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<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ ์ํผ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../recipes/recipes_index.html">๋ชจ๋ ๋ ์ํผ ๋ณด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../prototype/prototype_index.html">๋ชจ๋ ํ๋กํ ํ์
๋ ์ํผ ๋ณด๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ์์ํ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/intro.html">ํ์ดํ ์น(PyTorch) ๊ธฐ๋ณธ ์ตํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/quickstart_tutorial.html">๋น ๋ฅธ ์์(Quickstart)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/tensorqs_tutorial.html">ํ
์(Tensor)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/data_tutorial.html">Dataset๊ณผ Dataloader</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/transforms_tutorial.html">๋ณํ(Transform)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/buildmodel_tutorial.html">์ ๊ฒฝ๋ง ๋ชจ๋ธ ๊ตฌ์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/autogradqs_tutorial.html"><code class="docutils literal notranslate"><span class="pre">torch.autograd</span></code>๋ฅผ ์ฌ์ฉํ ์๋ ๋ฏธ๋ถ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/optimization_tutorial.html">๋ชจ๋ธ ๋งค๊ฐ๋ณ์ ์ต์ ํํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/basics/saveloadrun_tutorial.html">๋ชจ๋ธ ์ ์ฅํ๊ณ ๋ถ๋ฌ์ค๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ์ดํ ์น(PyTorch) ๋ฐฐ์ฐ๊ธฐ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/deep_learning_60min_blitz.html">PyTorch๋ก ๋ฅ๋ฌ๋ํ๊ธฐ: 60๋ถ๋ง์ ๋์ฅ๋ด๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/pytorch_with_examples.html">์์ ๋ก ๋ฐฐ์ฐ๋ ํ์ดํ ์น(PyTorch)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/nn_tutorial.html"><cite>torch.nn</cite> ์ด <em>์ค์ ๋ก</em> ๋ฌด์์ธ๊ฐ์?</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/tensorboard_tutorial.html">TensorBoard๋ก ๋ชจ๋ธ, ๋ฐ์ดํฐ, ํ์ต ์๊ฐํํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">์ด๋ฏธ์ง/๋น๋์ค</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/torchvision_tutorial.html">TorchVision ๊ฐ์ฒด ๊ฒ์ถ ๋ฏธ์ธ์กฐ์ (Finetuning) ํํ ๋ฆฌ์ผ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/transfer_learning_tutorial.html">์ปดํจํฐ ๋น์ (Vision)์ ์ํ ์ ์ดํ์ต(Transfer Learning)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/fgsm_tutorial.html">์ ๋์ ์์ ์์ฑ(Adversarial Example Generation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/dcgan_faces_tutorial.html">DCGAN Tutorial</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/vt_tutorial.html">Optimizing Vision Transformer Model for Deployment</a></li>
</ul>
<p class="caption"><span class="caption-text">์ค๋์ค</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_preprocessing_tutorial.html">Audio manipulation with torchaudio</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_preprocessing_tutorial.html#audio-i-o">Audio I/O</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_preprocessing_tutorial.html#resampling">Resampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_preprocessing_tutorial.html#data-augmentation">Data Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_preprocessing_tutorial.html#feature-extractions">Feature Extractions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_preprocessing_tutorial.html#feature-augmentation">Feature Augmentation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/audio_preprocessing_tutorial.html#datasets">Datasets</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/speech_command_recognition_with_torchaudio_tutorial.html">Speech Command Recognition with torchaudio</a></li>
</ul>
<p class="caption"><span class="caption-text">ํ
์คํธ</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../beginner/transformer_tutorial.html">nn.Transformer ์ TorchText ๋ก ์ํ์ค-ํฌ-์ํ์ค(Sequence-to-Sequence) ๋ชจ๋ธ๋งํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_classification_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_generation_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: ๋ฌธ์-๋จ์ RNN์ผ๋ก ์ด๋ฆ ์์ฑํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../intermediate/seq2seq_translation_tutorial.html">๊ธฐ์ด๋ถํฐ ์์ํ๋ NLP: Sequence to Sequence ๋คํธ์ํฌ์ Attention์ ์ด์ฉํ ๋ฒ์ญ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/text_sentiment_ngrams_tutorial.html">torchtext ๋ผ์ด๋ธ๋ฌ๋ฆฌ๋ก ํ
์คํธ ๋ถ๋ฅํ๊ธฐ</a></li>
<li class="toctree-l1"><a class="reference internal" href="../beginner/translation_transformer.html">nn.Transformer์ torchtext๋ก ์ธ์ด ๋ฒ์ญํ๊ธฐ</a></li>
</ul>
<p class="caption"><span class="caption-text">๊ฐํํ์ต</span></p>
<ul>
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<div class="section" id="beta-static-quantization-with-eager-mode-in-pytorch">
<h1>(beta) Static Quantization with Eager Mode in PyTorch<a class="headerlink" href="#beta-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>
<strong>Edited by</strong>: <a class="reference external" href="https://github.com/SethHWeidman/">Seth Weidman</a>, <a class="reference external" href="https:github.com/jerryzh168">Jerry Zhang</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.
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.
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.
Weโll start by doing the necessary imports:</p>
<div class="code python 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="kn">import</span> <span class="n">DataLoader</span>
<span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">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>Replacing addition with <code class="docutils literal notranslate"><span class="pre">nn.quantized.FloatFunctional</span></code></li>
<li>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.</li>
<li>Replace ReLU6 with ReLU</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="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.quantization</span> <span class="kn">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="fm">__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="fm">__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="fm">__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="code python 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="fm">__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="fm">__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">reshape</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>To run the code in this tutorial using the entire ImageNet dataset, first download imagenet by following the instructions at here <a class="reference external" href="http://www.image-net.org/download">ImageNet Data</a>. Unzip the downloaded file into the โdata_pathโ folder.</p>
<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="code python 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">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">ImageNet</span><span class="p">(</span>
<span class="n">data_path</span><span class="p">,</span> <span class="n">split</span><span class="o">=</span><span class="s2">"train"</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">ImageNet</span><span class="p">(</span>
<span class="n">data_path</span><span class="p">,</span> <span class="n">split</span><span class="o">=</span><span class="s2">"val"</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="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">data_path</span> <span class="o">=</span> <span class="s1">'~/.data/imagenet'</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">50</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>
<span class="c1"># Next, we'll "fuse modules"; this can both make the model faster by saving on memory access</span>
<span class="c1"># while also improving numerical accuracy. While this can be used with any model, this is</span>
<span class="c1"># especially common with quantized models.</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>Finally to get a โbaselineโ accuracy, letโs see the accuracy of our un-quantized model
with fused modules</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">num_eval_batches</span> <span class="o">=</span> <span class="mi">1000</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>On the entire model, we get an accuracy of 71.9% on the eval dataset of 50,000 images.</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="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">num_calibration_batches</span> <span class="o">=</span> <span class="mi">32</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>For this quantized model, we see an accuracy of 56.7% on the eval dataset. This is because we used a simple min/max observer to determine quantization parameters. 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>Quantizes weights on a per-channel basis</li>
<li>Uses a histogram observer that collects a histogram of activations and then picks
quantization parameters in an optimal manner.</li>
</ul>
<div class="code python 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>Changing just this quantization configuration method resulted in an increase
of the accuracy to over 67.3%! Still, this is 4% worse than the baseline of 71.9% 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>We can use the same model as before: there is no additional preparation needed for quantization-aware
training.</li>
<li>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</li>
</ul>
<p>We first define a training function:</p>
<div class="code python 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="code python 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="code python 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>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>Switch batch norm to use running mean and variance towards the end of training to better
match inference numerics.</li>
<li>We also freeze the quantizer parameters (scale and zero-point) and fine tune the weights.</li>
</ul>
<div class="code python 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"># QAT takes time and one needs to train over a few epochs.</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>
<span class="c1"># Freeze quantizer parameters</span>
<span class="n">qat_model</span><span class="o">.</span><span class="n">apply</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">disable_observer</span><span class="p">)</span>
<span class="k">if</span> <span class="n">nepoch</span> <span class="o">></span> <span class="mi">2</span><span class="p">:</span>
<span class="c1"># Freeze batch norm mean and variance estimates</span>
<span class="n">qat_model</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">intrinsic</span><span class="o">.</span><span class="n">qat</span><span class="o">.</span><span class="n">freeze_bn_stats</span><span class="p">)</span>
<span class="c1"># Check the accuracy after each epoch</span>
<span class="n">quantized_model</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">convert</span><span class="p">(</span><span class="n">qat_model</span><span class="o">.</span><span class="n">eval</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="n">quantized_model</span><span class="o">.</span><span class="n">eval</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">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">'Epoch </span><span class="si">%d</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">nepoch</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>Quantization-aware training yields an accuracy of over 71.5% on the entire imagenet dataset, which is close to the floating point accuracy of 71.9%.</p>
<p>More on quantization-aware training:</p>
<ul class="simple">
<li>QAT is a super-set of post training quant techniques that allows for more debugging.
For example, we can analyze if the accuracy of the model is limited by weight or activation
quantization.</li>
<li>We can also simulate the accuracy of a quantized model in floating point since
we are using fake-quantization to model the numerics of actual quantized arithmetic.</li>
<li>We can mimic post training quantization easily too.</li>
</ul>
<div class="section" id="speedup-from-quantization">
<h3>Speedup from quantization<a class="headerlink" href="#speedup-from-quantization" title="Permalink to this headline">ยถ</a></h3>
<p>Finally, letโs confirm something we alluded to above: do our quantized models actually perform inference
faster? Letโs test:</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">run_benchmark</span><span class="p">(</span><span class="n">model_file</span><span class="p">,</span> <span class="n">img_loader</span><span class="p">):</span>
<span class="n">elapsed</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</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">eval</span><span class="p">()</span>
<span class="n">num_batches</span> <span class="o">=</span> <span class="mi">5</span>
<span class="c1"># Run the scripted model on a few batches of images</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">images</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">img_loader</span><span class="p">):</span>
<span class="k">if</span> <span class="n">i</span> <span class="o"><</span> <span class="n">num_batches</span><span class="p">:</span>
<span class="n">start</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="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
<span class="n">end</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="n">elapsed</span> <span class="o">=</span> <span class="n">elapsed</span> <span class="o">+</span> <span class="p">(</span><span class="n">end</span><span class="o">-</span><span class="n">start</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">break</span>
<span class="n">num_images</span> <span class="o">=</span> <span class="n">images</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="o">*</span> <span class="n">num_batches</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Elapsed time: </span><span class="si">%3.0f</span><span class="s1"> ms'</span> <span class="o">%</span> <span class="p">(</span><span class="n">elapsed</span><span class="o">/</span><span class="n">num_images</span><span class="o">*</span><span class="mi">1000</span><span class="p">))</span>
<span class="k">return</span> <span class="n">elapsed</span>
<span class="n">run_benchmark</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> <span class="n">data_loader_test</span><span class="p">)</span>
<span class="n">run_benchmark</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> <span class="n">data_loader_test</span><span class="p">)</span>
</pre></div>
</div>
<p>Running this locally on a MacBook pro yielded 61 ms for the regular model, and
just 20 ms for the quantized model, illustrating the typical 2-4x speedup
we see for quantized models compared to floating point ones.</p>
</div>
</div>
<div class="section" id="conclusion">
<h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">ยถ</a></h2>
<p>In this tutorial, we showed two quantization methods - post-training static quantization,
and quantization-aware training - describing what they do โunder the hoodโ and how to use
them in PyTorch.</p>
<p>Thanks for reading! As always, we welcome any feedback, so please create an issue
<a class="reference external" href="https://github.com/pytorch/pytorch/issues">here</a> if you have any.</p>
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<li><a class="reference internal" href="#">(beta) Static Quantization with Eager Mode in PyTorch</a><ul>