span class="n">accuracy</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">topk</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">top1</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">acc1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">image</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
<span class="n">top5</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">acc5</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">image</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
<span class="n">avgloss</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">image</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
<span class="k">if</span> <span class="n">cnt</span> <span class="o">>=</span> <span class="n">ntrain_batches</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Loss'</span><span class="p">,</span> <span class="n">avgloss</span><span class="o">.</span><span class="n">avg</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Training: * Acc@1 </span><span class="si">{top1.avg:.3f}</span><span class="s1"> Acc@5 </span><span class="si">{top5.avg:.3f}</span><span class="s1">'</span>
<span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">top1</span><span class="o">=</span><span class="n">top1</span><span class="p">,</span> <span class="n">top5</span><span class="o">=</span><span class="n">top5</span><span class="p">))</span>
<span class="k">return</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Full imagenet train set: * Acc@1 </span><span class="si">{top1.global_avg:.3f}</span><span class="s1"> Acc@5 </span><span class="si">{top5.global_avg:.3f}</span><span class="s1">'</span>
<span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">top1</span><span class="o">=</span><span class="n">top1</span><span class="p">,</span> <span class="n">top5</span><span class="o">=</span><span class="n">top5</span><span class="p">))</span>
<span class="k">return</span>
</pre></div>
</div>
<p>We fuse modules as before</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">qat_model</span> <span class="o">=</span> <span class="n">load_model</span><span class="p">(</span><span class="n">saved_model_dir</span> <span class="o">+</span> <span class="n">float_model_file</span><span class="p">)</span>
<span class="n">qat_model</span><span class="o">.</span><span class="n">fuse_model</span><span class="p">()</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">qat_model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.0001</span><span class="p">)</span>
<span class="n">qat_model</span><span class="o">.</span><span class="n">qconfig</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">get_default_qat_qconfig</span><span class="p">(</span><span class="s1">'fbgemm'</span><span class="p">)</span>
</pre></div>
</div>
<p>Finally, <code class="docutils literal notranslate"><span class="pre">prepare_qat</span></code> performs the โfake quantizationโ, preparing the model for quantization-aware
training</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">prepare_qat</span><span class="p">(</span><span class="n">qat_model</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Inverted Residual Block: After preparation for QAT, note fake-quantization modules </span><span class="se">\n</span><span class="s1">'</span><span class="p">,</span><span class="n">qat_model</span><span class="o">.</span><span class="n">features</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">conv</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Inverted Residual Block: After preparation for QAT, note fake-quantization modules
Sequential(
(0): ConvBNReLU(
(0): ConvBnReLU2d(
32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False
(observer): FakeQuantize(
fake_quant_enabled=True, observer_enabled=True, scale=None, zero_point=None
(observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
)
(weight_fake_quant): FakeQuantize(
fake_quant_enabled=True, observer_enabled=True, scale=None, zero_point=None
(observer): MovingAveragePerChannelMinMaxObserver(min_val=None, max_val=None)
)
)
(1): Identity()
(2): Identity()
)
(1): ConvBn2d(
32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False
(observer): FakeQuantize(
fake_quant_enabled=True, observer_enabled=True, scale=None, zero_point=None
(observer): MovingAverageMinMaxObserver(min_val=None, max_val=None)
)
(weight_fake_quant): FakeQuantize(
fake_quant_enabled=True, observer_enabled=True, scale=None, zero_point=None
(observer): MovingAveragePerChannelMinMaxObserver(min_val=None, max_val=None)
)
)
(2): Identity()
)
</pre></div>
</div>
<p>Training a quantized model with high accuracy requires accurate modeling of numerics at
inference. For quantization aware training, therefore, we modify the training loop by:</p>
<ul class="simple">
<li><p>Switch batch norm to use running mean and variance towards the end of training to better
match inference numerics.</p></li>
<li><p>We also freeze the quantizer parameters (scale and zero-point) and fine tune the weights.</p></li>
</ul>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">num_train_batches</span> <span class="o">=</span> <span class="mi">20</span>
<span class="c1"># Train and check accuracy after each epoch</span>
<span class="k">for</span> <span class="n">nepoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">8</span><span class="p">):</span>
<span class="n">train_one_epoch</span><span class="p">(</span><span class="n">qat_model</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">data_loader</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cpu'</span><span class="p">),</span> <span class="n">num_train_batches</span><span class="p">)</span>
<span class="k">if</span> <span class="n">nepoch</span> <span class="o">></span> <span class="mi">3</span><span class="p">:</span>