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-<li class="toctree-l4"><a class="reference internal" href="../../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../install/index.html#installation-instructions">Installation Instructions</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../../install/index.html#installation-instructions">Installation Instructions</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../install/index.html#installation-instructions">Installation Instructions</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../install/index.html#installation-instructions">Installation Instructions</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../install/index.html#installation-instructions">Installation Instructions</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
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+<li class="toctree-l5"><a class="reference internal" href="../../install/install_host.html#from-pypi">From PyPI</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../install/index.html#installation-instructions">Installation Instructions</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../install/index.html#installation-instructions">Installation Instructions</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
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diff --git a/releases/1.32.0/api_docs/tensorflow_compress.html b/releases/1.32.0/api_docs/tensorflow_compress.html
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-<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
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+<li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#from-pypi">From PyPI</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../install/index.html#installation-instructions">Installation Instructions</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#from-pypi">From PyPI</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-tensorflow-gpu-debian-packages">Install tensorflow GPU debian packages</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../install/index.html#installation-instructions">Installation Instructions</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../install/index.html#advanced-installation-instructions">Advanced Installation Instructions</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install/install_host.html">Install in Host Machine</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#from-pypi">From PyPI</a></li>
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diff --git a/releases/1.32.0/api_docs/torch_adaround.html b/releases/1.32.0/api_docs/torch_adaround.html
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-<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
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+<li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#from-pypi">From PyPI</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-tensorflow-gpu-debian-packages">Install tensorflow GPU debian packages</a></li>
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 </li>
-<li class="toctree-l4"><a class="reference internal" href="install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
+<li class="toctree-l4"><a class="reference internal" href="install/install_host.html#install-aimet-packages">Install AIMET packages</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="install/install_host.html#from-pypi">From PyPI</a></li>
+<li class="toctree-l5"><a class="reference internal" href="install/install_host.html#from-release-package">From Release Package</a></li>
+</ul>
+</li>
 <li class="toctree-l4"><a class="reference internal" href="install/install_host.html#install-common-debian-packages">Install common debian packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="install/install_host.html#install-tensorflow-gpu-debian-packages">Install tensorflow GPU debian packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="install/install_host.html#install-torch-gpu-debian-packages">Install torch GPU debian packages</a></li>
diff --git a/releases/1.32.0/install/index.html b/releases/1.32.0/install/index.html
index 9f2cb65..d78ee3c 100644
--- a/releases/1.32.0/install/index.html
+++ b/releases/1.32.0/install/index.html
@@ -1022,9 +1022,10 @@
 </ul>
 </li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#"> Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="#release-packages">Release packages</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#quick-install">Quick Install</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#release-packages">Release Packages</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#system-requirements">System Requirements</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#installation-instructions">Installation Instructions</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#advanced-installation-instructions">Advanced Installation Instructions</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="install_host.html">Install in Host Machine</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="install_host.html#install-prerequisite-packages">Install prerequisite packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="install_host.html#install-gpu-packages">Install GPU packages</a><ul>
@@ -1032,7 +1033,11 @@
 <li class="toctree-l5"><a class="reference internal" href="install_host.html#install-gpu-packages-for-pytorch-2-1-or-tensorflow">Install GPU packages for PyTorch 2.1 or TensorFlow</a></li>
 </ul>
 </li>
-<li class="toctree-l4"><a class="reference internal" href="install_host.html#install-aimet-packages">Install AIMET packages</a></li>
+<li class="toctree-l4"><a class="reference internal" href="install_host.html#install-aimet-packages">Install AIMET packages</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="install_host.html#from-pypi">From PyPI</a></li>
+<li class="toctree-l5"><a class="reference internal" href="install_host.html#from-release-package">From Release Package</a></li>
+</ul>
+</li>
 <li class="toctree-l4"><a class="reference internal" href="install_host.html#install-common-debian-packages">Install common debian packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="install_host.html#install-tensorflow-gpu-debian-packages">Install tensorflow GPU debian packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="install_host.html#install-torch-gpu-debian-packages">Install torch GPU debian packages</a></li>
@@ -1091,30 +1096,47 @@
              
   <div class="section" id="aimet-installation">
 <span id="ug-installation"></span><h1>AIMET Installation<a class="headerlink" href="#aimet-installation" title="Permalink to this heading"></a></h1>
-<div class="section" id="release-packages">
-<h2>Release packages<a class="headerlink" href="#release-packages" title="Permalink to this heading"></a></h2>
-<p>AIMET release packages are hosted at <a class="reference external" href="https://github.com/quic/aimet/releases">https://github.com/quic/aimet/releases</a>. Each release includes multiple python packages of the following format:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">&lt;</span><span class="n">PACKAGE_PREFIX</span><span class="o">&gt;-&lt;</span><span class="n">VARIANT</span><span class="o">&gt;</span><span class="n">_</span><span class="o">&lt;</span><span class="n">VERSION</span><span class="o">&gt;-</span><span class="n">cp310</span><span class="o">-</span><span class="n">cp310</span><span class="o">-</span><span class="n">linux_x86_64</span><span class="o">.</span><span class="n">whl</span>
+<div class="section" id="quick-install">
+<h2>Quick Install<a class="headerlink" href="#quick-install" title="Permalink to this heading"></a></h2>
+<p>The AIMET PyTorch GPU PyPI packages are available for environments that meet the following requirements:</p>
+<ul class="simple">
+<li><p>64-bit Intel x86-compatible processor</p></li>
+<li><p>Linux Ubuntu 22.04 LTS [Python 3.10] or Linux Ubuntu 20.04 LTS [Python 3.8]</p></li>
+<li><p>Torch 1.13+cu117</p></li>
+</ul>
+<p><strong>Pip install:</strong></p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">apt</span><span class="o">-</span><span class="n">get</span> <span class="n">install</span> <span class="n">liblapacke</span>
+<span class="n">python3</span> <span class="o">-</span><span class="n">m</span> <span class="n">pip</span> <span class="n">install</span> <span class="n">aimet</span><span class="o">-</span><span class="n">torch</span>
 </pre></div>
 </div>
-<p>Please find more information below about each <em>VARIANT</em>.</p>
-<p>PyTorch</p>
-<ol class="arabic simple">
-<li><p><strong>torch-gpu</strong> for PyTorch 1.13 GPU package with Python 3.10 and CUDA 11.x - <em>Recommended for use</em> with PyTorch models</p></li>
-<li><p><strong>torch-cpu</strong> for PyTorch 1.13 CPU package with Python 3.10 - If installing on a machine without CUDA</p></li>
-<li><p><strong>torch-gpu-pt21</strong> for PyTorch 2.1 GPU package with Python 3.10 and CUDA 11.x</p></li>
-<li><p><strong>torch-cpu-pt21</strong> for PyTorch 2.1 CPU package with Python 3.10 - If installing on a machine without CUDA</p></li>
-</ol>
-<p>TensorFlow</p>
-<ol class="arabic simple">
-<li><p><strong>tf-gpu</strong> for TensorFlow 2.10 GPU package with Python 3.10 - <em>Recommended for use</em> with TensorFlow models</p></li>
-<li><p><strong>tf-cpu</strong> for TensorFlow 2.10 CPU package with Python 3.10 - If installing on a machine without CUDA</p></li>
-</ol>
-<p>ONNX</p>
-<ol class="arabic simple">
-<li><p><strong>onnx-gpu</strong> for ONNX 1.14 GPU package with Python 3.10 - <em>Recommended for use</em> with ONNX models</p></li>
-<li><p><strong>onnx-cpu</strong> for ONNX 1.14 CPU package with Python 3.10 - If installing on a machine without CUDA</p></li>
-</ol>
+</div>
+<div class="section" id="release-packages">
+<h2>Release Packages<a class="headerlink" href="#release-packages" title="Permalink to this heading"></a></h2>
+<p>For other aimet variants, install the latest version from the .whl files hosted at <a class="reference external" href="https://github.com/quic/aimet/releases">https://github.com/quic/aimet/releases</a></p>
+<p><strong>PyTorch</strong></p>
+<pre class="literal-block"># Pytorch 1.13 with CUDA 11.x
+python3 -m pip install https://github.com/quic/aimet/releases/download/1.31.0/aimet_torch_gpu_1.31.0-cp38-cp38-manylinux_2_34_x86_64.whl -f <a class="reference external" href="https://download.pytorch.org/whl/torch_stable.html">https://download.pytorch.org/whl/torch_stable.html</a>
+# Pytorch 1.13 CPU only
+python3 -m pip install https://github.com/quic/aimet/releases/download/1.31.0/aimet_torch_cpu_1.31.0-cp38-cp38-manylinux_2_34_x86_64.whl
+
+# Pytorch 2.1 with CUDA 11.x
+python3 -m pip install https://github.com/quic/aimet/releases/download/1.31.0/aimet_torch_gpu_pt21_1.31.0-cp38-cp38-manylinux_2_34_x86_64.whl -f <a class="reference external" href="https://download.pytorch.org/whl/torch_stable.html">https://download.pytorch.org/whl/torch_stable.html</a>
+# Pytorch 2.1 CPU only
+python3 -m pip install https://github.com/quic/aimet/releases/download/1.31.0/aimet_torch_cpu_pt21_1.31.0-cp38-cp38-manylinux_2_34_x86_64.whl</pre>
+<p><strong>TensorFlow</strong></p>
+<pre class="literal-block"># Tensorflow 2.10 GPU with CUDA 11.x
+python3 -m pip install https://github.com/quic/aimet/releases/download/1.31.0/aimet_tensorflow_gpu_1.31.0-cp38-cp38-manylinux_2_34_x86_64.whl
+# Tensorflow 2.10 CPU only
+python3 -m pip install https://github.com/quic/aimet/releases/download/1.31.0/aimet_tensorflow_cpu_1.31.0-cp38-cp38-manylinux_2_34_x86_64.whl</pre>
+<p><strong>Onnx</strong></p>
+<pre class="literal-block"># ONNX 1.14 GPU
+python3 -m pip install https://github.com/quic/aimet/releases/download/1.31.0/aimet_onnx_gpu_1.31.0-cp38-cp38-manylinux_2_34_x86_64.whl
+# ONNX 1.14 CPU
+python3 -m pip install https://github.com/quic/aimet/releases/download/1.31.0/aimet_onnx_cpu_1.31.0-cp38-cp38-manylinux_2_34_x86_64.whl</pre>
+<p>For previous AIMET releases, browse packages at <a class="reference external" href="https://github.com/quic/aimet/releases">https://github.com/quic/aimet/releases</a>. Each release includes multiple python packages of the following format:</p>
+<pre class="literal-block"># VARIANT in {torch_gpu, torch_cpu, tf_gpu, tf_cpu, onnx_gpu, onnx_cpu}
+# PACKAGE_PREFIX in {aimet_torch, aimet_tensorflow, aimet_onnx}
+&lt;PACKAGE_PREFIX&gt;-&lt;VARIANT&gt;_&lt;VERSION&gt;-cp38-cp38-manylinux_2_34_x86_64.whl</pre>
 </div>
 <div class="section" id="system-requirements">
 <h2>System Requirements<a class="headerlink" href="#system-requirements" title="Permalink to this heading"></a></h2>
@@ -1148,8 +1170,8 @@ <h2>System Requirements<a class="headerlink" href="#system-requirements" title="
 </ul>
 <p>While these are not minimum requirements, they are recommended for good performance when training large networks.</p>
 </div>
-<div class="section" id="installation-instructions">
-<h2>Installation Instructions<a class="headerlink" href="#installation-instructions" title="Permalink to this heading"></a></h2>
+<div class="section" id="advanced-installation-instructions">
+<h2>Advanced Installation Instructions<a class="headerlink" href="#advanced-installation-instructions" title="Permalink to this heading"></a></h2>
 <dl class="simple">
 <dt>There are two ways to setup and install AIMET:</dt><dd><ul class="simple">
 <li><p>On your host machine</p></li>
diff --git a/releases/1.32.0/install/install_docker.html b/releases/1.32.0/install/install_docker.html
index 269b131..8644688 100644
--- a/releases/1.32.0/install/install_docker.html
+++ b/releases/1.32.0/install/install_docker.html
@@ -1021,9 +1021,10 @@
 </ul>
 </li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html"> Installation</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="index.html#release-packages">Release packages</a></li>
+<li class="toctree-l2"><a class="reference internal" href="index.html#quick-install">Quick Install</a></li>
+<li class="toctree-l2"><a class="reference internal" href="index.html#release-packages">Release Packages</a></li>
 <li class="toctree-l2"><a class="reference internal" href="index.html#system-requirements">System Requirements</a></li>
-<li class="toctree-l2 current"><a class="reference internal" href="index.html#installation-instructions">Installation Instructions</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="index.html#advanced-installation-instructions">Advanced Installation Instructions</a><ul class="current">
 <li class="toctree-l3"><a class="reference internal" href="install_host.html">Install in Host Machine</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="install_host.html#install-prerequisite-packages">Install prerequisite packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="install_host.html#install-gpu-packages">Install GPU packages</a><ul>
@@ -1031,7 +1032,11 @@
 <li class="toctree-l5"><a class="reference internal" href="install_host.html#install-gpu-packages-for-pytorch-2-1-or-tensorflow">Install GPU packages for PyTorch 2.1 or TensorFlow</a></li>
 </ul>
 </li>
-<li class="toctree-l4"><a class="reference internal" href="install_host.html#install-aimet-packages">Install AIMET packages</a></li>
+<li class="toctree-l4"><a class="reference internal" href="install_host.html#install-aimet-packages">Install AIMET packages</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="install_host.html#from-pypi">From PyPI</a></li>
+<li class="toctree-l5"><a class="reference internal" href="install_host.html#from-release-package">From Release Package</a></li>
+</ul>
+</li>
 <li class="toctree-l4"><a class="reference internal" href="install_host.html#install-common-debian-packages">Install common debian packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="install_host.html#install-tensorflow-gpu-debian-packages">Install tensorflow GPU debian packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="install_host.html#install-torch-gpu-debian-packages">Install torch GPU debian packages</a></li>
@@ -1187,8 +1192,8 @@ <h3>From Release Package<a class="headerlink" href="#from-release-package" title
 <ol class="arabic simple">
 <li><p>For the PyTorch 1.13 GPU variant, use “torch_gpu”</p></li>
 <li><p>For the PyTorch 1.13 CPU variant, use “torch_cpu”</p></li>
-<li><p>For the PyTorch 2.1 GPU variant, use “torch_gpu-pt21”</p></li>
-<li><p>For the PyTorch 2.1 CPU variant, use “torch_cpu-pt21”</p></li>
+<li><p>For the PyTorch 2.1 GPU variant, use “torch_gpu_pt21”</p></li>
+<li><p>For the PyTorch 2.1 CPU variant, use “torch_cpu_pt21”</p></li>
 <li><p>For the TensorFlow GPU variant, use “tf_gpu”</p></li>
 <li><p>For the TensorFlow CPU variant, use “tf_cpu”</p></li>
 <li><p>For the ONNX GPU variant, use “onnx_gpu”</p></li>
@@ -1206,7 +1211,7 @@ <h3>From Release Package<a class="headerlink" href="#from-release-package" title
 </pre></div>
 </div>
 <p>Set the common suffix for the package files as follows:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">wheel_file_suffix</span><span class="o">=</span><span class="s2">&quot;cp310-cp310-linux_x86_64.whl&quot;</span>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">wheel_file_suffix</span><span class="o">=</span><span class="s2">&quot;cp310-cp310-manylinux_2_34_x86_64.whl&quot;</span>
 </pre></div>
 </div>
 <p>Install the AIMET packages in the order specified below:</p>
@@ -1219,11 +1224,11 @@ <h3>From Release Package<a class="headerlink" href="#from-release-package" title
 </dd>
 </dl>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install ONE of the following depending on the variant</span>
-python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_torch-<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span><span class="w"> </span>-f<span class="w"> </span>https://download.pytorch.org/whl/torch_stable.html
+python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span><span class="w"> </span>-f<span class="w"> </span>https://download.pytorch.org/whl/torch_stable.html
 <span class="c1"># OR</span>
-python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_tensorflow-<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span>
+python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span>
 <span class="c1"># OR</span>
-python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_onnx-<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span>
+python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span>
 </pre></div>
 </div>
 </div>
diff --git a/releases/1.32.0/install/install_host.html b/releases/1.32.0/install/install_host.html
index 44154cb..22e574b 100644
--- a/releases/1.32.0/install/install_host.html
+++ b/releases/1.32.0/install/install_host.html
@@ -1022,9 +1022,10 @@
 </ul>
 </li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html"> Installation</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="index.html#release-packages">Release packages</a></li>
+<li class="toctree-l2"><a class="reference internal" href="index.html#quick-install">Quick Install</a></li>
+<li class="toctree-l2"><a class="reference internal" href="index.html#release-packages">Release Packages</a></li>
 <li class="toctree-l2"><a class="reference internal" href="index.html#system-requirements">System Requirements</a></li>
-<li class="toctree-l2 current"><a class="reference internal" href="index.html#installation-instructions">Installation Instructions</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="index.html#advanced-installation-instructions">Advanced Installation Instructions</a><ul class="current">
 <li class="toctree-l3 current"><a class="current reference internal" href="#">Install in Host Machine</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="#install-prerequisite-packages">Install prerequisite packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="#install-gpu-packages">Install GPU packages</a><ul>
@@ -1032,7 +1033,11 @@
 <li class="toctree-l5"><a class="reference internal" href="#install-gpu-packages-for-pytorch-2-1-or-tensorflow">Install GPU packages for PyTorch 2.1 or TensorFlow</a></li>
 </ul>
 </li>
-<li class="toctree-l4"><a class="reference internal" href="#install-aimet-packages">Install AIMET packages</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#install-aimet-packages">Install AIMET packages</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="#from-pypi">From PyPI</a></li>
+<li class="toctree-l5"><a class="reference internal" href="#from-release-package">From Release Package</a></li>
+</ul>
+</li>
 <li class="toctree-l4"><a class="reference internal" href="#install-common-debian-packages">Install common debian packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="#install-tensorflow-gpu-debian-packages">Install tensorflow GPU debian packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="#install-torch-gpu-debian-packages">Install torch GPU debian packages</a></li>
@@ -1173,6 +1178,25 @@ <h3>Install GPU packages for PyTorch 2.1 or TensorFlow<a class="headerlink" href
 </div>
 <div class="section" id="install-aimet-packages">
 <h2>Install AIMET packages<a class="headerlink" href="#install-aimet-packages" title="Permalink to this heading"></a></h2>
+<div class="section" id="from-pypi">
+<h3>From PyPI<a class="headerlink" href="#from-pypi" title="Permalink to this heading"></a></h3>
+<p>Aimet Torch GPU can install from pypi through the following method:</p>
+<p>Go to <a class="reference external" href="https://pypi.org/project/aimet-torch">https://pypi.org/project/aimet-torch</a> to identify a version you wish to install</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>For PyTorch 1.13 GPU you should use aimet-torch==1.31.1</p></li>
+<li><p>For Pytorch 2.1.2 GPU you should use aimet-torch &gt;= 1.32.0</p></li>
+</ul>
+</div></blockquote>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>sudo<span class="w"> </span>apt-get<span class="w"> </span>install<span class="w"> </span>liblapacke<span class="w"> </span>-y
+pip<span class="w"> </span>install<span class="w"> </span>aimet-torch
+</pre></div>
+</div>
+</div>
+<div class="section" id="from-release-package">
+<h3>From Release Package<a class="headerlink" href="#from-release-package" title="Permalink to this heading"></a></h3>
+<p>Alternatively, we host .whl packages for each release at <a class="reference external" href="https://github.com/quic/aimet/releases">https://github.com/quic/aimet/releases</a>. Identify the release tag
+of the package you wish to install, then follow the instructions below to install AIMET from the .whl file.</p>
 <p>Go to <a class="reference external" href="https://github.com/quic/aimet/releases">https://github.com/quic/aimet/releases</a> and identify the release tag of the package you want to install.</p>
 <p>Set the &lt;variant_string&gt; to ONE of the following depending on your desired variant</p>
 <ol class="arabic simple">
@@ -1204,14 +1228,15 @@ <h2>Install AIMET packages<a class="headerlink" href="#install-aimet-packages" t
 <p>Install the AIMET packages in the order specified below:</p>
 <p><strong>NOTE:</strong> Python dependencies will automatically get installed.</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install ONE of the following depending on the variant</span>
-python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_torch-<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span><span class="w"> </span>-f<span class="w"> </span>https://download.pytorch.org/whl/torch_stable.html
+python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span><span class="w"> </span>-f<span class="w"> </span>https://download.pytorch.org/whl/torch_stable.html
 <span class="c1"># OR</span>
-python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_tensorflow-<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span>
+python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span>
 <span class="c1"># OR</span>
-python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_onnx-<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span>
+python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span>
 </pre></div>
 </div>
 </div>
+</div>
 <div class="section" id="install-common-debian-packages">
 <h2>Install common debian packages<a class="headerlink" href="#install-common-debian-packages" title="Permalink to this heading"></a></h2>
 <p><strong>NOTE:</strong> Do the following ONLY for the TensorFlow variant packages.</p>
diff --git a/releases/1.32.0/objects.inv b/releases/1.32.0/objects.inv
index ff6514d..f039d66 100644
Binary files a/releases/1.32.0/objects.inv and b/releases/1.32.0/objects.inv differ
diff --git a/releases/1.32.0/search.html b/releases/1.32.0/search.html
index 31b6853..d65c9ab 100644
--- a/releases/1.32.0/search.html
+++ b/releases/1.32.0/search.html
@@ -1022,9 +1022,10 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="install/index.html"> Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="install/index.html#release-packages">Release packages</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install/index.html#quick-install">Quick Install</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install/index.html#release-packages">Release Packages</a></li>
 <li class="toctree-l2"><a class="reference internal" href="install/index.html#system-requirements">System Requirements</a></li>
-<li class="toctree-l2"><a class="reference internal" href="install/index.html#installation-instructions">Installation Instructions</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="install/index.html#advanced-installation-instructions">Advanced Installation Instructions</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="install/install_host.html">Install in Host Machine</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="install/install_host.html#install-prerequisite-packages">Install prerequisite packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="install/install_host.html#install-gpu-packages">Install GPU packages</a><ul>
@@ -1032,7 +1033,11 @@
 <li class="toctree-l5"><a class="reference internal" href="install/install_host.html#install-gpu-packages-for-pytorch-2-1-or-tensorflow">Install GPU packages for PyTorch 2.1 or TensorFlow</a></li>
 </ul>
 </li>
-<li class="toctree-l4"><a class="reference internal" href="install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
+<li class="toctree-l4"><a class="reference internal" href="install/install_host.html#install-aimet-packages">Install AIMET packages</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="install/install_host.html#from-pypi">From PyPI</a></li>
+<li class="toctree-l5"><a class="reference internal" href="install/install_host.html#from-release-package">From Release Package</a></li>
+</ul>
+</li>
 <li class="toctree-l4"><a class="reference internal" href="install/install_host.html#install-common-debian-packages">Install common debian packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="install/install_host.html#install-tensorflow-gpu-debian-packages">Install tensorflow GPU debian packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="install/install_host.html#install-torch-gpu-debian-packages">Install torch GPU debian packages</a></li>
diff --git a/releases/1.32.0/searchindex.js b/releases/1.32.0/searchindex.js
index 4525ca0..b02dfca 100644
--- a/releases/1.32.0/searchindex.js
+++ b/releases/1.32.0/searchindex.js
@@ -1 +1 @@
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"AIMET Installation", "AIMET Installation in Docker", "AIMET Installation and Setup", "&lt;no title&gt;", "AIMET AdaRound", "AIMET AutoQuant", "AIMET BN Re-estimation", "AIMET Channel Pruning", "AIMET Compression Features Guidebook", "AIMET Examples", "AIMET Greedy Compression Ratio Selection", "AI Model Efficiency Toolkit User Guide", "AIMET Known Issues", "AIMET Model Compression", "Model Guidelines for PyTorch", "AIMET Model Quantization", "AIMET Post-Training Quantization Techniques", "AIMET QuantAnalyzer", "AIMET Quantization Aware Training", "Quantization Simulation Configuration", "AIMET Quantization Features Guidebook", "AIMET Quantization Simulation", "AIMET Release Notes", "AIMET Spatial SVD", "AIMET Visualization", "AIMET Visualization for Quantization", "AIMET Weight SVD", "AIMET Winnowing"], "terms": {"showcas": [0, 8, 16, 18, 21, 29, 32], "work": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 46, 48, 54, 59, 66, 68, 71, 81, 82, 86, 95, 98, 102, 103, 105, 108], "code": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 93], "how": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 36, 37, 39, 48, 54, 55, 57, 58, 59, 62, 68, 71, 72, 73, 74, 75, 77, 81, 86, 98, 102, 105, 106, 109, 110], "us": [0, 1, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 25, 26, 27, 28, 29, 30, 31, 32, 34, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 68, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 89, 91, 95, 96, 97, 98, 99, 100, 103, 105, 106, 107, 108, 109, 110, 111, 114], "aimet": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 41, 42, 55, 64, 72, 79, 80, 81, 82, 100, 103, 108], "appli": [0, 2, 3, 4, 7, 8, 11, 12, 18, 22, 23, 24, 26, 27, 28, 29, 36, 38, 39, 43, 45, 48, 57, 58, 61, 62, 65, 71, 73, 75, 76, 81, 83, 85, 88, 93, 94, 95, 98, 99, 102, 104, 105, 107, 108, 109, 110, 111, 113, 114], "post": [0, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 19, 20, 25, 26, 27, 28, 29, 30, 31, 45, 53, 54, 55, 58, 67, 68, 73, 85, 93, 94, 100, 102, 107, 110, 111], "techniqu": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 36, 38, 39, 45, 48, 49, 50, 53, 57, 58, 60, 61, 62, 67, 71, 73, 75, 76, 77, 85, 93, 94, 96, 97, 100, 104, 106, 107, 109, 110, 111, 112, 115], "aim": [0, 2, 3, 4, 7, 8, 11, 18, 22, 23, 24, 28, 29, 37], "improv": [0, 1, 2, 3, 4, 5, 7, 8, 11, 12, 14, 15, 18, 19, 20, 22, 23, 24, 28, 29, 30, 31, 34, 68, 71, 86, 97, 104, 107, 109, 114], "given": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 33, 38, 39, 43, 46, 55, 58, 60, 61, 62, 64, 65, 68, 69, 71, 73, 74, 76, 77, 83, 86, 94, 96, 99, 100, 102, 105, 112, 113, 115], "doe": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 36, 42, 44, 50, 52, 57, 61, 66, 76, 77, 79, 80, 84, 99, 101, 104, 109], "need": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 38, 40, 41, 42, 43, 46, 52, 55, 61, 63, 65, 71, 75, 76, 78, 82, 83, 86, 90, 94, 97, 102, 104, 105, 106, 107, 108, 110, 111, 113, 114], "ani": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 38, 41, 42, 43, 44, 45, 46, 52, 61, 65, 66, 68, 71, 73, 74, 76, 79, 80, 81, 83, 84, 85, 86, 90, 91, 93, 94, 98, 108, 111], "data": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 36, 37, 38, 43, 44, 46, 48, 49, 52, 54, 57, 58, 61, 65, 66, 68, 71, 73, 74, 75, 76, 80, 82, 84, 86, 87, 93, 95, 101, 104, 105, 106, 107, 109, 110], "sampl": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 36, 38, 43, 44, 46, 52, 54, 55, 57, 58, 60, 61, 65, 66, 68, 71, 73, 75, 84, 86, 96, 104, 105, 106, 107, 110], "help": [0, 2, 3, 4, 5, 6, 8, 16, 18, 19, 20, 21, 29, 32, 62, 65, 72, 81, 86, 88, 93, 99, 102, 104, 105, 106, 109, 113, 114], "recov": [0, 2, 3, 4, 8, 18, 22, 23, 24, 29, 100, 109, 110], "when": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 38, 41, 42, 44, 45, 46, 50, 55, 61, 64, 66, 73, 76, 77, 79, 80, 84, 85, 86, 89, 93, 100, 102, 104, 105, 106, 107, 108, 109, 110, 113, 114, 116], "sensit": [0, 8, 18, 29, 44, 45, 52, 53, 66, 67, 84, 85, 99, 104, 106, 109, 110, 111], "paramet": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 33, 37, 40, 42, 43, 44, 45, 46, 52, 53, 55, 58, 59, 62, 63, 64, 65, 66, 67, 68, 69, 72, 73, 74, 75, 77, 78, 80, 81, 83, 84, 85, 86, 87, 88, 93, 95, 96, 102, 103, 104, 105, 106, 107, 108, 114], "oppos": [0, 8, 18, 29, 104, 108], "activ": [0, 1, 5, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 41, 42, 43, 44, 46, 52, 54, 55, 60, 65, 66, 68, 71, 72, 75, 79, 80, 83, 84, 86, 104, 106, 107, 108, 109, 110], "To": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 36, 38, 39, 42, 46, 48, 49, 50, 57, 58, 60, 61, 62, 68, 71, 73, 75, 76, 77, 86, 89, 95, 98, 99, 102, 103, 106, 108, 109, 110, 113, 114], "learn": [0, 2, 3, 4, 5, 7, 8, 9, 11, 12, 13, 14, 17, 18, 19, 22, 23, 24, 25, 28, 29, 30, 36, 37, 38, 39, 46, 48, 49, 50, 57, 58, 59, 60, 61, 62, 68, 71, 73, 74, 75, 76, 77, 86, 95, 98, 102, 104, 107, 110, 111], "more": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 36, 38, 39, 43, 44, 46, 48, 49, 50, 52, 55, 57, 58, 60, 61, 62, 65, 66, 68, 71, 72, 73, 75, 76, 77, 81, 83, 84, 86, 89, 96, 97, 98, 99, 100, 102, 104, 105, 106, 107, 108, 109, 110, 113, 114], "about": [0, 7, 8, 13, 18, 28, 29, 36, 38, 39, 43, 46, 48, 49, 50, 54, 55, 57, 58, 60, 61, 62, 65, 68, 71, 73, 75, 76, 77, 83, 86, 89], "pleas": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 36, 37, 38, 39, 44, 46, 48, 49, 50, 52, 57, 58, 59, 60, 61, 62, 66, 68, 71, 73, 74, 75, 76, 77, 81, 84, 86, 89, 90, 91, 93, 96, 98, 100, 102, 106, 110], "refer": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 55, 60, 71, 75, 78, 85, 86, 93, 98, 100, 104, 106, 107, 108, 110], "free": [0, 2, 3, 4, 7, 8, 11, 12, 14, 15, 18, 19, 20, 22, 23, 24, 28, 29, 30, 31, 86, 90, 104, 105, 107], "through": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 42, 61, 71, 75, 80, 86, 88, 90, 105, 106, 110, 113, 114], "weight": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 33, 36, 38, 42, 43, 44, 45, 46, 52, 53, 55, 57, 58, 59, 60, 62, 65, 66, 67, 68, 71, 75, 81, 83, 84, 85, 86, 88, 93, 95, 97, 102, 104, 105, 106, 107, 108, 109, 110, 114], "bia": [0, 7, 11, 18, 39, 43, 50, 62, 65, 67, 72, 77, 80, 81, 83, 85, 93, 96, 104, 105, 108, 109, 111], "correct": [0, 9, 17, 18, 26, 27, 32, 43, 65, 67, 71, 73, 83, 85, 91, 93, 95, 104, 105, 109], "paper": [0, 8, 18, 29, 105], "from": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 46, 48, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 68, 69, 71, 72, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 84, 86, 87, 88, 93, 96, 97, 98, 99, 103, 104, 105, 106, 107, 108, 109, 110, 113, 116], "iccv": [0, 8, 18, 29, 102, 105], "2019": [0, 8, 18, 29, 105], "http": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 80, 89, 90, 91, 97, 98, 105, 111, 113], "arxiv": [0, 8, 18, 29, 105], "org": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 80, 90, 91, 98, 105], "ab": [0, 8, 18, 29, 105], "1906": [0, 8, 18, 29, 105], "04721": [0, 8, 18, 29, 105], "perform": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 16, 17, 18, 21, 22, 23, 24, 25, 29, 32, 33, 36, 37, 39, 44, 48, 50, 52, 55, 57, 58, 59, 60, 61, 62, 64, 66, 68, 71, 72, 73, 74, 75, 76, 77, 82, 83, 84, 85, 86, 88, 89, 94, 95, 96, 97, 99, 102, 104, 105, 106, 107, 109], "follow": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 38, 39, 40, 41, 42, 43, 45, 46, 50, 51, 53, 54, 55, 58, 59, 61, 62, 63, 64, 65, 67, 68, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 89, 90, 91, 93, 94, 95, 96, 97, 98, 99, 100, 102, 103, 104, 106, 107, 108, 110, 112, 115, 116], "step": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 43, 49, 59, 64, 65, 71, 73, 74, 80, 86, 90, 93, 94, 95, 96, 97, 99, 102, 104, 105, 107, 109, 110], "run": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 38, 40, 42, 44, 45, 46, 48, 49, 51, 54, 55, 57, 58, 59, 60, 61, 63, 66, 68, 72, 73, 74, 76, 78, 80, 81, 84, 86, 90, 95, 100, 102, 104, 105, 106, 110, 111, 113], "norm": [0, 8, 18, 29, 43, 59, 65, 72, 74, 83, 93, 95, 104, 105, 106], "bn": [0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 14, 15, 17, 18, 19, 20, 21, 25, 29, 30, 31, 37, 43, 60, 62, 64, 65, 67, 75, 83, 85, 104, 111], "conv": [0, 2, 3, 4, 5, 6, 7, 8, 11, 18, 19, 20, 21, 29, 33, 37, 43, 57, 59, 60, 61, 65, 71, 74, 75, 80, 83, 88, 101, 108, 111, 112, 115, 116], "immedi": [0, 8, 18, 27, 29], "befor": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 27, 28, 29, 30, 31, 40, 41, 42, 43, 45, 49, 51, 60, 63, 64, 65, 71, 73, 78, 83, 85, 86, 88, 93, 94, 95, 102, 104, 107, 113, 114], "after": [0, 2, 3, 5, 7, 8, 9, 11, 12, 17, 18, 19, 20, 22, 23, 25, 26, 27, 28, 29, 30, 31, 33, 36, 37, 43, 49, 57, 59, 60, 62, 65, 71, 73, 74, 80, 81, 83, 86, 88, 93, 94, 95, 97, 102, 104, 107, 109, 113, 114], "scale": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 37, 39, 43, 50, 55, 59, 62, 65, 71, 74, 77, 83, 86, 95, 104, 105, 106, 107, 110], "set": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 36, 38, 42, 43, 48, 49, 54, 55, 57, 58, 60, 61, 64, 65, 68, 71, 73, 76, 80, 81, 83, 86, 91, 93, 97, 99, 100, 102, 103, 105, 106, 107, 108, 109, 110, 116], "consecut": [0, 8, 18, 29, 43, 65, 83, 104, 105], "rang": [0, 1, 5, 7, 8, 9, 11, 12, 13, 14, 17, 18, 19, 25, 26, 27, 28, 29, 30, 42, 44, 52, 59, 66, 68, 69, 71, 73, 74, 80, 84, 86, 88, 93, 95, 98, 99, 104, 105, 106, 107, 109, 110, 111, 114], "tensor": [0, 1, 2, 3, 4, 5, 6, 8, 9, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 33, 36, 40, 42, 45, 46, 53, 54, 55, 57, 60, 66, 67, 68, 71, 72, 73, 76, 77, 78, 79, 80, 83, 84, 85, 86, 93, 96, 103, 104, 106, 108, 109, 110, 111, 112, 115], "valu": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 36, 38, 43, 44, 52, 55, 57, 58, 61, 65, 66, 71, 73, 75, 76, 78, 80, 83, 84, 86, 93, 99, 102, 104, 105, 106, 107, 110, 112, 114, 115], "per": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38, 44, 52, 55, 59, 61, 65, 66, 74, 76, 78, 84, 86, 95, 104, 105, 106, 108, 109, 110, 111], "channel": [0, 1, 8, 14, 15, 18, 23, 25, 28, 29, 30, 31, 32, 38, 55, 59, 69, 72, 74, 88, 95, 97, 98, 99, 101, 102, 105, 106, 108, 109, 110, 111, 112, 114, 115, 116], "up": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 36, 38, 42, 44, 46, 52, 57, 61, 64, 66, 68, 71, 76, 84, 86, 91, 102, 107, 108, 110, 116], "down": [0, 5, 8, 9, 17, 18, 25, 29, 55, 71], "correspond": [0, 2, 3, 4, 5, 6, 7, 8, 17, 18, 19, 20, 21, 22, 23, 24, 27, 29, 33, 36, 37, 40, 43, 57, 59, 60, 63, 65, 71, 74, 78, 83, 86, 91, 96, 99, 104, 106, 116], "subsequ": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 26, 29, 30, 31, 71, 86, 103, 105, 107, 108], "high": [0, 2, 3, 4, 8, 18, 22, 23, 24, 29, 39, 43, 50, 62, 65, 77, 83, 93, 95, 97, 99, 100, 105, 109, 111, 114], "mai": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 42, 55, 71, 73, 80, 86, 93, 97, 102, 104, 105, 106, 108, 109, 110], "result": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 38, 42, 44, 58, 61, 66, 71, 73, 76, 84, 86, 93, 94, 96, 97, 100, 105, 106, 107, 108, 110], "some": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 38, 42, 43, 45, 46, 61, 65, 68, 71, 76, 80, 83, 85, 86, 88, 93, 97, 99, 102, 103, 104, 105, 107, 109, 110], "cover": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 71, 86, 95, 108, 110], "instanti": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 79, 82, 107, 113], "load": [0, 1, 12, 33, 40, 43, 46, 51, 59, 60, 62, 63, 64, 65, 68, 69, 71, 73, 74, 75, 76, 78, 80, 81, 86, 102], "find": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 25, 26, 27, 29, 30, 31, 43, 61, 65, 71, 75, 81, 83, 86, 88, 89, 99, 104, 106, 107, 110], "fake": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 25, 28, 29, 30, 31, 33, 71, 86], "op": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 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40], "decid": [0, 1, 5, 9, 17, 22, 23, 24, 25, 29, 30, 31, 32, 93, 113], "whether": [0, 1, 2, 3, 4, 5, 9, 17, 22, 23, 24, 25, 28, 29, 30, 31, 32, 40, 45, 51, 61, 63, 73, 78, 80, 81, 85, 93, 107], "cpu": [0, 1, 2, 3, 4, 5, 6, 8, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 61, 71, 73, 74, 78, 80, 83, 86, 88, 89, 90, 91, 104, 111], "cuda": [0, 1, 2, 3, 4, 5, 6, 8, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 73, 74, 75, 76, 84, 86, 87, 89, 91], "devic": [0, 1, 2, 3, 4, 5, 6, 7, 8, 11, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 40, 51, 63, 71, 73, 74, 75, 78, 80, 83, 86, 87, 88, 110], "current": [0, 2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 33, 38, 42, 46, 61, 65, 66, 68, 72, 76, 81, 82, 96, 100, 101, 102, 103, 108, 112, 115], "environ": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 14, 15, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 98], "chang": [0, 1, 2, 3, 4, 7, 8, 11, 12, 14, 15, 19, 20, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 45, 55, 71, 73, 79, 80, 85, 86, 88, 93, 102, 106, 107, 108, 110, 114, 116], "logic": [0, 1, 22, 23, 24, 25, 29, 30, 31, 32, 111], "forc": [0, 1, 22, 23, 24, 25, 29, 30, 31, 32], "placement": [0, 1, 22, 23, 24, 25, 29, 30, 31, 32, 108], "cudnn_conv_algo_search": [0, 1], "fix": [0, 1, 55, 81, 100, 104, 109, 110, 111], "default": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 36, 38, 42, 44, 46, 57, 58, 60, 61, 66, 68, 71, 73, 75, 76, 80, 84, 86, 91, 93, 99, 102, 108, 110, 111, 113], "avoid": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 41, 46, 52, 71, 79, 86, 97], "everi": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 40, 44, 52, 57, 63, 66, 71, 78, 84, 86, 99, 102, 107, 114], "cudaexecutionprovid": [0, 1], "get_available_provid": [0, 1], "cpuexecutionprovid": [0, 1], "use_cuda": [0, 1, 2, 3, 4, 5, 6, 7, 8, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 33, 38, 48, 51, 54, 57, 60, 61, 63, 66, 68, 71, 74, 76, 86], "let": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 29, 30, 31, 71, 80, 86], "session": [0, 1, 2, 3, 4, 5, 6, 7, 8, 19, 20, 21, 33, 36, 38, 49, 51, 52, 54, 57, 58, 59, 60, 61, 62, 63, 65, 66, 68, 69, 87], "point": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 28, 29, 30, 31, 42, 43, 44, 45, 52, 53, 55, 66, 67, 75, 83, 84, 85, 86, 100, 102, 104, 106, 109, 110, 114], "32": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 29, 30, 31, 33, 36, 41, 42, 44, 49, 55, 57, 58, 61, 66, 71, 72, 80, 81, 90, 109], "bit": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 55, 68, 71, 89, 93, 95, 104, 109, 110, 111], "print": [0, 1, 2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38, 49, 61, 68, 71, 72, 73, 76, 80, 81, 86, 91, 104, 106], "quantizationsimmodel": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 36, 37, 40, 43, 46, 48, 51, 54, 57, 59, 63, 68, 71, 73, 74, 78, 86, 93, 95], "batchnorm": [0, 1, 2, 3, 4, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 39, 42, 43, 45, 50, 58, 62, 64, 65, 72, 73, 75, 77, 83, 88, 94, 105, 116], "These": [0, 1, 5, 8, 9, 12, 14, 15, 17, 18, 19, 20, 25, 27, 29, 30, 31, 33, 36, 46, 57, 58, 68, 73, 86, 90, 91, 94, 95, 96, 97, 103, 104, 105, 106, 109, 110], "adjac": [0, 1, 2, 3, 4, 5, 6, 8, 9, 14, 15, 17, 18, 19, 20, 21, 25, 29, 30, 31, 108], "convolut": [0, 1, 2, 3, 4, 5, 8, 9, 13, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 29, 30, 31, 38, 42, 43, 61, 65, 72, 95, 97, 102, 109], "cannot": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 72, 80, 81], "thei": [0, 1, 2, 3, 4, 5, 8, 9, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 41, 43, 61, 65, 80, 83, 85, 89, 108, 113], "why": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 109], "On": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 55, 89], "runtim": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 46, 54, 55, 61, 68, 71, 76, 86, 97, 100, 102, 104, 106, 108, 110, 111], "tflite": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31], "snapdragon": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31], "neural": [0, 1, 5, 6, 8, 9, 10, 14, 15, 17, 18, 19, 20, 25, 26, 27, 29, 30, 31, 94, 97, 100, 102, 104, 107, 109, 110, 115], "process": [0, 1, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 19, 20, 25, 26, 27, 29, 30, 31, 38, 40, 51, 61, 63, 68, 71, 78, 86, 87, 94, 100, 102, 104, 105, 110], "sdk": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 100], "etc": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 55, 61, 64, 71, 86, 90, 91, 97, 104], "practic": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 71, 86, 102], "so": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 36, 37, 44, 46, 52, 57, 61, 66, 68, 71, 76, 80, 81, 82, 84, 86, 91, 103, 106, 113], "sec": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31], "speedup": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31], "sinc": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 54, 71, 86, 95, 97, 99, 110], "unnecessari": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 116], "comput": [0, 2, 3, 4, 6, 9, 11, 12, 16, 17, 21, 22, 23, 24, 25, 29, 30, 31, 32, 36, 38, 40, 44, 46, 51, 52, 54, 55, 57, 58, 61, 63, 66, 68, 71, 73, 76, 78, 82, 84, 86, 89, 91, 93, 102, 103, 104, 105, 106, 110, 113, 116], "now": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 38, 42, 71, 80, 81, 86, 104, 111, 116], "perspect": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31], "mathemat": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 79], "equival": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 26, 29, 30, 31, 32, 71, 79, 86], "produc": [0, 1, 5, 8, 9, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 55, 73, 80, 99, 106, 113], "same": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 13, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 28, 29, 30, 31, 36, 40, 42, 51, 57, 63, 71, 73, 78, 80, 81, 86, 95, 105, 108, 110, 114], "howev": [0, 1, 5, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 25, 26, 27, 29, 30, 31, 32, 41, 42, 61, 71, 76, 86, 104, 105, 107, 108, 110], "increas": [0, 1, 2, 3, 4, 5, 8, 9, 14, 15, 17, 18, 19, 20, 22, 24, 25, 29, 30, 31, 38, 61, 76, 88, 99, 105, 108], "And": [0, 1, 8, 9, 14, 15, 17, 18, 19, 20, 29, 30, 31, 43, 65, 83, 102], "neg": [0, 1, 5, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31], "impact": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 99, 109], "especi": [0, 1, 2, 3, 4, 5, 6, 8, 9, 14, 15, 17, 18, 19, 20, 21, 25, 26, 27, 29, 30, 31, 89, 104, 107, 109], "int8": [0, 1, 5, 7, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 71, 107, 110, 114], "lower": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 71, 99, 104, 109], "precis": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 55, 71, 104], "want": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 40, 43, 46, 51, 58, 63, 65, 66, 68, 71, 78, 80, 83, 85, 86, 90, 91], "target": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38, 40, 42, 46, 49, 51, 54, 55, 59, 61, 63, 68, 71, 74, 76, 78, 86, 95, 97, 99, 100, 102, 104, 109, 110, 111], "behavior": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 80, 100], "here": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 36, 37, 38, 39, 57, 58, 59, 62, 68, 71, 73, 74, 77, 80, 86, 97, 98, 107, 113], "call": [0, 1, 2, 3, 4, 5, 6, 8, 9, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 37, 38, 39, 41, 42, 43, 52, 55, 58, 61, 62, 65, 71, 73, 76, 77, 80, 83, 86, 95, 102, 104, 106, 108, 110, 111, 112, 115], "aimet_onnx": [0, 1, 48, 49, 50, 51, 52, 54, 90, 91], "batch_norm_fold": [0, 1, 5, 7, 8, 9, 11, 14, 15, 17, 18, 19, 20, 25, 28, 29, 30, 31, 37, 43, 59, 60, 65, 74, 83, 88], "fold_all_batch_norms_to_weight": [0, 1], "_": [0, 1, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 25, 26, 27, 29, 30, 31, 32, 33, 36, 40, 42, 43, 44, 51, 52, 54, 57, 58, 60, 61, 62, 63, 65, 66, 68, 69, 73, 78, 84, 89, 90, 91], "basic": [0, 1, 7, 8, 11, 12, 14, 15, 18, 19, 20, 28, 29, 30, 31, 71, 86, 91], "mean": [0, 1, 2, 3, 4, 5, 7, 8, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 45, 59, 71, 74, 86, 88, 96, 108, 110], "graph": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 14, 15, 17, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 38, 42, 55, 57, 58, 60, 61, 62, 64, 65, 68, 69, 71, 72, 73, 80, 83, 86, 87, 103, 104, 110, 113], "configur": [0, 1, 5, 7, 8, 11, 12, 14, 15, 19, 20, 25, 28, 29, 30, 31, 36, 44, 55, 57, 58, 71, 73, 75, 84, 86, 97, 101, 111], "them": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 42, 46, 52, 60, 66, 71, 73, 80, 81, 86, 93, 116], "few": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 71, 86, 97, 104, 109, 110], "explain": [0, 1, 2, 3, 4, 5, 7, 8, 11, 12, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 28, 29, 30, 31, 32, 71, 86, 96, 102, 105, 110, 116], "quant_schem": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 36, 44, 46, 52, 54, 55, 57, 66, 68, 71, 73, 75, 84, 86], "quantschem": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 36, 44, 46, 52, 54, 57, 58, 59, 66, 68, 71, 73, 74, 75, 84, 86, 94], "post_training_tf_enhanc": [0, 1, 5, 7, 8, 11, 12, 14, 16, 18, 19, 21, 25, 28, 29, 30, 32, 36, 44, 46, 52, 55, 57, 58, 66, 68, 71, 73, 75, 84, 86], "support": [0, 1, 2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 14, 18, 19, 20, 21, 28, 29, 30, 31, 33, 34, 38, 39, 41, 42, 44, 46, 54, 57, 58, 61, 62, 64, 66, 68, 71, 73, 75, 76, 79, 80, 84, 86, 89, 96, 97, 100, 101, 102, 103, 104, 105, 108, 109, 110, 111, 112, 115, 116], "option": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 38, 43, 44, 46, 49, 57, 58, 60, 61, 66, 68, 71, 72, 73, 74, 75, 76, 80, 83, 84, 86, 88, 91, 93, 98, 106, 108, 110, 113], "tf_enhanc": [0, 1, 7, 8, 11, 12, 14, 18, 19, 28, 29, 30, 46, 60, 68, 75], "tf": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 28, 29, 30, 31, 32, 33, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 54, 57, 58, 59, 60, 61, 62, 63, 65, 66, 68, 69, 75, 84, 89, 90, 106, 110, 111], "quant": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 28, 29, 30, 31, 36, 45, 46, 54, 57, 60, 68, 71, 73, 75, 85, 86, 95], "scheme": [0, 1, 2, 3, 4, 5, 7, 8, 11, 12, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 28, 29, 30, 31, 32, 33, 36, 38, 44, 46, 54, 57, 58, 60, 61, 66, 68, 71, 73, 75, 76, 78, 84, 86, 94, 95, 99, 102, 106], "enum": [0, 1, 7, 8, 11, 12, 14, 18, 19, 28, 29, 30, 38, 61, 75, 76], "post_training_tf": [0, 1, 7, 8, 9, 11, 12, 14, 17, 18, 19, 28, 29, 30, 36, 44, 46, 54, 55, 57, 58, 66, 68, 71, 73, 75, 84, 86], "default_output_bw": [0, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 36, 44, 46, 57, 58, 66, 68, 71, 73, 84, 86], "8": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 28, 29, 30, 31, 32, 36, 43, 44, 46, 48, 52, 54, 55, 57, 58, 61, 66, 68, 71, 73, 75, 76, 80, 81, 84, 86, 87, 91, 104, 116], "essenti": [0, 1, 7, 8, 11, 12, 14, 15, 18, 19, 20, 28, 29, 30, 31], "ask": [0, 1, 5, 7, 8, 11, 12, 14, 15, 16, 18, 19, 20, 21, 28, 29, 30, 31, 32], "all": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 26, 28, 29, 30, 31, 32, 37, 39, 41, 42, 43, 46, 52, 55, 59, 61, 62, 65, 66, 68, 71, 74, 75, 76, 80, 81, 83, 86, 88, 90, 96, 99, 102, 105, 106, 108, 109], "integ": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 36, 44, 46, 52, 55, 57, 66, 68, 84, 86, 93, 104, 106], "default_param_bw": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 36, 44, 46, 48, 52, 54, 57, 58, 66, 68, 71, 73, 84, 86], "num_batch": [0, 5, 6, 7, 8, 9, 10, 11, 16, 17, 18, 21, 25, 26, 27, 28, 29, 32, 36, 44, 48, 49, 52, 57, 58, 59, 66, 71, 73, 74, 84], "number": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 36, 37, 38, 42, 44, 46, 52, 57, 58, 59, 61, 66, 68, 71, 73, 74, 75, 76, 80, 84, 86, 90, 93, 99, 100, 102, 107, 110, 111, 113, 116], "while": [0, 2, 3, 4, 5, 8, 9, 12, 15, 17, 18, 22, 23, 24, 25, 29, 38, 58, 61, 71, 76, 89, 93, 99, 103, 104, 107, 109, 110, 113], "calcul": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 36, 46, 57, 58, 61, 65, 66, 68, 71, 73, 83, 86, 99, 105, 106, 110], "encod": [0, 2, 3, 4, 6, 9, 10, 11, 12, 17, 25, 26, 27, 29, 30, 31, 36, 40, 44, 46, 48, 51, 52, 54, 57, 58, 59, 63, 66, 68, 71, 73, 74, 78, 82, 84, 86, 93, 95, 104, 106, 107, 111], "onli": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 38, 40, 41, 46, 48, 51, 52, 54, 60, 61, 63, 64, 68, 71, 72, 73, 75, 78, 79, 80, 82, 83, 86, 90, 91, 95, 101, 104, 106, 107, 108, 111, 116], "5": [0, 2, 3, 4, 5, 8, 12, 17, 19, 20, 22, 23, 24, 29, 30, 31, 33, 38, 48, 49, 59, 61, 73, 74, 76, 80, 86, 87, 89, 97, 107, 109], "In": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 38, 41, 42, 44, 45, 49, 52, 55, 58, 61, 64, 71, 72, 73, 76, 79, 80, 81, 84, 85, 86, 93, 94, 97, 99, 102, 104, 105, 107, 108, 110, 114, 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19, 20, 25, 29, 30, 31, 71, 73, 84, 86, 96, 106], "statist": [0, 1, 2, 3, 4, 5, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 29, 30, 31, 37, 38, 44, 45, 52, 59, 61, 66, 67, 69, 71, 74, 76, 84, 85, 86, 88, 95, 104, 106, 114], "sometim": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 71, 86, 96, 102, 105, 106], "calibr": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 44, 48, 52, 54, 66, 68, 71, 84, 86, 104, 106, 107, 109, 110], "simpli": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 28, 29, 30, 31, 36, 44, 46, 52, 57, 66, 68, 71, 80, 84, 86, 116], "fairli": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 71, 86], "simpl": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 36, 44, 52, 57, 61, 66, 71, 76, 80, 84, 86, 104, 116], "loader": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 28, 29, 30, 31, 32, 44, 52, 54, 66, 68, 71, 74, 75, 84, 86, 93], "extract": [0, 1, 5, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 71, 86, 105], "don": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 71, 80, 86, 93], "t": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 71, 73, 80, 82, 86, 90, 93], "loss": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 36, 44, 46, 52, 57, 66, 71, 84, 86, 93, 100, 104, 106, 110], "metric": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 33, 38, 44, 46, 61, 71, 76, 86, 106, 110], "just": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 66, 71, 86, 110, 113, 116], "ignor": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 38, 60, 61, 62, 65, 71, 75, 76, 80, 86], "pointer": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 71, 86], "regard": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 71, 86], "veri": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 27, 29, 30, 31, 32, 71, 86, 95, 97, 102, 106, 114, 116], "small": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 27, 29, 30, 31, 32, 71, 86, 95, 100, 104], "percentag": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 71, 86], "1m": [0, 1, 5, 7, 8, 9, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 71, 86], "500": [0, 1, 5, 7, 8, 9, 14, 15, 16, 17, 18, 19, 20, 21, 25, 26, 27, 29, 30, 31, 32, 49, 71, 76, 86, 93, 105, 106], "It": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 38, 39, 42, 43, 44, 49, 52, 55, 61, 62, 65, 66, 71, 76, 80, 83, 84, 86, 95, 98, 99, 104, 105, 108, 113, 114, 116], "benefici": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 71, 86, 93, 106, 107], "well": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 52, 61, 66, 71, 72, 81, 86, 97, 102, 104, 105, 106, 110, 112], "distribut": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 33, 38, 61, 71, 76, 86, 105, 109, 110], "look": [0, 1, 5, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 42, 60, 71, 86, 113], "definit": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 26, 29, 30, 31, 32, 41, 45, 79, 80, 85, 104], "extrem": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 71, 86], "scenario": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 71, 86, 95, 102, 104, 116], "dark": [0, 1, 5, 7, 8, 9, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 71, 86], "light": [0, 1, 5, 7, 8, 9, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 71, 86], "pictur": [0, 1, 5, 7, 8, 9, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 71, 86, 96, 100], "captur": [0, 1, 5, 7, 8, 9, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 40, 51, 63, 71, 78, 80, 86, 99], "night": [0, 1, 5, 7, 8, 9, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 71, 86], "might": [0, 1, 5, 7, 8, 9, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 55, 71, 86, 102, 106], "give": [0, 1, 5, 7, 8, 9, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 71, 86, 112, 115], "ideal": [0, 1, 3, 4, 5, 7, 8, 9, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 71, 86], "mani": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 80, 93, 105, 110], "wai": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 38, 61, 76, 78, 86, 89, 98, 99], "pass_calibration_data": [0, 1, 5, 7, 8, 9, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 48, 54, 68, 71, 86], "get_input": [0, 1], "name": [0, 1, 2, 3, 4, 5, 6, 7, 8, 11, 13, 15, 16, 19, 20, 21, 32, 33, 38, 40, 42, 51, 55, 57, 58, 59, 60, 61, 62, 63, 65, 66, 68, 75, 78, 86, 90, 105, 110, 111, 113], "batch_cntr": [0, 1, 5, 7, 8, 9, 14, 15, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 49], "input_data": [0, 1, 25, 28, 29, 30, 31, 32, 36, 49, 54, 57, 61, 66, 68, 71, 86], "target_data": [0, 1, 25, 28, 29, 30, 31, 32, 71, 86], "inputs_batch": [0, 1, 25, 28, 29, 30, 31, 32, 71, 86], "numpi": [0, 1, 6, 7, 13, 33, 36, 38, 44, 46, 49, 52, 54, 57, 58, 61, 66], "break": [0, 1, 5, 7, 8, 9, 14, 15, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 38, 49, 54, 68, 71, 86], "abov": [0, 1, 2, 3, 4, 5, 8, 9, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 36, 44, 52, 57, 61, 66, 71, 76, 80, 82, 84, 86, 90, 91, 94, 95, 98, 99, 100, 102, 103, 105, 109, 110, 116], "compute_encod": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 25, 28, 29, 30, 31, 36, 46, 54, 57, 68, 71, 86], "forward_pass_callback": [0, 1, 5, 7, 8, 9, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 36, 44, 46, 52, 57, 66, 68, 84, 86], "forward_pass_callback_arg": [0, 1, 5, 7, 8, 9, 11, 14, 15, 17, 18, 19, 20, 25, 28, 29, 30, 31, 46, 48, 68, 71, 86], "first": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 33, 38, 42, 45, 60, 61, 71, 80, 85, 97, 102, 104, 107, 113], "u": [0, 1, 5, 7, 8, 9, 12, 17, 19, 20, 25, 29, 30, 31, 71, 90, 109], "saw": [0, 1, 5, 7, 8, 9, 17, 19, 20, 25, 29, 30, 31], "next": [0, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 25, 28, 29, 30, 31, 33, 43, 65, 71, 83, 86, 90, 109], "As": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 28, 29, 30, 31, 79, 86, 94, 96, 97, 99, 102, 104, 105, 106, 110, 112, 115], "note": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 42, 43, 44, 46, 49, 50, 51, 52, 54, 58, 61, 65, 66, 68, 73, 76, 77, 82, 83, 84, 86, 90, 91, 96, 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52, 58, 66, 73, 82, 84, 102, 104, 105, 106], "variou": [2, 3, 4, 6, 10, 16, 21, 22, 23, 24, 26, 27, 32, 38, 61, 76, 94, 97, 102, 104, 109, 110, 111, 114], "measur": [2, 3, 4, 22, 23, 24, 27, 38, 61, 76], "tri": [2, 3, 4, 22, 23, 24, 97, 104], "translat": [2, 3, 4, 5, 9, 17, 22, 23, 24, 25, 71], "33": [2, 3, 4, 22, 23, 24], "66": [2, 3, 4, 22, 23, 24, 97], "00": [2, 3, 4, 22, 23, 24], "higher": [2, 3, 4, 22, 23, 24, 61, 71, 76, 95, 99, 107, 109], "candid": [2, 3, 4, 22, 23, 24, 38, 39, 60, 61, 62, 73, 76, 99, 102], "granular": [2, 3, 4, 12, 22, 23, 24, 38, 61, 76, 102, 109, 110, 114], "time": [2, 3, 4, 6, 10, 16, 21, 22, 23, 24, 26, 27, 32, 38, 43, 55, 61, 65, 76, 80, 81, 94, 102, 103, 107, 113], "taken": [2, 3, 4, 22, 23, 24, 42, 116], "complet": [2, 3, 4, 6, 10, 21, 22, 23, 24, 26, 27, 38, 95, 109], "modules_to_ignor": [2, 3, 4, 22, 23, 24, 38, 61, 76, 84, 87, 101], "interact": [2, 3, 4, 22, 23, 24], "too": [2, 3, 4, 22, 23, 24], "choss": [2, 3, 4, 22, 23, 24], "auto": [2, 3, 4, 22, 23, 24, 34, 38, 39, 43, 50, 55, 61, 62, 65, 76, 77, 83, 87], "analysi": [2, 3, 4, 12, 22, 23, 24, 26, 27, 38, 44, 52, 61, 66, 76, 84, 102, 109], "much": [2, 3, 4, 6, 10, 15, 20, 22, 23, 24, 26, 31, 116], "altern": [2, 3, 4, 22, 23, 24, 90, 91, 102], "manual": [2, 3, 4, 6, 10, 22, 23, 24, 33, 38, 55, 61, 76, 83, 94, 102], "retriev": [2, 3, 4, 22, 24], "those": [2, 3, 4, 13, 15, 20, 22, 23, 24, 31, 52, 102], "num_reconstruction_sampl": [2, 3, 4, 22, 24, 61, 76], "last": [2, 3, 4, 99, 101, 109], "stage": [2, 3, 4, 94], "map": [2, 3, 4, 6, 9, 10, 11, 14, 15, 17, 21, 44, 46, 55, 58, 65, 73, 81, 106, 108], "linear": [2, 3, 4, 37, 43, 57, 59, 60, 65, 71, 74, 80, 81, 83, 88, 95, 96], "regress": [2, 3, 4, 96], "attempt": [2, 3, 4, 96, 104, 105], "done": [2, 3, 4, 7, 17, 18, 19, 20, 22, 23, 24, 28, 30, 31, 41, 90, 96, 102, 108, 110, 116], "random": [2, 3, 4, 13, 26, 27, 33, 36, 42, 44, 46, 49, 52, 54, 57, 58, 61, 66, 73, 96, 106], "ridicul": [2, 3, 4, 22, 24], "low": [2, 3, 4, 5, 8, 9, 17, 22, 24, 25, 29, 93, 95, 102, 104, 105, 109], "enabl": [2, 3, 4, 15, 17, 20, 22, 24, 31, 34, 38, 44, 59, 61, 66, 68, 74, 76, 84, 89, 90, 95, 100, 104, 106, 108, 110, 111], "allow_custom_downsample_op": [2, 3, 4, 22, 24, 61, 76], "flag": [2, 3, 4, 22, 24, 43, 60, 64, 65, 73, 80], "downsampl": [2, 3, 4, 22, 24], "consid": [2, 3, 4, 12, 22, 24, 65, 72, 93, 99, 104, 109], "bandwidth": [2, 3, 4, 22, 24, 97], "overhead": [2, 3, 4, 22, 24], "trade": [2, 3, 4, 22, 24, 36, 57, 71], "off": [2, 3, 4, 22, 24, 36, 46, 57, 68, 71, 86, 104, 105, 108], "suggest": [2, 3, 4, 22, 24, 46, 86, 99, 102, 105], "eval_callback": [2, 3, 4, 6, 10, 11, 16, 21, 22, 23, 24, 26, 27, 32, 33, 38, 44, 49, 52, 58, 61, 66, 73, 76, 84, 87], "expect": [2, 3, 4, 7, 11, 22, 23, 24, 28, 38, 41, 43, 44, 46, 48, 52, 54, 58, 60, 61, 65, 66, 68, 71, 72, 73, 76, 80, 81, 83, 84, 86, 102, 104, 106], "signatur": [2, 3, 4, 22, 23, 24, 36, 38, 44, 49, 52, 57, 61, 66, 76, 84], "function_nam": [2, 3, 4, 22, 23, 24], "eval_iter": [2, 3, 4, 22, 23, 24, 33, 38, 61, 76, 87], "batch": [2, 3, 4, 6, 7, 10, 11, 21, 22, 23, 24, 28, 36, 37, 38, 40, 43, 44, 52, 54, 57, 58, 59, 60, 61, 63, 65, 66, 68, 71, 72, 74, 78, 83, 84, 86, 93, 95, 104, 105, 106], "choos": [2, 3, 4, 22, 23, 24, 66, 68, 86, 88, 96, 97, 102], "enough": [2, 3, 4, 22, 23, 24, 72], "trust": [2, 3, 4, 22, 23, 24], "callback": [2, 3, 4, 12, 14, 15, 16, 21, 22, 23, 24, 32, 38, 44, 46, 49, 52, 58, 61, 66, 68, 73, 76, 84, 86, 106, 110], "invoc": [2, 3, 4, 22, 23, 24], "compress_schem": [2, 3, 4, 22, 23, 24, 33, 38, 61, 76, 87], "cost_metr": [2, 3, 4, 22, 23, 24, 33, 38, 61, 76, 87], "actual": [2, 3, 4, 7, 8, 11, 13, 19, 20, 28, 30, 31, 40, 44, 49, 51, 52, 58, 63, 66, 71, 73, 78, 82, 84, 86, 91, 97, 104], "greedi": [2, 3, 4, 102, 113], "select": [2, 3, 4, 36, 57, 60, 71, 73, 86, 88, 94, 97, 106, 110, 113, 116], "among": [2, 3, 4, 71, 73], "reach": [2, 3, 4, 6, 10, 26, 27, 94, 97], "previou": [2, 3, 4, 8, 29, 38, 43, 61, 73, 76, 83, 97, 99, 109], "rule": [2, 3, 4, 46, 66, 68, 108], "thumb": [2, 3, 4], "one": [2, 3, 4, 5, 8, 12, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 31, 32, 43, 55, 60, 61, 64, 65, 68, 71, 72, 73, 80, 81, 83, 86, 90, 96, 98, 102, 107, 108, 111, 112, 115], "found": [2, 3, 4, 13, 33, 43, 83, 107, 110], "compressionschem": [2, 3, 4, 22, 23, 24, 33, 38, 61, 76, 87], "costmetr": [2, 3, 4, 22, 23, 24, 33, 38, 61, 76, 87], "greedyselectionparamet": [2, 3, 4, 22, 23, 24, 38, 61, 76, 87], "channelpruningparamet": [2, 4, 22, 24, 61, 76], "decim": [2, 3, 4, 22, 23, 24, 38, 61, 76, 87], "greedy_param": [2, 3, 4, 22, 23, 24, 38, 61, 76, 87], "get_operation_by_nam": [2, 3, 4, 5, 7, 8, 19, 20, 21, 33, 60, 61, 65, 68, 69], "conv1_conv": [2, 3, 4, 69], "auto_param": [2, 3, 4, 22, 23, 24, 38, 61, 76, 87], "automodeparam": [2, 3, 4, 22, 23, 24, 38, 61, 76, 87], "greedy_select_param": [2, 3, 4, 22, 23, 24, 38, 61, 76], "data_set": [2, 4, 5, 8, 9, 17, 36, 57, 60, 61], "channel_prun": [2, 4, 22, 24, 38, 61, 76], "modelcompressor": [2, 3, 4, 22, 23, 24, 33, 38, 61, 76, 87], "compress_model": [2, 3, 4, 22, 23, 24, 33, 38, 61, 76, 87, 113], "relev": [2, 3, 4, 22, 23, 24], "our": [2, 3, 4, 7, 8, 11, 12, 14, 15, 18, 19, 22, 23, 24, 28, 37, 52, 89, 91, 99, 109, 110], "new": [2, 4, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 26, 29, 30, 31, 32, 41, 42, 43, 55, 62, 65, 71, 79, 80, 86, 90, 104, 108, 111], "final": [2, 4, 7, 11, 12, 14, 15, 16, 21, 28, 32, 38, 42, 61, 76, 81, 87, 96, 97, 99, 107, 109, 113], "makedir": [2, 3, 4, 7, 8, 9, 11, 17, 18, 19, 20, 22, 23, 24, 25, 28, 29, 30, 31], "exist_ok": [2, 3, 4, 7, 8, 9, 11, 17, 18, 19, 20, 22, 23, 24, 25, 28, 29, 30, 31], "compressed_sess": [2, 3, 33], "comp_stat": [2, 3, 22, 23], "working_dir": [2, 3, 4, 33, 61], "fell": [2, 3, 4, 22, 23, 24], "sharpli": [2, 3, 4, 22, 23, 24], "15": [2, 3, 4, 7, 11, 12, 14, 15, 19, 20, 22, 23, 24, 28, 30, 31, 86, 102, 107], "job": [2, 3, 4, 7, 11, 12, 14, 15, 19, 20, 22, 23, 24, 28, 30, 31, 86], "hyper": [2, 3, 4, 7, 8, 11, 12, 19, 20, 22, 23, 24, 28, 30, 31, 86, 93, 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Create a quantization simulation model and determine quantized accuracy": [[0, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [1, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [5, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [8, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [9, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [17, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [18, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [19, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [20, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [25, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [29, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [30, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [31, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"]], "Fold Batch Normalization layers": [[0, "Fold-Batch-Normalization-layers"], [1, "Fold-Batch-Normalization-layers"], [5, "Fold-Batch-Normalization-layers"], [8, "Fold-Batch-Normalization-layers"], [9, "Fold-Batch-Normalization-layers"], [14, "Fold-Batch-Normalization-layers"], [15, "Fold-Batch-Normalization-layers"], [17, "Fold-Batch-Normalization-layers"], [18, "Fold-Batch-Normalization-layers"], [19, "Fold-Batch-Normalization-layers"], [20, "Fold-Batch-Normalization-layers"], [25, "Fold-Batch-Normalization-layers"], [29, "Fold-Batch-Normalization-layers"], [30, "Fold-Batch-Normalization-layers"], [31, "Fold-Batch-Normalization-layers"]], "Create Quantization Sim Model": [[0, "Create-Quantization-Sim-Model"], [1, "Create-Quantization-Sim-Model"], [5, "Create-Quantization-Sim-Model"], [7, "Create-Quantization-Sim-Model"], [8, "Create-Quantization-Sim-Model"], [14, "Create-Quantization-Sim-Model"], [15, "Create-Quantization-Sim-Model"], [18, "Create-Quantization-Sim-Model"], [19, "Create-Quantization-Sim-Model"], [20, "Create-Quantization-Sim-Model"], [28, "Create-Quantization-Sim-Model"], [29, "Create-Quantization-Sim-Model"], [30, "Create-Quantization-Sim-Model"], [31, "Create-Quantization-Sim-Model"]], "4. 1 Cross Layer Equalization": [[0, "4.-1-Cross-Layer-Equalization"], [8, "4.-1-Cross-Layer-Equalization"], [29, "4.-1-Cross-Layer-Equalization"]], "Summary": [[0, "Summary"], [1, "Summary"], [2, "Summary"], [3, "Summary"], [4, "Summary"], [5, "Summary"], [6, "Summary"], [7, "Summary"], [8, "Summary"], [9, "Summary"], [10, "Summary"], [11, "Summary"], [13, "Summary"], [14, "Summary"], [15, "Summary"], [17, "Summary"], [18, "Summary"], [19, "Summary"], [20, "Summary"], [22, "Summary"], [23, "Summary"], [24, "Summary"], [25, "Summary"], [26, "Summary"], [27, "Summary"], [28, "Summary"], [29, "Summary"], [30, "Summary"], [31, "Summary"]], "Quantization Simulation": [[1, "Quantization-Simulation"]], "1. Example evaluation pipeline": [[1, "1.-Example-evaluation-pipeline"]], "Compute Encodings": [[1, "Compute-Encodings"], [5, "Compute-Encodings"], [7, "Compute-Encodings"], [8, "Compute-Encodings"], [14, "Compute-Encodings"], [15, "Compute-Encodings"], [18, "Compute-Encodings"], [19, "Compute-Encodings"], [20, "Compute-Encodings"]], "Model Compression Using Channel Pruning": [[2, "Model-Compression-Using-Channel-Pruning"]], "2. Load the model and evaluate it to find the baseline accuracy": [[2, "2.-Load-the-model-and-evaluate-it-to-find-the-baseline-accuracy"], [3, "2.-Load-the-model-and-evaluate-it-to-find-the-baseline-accuracy"], [4, "2.-Load-the-model-and-evaluate-it-to-find-the-baseline-accuracy"], [22, "2.-Load-the-model-and-evaluate-it-to-find-the-baseline-accuracy"], [23, "2.-Load-the-model-and-evaluate-it-to-find-the-baseline-accuracy"], [24, "2.-Load-the-model-and-evaluate-it-to-find-the-baseline-accuracy"]], "3. Compress the model and fine-tune": [[2, "3.-Compress-the-model-and-fine-tune"], [3, "3.-Compress-the-model-and-fine-tune"], [4, "3.-Compress-the-model-and-fine-tune"], [22, "3.-Compress-the-model-and-fine-tune"], [23, "3.-Compress-the-model-and-fine-tune"], [24, "3.-Compress-the-model-and-fine-tune"]], "3.1. Compress model using Channel Pruning and evaluate it to find post-compression accuracy": [[2, "3.1.-Compress-model-using-Channel-Pruning-and-evaluate-it-to-find-post-compression-accuracy"], [3, "3.1.-Compress-model-using-Channel-Pruning-and-evaluate-it-to-find-post-compression-accuracy"], [4, "3.1.-Compress-model-using-Channel-Pruning-and-evaluate-it-to-find-post-compression-accuracy"], [22, "3.1.-Compress-model-using-Channel-Pruning-and-evaluate-it-to-find-post-compression-accuracy"]], "3.2. Fine-tune the model": [[2, "3.2.-Fine-tune-the-model"], [3, "3.2.-Fine-tune-the-model"], [22, "3.2.-Fine-tune-the-model"], [23, "3.2.-Fine-tune-the-model"]], "Model compression Using Spatial SVD": [[3, "Model-compression-Using-Spatial-SVD"]], "Model Compression Using Spatial SVD Followed by Channel Pruning": [[4, "Model-Compression-Using-Spatial-SVD-Followed-by-Channel-Pruning"]], "3.2. Fine-tune the model after Spatial SVD": [[4, "3.2.-Fine-tune-the-model-after-Spatial-SVD"], [24, "3.2.-Fine-tune-the-model-after-Spatial-SVD"]], "3.3. Compress model using Channel Pruning and evaluate it to find post-compression accuracy": [[4, "3.3.-Compress-model-using-Channel-Pruning-and-evaluate-it-to-find-post-compression-accuracy"], [24, "3.3.-Compress-model-using-Channel-Pruning-and-evaluate-it-to-find-post-compression-accuracy"]], "3.4. Fine-tune the model after Channel Pruning": [[4, "3.4.-Fine-tune-the-model-after-Channel-Pruning"], [24, "3.4.-Fine-tune-the-model-after-Channel-Pruning"]], "Adaptive Rounding (AdaRound)": [[5, "Adaptive-Rounding-(AdaRound)"], [25, "Adaptive-Rounding-(AdaRound)"]], "1. Example Evaluation and Training Pipeline": [[5, "1.-Example-Evaluation-and-Training-Pipeline"], [20, "1.-Example-Evaluation-and-Training-Pipeline"]], "2. Load the model and evaluate to get a baseline FP32 accuracy score": [[5, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"], [8, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"], [9, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"], [17, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"], [18, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"], [19, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"], [20, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"], [25, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"], [29, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"], [30, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"], [31, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"]], "4. Apply AdaRound": [[5, "4.-Apply-AdaRound"]], "AutoQuant": [[6, "AutoQuant"], [10, "AutoQuant"], [26, "AutoQuant"], [27, "AutoQuant"]], "2. Load a pretrained FP32 model": [[6, "2.-Load-a-pretrained-FP32-model"], [10, "2.-Load-a-pretrained-FP32-model"], [14, "2.-Load-a-pretrained-FP32-model"], [15, "2.-Load-a-pretrained-FP32-model"], [16, "2.-Load-a-pretrained-FP32-model"], [26, "2.-Load-a-pretrained-FP32-model"], [27, "2.-Load-a-pretrained-FP32-model"]], "3. Determine the baseline FP32 accuracy": [[6, "3.-Determine-the-baseline-FP32-accuracy"], [10, "3.-Determine-the-baseline-FP32-accuracy"], [14, "3.-Determine-the-baseline-FP32-accuracy"], [15, "3.-Determine-the-baseline-FP32-accuracy"]], "4. Define Constants and Helper functions": [[6, "4.-Define-Constants-and-Helper-functions"], [10, "4.-Define-Constants-and-Helper-functions"]], "Prepare unlabeled dataset": [[6, "Prepare-unlabeled-dataset"]], "Prepare the evaluation callback function": [[6, "Prepare-the-evaluation-callback-function"], [10, "Prepare-the-evaluation-callback-function"], [11, "Prepare-the-evaluation-callback-function"]], "5. Apply AutoQuant": [[6, "5.-Apply-AutoQuant"], [10, "5.-Apply-AutoQuant"]], "Optionally set AdaRound Parameters": [[6, "Optionally-set-AdaRound-Parameters"], [10, "Optionally-set-AdaRound-Parameters"]], "Run AutoQuant": [[6, "Run-AutoQuant"], [10, "Run-AutoQuant"], [26, "Run-AutoQuant"]], "Quantization-Aware Training with BatchNorm Re-estimation": [[7, "Quantization-Aware-Training-with-BatchNorm-Re-estimation"], [11, "Quantization-Aware-Training-with-BatchNorm-Re-estimation"], [28, "Quantization-Aware-Training-with-BatchNorm-Re-estimation"]], "2. Load FP32 model": [[7, "2.-Load-FP32-model"], [28, "2.-Load-FP32-model"]], "BatchNorm Rewriter": [[7, "BatchNorm-Rewriter"]], "3. Create a quantization simulation model and Perform QAT": [[7, "3.-Create-a-quantization-simulation-model-and-Perform-QAT"], [28, "3.-Create-a-quantization-simulation-model-and-Perform-QAT"]], "Perform QAT": [[7, "Perform-QAT"], [28, "Perform-QAT"]], "4. Perform BatchNorm Reestimation": [[7, "4.-Perform-BatchNorm-Reestimation"], [28, "4.-Perform-BatchNorm-Reestimation"]], "Re-estimate BatchNorm Statistics": [[7, "Re-estimate-BatchNorm-Statistics"], [28, "Re-estimate-BatchNorm-Statistics"]], "Fold BatchNorm Layers": [[7, "Fold-BatchNorm-Layers"], [11, "Fold-BatchNorm-Layers"], [28, "Fold-BatchNorm-Layers"]], "5. Export Model": [[7, "5.-Export-Model"], [11, "5.-Export-Model"], [28, "5.-Export-Model"]], "Cross-Layer Equalization (CLE) and Bias Correction (BC)": [[8, "Cross-Layer-Equalization-(CLE)-and-Bias-Correction-(BC)"], [29, "Cross-Layer-Equalization-(CLE)-and-Bias-Correction-(BC)"]], "4. 2 Bias Correction": [[8, "4.-2-Bias-Correction"], [29, "4.-2-Bias-Correction"]], "Adaptive Rounding (Adaround)": [[9, "Adaptive-Rounding-(Adaround)"]], "4. Apply Adaround": [[9, "4.-Apply-Adaround"], [17, "4.-Apply-Adaround"], [25, "4.-Apply-Adaround"]], "1. Instantiate the example evaluation and training pipeline": [[11, "1.-Instantiate-the-example-evaluation-and-training-pipeline"]], "2. Define Constants and Datasets Prepare": [[11, "2.-Define-Constants-and-Datasets-Prepare"]], "2. Create the model in Keras": [[11, "2.-Create-the-model-in-Keras"]], "3. Train and evaluate the model": [[11, "3.-Train-and-evaluate-the-model"]], "4. Create a QuantizationSim Model": [[11, "4.-Create-a-QuantizationSim-Model"]], "5. Perform QAT": [[11, "5.-Perform-QAT"], [14, "5.-Perform-QAT"], [15, "5.-Perform-QAT"]], "Quantization-Aware Training with a Keras Transformer Model": [[12, "Quantization-Aware-Training-with-a-Keras-Transformer-Model"]], "Keras Model Preparer": [[13, "Keras-Model-Preparer"]], "1. Creating a Keras model with subclass layers": [[13, "1.-Creating-a-Keras-model-with-subclass-layers"]], "2. Converting the Keras model with subclass layers to a Keras model with functional layers": [[13, "2.-Converting-the-Keras-model-with-subclass-layers-to-a-Keras-model-with-functional-layers"]], "3. Showing similarities and differences between the original and converted models": [[13, "3.-Showing-similarities-and-differences-between-the-original-and-converted-models"]], "4. Discussing the limitations of the Keras Model Preparer": [[13, "4.-Discussing-the-limitations-of-the-Keras-Model-Preparer"]], "Quantization-Aware Training": [[14, "Quantization-Aware-Training"], [19, "Quantization-Aware-Training"], [30, "Quantization-Aware-Training"]], "Example evaluation and training pipeline": [[14, "Example-evaluation-and-training-pipeline"], [15, "Example-evaluation-and-training-pipeline"]], "1. Load the dataset": [[14, "1.-Load-the-dataset"], [15, "1.-Load-the-dataset"]], "4. Create a QuantizationSim Model and determine quantized accuracy": [[14, "4.-Create-a-QuantizationSim-Model-and-determine-quantized-accuracy"], [15, "4.-Create-a-QuantizationSim-Model-and-determine-quantized-accuracy"]], "Compile the model": [[14, "Compile-the-model"], [15, "Compile-the-model"]], "Evaluate the performance of the quantized model": [[14, "Evaluate-the-performance-of-the-quantized-model"], [15, "Evaluate-the-performance-of-the-quantized-model"]], "6. Evaluate validation accuracy after QAT": [[14, "6.-Evaluate-validation-accuracy-after-QAT"], [15, "6.-Evaluate-validation-accuracy-after-QAT"]], "7. Export the encodings": [[14, "7.-Export-the-encodings"], [15, "7.-Export-the-encodings"]], "Quantization-Aware Training with Range Learning": [[15, "Quantization-Aware-Training-with-Range-Learning"], [20, "Quantization-Aware-Training-with-Range-Learning"], [31, "Quantization-Aware-Training-with-Range-Learning"]], "Quant Analyzer": [[16, "Quant-Analyzer"], [21, "Quant-Analyzer"], [32, "Quant-Analyzer"]], "3. Apply QuantAnalyzer to the model": [[16, "3.-Apply-QuantAnalyzer-to-the-model"], [21, "3.-Apply-QuantAnalyzer-to-the-model"], [32, "3.-Apply-QuantAnalyzer-to-the-model"]], "Per-layer analysis by enabling/disabling quantization wrappers": [[16, "Per-layer-analysis-by-enabling/disabling-quantization-wrappers"], [32, "Per-layer-analysis-by-enabling/disabling-quantization-wrappers"]], "Encoding min/max ranges": [[16, "Encoding-min/max-ranges"], [21, "Encoding-min/max-ranges"], [32, "Encoding-min/max-ranges"]], "PDF of statistics": [[16, "PDF-of-statistics"], [21, "PDF-of-statistics"], [32, "PDF-of-statistics"]], "Per-layer MSE loss": [[16, "Per-layer-MSE-loss"], [32, "Per-layer-MSE-loss"]], "Quantsim and Adaround - Per Channel Quantization (PCQ)": [[17, "Quantsim-and-Adaround---Per-Channel-Quantization-(PCQ)"]], "Cross-Layer Equalization (CLE) with QuantSim": [[18, "Cross-Layer-Equalization-(CLE)-with-QuantSim"]], "4 Cross Layer Equalization": [[18, "4-Cross-Layer-Equalization"]], "5 Exporting": [[18, "5-Exporting"]], "4. Perform QAT": [[19, "4.-Perform-QAT"], [20, "4.-Perform-QAT"], [30, "4.-Perform-QAT"], [31, "4.-Perform-QAT"]], "2. Load the model": [[21, "2.-Load-the-model"], [32, "2.-Load-the-model"]], "Per op analysis by enabling/disabling quantization ops": [[21, "Per-op-analysis-by-enabling/disabling-quantization-ops"]], "Per op MSE loss": [[21, "Per-op-MSE-loss"]], "Model compression using Channel Pruning": [[22, "Model-compression-using-Channel-Pruning"]], "Model compression using Spatial SVD": [[23, "Model-compression-using-Spatial-SVD"]], "3.1. Compress model using Spatial SVD and evaluate it to find post-compression accuracy": [[23, "3.1.-Compress-model-using-Spatial-SVD-and-evaluate-it-to-find-post-compression-accuracy"], [24, "3.1.-Compress-model-using-Spatial-SVD-and-evaluate-it-to-find-post-compression-accuracy"]], "Model compression using Spatial SVD followed by Channel Pruning": [[24, "Model-compression-using-Spatial-SVD-followed-by-Channel-Pruning"]], "1. Define Constants and Helper functions": [[26, "1.-Define-Constants-and-Helper-functions"], [27, "1.-Define-Constants-and-Helper-functions"]], "3. Run AutoQuant": [[26, "3.-Run-AutoQuant"], [27, "3.-Run-AutoQuant"]], "Prepare and Validate Model": [[26, "Prepare-and-Validate-Model"]], "Create AutoQuant Object": [[26, "Create-AutoQuant-Object"], [27, "Create-AutoQuant-Object"]], "Set AdaRound Parameters (optional)": [[26, "Set-AdaRound-Parameters-(optional)"], [27, "Set-AdaRound-Parameters-(optional)"]], "Run AutoQuant Inference": [[27, "Run-AutoQuant-Inference"]], "Run AutoQuant Optimization": [[27, "Run-AutoQuant-Optimization"]], "Using AIMET Tensorflow APIs with Keras Models": [[33, "using-aimet-tensorflow-apis-with-keras-models"]], "Introduction": [[33, "introduction"], [37, "introduction"], [38, "introduction"], [39, "introduction"], [43, "introduction"], [50, "introduction"], [59, "introduction"], [61, "introduction"], [62, "introduction"], [65, "introduction"], [74, "introduction"], [76, "introduction"], [77, "introduction"], [83, "introduction"]], "APIs": [[33, "apis"]], "Code Example": [[33, "code-example"], [37, "code-example"], [39, "code-example"], [40, "code-example"], [50, "code-example"], [51, "code-example"], [62, "code-example"], [63, "code-example"], [66, "code-example"], [77, "code-example"], [78, "code-example"]], "Utility Functions": [[33, "utility-functions"]], "Welcome to AI Model Efficiency Toolkit API Docs!": [[34, "welcome-to-ai-model-efficiency-toolkit-api-docs"]], "Indices and tables": [[34, "indices-and-tables"]], "AIMET Keras APIs": [[35, "aimet-keras-apis"]], "AIMET Keras AdaRound API": [[36, "aimet-keras-adaround-api"]], "User Guide Link": [[36, "user-guide-link"], [39, "user-guide-link"], [46, "user-guide-link"], [48, "user-guide-link"], [49, "user-guide-link"], [50, "user-guide-link"], [57, "user-guide-link"], [58, "user-guide-link"], [60, "user-guide-link"], [62, "user-guide-link"], [68, "user-guide-link"], [71, "user-guide-link"], [73, "user-guide-link"], [75, "user-guide-link"], [77, "user-guide-link"], [86, "user-guide-link"]], "Examples Notebook Link": [[36, "examples-notebook-link"], [37, "examples-notebook-link"], [39, "examples-notebook-link"], [57, "examples-notebook-link"], [58, "examples-notebook-link"], [59, "examples-notebook-link"], [62, "examples-notebook-link"], [68, "examples-notebook-link"], [71, "examples-notebook-link"], [73, "examples-notebook-link"], [74, "examples-notebook-link"], [77, "examples-notebook-link"], [86, "examples-notebook-link"]], "Top-level API": [[36, "top-level-api"], [40, "top-level-api"], [42, "top-level-api"], [44, "top-level-api"], [46, "top-level-api"], [48, "top-level-api"], [49, "top-level-api"], [51, "top-level-api"], [52, "top-level-api"], [54, "top-level-api"], [57, "top-level-api"], [58, "top-level-api"], [63, "top-level-api"], [66, "top-level-api"], [68, "top-level-api"], [71, "top-level-api"], [73, "top-level-api"], [78, "top-level-api"], [80, "top-level-api"], [84, "top-level-api"], [86, "top-level-api"]], "Adaround Parameters": [[36, "adaround-parameters"], [48, "adaround-parameters"], [57, "adaround-parameters"], [71, "adaround-parameters"]], "Enum Definition": [[36, "enum-definition"], [57, "enum-definition"], [71, "enum-definition"], [78, "enum-definition"], [86, "enum-definition"]], "Code Examples": [[36, "code-examples"], [38, "code-examples"], [42, "code-examples"], [44, "code-examples"], [46, "code-examples"], [49, "code-examples"], [52, "code-examples"], [54, "code-examples"], [57, "code-examples"], [58, "code-examples"], [61, "code-examples"], [68, "code-examples"], [73, "code-examples"], [76, "code-examples"], [80, "code-examples"], [84, "code-examples"], [87, "code-examples"], [88, "code-examples"]], "AIMET Keras BatchNorm Re-estimation APIs": [[37, "aimet-keras-batchnorm-re-estimation-apis"]], "Top-level APIs": [[37, "top-level-apis"], [59, "top-level-apis"], [74, "top-level-apis"]], "Limitations": [[37, "limitations"], [42, "limitations"]], "AIMET Keras Compression API": [[38, "aimet-keras-compression-api"]], "Top-level API for Compression": [[38, "top-level-api-for-compression"], [61, "top-level-api-for-compression"], [76, "top-level-api-for-compression"]], "Greedy Selection Parameters": [[38, "greedy-selection-parameters"], [61, "greedy-selection-parameters"], [76, "greedy-selection-parameters"]], "Spatial SVD Configuration": [[38, "spatial-svd-configuration"], [61, "spatial-svd-configuration"], [76, "spatial-svd-configuration"]], "Configuration Definitions": [[38, "configuration-definitions"], [61, "configuration-definitions"], [76, "configuration-definitions"]], "AIMET Keras Cross Layer Equalization APIs": [[39, "aimet-keras-cross-layer-equalization-apis"]], "Cross Layer Equalization API": [[39, "cross-layer-equalization-api"], [50, "cross-layer-equalization-api"], [62, "cross-layer-equalization-api"], [77, "cross-layer-equalization-api"]], "Primitive APIs": [[39, "primitive-apis"], [62, "primitive-apis"], [77, "primitive-apis"]], "AIMET Keras Layer Output Generation API": [[40, "aimet-keras-layer-output-generation-api"]], "Keras Model Guidelines": [[41, "keras-model-guidelines"]], "Model Preparer API": [[42, "model-preparer-api"], [80, "model-preparer-api"]], "AIMET Keras Cross Layer Equalization Primitive API": [[43, "aimet-keras-cross-layer-equalization-primitive-api"]], "Higher Level APIs for Cross Layer Equalization": [[43, "higher-level-apis-for-cross-layer-equalization"], [65, "higher-level-apis-for-cross-layer-equalization"], [83, "higher-level-apis-for-cross-layer-equalization"]], "Code Examples for Higher Level APIs": [[43, "code-examples-for-higher-level-apis"], [65, "code-examples-for-higher-level-apis"], [83, "code-examples-for-higher-level-apis"]], "Lower Level APIs for Cross Layer Equalization": [[43, "lower-level-apis-for-cross-layer-equalization"], [65, "lower-level-apis-for-cross-layer-equalization"], [83, "lower-level-apis-for-cross-layer-equalization"]], "Custom Datatype used": [[43, "custom-datatype-used"], [65, "custom-datatype-used"]], "Code Example for Lower level APIs": [[43, 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[[88, "aimet_torch.visualize_model.visualize_weight_ranges"]]}})
\ No newline at end of file
+Search.setIndex({"docnames": ["Examples/onnx/quantization/cle", "Examples/onnx/quantization/quantsim", "Examples/tensorflow/compression/channel_pruning", "Examples/tensorflow/compression/spatial_svd", "Examples/tensorflow/compression/spatial_svd_channel_pruning", "Examples/tensorflow/quantization/adaround", "Examples/tensorflow/quantization/autoquant", "Examples/tensorflow/quantization/bn_reestimation", "Examples/tensorflow/quantization/cle_bc", "Examples/tensorflow/quantization/keras/adaround", "Examples/tensorflow/quantization/keras/autoquant", "Examples/tensorflow/quantization/keras/bn_reestimation", "Examples/tensorflow/quantization/keras/keras_transformer_qat", "Examples/tensorflow/quantization/keras/model_preparer", "Examples/tensorflow/quantization/keras/qat", "Examples/tensorflow/quantization/keras/qat_range_learning", "Examples/tensorflow/quantization/keras/quant_analyzer", "Examples/tensorflow/quantization/keras/quantsim_adaround_pcq", "Examples/tensorflow/quantization/keras/quantsim_cle", "Examples/tensorflow/quantization/qat", "Examples/tensorflow/quantization/qat_range_learning", "Examples/tensorflow/quantization/quant_analyzer", "Examples/torch/compression/channel_pruning", "Examples/torch/compression/spatial_svd", "Examples/torch/compression/spatial_svd_channel_pruning", "Examples/torch/quantization/adaround", "Examples/torch/quantization/autoquant_v1", "Examples/torch/quantization/autoquant_v2", "Examples/torch/quantization/bn_reestimation", "Examples/torch/quantization/cle_bc", "Examples/torch/quantization/qat", "Examples/torch/quantization/qat_range_learning", "Examples/torch/quantization/quant_analyzer", "api_docs/convert_tf_sess_to_keras", "api_docs/index", "api_docs/keras", "api_docs/keras_adaround", "api_docs/keras_batchnorm_re_estimation", "api_docs/keras_compression", "api_docs/keras_cross_layer_equalization", "api_docs/keras_layer_output_generation", "api_docs/keras_model_guidelines", 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81, 82, 83, 85, 86, 87, 89, 90, 91, 93, 94, 95, 96, 97, 98, 99, 100, 102, 103, 104, 106, 107, 108, 110, 112, 115, 116], "step": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 43, 49, 59, 64, 65, 71, 73, 74, 80, 86, 90, 93, 94, 95, 96, 97, 99, 102, 104, 105, 107, 109, 110], "run": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 38, 40, 42, 44, 45, 46, 48, 49, 51, 54, 55, 57, 58, 59, 60, 61, 63, 66, 68, 72, 73, 74, 76, 78, 80, 81, 84, 86, 90, 95, 100, 102, 104, 105, 106, 110, 111, 113], "norm": [0, 8, 18, 29, 43, 59, 65, 72, 74, 83, 93, 95, 104, 105, 106], "bn": [0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 14, 15, 17, 18, 19, 20, 21, 25, 29, 30, 31, 37, 43, 60, 62, 64, 65, 67, 75, 83, 85, 104, 111], "conv": [0, 2, 3, 4, 5, 6, 7, 8, 11, 18, 19, 20, 21, 29, 33, 37, 43, 57, 59, 60, 61, 65, 71, 74, 75, 80, 83, 88, 101, 108, 111, 112, 115, 116], "immedi": [0, 8, 18, 27, 29], "befor": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 27, 28, 29, 30, 31, 40, 41, 42, 43, 45, 49, 51, 60, 63, 64, 65, 71, 73, 78, 83, 85, 86, 88, 93, 94, 95, 102, 104, 107, 113, 114], "after": [0, 2, 3, 5, 7, 8, 9, 11, 12, 17, 18, 19, 20, 22, 23, 25, 26, 27, 28, 29, 30, 31, 33, 36, 37, 43, 49, 57, 59, 60, 62, 65, 71, 73, 74, 80, 81, 83, 86, 88, 93, 94, 95, 97, 102, 104, 107, 109, 113, 114], "scale": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 37, 39, 43, 50, 55, 59, 62, 65, 71, 74, 77, 83, 86, 95, 104, 105, 106, 107, 110], "set": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 36, 38, 42, 43, 48, 49, 54, 55, 57, 58, 60, 61, 64, 65, 68, 71, 73, 76, 80, 81, 83, 86, 91, 93, 97, 99, 100, 102, 103, 105, 106, 107, 108, 109, 110, 116], "consecut": [0, 8, 18, 29, 43, 65, 83, 104, 105], "rang": [0, 1, 5, 7, 8, 9, 11, 12, 13, 14, 17, 18, 19, 25, 26, 27, 28, 29, 30, 42, 44, 52, 59, 66, 68, 69, 71, 73, 74, 80, 84, 86, 88, 93, 95, 98, 99, 104, 105, 106, 107, 109, 110, 111, 114], "tensor": [0, 1, 2, 3, 4, 5, 6, 8, 9, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 33, 36, 40, 42, 45, 46, 53, 54, 55, 57, 60, 66, 67, 68, 71, 72, 73, 76, 77, 78, 79, 80, 83, 84, 85, 86, 93, 96, 103, 104, 106, 108, 109, 110, 111, 112, 115], "valu": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 36, 38, 43, 44, 52, 55, 57, 58, 61, 65, 66, 71, 73, 75, 76, 78, 80, 83, 84, 86, 93, 99, 102, 104, 105, 106, 107, 110, 112, 114, 115], "per": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38, 44, 52, 55, 59, 61, 65, 66, 74, 76, 78, 84, 86, 95, 104, 105, 106, 108, 109, 110, 111], "channel": [0, 1, 8, 14, 15, 18, 23, 25, 28, 29, 30, 31, 32, 38, 55, 59, 69, 72, 74, 88, 95, 97, 98, 99, 101, 102, 105, 106, 108, 109, 110, 111, 112, 114, 115, 116], "up": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 36, 38, 42, 44, 46, 52, 57, 61, 64, 66, 68, 71, 76, 84, 86, 91, 102, 107, 108, 110, 116], "down": [0, 5, 8, 9, 17, 18, 25, 29, 55, 71], "correspond": [0, 2, 3, 4, 5, 6, 7, 8, 17, 18, 19, 20, 21, 22, 23, 24, 27, 29, 33, 36, 37, 40, 43, 57, 59, 60, 63, 65, 71, 74, 78, 83, 86, 91, 96, 99, 104, 106, 116], "subsequ": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 26, 29, 30, 31, 71, 86, 103, 105, 107, 108], "high": [0, 2, 3, 4, 8, 18, 22, 23, 24, 29, 39, 43, 50, 62, 65, 77, 83, 93, 95, 97, 99, 100, 105, 109, 111, 114], "mai": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 42, 55, 71, 73, 80, 86, 93, 97, 102, 104, 105, 106, 108, 109, 110], "result": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 38, 42, 44, 58, 61, 66, 71, 73, 76, 84, 86, 93, 94, 96, 97, 100, 105, 106, 107, 108, 110], "some": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 38, 42, 43, 45, 46, 61, 65, 68, 71, 76, 80, 83, 85, 86, 88, 93, 97, 99, 102, 103, 104, 105, 107, 109, 110], "cover": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 71, 86, 95, 108, 110], "instanti": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 79, 82, 107, 113], "load": [0, 1, 12, 33, 40, 43, 46, 51, 59, 60, 62, 63, 64, 65, 68, 69, 71, 73, 74, 75, 76, 78, 80, 81, 86, 102], "find": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 25, 26, 27, 29, 30, 31, 43, 61, 65, 71, 75, 81, 83, 86, 88, 99, 104, 106, 107, 110], "fake": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 25, 28, 29, 30, 31, 33, 71, 86], "op": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 22, 24, 25, 28, 29, 30, 31, 33, 38, 43, 46, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 71, 72, 73, 81, 86, 104, 108, 111], "insert": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 22, 24, 25, 28, 29, 30, 31, 71, 80, 86, 104, 110], "simuat": [0, 5, 8, 9, 17, 18, 19, 20, 25, 29, 30, 31], "get": [0, 1, 2, 3, 4, 6, 11, 12, 13, 14, 15, 16, 22, 23, 24, 33, 38, 40, 43, 51, 60, 61, 62, 63, 65, 68, 69, 72, 76, 78, 80, 86, 88, 89, 90, 91, 93, 96, 104, 114], "score": [0, 1, 7, 11, 14, 15, 27, 28, 38, 58, 61, 71, 73, 76, 87, 99, 102, 113], "finetun": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 54, 59, 68, 86], "design": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, 81, 105], "show": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 36, 37, 39, 48, 57, 58, 59, 62, 68, 69, 71, 72, 73, 74, 77, 81, 86, 88, 100, 105, 109], "state": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 64, 102], "art": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32], "For": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 36, 37, 38, 39, 40, 41, 42, 51, 52, 54, 57, 58, 59, 61, 62, 63, 68, 71, 73, 74, 76, 77, 78, 79, 80, 81, 82, 86, 88, 89, 90, 91, 93, 96, 97, 98, 99, 100, 101, 102, 104, 106, 108, 110, 113, 116], "rel": [0, 1, 5, 6, 8, 9, 10, 14, 15, 16, 17, 18, 19, 20, 21, 25, 26, 27, 29, 30, 31, 32, 38, 61, 69, 76, 88, 97, 104, 109, 114], "friendli": [0, 1, 5, 6, 8, 9, 10, 14, 15, 16, 17, 18, 19, 20, 21, 25, 26, 27, 29, 30, 31, 32, 94, 104, 105], "like": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 39, 42, 43, 62, 71, 77, 83, 86, 100, 102, 104, 106, 107, 108, 113], "resnet18": [0, 1, 6, 8, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 59, 71, 73, 74, 77, 83, 84, 86, 87, 88], "also": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 44, 46, 52, 55, 57, 58, 60, 66, 68, 71, 73, 79, 80, 84, 86, 96, 97, 98, 99, 104, 106, 108, 109, 110, 111, 113, 114, 116], "optim": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 36, 38, 44, 45, 46, 49, 53, 54, 57, 61, 67, 71, 72, 73, 76, 85, 86, 87, 88, 93, 94, 100, 102, 104, 107, 110, 111, 113], "ar": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 51, 52, 54, 57, 58, 59, 61, 62, 63, 64, 65, 66, 68, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 83, 84, 85, 86, 89, 93, 94, 95, 96, 97, 99, 102, 103, 104, 105, 106, 107, 108, 109, 110, 113, 114, 116], "deliber": [0, 2, 3, 4, 5, 6, 8, 9, 10, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32], "chosen": [0, 2, 3, 4, 5, 6, 8, 9, 10, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 98, 102], "have": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 42, 50, 52, 55, 61, 65, 66, 71, 72, 77, 78, 80, 81, 86, 88, 91, 99, 102, 104, 105, 106, 109, 110], "execut": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 71, 73, 80, 87, 98, 99, 113], "quickli": [0, 2, 3, 4, 5, 6, 8, 9, 10, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 99], "reli": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], "imagenet": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 38, 54, 58, 59, 60, 62, 65, 68, 69, 71, 74, 75, 86, 98], "task": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 55, 113, 114], "imag": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 44, 52, 58, 66, 73, 75, 84, 89, 93, 98, 106], "classif": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 42, 102], "If": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 36, 38, 39, 41, 42, 43, 46, 55, 57, 58, 60, 61, 62, 64, 65, 66, 68, 71, 73, 75, 76, 77, 78, 79, 80, 81, 83, 85, 86, 87, 88, 90, 91, 94, 103, 104, 105, 106, 108, 109, 113, 114, 116], "you": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 38, 58, 61, 64, 68, 71, 73, 76, 80, 82, 86, 90, 91, 98, 99, 103, 112, 115], "alreadi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 90, 99, 109], "version": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 51, 71, 73, 80, 86, 89, 90, 91, 98, 100], "readili": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], "avail": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 39, 62, 71, 80, 86, 89, 103, 106, 108, 109], "els": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 38, 43, 49, 74, 80, 81, 105], "download": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 89, 90, 91], "appropri": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 38, 61, 71, 76, 83, 86, 89, 90, 91, 97, 98, 99, 102, 109], "locat": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 98], "e": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 36, 44, 45, 46, 52, 54, 55, 57, 66, 68, 71, 73, 75, 84, 86, 95, 97, 100, 107, 109, 116], "g": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 36, 44, 46, 52, 55, 57, 66, 68, 71, 84, 86, 90, 95, 97, 100, 109, 116], "net": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 98], "challeng": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], "lsvrc": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 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24, 25, 26, 27, 28, 29, 30, 31, 32], "valid": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 38, 44, 52, 54, 61, 62, 65, 66, 68, 71, 75, 76, 84, 85, 86, 94, 104, 111], "see": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 36, 37, 38, 39, 42, 46, 48, 49, 50, 57, 58, 59, 60, 61, 62, 68, 71, 73, 74, 75, 76, 77, 86, 96, 99, 100, 102, 104, 108, 109, 110, 112, 113, 114, 115], "descript": [0, 1, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 103], "detail": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 61, 71, 73, 80, 86, 96, 98, 99, 100, 102, 104, 109, 110, 113, 114], "A": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 38, 43, 44, 46, 52, 58, 61, 65, 66, 68, 71, 72, 73, 74, 75, 76, 77, 82, 83, 84, 86, 87, 88, 97, 104, 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71, 75, 77, 83, 86, 107, 108], "origin": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 38, 40, 42, 51, 61, 63, 68, 76, 78, 80, 86, 96, 97, 102, 104, 105, 106, 107, 110, 113], "do": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 33, 37, 42, 46, 68, 71, 80, 82, 88, 91, 102, 106, 110], "infer": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 38, 45, 46, 49, 52, 53, 54, 55, 57, 67, 68, 71, 73, 85, 86, 95, 97, 100, 105, 107, 110, 111], "put": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32], "interfac": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 89], "method": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 33, 40, 46, 58, 61, 63, 64, 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16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 48, 49, 50, 51, 52, 54, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 68, 69, 71, 72, 73, 74, 75, 76, 77, 78, 80, 81, 83, 84, 86, 87, 88, 91, 95, 96, 109], "torch": [0, 1, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 71, 72, 73, 74, 76, 78, 79, 81, 82, 83, 84, 86, 87, 88, 89, 90, 98, 103, 111], "onnxruntim": [0, 1, 49, 51, 52], "ort": [0, 1, 49], "common": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 58, 61, 68, 86, 90, 109, 114], "image_net_config": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 59], "util": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 36, 42, 43, 44, 46, 57, 58, 59, 60, 64, 65, 66, 68, 72, 73, 83, 86, 87, 95, 98, 104], "image_net_evalu": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 66], "imagenetevalu": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 66], "image_net_data_load": [0, 1, 22, 24, 25, 28, 29, 30, 31, 32], "imagenetdataload": [0, 1, 2, 3, 4, 5, 6, 7, 8, 16, 19, 20, 21, 22, 24, 25, 28, 29, 30, 31, 32], "imagenetdatapipelin": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 59, 71, 74, 75, 86], "staticmethod": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 80], "def": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 36, 38, 39, 41, 42, 43, 44, 46, 48, 49, 50, 52, 54, 57, 58, 59, 60, 61, 62, 65, 66, 68, 69, 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 83, 84, 86, 87, 88], "get_val_dataload": [0, 1, 2, 3, 4, 5, 6, 7, 8, 19, 20, 21, 22, 24, 25, 28, 29, 30, 31, 32, 71, 75, 86], "return": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 49, 52, 54, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 68, 69, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 83, 84, 86, 87, 88, 94, 99, 100, 106, 110], "data_load": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 48, 49, 54, 68, 71, 73, 75, 76, 86], "image_s": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 38, 66, 73], "batch_siz": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 36, 37, 38, 44, 49, 52, 54, 57, 58, 60, 61, 66, 68, 71, 73, 76, 84, 86], "is_train": [0, 1, 22, 24, 25, 28, 29, 30, 31, 32], "fals": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 17, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 33, 38, 42, 46, 51, 54, 55, 57, 58, 60, 61, 63, 64, 66, 68, 72, 73, 75, 76, 80, 81, 86, 103, 108], "num_work": [0, 1, 22, 23, 24, 25, 28, 29, 30, 31, 32], "sess": [0, 1, 2, 3, 4, 5, 6, 7, 8, 19, 20, 21, 33, 38, 58, 59, 60, 61, 62, 65, 68, 69], "inferencesess": [0, 1, 49, 51, 52], "float": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 36, 38, 44, 46, 49, 52, 55, 57, 58, 61, 66, 68, 71, 73, 75, 76, 84, 86, 104, 106, 109, 110, 114], "its": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 38, 42, 69, 98, 100, 104, 106, 110, 116], "top": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 60, 96, 113], "param": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 33, 36, 38, 39, 43, 44, 46, 48, 49, 52, 54, 57, 60, 61, 65, 66, 68, 71, 73, 76, 84, 86, 87, 108], "iter": [0, 1, 2, 3, 4, 5, 9, 10, 11, 17, 18, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 33, 36, 38, 44, 49, 52, 57, 60, 61, 66, 71, 73, 76, 84, 93, 105], "none": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 36, 38, 40, 42, 43, 44, 46, 48, 49, 51, 52, 54, 57, 58, 60, 61, 63, 65, 66, 68, 71, 72, 73, 74, 75, 76, 77, 78, 80, 83, 84, 86, 87, 88, 91, 113], "go": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 42, 59, 71, 74, 86, 90, 91, 98, 113], "pretrain": [0, 1, 2, 3, 4, 5, 7, 8, 9, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 59, 71, 73, 74, 77, 83, 84, 86, 87, 88, 106, 107, 110], "torchvis": [0, 1, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 71, 73, 74, 77, 83, 84, 86, 87, 88], "similarli": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 71, 86, 109], "instead": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 38, 41, 71, 79, 80, 81, 86, 104, 105], "differ": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 38, 58, 61, 76, 80, 96, 98, 99, 102, 104, 105, 107, 108, 109, 110], "framework": [0, 1, 34, 54, 100, 104, 108, 110], "altogeth": [0, 1, 108], "input_shap": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 13, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 33, 39, 41, 44, 49, 52, 54, 58, 59, 60, 61, 62, 65, 69, 71, 73, 75, 76, 77, 80, 83, 84, 86, 87], "224": [0, 1, 2, 3, 4, 5, 6, 7, 8, 11, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 38, 39, 44, 46, 49, 52, 54, 58, 59, 60, 61, 62, 65, 66, 69, 71, 73, 75, 77, 78, 83, 84, 86, 87, 88], "shape": [0, 1, 11, 12, 13, 17, 25, 28, 29, 30, 31, 32, 33, 41, 42, 61, 72, 76, 77, 80, 81, 83, 106], "x": [0, 1, 9, 12, 13, 16, 17, 21, 25, 28, 29, 30, 31, 32, 38, 41, 42, 46, 64, 68, 72, 79, 80, 81, 89, 97, 103, 106], "height": [0, 1, 12, 25, 28, 29, 30, 31, 32, 112, 115, 116], "width": [0, 1, 5, 9, 17, 25, 28, 29, 30, 31, 32, 55, 93, 109, 110, 112, 115, 116], "dummy_input": [0, 1, 25, 26, 27, 28, 29, 30, 31, 32, 37, 40, 49, 51, 52, 54, 71, 72, 73, 77, 78, 79, 83, 84, 86], "randn": [0, 1, 26, 27, 46, 49, 52, 54, 71, 73, 80, 81, 84, 86], "filenam": [0, 1, 14, 15, 46, 57, 61, 68, 71, 86, 91], "resnet": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 17, 18, 19, 20, 21, 38, 58, 59, 97], "18": [0, 1], "pt_model": [0, 1], "true": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 38, 43, 46, 48, 55, 60, 61, 64, 65, 66, 68, 71, 72, 73, 74, 75, 76, 77, 80, 81, 83, 84, 86, 87, 88, 103, 108], "export": [0, 1, 5, 8, 9, 12, 17, 19, 20, 25, 29, 30, 31, 40, 44, 46, 51, 52, 54, 55, 59, 63, 66, 68, 71, 73, 74, 78, 79, 84, 86, 90, 91, 95, 98, 100, 102, 103, 104, 107, 110, 111], "eval": [0, 1, 2, 3, 4, 5, 6, 7, 8, 11, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 36, 38, 44, 49, 52, 57, 58, 61, 66, 71, 73, 75, 76, 77, 80, 82, 83, 84, 86, 87, 88, 99, 102, 113], "trainingmod": [0, 1], "preserv": [0, 1, 2, 3, 4, 22, 23, 24, 80], "export_param": [0, 1], "do_constant_fold": [0, 1], "input_nam": [0, 1, 86], "input": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 28, 32, 33, 38, 40, 41, 42, 44, 49, 51, 52, 54, 58, 59, 61, 63, 65, 66, 68, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 83, 84, 86, 96, 102, 106, 108, 110, 112, 113, 115, 116], "output_nam": [0, 1, 86], "output": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 33, 37, 38, 41, 42, 44, 55, 57, 58, 59, 60, 61, 62, 65, 66, 67, 68, 69, 71, 72, 73, 75, 79, 80, 81, 84, 85, 86, 88, 96, 102, 105, 106, 108, 110, 111, 112, 115, 116], "dynamic_ax": [0, 1], "0": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 36, 37, 38, 42, 46, 49, 54, 57, 58, 61, 66, 68, 71, 72, 73, 75, 76, 80, 81, 83, 86, 87, 89, 90, 91, 93, 97, 99, 103, 108], "load_model": [0, 1, 40], "decid": [0, 1, 5, 9, 17, 22, 23, 24, 25, 29, 30, 31, 32, 93, 113], "whether": [0, 1, 2, 3, 4, 5, 9, 17, 22, 23, 24, 25, 28, 29, 30, 31, 32, 40, 45, 51, 61, 63, 73, 78, 80, 81, 85, 93, 107], "cpu": [0, 1, 2, 3, 4, 5, 6, 8, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 61, 71, 73, 74, 78, 80, 83, 86, 88, 89, 90, 91, 104, 111], "cuda": [0, 1, 2, 3, 4, 5, 6, 8, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 73, 74, 75, 76, 84, 86, 87, 89, 91], "devic": [0, 1, 2, 3, 4, 5, 6, 7, 8, 11, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 40, 51, 63, 71, 73, 74, 75, 78, 80, 83, 86, 87, 88, 110], "current": [0, 2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 33, 38, 42, 46, 61, 65, 66, 68, 72, 76, 81, 82, 96, 100, 101, 102, 103, 108, 112, 115], "environ": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 14, 15, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 89, 98], "chang": [0, 1, 2, 3, 4, 7, 8, 11, 12, 14, 15, 19, 20, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 45, 55, 71, 73, 79, 80, 85, 86, 88, 93, 102, 106, 107, 108, 110, 114, 116], "logic": [0, 1, 22, 23, 24, 25, 29, 30, 31, 32, 111], "forc": [0, 1, 22, 23, 24, 25, 29, 30, 31, 32], "placement": [0, 1, 22, 23, 24, 25, 29, 30, 31, 32, 108], "cudnn_conv_algo_search": [0, 1], "fix": [0, 1, 55, 81, 100, 104, 109, 110, 111], "default": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 36, 38, 42, 44, 46, 57, 58, 60, 61, 66, 68, 71, 73, 75, 76, 80, 84, 86, 91, 93, 99, 102, 108, 110, 111, 113], "avoid": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 41, 46, 52, 71, 79, 86, 97], "everi": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 40, 44, 52, 57, 63, 66, 71, 78, 84, 86, 99, 102, 107, 114], "cudaexecutionprovid": [0, 1], "get_available_provid": [0, 1], "cpuexecutionprovid": [0, 1], "use_cuda": [0, 1, 2, 3, 4, 5, 6, 7, 8, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 33, 38, 48, 51, 54, 57, 60, 61, 63, 66, 68, 71, 74, 76, 86], "let": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 29, 30, 31, 71, 80, 86], "session": [0, 1, 2, 3, 4, 5, 6, 7, 8, 19, 20, 21, 33, 36, 38, 49, 51, 52, 54, 57, 58, 59, 60, 61, 62, 63, 65, 66, 68, 69, 87], "point": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 28, 29, 30, 31, 42, 43, 44, 45, 52, 53, 55, 66, 67, 75, 83, 84, 85, 86, 100, 102, 104, 106, 109, 110, 114], "32": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 29, 30, 31, 33, 36, 41, 42, 44, 49, 55, 57, 58, 61, 66, 71, 72, 80, 81, 90, 91, 109], "bit": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 55, 68, 71, 89, 93, 95, 104, 109, 110, 111], "print": [0, 1, 2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38, 49, 61, 68, 71, 72, 73, 76, 80, 81, 86, 91, 104, 106], "quantizationsimmodel": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 36, 37, 40, 43, 46, 48, 51, 54, 57, 59, 63, 68, 71, 73, 74, 78, 86, 93, 95], "batchnorm": [0, 1, 2, 3, 4, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 39, 42, 43, 45, 50, 58, 62, 64, 65, 72, 73, 75, 77, 83, 88, 94, 105, 116], "These": [0, 1, 5, 8, 9, 12, 14, 15, 17, 18, 19, 20, 25, 27, 29, 30, 31, 33, 36, 46, 57, 58, 68, 73, 86, 90, 91, 94, 95, 96, 97, 103, 104, 105, 106, 109, 110], "adjac": [0, 1, 2, 3, 4, 5, 6, 8, 9, 14, 15, 17, 18, 19, 20, 21, 25, 29, 30, 31, 108], "convolut": [0, 1, 2, 3, 4, 5, 8, 9, 13, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 29, 30, 31, 38, 42, 43, 61, 65, 72, 95, 97, 102, 109], "cannot": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 72, 80, 81], "thei": [0, 1, 2, 3, 4, 5, 8, 9, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 41, 43, 61, 65, 80, 83, 85, 89, 108, 113], "why": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 109], "On": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 55, 89], "runtim": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 46, 54, 55, 61, 68, 71, 76, 86, 97, 100, 102, 104, 106, 108, 110, 111], "tflite": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31], "snapdragon": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31], "neural": [0, 1, 5, 6, 8, 9, 10, 14, 15, 17, 18, 19, 20, 25, 26, 27, 29, 30, 31, 94, 97, 100, 102, 104, 107, 109, 110, 115], "process": [0, 1, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 19, 20, 25, 26, 27, 29, 30, 31, 38, 40, 51, 61, 63, 68, 71, 78, 86, 87, 94, 100, 102, 104, 105, 110], "sdk": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 100], "etc": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 55, 61, 64, 71, 86, 90, 91, 97, 104], "practic": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 71, 86, 102], "so": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 36, 37, 44, 46, 52, 57, 61, 66, 68, 71, 76, 80, 81, 82, 84, 86, 91, 103, 106, 113], "sec": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31], "speedup": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31], "sinc": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 54, 71, 86, 95, 97, 99, 110], "unnecessari": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 116], "comput": [0, 2, 3, 4, 6, 9, 11, 12, 16, 17, 21, 22, 23, 24, 25, 29, 30, 31, 32, 36, 38, 40, 44, 46, 51, 52, 54, 55, 57, 58, 61, 63, 66, 68, 71, 73, 76, 78, 82, 84, 86, 89, 91, 93, 102, 103, 104, 105, 106, 110, 113, 116], "now": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 38, 42, 71, 80, 81, 86, 104, 111, 116], "perspect": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31], "mathemat": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 79], "equival": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 26, 29, 30, 31, 32, 71, 79, 86], "produc": [0, 1, 5, 8, 9, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 55, 73, 80, 99, 106, 113], "same": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 13, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 28, 29, 30, 31, 36, 40, 42, 51, 57, 63, 71, 73, 78, 80, 81, 86, 95, 105, 108, 110, 114], "howev": [0, 1, 5, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 25, 26, 27, 29, 30, 31, 32, 41, 42, 61, 71, 76, 86, 104, 105, 107, 108, 110], "increas": [0, 1, 2, 3, 4, 5, 8, 9, 14, 15, 17, 18, 19, 20, 22, 24, 25, 29, 30, 31, 38, 61, 76, 88, 99, 105, 108], "And": [0, 1, 8, 9, 14, 15, 17, 18, 19, 20, 29, 30, 31, 43, 65, 83, 102], "neg": [0, 1, 5, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31], "impact": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 99, 109], "especi": [0, 1, 2, 3, 4, 5, 6, 8, 9, 14, 15, 17, 18, 19, 20, 21, 25, 26, 27, 29, 30, 31, 89, 104, 107, 109], "int8": [0, 1, 5, 7, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 71, 107, 110, 114], "lower": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 71, 99, 104, 109], "precis": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 55, 71, 104], "want": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 40, 43, 46, 51, 58, 63, 65, 66, 68, 71, 78, 80, 83, 85, 86, 90, 91], "target": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38, 40, 42, 46, 49, 51, 54, 55, 59, 61, 63, 68, 71, 74, 76, 78, 86, 95, 97, 99, 100, 102, 104, 109, 110, 111], "behavior": [0, 1, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 80, 100], "here": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 36, 37, 38, 39, 57, 58, 59, 62, 68, 71, 73, 74, 77, 80, 86, 97, 98, 107, 113], "call": [0, 1, 2, 3, 4, 5, 6, 8, 9, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 37, 38, 39, 41, 42, 43, 52, 55, 58, 61, 62, 65, 71, 73, 76, 77, 80, 83, 86, 95, 102, 104, 106, 108, 110, 111, 112, 115], "aimet_onnx": [0, 1, 48, 49, 50, 51, 52, 54, 89, 91], "batch_norm_fold": [0, 1, 5, 7, 8, 9, 11, 14, 15, 17, 18, 19, 20, 25, 28, 29, 30, 31, 37, 43, 59, 60, 65, 74, 83, 88], "fold_all_batch_norms_to_weight": [0, 1], "_": [0, 1, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 25, 26, 27, 29, 30, 31, 32, 33, 36, 40, 42, 43, 44, 51, 52, 54, 57, 58, 60, 61, 62, 63, 65, 66, 68, 69, 73, 78, 84, 89, 90, 91], "basic": [0, 1, 7, 8, 11, 12, 14, 15, 18, 19, 20, 28, 29, 30, 31, 71, 86, 91], "mean": [0, 1, 2, 3, 4, 5, 7, 8, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 45, 59, 71, 74, 86, 88, 96, 108, 110], "graph": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 14, 15, 17, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 38, 42, 55, 57, 58, 60, 61, 62, 64, 65, 68, 69, 71, 72, 73, 80, 83, 86, 87, 103, 104, 110, 113], "configur": [0, 1, 5, 7, 8, 11, 12, 14, 15, 19, 20, 25, 28, 29, 30, 31, 36, 44, 55, 57, 58, 71, 73, 75, 84, 86, 97, 101, 111], "them": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 42, 46, 52, 60, 66, 71, 73, 80, 81, 86, 93, 116], "few": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 71, 86, 97, 104, 109, 110], "explain": [0, 1, 2, 3, 4, 5, 7, 8, 11, 12, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 28, 29, 30, 31, 32, 71, 86, 96, 102, 105, 110, 116], "quant_schem": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 36, 44, 46, 52, 54, 55, 57, 66, 68, 71, 73, 75, 84, 86], "quantschem": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 36, 44, 46, 52, 54, 57, 58, 59, 66, 68, 71, 73, 74, 75, 84, 86, 94], 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19, 20, 28, 29, 30, 31, 36, 45, 46, 54, 57, 60, 68, 71, 73, 75, 85, 86, 95], "scheme": [0, 1, 2, 3, 4, 5, 7, 8, 11, 12, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 28, 29, 30, 31, 32, 33, 36, 38, 44, 46, 54, 57, 58, 60, 61, 66, 68, 71, 73, 75, 76, 78, 84, 86, 94, 95, 99, 102, 106], "enum": [0, 1, 7, 8, 11, 12, 14, 18, 19, 28, 29, 30, 38, 61, 75, 76], "post_training_tf": [0, 1, 7, 8, 9, 11, 12, 14, 17, 18, 19, 28, 29, 30, 36, 44, 46, 54, 55, 57, 58, 66, 68, 71, 73, 75, 84, 86], "default_output_bw": [0, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 36, 44, 46, 57, 58, 66, 68, 71, 73, 84, 86], "8": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 28, 29, 30, 31, 32, 36, 43, 44, 46, 48, 52, 54, 55, 57, 58, 61, 66, 68, 71, 73, 75, 76, 80, 81, 84, 86, 87, 89, 91, 104, 116], "essenti": [0, 1, 7, 8, 11, 12, 14, 15, 18, 19, 20, 28, 29, 30, 31], "ask": [0, 1, 5, 7, 8, 11, 12, 14, 15, 16, 18, 19, 20, 21, 28, 29, 30, 31, 32], "all": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 26, 28, 29, 30, 31, 32, 37, 39, 41, 42, 43, 46, 52, 55, 59, 61, 62, 65, 66, 68, 71, 74, 75, 76, 80, 81, 83, 86, 88, 90, 96, 99, 102, 105, 106, 108, 109], "integ": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 36, 44, 46, 52, 55, 57, 66, 68, 84, 86, 93, 104, 106], "default_param_bw": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 36, 44, 46, 48, 52, 54, 57, 58, 66, 68, 71, 73, 84, 86], "num_batch": [0, 5, 6, 7, 8, 9, 10, 11, 16, 17, 18, 21, 25, 26, 27, 28, 29, 32, 36, 44, 48, 49, 52, 57, 58, 59, 66, 71, 73, 74, 84], "number": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 36, 37, 38, 42, 44, 46, 52, 57, 58, 59, 61, 66, 68, 71, 73, 74, 75, 76, 80, 84, 86, 90, 93, 99, 100, 102, 107, 110, 111, 113, 116], "while": [0, 2, 3, 4, 5, 8, 9, 12, 15, 17, 18, 22, 23, 24, 25, 29, 38, 58, 61, 71, 76, 89, 93, 99, 103, 104, 107, 109, 110, 113], "calcul": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 36, 46, 57, 58, 61, 65, 66, 68, 71, 73, 83, 86, 99, 105, 106, 110], "encod": [0, 2, 3, 4, 6, 9, 10, 11, 12, 17, 25, 26, 27, 29, 30, 31, 36, 40, 44, 46, 48, 51, 52, 54, 57, 58, 59, 63, 66, 68, 71, 73, 74, 78, 82, 84, 86, 93, 95, 104, 106, 107, 111], "onli": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 38, 40, 41, 46, 48, 51, 52, 54, 60, 61, 63, 64, 68, 71, 72, 73, 75, 78, 79, 80, 82, 83, 86, 89, 90, 91, 95, 101, 104, 106, 107, 108, 111, 116], "5": [0, 2, 3, 4, 5, 8, 12, 17, 19, 20, 22, 23, 24, 29, 30, 31, 33, 38, 48, 49, 59, 61, 73, 74, 76, 80, 86, 87, 89, 97, 107, 109], "In": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 38, 41, 42, 44, 45, 49, 52, 55, 58, 61, 64, 71, 72, 73, 76, 79, 80, 81, 84, 85, 86, 93, 94, 97, 99, 102, 104, 105, 107, 108, 110, 114, 116], "addit": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 55, 59, 86, 94, 104, 107, 108, 111], "suffici": [0, 8, 18, 29, 104, 106, 107, 110], "rounding_mod": [0, 8, 9, 12, 14, 15, 17, 18, 29, 44, 46, 54, 66, 68, 73, 86], "round": [0, 8, 17, 18, 29, 36, 38, 44, 45, 46, 53, 57, 58, 60, 61, 66, 67, 68, 73, 75, 76, 85, 86, 93, 98, 104, 106, 110], "mode": [0, 2, 3, 4, 5, 6, 8, 18, 19, 20, 21, 22, 23, 24, 29, 33, 38, 39, 46, 50, 58, 61, 62, 68, 73, 75, 76, 77, 80, 82, 83, 86, 87, 103, 104, 108], "There": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 78, 81, 86, 89, 93, 103, 105, 107, 113, 114], "two": [0, 2, 3, 4, 5, 8, 9, 12, 13, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 33, 38, 42, 43, 60, 61, 65, 76, 80, 81, 83, 89, 99, 100, 102, 104, 105, 106, 107, 110, 112, 113, 114, 115], "possibl": 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18, 19, 20, 21, 26, 27, 28, 29, 30, 31, 32, 34, 87, 97, 98, 100, 111], "aimet_common": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116], "quantsim": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 19, 20, 25, 28, 29, 30, 31, 36, 40, 46, 48, 51, 52, 54, 57, 59, 63, 68, 71, 74, 75, 78, 86, 98, 104, 107, 108, 111], "default_activation_bw": [0, 1, 48, 52, 54], "even": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 71, 86, 104], "though": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 71, 86, 108], "ad": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 17, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 55, 61, 68, 71, 81, 86, 101, 104, 108, 111], "node": [0, 1, 5, 7, 8, 9, 11, 12, 17, 19, 20, 25, 29, 30, 31, 46, 61, 68, 71, 72, 73, 79, 80, 86, 107, 110], "readi": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 46, 68, 71, 86, 109], "yet": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 71, 86], "offset": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 44, 52, 55, 66, 71, 84, 86, 104, 106, 107, 110], "pass": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 36, 38, 40, 42, 43, 44, 46, 48, 51, 52, 54, 57, 58, 60, 61, 63, 65, 66, 68, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 86, 100, 103, 104, 105, 106, 107, 109, 110, 111, 113], "unlabel": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 49, 52, 54, 58, 66, 68, 71, 73, 84, 86, 93, 104, 106, 110], "collect": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 71, 73, 84, 86, 96, 106], "statist": [0, 1, 2, 3, 4, 5, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 29, 30, 31, 37, 38, 44, 45, 52, 59, 61, 66, 67, 69, 71, 74, 76, 84, 85, 86, 88, 95, 104, 106, 114], "sometim": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 71, 86, 96, 102, 105, 106], "calibr": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 44, 48, 52, 54, 66, 68, 71, 84, 86, 104, 106, 107, 109, 110], "simpli": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 17, 18, 19, 20, 22, 23, 24, 25, 28, 29, 30, 31, 36, 44, 46, 52, 57, 66, 68, 71, 80, 84, 86, 116], "fairli": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 71, 86], "simpl": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 36, 44, 52, 57, 61, 66, 71, 76, 80, 84, 86, 104, 116], "loader": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 28, 29, 30, 31, 32, 44, 52, 54, 66, 68, 71, 74, 75, 84, 86, 93], "extract": [0, 1, 5, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 71, 86, 105], "don": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 29, 30, 31, 32, 71, 80, 86, 93], "t": [0, 1, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 25, 28, 29, 30, 31, 32, 71, 73, 80, 82, 86, 90, 93], "loss": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 19, 20, 25, 29, 30, 31, 36, 44, 46, 52, 57, 66, 71, 84, 86, 93, 100, 104, 106, 110], "metric": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 33, 38, 44, 46, 61, 71, 76, 86, 106, 110], "just": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 66, 71, 86, 110, 113, 116], "ignor": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 32, 38, 60, 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38, 44, 46, 49, 55, 57, 58, 59, 60, 61, 66, 68, 71, 73, 74, 75, 76, 84, 86], "bool": [2, 3, 4, 22, 23, 24, 25, 28, 29, 30, 31, 32, 38, 43, 46, 60, 61, 64, 65, 66, 68, 73, 75, 76, 80, 83, 86], "maximum": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 16, 17, 18, 19, 20, 21, 32, 36, 57, 58, 71, 73, 86], "training_input": [2, 3, 4, 5, 6, 7, 8, 19, 20, 21, 66], "keras_learning_phas": [2, 3, 4, 5, 6, 7, 8, 19, 20, 21, 66], "data_input": [2, 3, 4, 5, 6, 7, 8, 19, 20, 21, 66, 68], "input_1": [2, 3, 4, 5, 6, 7, 8, 19, 20, 21, 33, 60, 61, 62, 65, 66], "validation_input": [2, 3, 4, 5, 6, 7, 8, 19, 20, 21, 66], "update_ops_nam": [2, 3, 4, 5, 7, 8, 19, 20, 59], "str": [2, 3, 4, 5, 7, 8, 19, 20, 33, 38, 40, 44, 46, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 68, 71, 73, 75, 78, 79, 80, 84, 86, 88], "learning_r": [2, 3, 4, 5, 7, 8, 19, 20, 22, 23, 24, 28, 30, 31, 59, 74, 86], "decay_step": [2, 3, 4, 5, 7, 8, 19, 20, 59], "updat": [2, 3, 4, 5, 7, 8, 9, 14, 15, 17, 18, 19, 20, 21, 25, 29, 30, 31, 55, 59, 60, 62, 64, 65, 71, 72, 86, 87, 91, 98, 104, 105, 107, 110, 111], "mostli": [2, 3, 4, 5, 7, 8, 19, 20, 24], "move": [2, 3, 4, 5, 7, 8, 19, 20, 82], "averag": [2, 3, 4, 5, 7, 8, 19, 20], "graphkei": [2, 3, 4, 5, 7, 8, 19, 20, 21], "update_op": [2, 3, 4, 5, 7, 8, 19, 20, 21], "alwai": [2, 3, 4, 5, 7, 8, 19, 20, 61, 89, 99], "dure": [2, 3, 4, 5, 7, 8, 9, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 28, 30, 31, 32, 36, 37, 38, 42, 57, 61, 65, 68, 71, 73, 76, 86, 87, 93, 100, 102, 104, 107, 108, 110, 113, 114], "rate": [2, 3, 4, 5, 7, 8, 11, 12, 13, 14, 15, 19, 20, 22, 23, 24, 28, 30, 31, 42, 86, 102, 107], "adjust": [2, 3, 4, 5, 6, 7, 8, 19, 20, 29, 36, 57, 58, 71, 72, 86, 95, 96, 97, 104, 105, 109], "decai": [2, 3, 4, 5, 7, 8, 19, 20, 102], "trainer": [2, 3, 4, 5, 7, 8, 19, 20, 22, 23, 24, 28, 30, 31, 38, 61, 76, 98], "num_epoch": [2, 3, 4, 5, 7, 8, 19, 20], "resnet50": [2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 17, 18, 19, 20, 21, 33, 38, 39, 43, 44, 46, 58, 59, 60, 62, 65, 69], "kera": [2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 17, 18, 19, 20, 21, 34, 42, 56, 58, 59, 60, 61, 62, 64, 65, 69, 95, 98, 100, 104, 108, 110, 111], "covert": [2, 3, 4, 5, 6, 7, 8, 19, 20, 21], "clear_sess": [2, 3, 4, 5, 6, 7, 8, 11, 19, 20, 21, 33, 36, 59, 60, 62, 65, 69], "releas": [2, 3, 4, 5, 6, 8, 19, 20, 21, 98, 103], "global": [2, 3, 4, 5, 6, 8, 19, 20, 21, 109], "clutter": [2, 3, 4, 5, 6, 8, 19, 20, 21], "old": [2, 3, 4, 5, 6, 8, 19, 20, 21], "By": [2, 3, 4, 5, 7, 8, 11, 19, 20, 21, 25, 28, 29, 30, 31, 37, 38, 42, 58, 61, 73, 76, 102, 108, 110], "depend": [2, 3, 4, 5, 7, 8, 9, 17, 19, 20, 21, 22, 23, 24, 25, 29, 30, 31, 61, 80, 90, 91, 97, 98, 99, 104, 108, 111], "train_op": [2, 3, 4, 5, 8, 19, 20, 21], "fold": [2, 3, 4, 6, 21, 37, 39, 43, 50, 58, 59, 62, 65, 67, 72, 73, 74, 77, 83, 85, 88, 93, 94, 95, 104, 105, 106, 111], "applic": [2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 17, 18, 19, 20, 21, 33, 38, 39, 43, 44, 46, 55, 58, 59, 60, 61, 62, 65, 69, 83, 99, 103], "backend": [2, 3, 4, 5, 6, 7, 8, 11, 19, 20, 21, 33, 36, 58, 59, 60, 62, 65, 69], "allow": [2, 3, 4, 5, 6, 8, 10, 12, 19, 20, 21, 26, 27, 38, 40, 42, 45, 46, 51, 53, 55, 58, 61, 63, 67, 73, 76, 78, 79, 80, 85, 86, 94, 100, 102, 104, 106, 107, 108, 109, 110, 111, 113], "easili": [2, 3, 4, 5, 6, 8, 19, 20, 21, 61, 76], "read": [2, 3, 4, 5, 6, 8, 19, 20, 21, 106], "eventu": [2, 3, 4, 5, 6, 8, 19, 20, 21], "aimet_tensorflow": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 33, 36, 37, 38, 39, 40, 42, 43, 44, 46, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 68, 69, 89, 91], "update_keras_bn_ops_trainable_flag": [2, 3, 4, 5, 6, 8, 19, 20, 21, 58, 64], "load_save_path": [2, 3, 4, 5, 6, 8, 19, 20, 21, 58, 64], "trainabl": [2, 3, 4, 5, 8, 15, 19, 20, 21, 31, 64, 68, 104], "add_image_net_computational_nodes_in_graph": [2, 3, 4, 5, 6, 7, 8, 19, 20, 21, 59], "an": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 36, 37, 38, 39, 41, 42, 44, 46, 52, 54, 55, 57, 58, 59, 60, 61, 62, 66, 68, 71, 73, 74, 75, 76, 77, 79, 80, 81, 84, 86, 89, 93, 94, 96, 99, 100, 102, 103, 104, 106, 107, 108, 109, 110, 114, 116], "softmax": [2, 3, 4, 5, 6, 8, 12, 13, 19, 20, 21, 33, 42, 57, 60, 61, 62, 65], "add_computational_nodes_in_graph": [2, 3, 4, 5, 6, 7, 8, 19, 20, 21, 59], "get_sess": [2, 3, 4, 5, 6, 7, 8, 19, 20, 21, 33, 58, 59, 60, 62, 65, 69], "creat": [2, 3, 4, 6, 10, 12, 16, 21, 32, 33, 36, 38, 42, 43, 44, 46, 49, 51, 52, 57, 58, 59, 61, 64, 65, 66, 68, 71, 73, 74, 76, 80, 82, 83, 84, 86, 88, 93, 95, 102, 103, 104, 107, 110], "within": [2, 3, 4, 5, 6, 8, 12, 20, 33, 97, 106, 110], "images_class": [2, 3, 4, 5, 6, 7, 8, 19, 20, 21, 59], "identifi": [2, 3, 4, 5, 6, 8, 19, 20, 21, 75, 81, 90, 91, 98, 106, 109, 111, 116], "input_op_nam": [2, 3, 4, 5, 6, 7, 8, 19, 20, 33, 38, 58, 59, 60, 61, 62, 65], "output_op_nam": [2, 3, 4, 5, 6, 7, 8, 19, 20, 21, 33, 38, 57, 58, 59, 60, 61, 62, 63, 65, 66, 68], "starting_op_nam": [2, 3, 4, 5, 6, 8, 19, 20, 21, 57, 58, 59, 63, 68], "append": [2, 3, 4, 7, 43, 65, 76], "test": [2, 3, 4, 5, 6, 8, 11, 12, 13, 19, 20, 21, 44, 52, 59, 66, 68, 72, 74, 84, 91], "is_gpu_avail": [2, 3, 4, 5, 6, 8, 19, 20, 21], "cuda_onli": [2, 3, 4, 5, 6, 8, 19, 20, 21], "": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 36, 38, 39, 41, 42, 43, 44, 46, 49, 52, 54, 55, 57, 59, 60, 61, 62, 64, 65, 66, 68, 71, 73, 74, 75, 76, 80, 83, 84, 86, 88, 91, 97, 101, 102, 104, 105, 106, 107, 109, 110, 113, 114, 116], "determin": [2, 3, 4, 7, 12, 22, 23, 24, 27, 36, 38, 44, 46, 52, 55, 57, 61, 62, 65, 66, 68, 71, 76, 84, 86, 94, 97, 102, 104, 105, 106], "fp32": [2, 3, 4, 11, 12, 21, 22, 23, 24, 32, 40, 44, 51, 63, 66, 78, 84, 93, 100, 105, 106, 107, 109, 110], "defin": [2, 3, 4, 8, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 29, 30, 31, 32, 33, 36, 41, 42, 44, 45, 49, 52, 55, 57, 58, 61, 64, 66, 71, 73, 76, 79, 80, 81, 84, 85, 86, 103, 104, 106, 108, 110], "target_comp_ratio": [2, 3, 4, 22, 23, 24, 38, 61, 76, 87], "desir": [2, 3, 4, 22, 23, 24, 38, 46, 52, 61, 68, 76, 86, 90, 91, 97, 102, 104, 109], "compess": [2, 3, 4], "ratio": [2, 3, 4, 22, 23, 24, 38, 61, 76, 87, 96, 97, 113], "denot": [2, 3, 4, 22, 23, 24, 38, 94], "20": [2, 3, 4, 6, 7, 10, 11, 12, 13, 14, 15, 19, 20, 22, 23, 24, 28, 30, 31, 36, 38, 42, 55, 57, 61, 71, 86, 89, 93, 107], "size": [2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 17, 25, 42, 46, 54, 58, 60, 61, 66, 68, 71, 72, 73, 81, 84, 86, 93, 102, 103, 112, 115], "would": [2, 3, 4, 5, 7, 8, 9, 11, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 38, 39, 42, 62, 71, 77, 86, 97, 102, 108, 111, 113], "80": [2, 3, 4, 38], "pre": [2, 3, 4, 14, 15, 40, 51, 63, 78, 89, 90, 91, 98, 100, 105], "9": [2, 3, 4, 22, 24, 25, 26, 29, 30, 31, 32, 38, 61, 71, 80, 86, 91, 109], "10": [2, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 22, 23, 24, 28, 30, 31, 36, 38, 41, 42, 43, 46, 57, 60, 61, 68, 74, 76, 80, 81, 86, 87, 89, 90, 91, 99, 102, 107], "part": [2, 3, 4, 22, 23, 24, 42, 44, 49, 52, 58, 66, 73, 82, 84, 102, 104, 105, 106], "variou": [2, 3, 4, 6, 10, 16, 21, 22, 23, 24, 26, 27, 32, 38, 61, 76, 94, 97, 102, 104, 109, 110, 111, 114], "measur": [2, 3, 4, 22, 23, 24, 27, 38, 61, 76], "tri": [2, 3, 4, 22, 23, 24, 97, 104], "translat": [2, 3, 4, 5, 9, 17, 22, 23, 24, 25, 71], "33": [2, 3, 4, 22, 23, 24], "66": [2, 3, 4, 22, 23, 24, 97], "00": [2, 3, 4, 22, 23, 24], "higher": [2, 3, 4, 22, 23, 24, 61, 71, 76, 95, 99, 107, 109], "candid": [2, 3, 4, 22, 23, 24, 38, 39, 60, 61, 62, 73, 76, 99, 102], "granular": [2, 3, 4, 12, 22, 23, 24, 38, 61, 76, 102, 109, 110, 114], "time": [2, 3, 4, 6, 10, 16, 21, 22, 23, 24, 26, 27, 32, 38, 43, 55, 61, 65, 76, 80, 81, 94, 102, 103, 107, 113], "taken": [2, 3, 4, 22, 23, 24, 42, 116], "complet": [2, 3, 4, 6, 10, 21, 22, 23, 24, 26, 27, 38, 95, 109], "modules_to_ignor": [2, 3, 4, 22, 23, 24, 38, 61, 76, 84, 87, 101], "interact": [2, 3, 4, 22, 23, 24], "too": [2, 3, 4, 22, 23, 24], "choss": [2, 3, 4, 22, 23, 24], "auto": [2, 3, 4, 22, 23, 24, 34, 38, 39, 43, 50, 55, 61, 62, 65, 76, 77, 83, 87], "analysi": [2, 3, 4, 12, 22, 23, 24, 26, 27, 38, 44, 52, 61, 66, 76, 84, 102, 109], "much": [2, 3, 4, 6, 10, 15, 20, 22, 23, 24, 26, 31, 116], "altern": [2, 3, 4, 22, 23, 24, 90, 91, 102], "manual": [2, 3, 4, 6, 10, 22, 23, 24, 33, 38, 55, 61, 76, 83, 94, 102], "retriev": [2, 3, 4, 22, 24], "those": [2, 3, 4, 13, 15, 20, 22, 23, 24, 31, 52, 102], "num_reconstruction_sampl": [2, 3, 4, 22, 24, 61, 76], "last": [2, 3, 4, 99, 101, 109], "stage": [2, 3, 4, 94], "map": [2, 3, 4, 6, 9, 10, 11, 14, 15, 17, 21, 44, 46, 55, 58, 65, 73, 81, 106, 108], "linear": [2, 3, 4, 37, 43, 57, 59, 60, 65, 71, 74, 80, 81, 83, 88, 95, 96], "regress": [2, 3, 4, 96], "attempt": [2, 3, 4, 96, 104, 105], "done": [2, 3, 4, 7, 17, 18, 19, 20, 22, 23, 24, 28, 30, 31, 41, 90, 96, 102, 108, 110, 116], "random": [2, 3, 4, 13, 26, 27, 33, 36, 42, 44, 46, 49, 52, 54, 57, 58, 61, 66, 73, 96, 106], "ridicul": [2, 3, 4, 22, 24], "low": [2, 3, 4, 5, 8, 9, 17, 22, 24, 25, 29, 93, 95, 102, 104, 105, 109], "enabl": [2, 3, 4, 15, 17, 20, 22, 24, 31, 34, 38, 44, 59, 61, 66, 68, 74, 76, 84, 89, 90, 95, 100, 104, 106, 108, 110, 111], "allow_custom_downsample_op": [2, 3, 4, 22, 24, 61, 76], "flag": [2, 3, 4, 22, 24, 43, 60, 64, 65, 73, 80], "downsampl": [2, 3, 4, 22, 24], "consid": [2, 3, 4, 12, 22, 24, 65, 72, 93, 99, 104, 109], "bandwidth": [2, 3, 4, 22, 24, 97], "overhead": [2, 3, 4, 22, 24], "trade": [2, 3, 4, 22, 24, 36, 57, 71], "off": [2, 3, 4, 22, 24, 36, 46, 57, 68, 71, 86, 104, 105, 108], "suggest": [2, 3, 4, 22, 24, 46, 86, 99, 102, 105], "eval_callback": [2, 3, 4, 6, 10, 11, 16, 21, 22, 23, 24, 26, 27, 32, 33, 38, 44, 49, 52, 58, 61, 66, 73, 76, 84, 87], "expect": [2, 3, 4, 7, 11, 22, 23, 24, 28, 38, 41, 43, 44, 46, 48, 52, 54, 58, 60, 61, 65, 66, 68, 71, 72, 73, 76, 80, 81, 83, 84, 86, 102, 104, 106], "signatur": [2, 3, 4, 22, 23, 24, 36, 38, 44, 49, 52, 57, 61, 66, 76, 84], "function_nam": [2, 3, 4, 22, 23, 24], "eval_iter": [2, 3, 4, 22, 23, 24, 33, 38, 61, 76, 87], "batch": [2, 3, 4, 6, 7, 10, 11, 21, 22, 23, 24, 28, 36, 37, 38, 40, 43, 44, 52, 54, 57, 58, 59, 60, 61, 63, 65, 66, 68, 71, 72, 74, 78, 83, 84, 86, 93, 95, 104, 105, 106], "choos": [2, 3, 4, 22, 23, 24, 66, 68, 86, 88, 96, 97, 102], "enough": [2, 3, 4, 22, 23, 24, 72], "trust": [2, 3, 4, 22, 23, 24], "callback": [2, 3, 4, 12, 14, 15, 16, 21, 22, 23, 24, 32, 38, 44, 46, 49, 52, 58, 61, 66, 68, 73, 76, 84, 86, 106, 110], "invoc": [2, 3, 4, 22, 23, 24], "compress_schem": [2, 3, 4, 22, 23, 24, 33, 38, 61, 76, 87], "cost_metr": [2, 3, 4, 22, 23, 24, 33, 38, 61, 76, 87], "actual": [2, 3, 4, 7, 8, 11, 13, 19, 20, 28, 30, 31, 40, 44, 49, 51, 52, 58, 63, 66, 71, 73, 78, 82, 84, 86, 91, 97, 104], "greedi": [2, 3, 4, 102, 113], "select": [2, 3, 4, 36, 57, 60, 71, 73, 86, 88, 94, 97, 106, 110, 113, 116], "among": [2, 3, 4, 71, 73], "reach": [2, 3, 4, 6, 10, 26, 27, 94, 97], "previou": [2, 3, 4, 8, 29, 38, 43, 61, 73, 76, 83, 89, 97, 99, 109], "rule": [2, 3, 4, 46, 66, 68, 108], "thumb": [2, 3, 4], "one": [2, 3, 4, 5, 8, 12, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 31, 32, 43, 55, 60, 61, 64, 65, 68, 71, 72, 73, 80, 81, 83, 86, 90, 96, 98, 102, 107, 108, 111, 112, 115], "found": [2, 3, 4, 13, 33, 43, 83, 107, 110], "compressionschem": [2, 3, 4, 22, 23, 24, 33, 38, 61, 76, 87], "costmetr": [2, 3, 4, 22, 23, 24, 33, 38, 61, 76, 87], "greedyselectionparamet": [2, 3, 4, 22, 23, 24, 38, 61, 76, 87], "channelpruningparamet": [2, 4, 22, 24, 61, 76], "decim": [2, 3, 4, 22, 23, 24, 38, 61, 76, 87], "greedy_param": [2, 3, 4, 22, 23, 24, 38, 61, 76, 87], "get_operation_by_nam": [2, 3, 4, 5, 7, 8, 19, 20, 21, 33, 60, 61, 65, 68, 69], "conv1_conv": [2, 3, 4, 69], "auto_param": [2, 3, 4, 22, 23, 24, 38, 61, 76, 87], "automodeparam": [2, 3, 4, 22, 23, 24, 38, 61, 76, 87], "greedy_select_param": [2, 3, 4, 22, 23, 24, 38, 61, 76], "data_set": [2, 4, 5, 8, 9, 17, 36, 57, 60, 61], "channel_prun": [2, 4, 22, 24, 38, 61, 76], "modelcompressor": [2, 3, 4, 22, 23, 24, 33, 38, 61, 76, 87], "compress_model": [2, 3, 4, 22, 23, 24, 33, 38, 61, 76, 87, 113], "relev": [2, 3, 4, 22, 23, 24], "our": [2, 3, 4, 7, 8, 11, 12, 14, 15, 18, 19, 22, 23, 24, 28, 37, 52, 89, 91, 99, 109, 110], "new": [2, 4, 5, 8, 9, 14, 15, 17, 18, 19, 20, 25, 26, 29, 30, 31, 32, 41, 42, 43, 55, 62, 65, 71, 79, 80, 86, 90, 104, 108, 111], "final": [2, 4, 7, 11, 12, 14, 15, 16, 21, 28, 32, 38, 42, 61, 76, 81, 87, 96, 97, 99, 107, 109, 113], "makedir": [2, 3, 4, 7, 8, 9, 11, 17, 18, 19, 20, 22, 23, 24, 25, 28, 29, 30, 31], "exist_ok": [2, 3, 4, 7, 8, 9, 11, 17, 18, 19, 20, 22, 23, 24, 25, 28, 29, 30, 31], "compressed_sess": [2, 3, 33], "comp_stat": [2, 3, 22, 23], "working_dir": [2, 3, 4, 33, 61], "fell": [2, 3, 4, 22, 23, 24], "sharpli": [2, 3, 4, 22, 23, 24], "15": [2, 3, 4, 7, 11, 12, 14, 15, 19, 20, 22, 23, 24, 28, 30, 31, 86, 102, 107], "job": [2, 3, 4, 7, 11, 12, 14, 15, 19, 20, 22, 23, 24, 28, 30, 31, 86], 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Example evaluation and training pipeline": [[0, "1.-Example-evaluation-and-training-pipeline"], [3, "1.-Example-evaluation-and-training-pipeline"], [4, "1.-Example-evaluation-and-training-pipeline"], [6, "1.-Example-evaluation-and-training-pipeline"], [7, "1.-Example-evaluation-and-training-pipeline"], [8, "1.-Example-evaluation-and-training-pipeline"], [9, "1.-Example-evaluation-and-training-pipeline"], [10, "1.-Example-evaluation-and-training-pipeline"], [16, "1.-Example-evaluation-and-training-pipeline"], [17, "1.-Example-evaluation-and-training-pipeline"], [18, "1.-Example-evaluation-and-training-pipeline"], [19, "1.-Example-evaluation-and-training-pipeline"], [21, "1.-Example-evaluation-and-training-pipeline"], [22, "1.-Example-evaluation-and-training-pipeline"], [23, "1.-Example-evaluation-and-training-pipeline"], [24, "1.-Example-evaluation-and-training-pipeline"], [25, "1.-Example-evaluation-and-training-pipeline"], [28, "1.-Example-evaluation-and-training-pipeline"], [29, "1.-Example-evaluation-and-training-pipeline"], [30, "1.-Example-evaluation-and-training-pipeline"], [31, "1.-Example-evaluation-and-training-pipeline"], [32, "1.-Example-evaluation-and-training-pipeline"]], "2. Convert an FP32 PyTorch model to ONNX and evaluate the model\u2019s baseline FP32 accuracy": [[0, "2.-Convert-an-FP32-PyTorch-model-to-ONNX-and-evaluate-the-model's-baseline-FP32-accuracy"], [1, "2.-Convert-an-FP32-PyTorch-model-to-ONNX-and-evaluate-the-model's-baseline-FP32-accuracy"]], "3. Create a quantization simulation model and determine quantized accuracy": [[0, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [1, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [5, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [8, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [9, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [17, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [18, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [19, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [20, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [25, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [29, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [30, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"], [31, "3.-Create-a-quantization-simulation-model-and-determine-quantized-accuracy"]], "Fold Batch Normalization layers": [[0, "Fold-Batch-Normalization-layers"], [1, "Fold-Batch-Normalization-layers"], [5, "Fold-Batch-Normalization-layers"], [8, "Fold-Batch-Normalization-layers"], [9, "Fold-Batch-Normalization-layers"], [14, "Fold-Batch-Normalization-layers"], [15, "Fold-Batch-Normalization-layers"], [17, "Fold-Batch-Normalization-layers"], [18, "Fold-Batch-Normalization-layers"], [19, "Fold-Batch-Normalization-layers"], [20, "Fold-Batch-Normalization-layers"], [25, "Fold-Batch-Normalization-layers"], [29, "Fold-Batch-Normalization-layers"], [30, "Fold-Batch-Normalization-layers"], [31, "Fold-Batch-Normalization-layers"]], "Create Quantization Sim Model": [[0, "Create-Quantization-Sim-Model"], [1, "Create-Quantization-Sim-Model"], [5, "Create-Quantization-Sim-Model"], [7, "Create-Quantization-Sim-Model"], [8, "Create-Quantization-Sim-Model"], [14, "Create-Quantization-Sim-Model"], [15, "Create-Quantization-Sim-Model"], [18, "Create-Quantization-Sim-Model"], [19, "Create-Quantization-Sim-Model"], [20, "Create-Quantization-Sim-Model"], [28, "Create-Quantization-Sim-Model"], [29, "Create-Quantization-Sim-Model"], [30, "Create-Quantization-Sim-Model"], [31, "Create-Quantization-Sim-Model"]], "4. 1 Cross Layer Equalization": [[0, "4.-1-Cross-Layer-Equalization"], [8, "4.-1-Cross-Layer-Equalization"], [29, "4.-1-Cross-Layer-Equalization"]], "Summary": [[0, "Summary"], [1, "Summary"], [2, "Summary"], [3, "Summary"], [4, "Summary"], [5, "Summary"], [6, "Summary"], [7, "Summary"], [8, "Summary"], [9, "Summary"], [10, "Summary"], [11, "Summary"], [13, "Summary"], [14, "Summary"], [15, "Summary"], [17, "Summary"], [18, "Summary"], [19, "Summary"], [20, "Summary"], [22, "Summary"], [23, "Summary"], [24, "Summary"], [25, "Summary"], [26, "Summary"], [27, "Summary"], [28, "Summary"], [29, "Summary"], [30, "Summary"], [31, "Summary"]], "Quantization Simulation": [[1, "Quantization-Simulation"]], "1. Example evaluation pipeline": [[1, "1.-Example-evaluation-pipeline"]], "Compute Encodings": [[1, "Compute-Encodings"], [5, "Compute-Encodings"], [7, "Compute-Encodings"], [8, "Compute-Encodings"], [14, "Compute-Encodings"], [15, "Compute-Encodings"], [18, "Compute-Encodings"], [19, "Compute-Encodings"], [20, "Compute-Encodings"]], "Model Compression Using Channel Pruning": [[2, "Model-Compression-Using-Channel-Pruning"]], "2. Load the model and evaluate it to find the baseline accuracy": [[2, "2.-Load-the-model-and-evaluate-it-to-find-the-baseline-accuracy"], [3, "2.-Load-the-model-and-evaluate-it-to-find-the-baseline-accuracy"], [4, "2.-Load-the-model-and-evaluate-it-to-find-the-baseline-accuracy"], [22, "2.-Load-the-model-and-evaluate-it-to-find-the-baseline-accuracy"], [23, "2.-Load-the-model-and-evaluate-it-to-find-the-baseline-accuracy"], [24, "2.-Load-the-model-and-evaluate-it-to-find-the-baseline-accuracy"]], "3. Compress the model and fine-tune": [[2, "3.-Compress-the-model-and-fine-tune"], [3, "3.-Compress-the-model-and-fine-tune"], [4, "3.-Compress-the-model-and-fine-tune"], [22, "3.-Compress-the-model-and-fine-tune"], [23, "3.-Compress-the-model-and-fine-tune"], [24, "3.-Compress-the-model-and-fine-tune"]], "3.1. Compress model using Channel Pruning and evaluate it to find post-compression accuracy": [[2, "3.1.-Compress-model-using-Channel-Pruning-and-evaluate-it-to-find-post-compression-accuracy"], [3, "3.1.-Compress-model-using-Channel-Pruning-and-evaluate-it-to-find-post-compression-accuracy"], [4, "3.1.-Compress-model-using-Channel-Pruning-and-evaluate-it-to-find-post-compression-accuracy"], [22, "3.1.-Compress-model-using-Channel-Pruning-and-evaluate-it-to-find-post-compression-accuracy"]], "3.2. 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Load the model and evaluate to get a baseline FP32 accuracy score": [[5, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"], [8, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"], [9, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"], [17, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"], [18, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"], [19, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"], [20, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"], [25, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"], [29, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"], [30, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"], [31, "2.-Load-the-model-and-evaluate-to-get-a-baseline-FP32-accuracy-score"]], "4. 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Define Constants and Helper functions": [[6, "4.-Define-Constants-and-Helper-functions"], [10, "4.-Define-Constants-and-Helper-functions"]], "Prepare unlabeled dataset": [[6, "Prepare-unlabeled-dataset"]], "Prepare the evaluation callback function": [[6, "Prepare-the-evaluation-callback-function"], [10, "Prepare-the-evaluation-callback-function"], [11, "Prepare-the-evaluation-callback-function"]], "5. Apply AutoQuant": [[6, "5.-Apply-AutoQuant"], [10, "5.-Apply-AutoQuant"]], "Optionally set AdaRound Parameters": [[6, "Optionally-set-AdaRound-Parameters"], [10, "Optionally-set-AdaRound-Parameters"]], "Run AutoQuant": [[6, "Run-AutoQuant"], [10, "Run-AutoQuant"], [26, "Run-AutoQuant"]], "Quantization-Aware Training with BatchNorm Re-estimation": [[7, "Quantization-Aware-Training-with-BatchNorm-Re-estimation"], [11, "Quantization-Aware-Training-with-BatchNorm-Re-estimation"], [28, "Quantization-Aware-Training-with-BatchNorm-Re-estimation"]], "2. Load FP32 model": [[7, "2.-Load-FP32-model"], [28, "2.-Load-FP32-model"]], "BatchNorm Rewriter": [[7, "BatchNorm-Rewriter"]], "3. Create a quantization simulation model and Perform QAT": [[7, "3.-Create-a-quantization-simulation-model-and-Perform-QAT"], [28, "3.-Create-a-quantization-simulation-model-and-Perform-QAT"]], "Perform QAT": [[7, "Perform-QAT"], [28, "Perform-QAT"]], "4. Perform BatchNorm Reestimation": [[7, "4.-Perform-BatchNorm-Reestimation"], [28, "4.-Perform-BatchNorm-Reestimation"]], "Re-estimate BatchNorm Statistics": [[7, "Re-estimate-BatchNorm-Statistics"], [28, "Re-estimate-BatchNorm-Statistics"]], "Fold BatchNorm Layers": [[7, "Fold-BatchNorm-Layers"], [11, "Fold-BatchNorm-Layers"], [28, "Fold-BatchNorm-Layers"]], "5. Export Model": [[7, "5.-Export-Model"], [11, "5.-Export-Model"], [28, "5.-Export-Model"]], "Cross-Layer Equalization (CLE) and Bias Correction (BC)": [[8, "Cross-Layer-Equalization-(CLE)-and-Bias-Correction-(BC)"], [29, "Cross-Layer-Equalization-(CLE)-and-Bias-Correction-(BC)"]], "4. 2 Bias Correction": [[8, "4.-2-Bias-Correction"], [29, "4.-2-Bias-Correction"]], "Adaptive Rounding (Adaround)": [[9, "Adaptive-Rounding-(Adaround)"]], "4. Apply Adaround": [[9, "4.-Apply-Adaround"], [17, "4.-Apply-Adaround"], [25, "4.-Apply-Adaround"]], "1. Instantiate the example evaluation and training pipeline": [[11, "1.-Instantiate-the-example-evaluation-and-training-pipeline"]], "2. Define Constants and Datasets Prepare": [[11, "2.-Define-Constants-and-Datasets-Prepare"]], "2. Create the model in Keras": [[11, "2.-Create-the-model-in-Keras"]], "3. Train and evaluate the model": [[11, "3.-Train-and-evaluate-the-model"]], "4. Create a QuantizationSim Model": [[11, "4.-Create-a-QuantizationSim-Model"]], "5. Perform QAT": [[11, "5.-Perform-QAT"], [14, "5.-Perform-QAT"], [15, "5.-Perform-QAT"]], "Quantization-Aware Training with a Keras Transformer Model": [[12, "Quantization-Aware-Training-with-a-Keras-Transformer-Model"]], "Keras Model Preparer": [[13, "Keras-Model-Preparer"]], "1. Creating a Keras model with subclass layers": [[13, "1.-Creating-a-Keras-model-with-subclass-layers"]], "2. Converting the Keras model with subclass layers to a Keras model with functional layers": [[13, "2.-Converting-the-Keras-model-with-subclass-layers-to-a-Keras-model-with-functional-layers"]], "3. Showing similarities and differences between the original and converted models": [[13, "3.-Showing-similarities-and-differences-between-the-original-and-converted-models"]], "4. Discussing the limitations of the Keras Model Preparer": [[13, "4.-Discussing-the-limitations-of-the-Keras-Model-Preparer"]], "Quantization-Aware Training": [[14, "Quantization-Aware-Training"], [19, "Quantization-Aware-Training"], [30, "Quantization-Aware-Training"]], "Example evaluation and training pipeline": [[14, "Example-evaluation-and-training-pipeline"], [15, "Example-evaluation-and-training-pipeline"]], "1. Load the dataset": [[14, "1.-Load-the-dataset"], [15, "1.-Load-the-dataset"]], "4. Create a QuantizationSim Model and determine quantized accuracy": [[14, "4.-Create-a-QuantizationSim-Model-and-determine-quantized-accuracy"], [15, "4.-Create-a-QuantizationSim-Model-and-determine-quantized-accuracy"]], "Compile the model": [[14, "Compile-the-model"], [15, "Compile-the-model"]], "Evaluate the performance of the quantized model": [[14, "Evaluate-the-performance-of-the-quantized-model"], [15, "Evaluate-the-performance-of-the-quantized-model"]], "6. Evaluate validation accuracy after QAT": [[14, "6.-Evaluate-validation-accuracy-after-QAT"], [15, "6.-Evaluate-validation-accuracy-after-QAT"]], "7. Export the encodings": [[14, "7.-Export-the-encodings"], [15, "7.-Export-the-encodings"]], "Quantization-Aware Training with Range Learning": [[15, "Quantization-Aware-Training-with-Range-Learning"], [20, "Quantization-Aware-Training-with-Range-Learning"], [31, "Quantization-Aware-Training-with-Range-Learning"]], "Quant Analyzer": [[16, "Quant-Analyzer"], [21, "Quant-Analyzer"], [32, "Quant-Analyzer"]], "3. Apply QuantAnalyzer to the model": [[16, "3.-Apply-QuantAnalyzer-to-the-model"], [21, "3.-Apply-QuantAnalyzer-to-the-model"], [32, "3.-Apply-QuantAnalyzer-to-the-model"]], "Per-layer analysis by enabling/disabling quantization wrappers": [[16, "Per-layer-analysis-by-enabling/disabling-quantization-wrappers"], [32, "Per-layer-analysis-by-enabling/disabling-quantization-wrappers"]], "Encoding min/max ranges": [[16, "Encoding-min/max-ranges"], [21, "Encoding-min/max-ranges"], [32, "Encoding-min/max-ranges"]], "PDF of statistics": [[16, "PDF-of-statistics"], [21, "PDF-of-statistics"], [32, "PDF-of-statistics"]], "Per-layer MSE loss": [[16, "Per-layer-MSE-loss"], [32, "Per-layer-MSE-loss"]], "Quantsim and Adaround - Per Channel Quantization (PCQ)": [[17, "Quantsim-and-Adaround---Per-Channel-Quantization-(PCQ)"]], "Cross-Layer Equalization (CLE) with QuantSim": [[18, "Cross-Layer-Equalization-(CLE)-with-QuantSim"]], "4 Cross Layer Equalization": [[18, "4-Cross-Layer-Equalization"]], "5 Exporting": [[18, "5-Exporting"]], "4. Perform QAT": [[19, "4.-Perform-QAT"], [20, "4.-Perform-QAT"], [30, "4.-Perform-QAT"], [31, "4.-Perform-QAT"]], "2. Load the model": [[21, "2.-Load-the-model"], [32, "2.-Load-the-model"]], "Per op analysis by enabling/disabling quantization ops": [[21, "Per-op-analysis-by-enabling/disabling-quantization-ops"]], "Per op MSE loss": [[21, "Per-op-MSE-loss"]], "Model compression using Channel Pruning": [[22, "Model-compression-using-Channel-Pruning"]], "Model compression using Spatial SVD": [[23, "Model-compression-using-Spatial-SVD"]], "3.1. Compress model using Spatial SVD and evaluate it to find post-compression accuracy": [[23, "3.1.-Compress-model-using-Spatial-SVD-and-evaluate-it-to-find-post-compression-accuracy"], [24, "3.1.-Compress-model-using-Spatial-SVD-and-evaluate-it-to-find-post-compression-accuracy"]], "Model compression using Spatial SVD followed by Channel Pruning": [[24, "Model-compression-using-Spatial-SVD-followed-by-Channel-Pruning"]], "1. Define Constants and Helper functions": [[26, "1.-Define-Constants-and-Helper-functions"], [27, "1.-Define-Constants-and-Helper-functions"]], "3. Run AutoQuant": [[26, "3.-Run-AutoQuant"], [27, "3.-Run-AutoQuant"]], "Prepare and Validate Model": [[26, "Prepare-and-Validate-Model"]], "Create AutoQuant Object": [[26, "Create-AutoQuant-Object"], [27, "Create-AutoQuant-Object"]], "Set AdaRound Parameters (optional)": [[26, "Set-AdaRound-Parameters-(optional)"], [27, "Set-AdaRound-Parameters-(optional)"]], "Run AutoQuant Inference": [[27, "Run-AutoQuant-Inference"]], "Run AutoQuant Optimization": [[27, "Run-AutoQuant-Optimization"]], "Using AIMET Tensorflow APIs with Keras Models": [[33, "using-aimet-tensorflow-apis-with-keras-models"]], "Introduction": [[33, "introduction"], [37, "introduction"], [38, "introduction"], [39, "introduction"], [43, "introduction"], [50, "introduction"], [59, "introduction"], [61, "introduction"], [62, "introduction"], [65, "introduction"], [74, "introduction"], [76, "introduction"], [77, "introduction"], [83, "introduction"]], "APIs": [[33, "apis"]], "Code Example": [[33, "code-example"], [37, "code-example"], [39, "code-example"], [40, "code-example"], [50, "code-example"], [51, "code-example"], [62, "code-example"], [63, "code-example"], [66, "code-example"], [77, "code-example"], [78, "code-example"]], "Utility Functions": [[33, "utility-functions"]], "Welcome to AI Model Efficiency Toolkit API Docs!": [[34, "welcome-to-ai-model-efficiency-toolkit-api-docs"]], "Indices and tables": [[34, "indices-and-tables"]], "AIMET Keras APIs": [[35, "aimet-keras-apis"]], "AIMET Keras AdaRound API": [[36, "aimet-keras-adaround-api"]], "User Guide Link": [[36, "user-guide-link"], [39, "user-guide-link"], [46, "user-guide-link"], [48, "user-guide-link"], [49, "user-guide-link"], [50, "user-guide-link"], [57, "user-guide-link"], [58, "user-guide-link"], [60, "user-guide-link"], [62, "user-guide-link"], [68, "user-guide-link"], [71, "user-guide-link"], [73, "user-guide-link"], [75, "user-guide-link"], [77, "user-guide-link"], [86, "user-guide-link"]], "Examples Notebook Link": [[36, "examples-notebook-link"], [37, "examples-notebook-link"], [39, "examples-notebook-link"], [57, "examples-notebook-link"], [58, "examples-notebook-link"], [59, "examples-notebook-link"], [62, "examples-notebook-link"], [68, "examples-notebook-link"], [71, "examples-notebook-link"], [73, "examples-notebook-link"], [74, "examples-notebook-link"], [77, "examples-notebook-link"], [86, "examples-notebook-link"]], "Top-level API": [[36, "top-level-api"], [40, "top-level-api"], [42, "top-level-api"], [44, "top-level-api"], [46, "top-level-api"], [48, "top-level-api"], [49, "top-level-api"], [51, "top-level-api"], [52, "top-level-api"], [54, "top-level-api"], [57, "top-level-api"], [58, "top-level-api"], [63, "top-level-api"], [66, "top-level-api"], [68, "top-level-api"], [71, "top-level-api"], [73, "top-level-api"], [78, "top-level-api"], [80, "top-level-api"], [84, "top-level-api"], [86, "top-level-api"]], "Adaround Parameters": [[36, "adaround-parameters"], [48, "adaround-parameters"], [57, "adaround-parameters"], [71, "adaround-parameters"]], "Enum Definition": [[36, "enum-definition"], [57, "enum-definition"], [71, "enum-definition"], [78, "enum-definition"], [86, "enum-definition"]], "Code Examples": [[36, "code-examples"], [38, "code-examples"], [42, "code-examples"], [44, "code-examples"], [46, "code-examples"], [49, "code-examples"], [52, "code-examples"], [54, "code-examples"], [57, "code-examples"], [58, "code-examples"], [61, "code-examples"], [68, "code-examples"], [73, "code-examples"], [76, "code-examples"], [80, "code-examples"], [84, "code-examples"], [87, "code-examples"], [88, "code-examples"]], "AIMET Keras BatchNorm Re-estimation APIs": [[37, "aimet-keras-batchnorm-re-estimation-apis"]], "Top-level APIs": [[37, "top-level-apis"], [59, "top-level-apis"], [74, "top-level-apis"]], "Limitations": [[37, "limitations"], [42, "limitations"]], "AIMET Keras Compression API": [[38, "aimet-keras-compression-api"]], "Top-level API for Compression": [[38, "top-level-api-for-compression"], [61, "top-level-api-for-compression"], [76, "top-level-api-for-compression"]], "Greedy Selection Parameters": [[38, "greedy-selection-parameters"], [61, "greedy-selection-parameters"], [76, "greedy-selection-parameters"]], "Spatial SVD Configuration": [[38, "spatial-svd-configuration"], [61, "spatial-svd-configuration"], [76, "spatial-svd-configuration"]], "Configuration Definitions": [[38, "configuration-definitions"], [61, "configuration-definitions"], [76, "configuration-definitions"]], "AIMET Keras Cross Layer Equalization APIs": [[39, "aimet-keras-cross-layer-equalization-apis"]], "Cross Layer Equalization API": [[39, "cross-layer-equalization-api"], [50, "cross-layer-equalization-api"], [62, "cross-layer-equalization-api"], [77, "cross-layer-equalization-api"]], "Primitive APIs": [[39, "primitive-apis"], [62, "primitive-apis"], [77, "primitive-apis"]], "AIMET Keras Layer Output Generation API": [[40, "aimet-keras-layer-output-generation-api"]], "Keras Model Guidelines": [[41, "keras-model-guidelines"]], "Model Preparer API": [[42, "model-preparer-api"], [80, "model-preparer-api"]], "AIMET Keras Cross Layer Equalization Primitive API": [[43, "aimet-keras-cross-layer-equalization-primitive-api"]], "Higher Level APIs for Cross Layer Equalization": [[43, "higher-level-apis-for-cross-layer-equalization"], [65, "higher-level-apis-for-cross-layer-equalization"], [83, "higher-level-apis-for-cross-layer-equalization"]], "Code Examples for Higher Level APIs": [[43, "code-examples-for-higher-level-apis"], [65, "code-examples-for-higher-level-apis"], [83, "code-examples-for-higher-level-apis"]], "Lower Level APIs for Cross Layer Equalization": [[43, "lower-level-apis-for-cross-layer-equalization"], [65, "lower-level-apis-for-cross-layer-equalization"], [83, "lower-level-apis-for-cross-layer-equalization"]], "Custom Datatype used": [[43, "custom-datatype-used"], [65, "custom-datatype-used"]], "Code Example for Lower level APIs": [[43, "code-example-for-lower-level-apis"], [65, "code-example-for-lower-level-apis"]], "Example helper methods to perform CLE in manual mode": [[43, "example-helper-methods-to-perform-cle-in-manual-mode"], [65, "example-helper-methods-to-perform-cle-in-manual-mode"]], "AIMET Keras Quant Analyzer API": [[44, "aimet-keras-quant-analyzer-api"]], "AIMET Keras Quantization APIs": [[45, "aimet-keras-quantization-apis"]], "AIMET Keras Quantization SIM API": [[46, "aimet-keras-quantization-sim-api"]], "AIMET ONNX APIs": [[47, "aimet-onnx-apis"]], "AIMET ONNX AdaRound API": [[48, "aimet-onnx-adaround-api"]], "Code Example - Adaptive Rounding (AdaRound)": [[48, "code-example-adaptive-rounding-adaround"], [71, "code-example-adaptive-rounding-adaround"]], "AIMET ONNX AutoQuant API": [[49, "aimet-onnx-autoquant-api"]], "AIMET ONNX Cross Layer Equalization APIs": [[50, "aimet-onnx-cross-layer-equalization-apis"]], "AIMET ONNX Layer Output Generation API": [[51, "aimet-onnx-layer-output-generation-api"]], "AIMET ONNX Quant Analyzer API": [[52, "aimet-onnx-quant-analyzer-api"]], "Run specific utility": [[52, "run-specific-utility"]], "AIMET ONNX Quantization APIs": [[53, "aimet-onnx-quantization-apis"]], "AIMET ONNX Quantization SIM API": [[54, "aimet-onnx-quantization-sim-api"]], "Encoding Format Specification": [[55, "encoding-format-specification"]], "1. Versioning": [[55, "versioning"]], "2. Version 0.4.0 (up to)": [[55, "version-0-4-0-up-to"]], "2.1. Encoding Specification": [[55, "encoding-specification"]], "2.2. Encoding File Example for PyTorch": [[55, "encoding-file-example-for-pytorch"]], "2.3. Encoding File Example for TensorFlow": [[55, "encoding-file-example-for-tensorflow"]], "3. Version 0.5.0": [[55, "version-0-5-0"]], "3.1. Encoding Specification": [[55, "id1"]], "3.2. Encoding File Example for PyTorch": [[55, "id2"]], "3.3. Encoding File Example for TensorFlow": [[55, "id3"]], "4. Version 0.6.1": [[55, "version-0-6-1"]], "4.1. Encoding Specification": [[55, "id4"]], "AIMET TensorFlow APIs": [[56, "aimet-tensorflow-apis"]], "AIMET TensorFlow AdaRound API": [[57, "aimet-tensorflow-adaround-api"]], "AIMET TensorFlow AutoQuant API": [[58, "aimet-tensorflow-autoquant-api"]], "AIMET TensorFlow BatchNorm Re-estimation APIs": [[59, "aimet-tensorflow-batchnorm-re-estimation-apis"]], "Code Example - BN-Reestimation": [[59, "code-example-bn-reestimation"], [74, "code-example-bn-reestimation"]], "AIMET TensorFlow Bias Correction API": [[60, "aimet-tensorflow-bias-correction-api"]], "Bias Correction API": [[60, "bias-correction-api"], [75, "bias-correction-api"]], "Input Parameter Types": [[60, "input-parameter-types"]], "Data Input Type": [[60, "data-input-type"]], "Code Examples for Bias Correction": [[60, "code-examples-for-bias-correction"]], "Bias Correction Per Layer API": [[60, "bias-correction-per-layer-api"]], "Code Example for Per-Layer Bias Correction": [[60, "code-example-for-per-layer-bias-correction"]], "AIMET TensorFlow Compression API": [[61, "aimet-tensorflow-compression-api"]], "Channel Pruning Configuration": [[61, "channel-pruning-configuration"], [76, "channel-pruning-configuration"]], "Weight SVD Top-level API": [[61, "weight-svd-top-level-api"]], "Code Examples for Weight SVD": [[61, "code-examples-for-weight-svd"]], "AIMET TensorFlow Cross Layer Equalization APIs": [[62, "aimet-tensorflow-cross-layer-equalization-apis"]], "AIMET Tensorflow Layer Output Generation API": [[63, "aimet-tensorflow-layer-output-generation-api"]], "TensorFlow Model Guidelines": [[64, "tensorflow-model-guidelines"]], "AIMET TensorFlow Cross Layer Equalization Primitive API": [[65, "aimet-tensorflow-cross-layer-equalization-primitive-api"]], "AIMET Tensorflow Quant Analyzer API": [[66, "aimet-tensorflow-quant-analyzer-api"]], "AIMET TensorFlow Quantization APIs": [[67, "aimet-tensorflow-quantization-apis"]], "AIMET TensorFlow Quantization SIM API": [[68, "aimet-tensorflow-quantization-sim-api"]], "AIMET Visualization for Quantization for TensorFlow API": [[69, "aimet-visualization-for-quantization-for-tensorflow-api"]], "Top-level API for Visualization of Weight tensors": [[69, "top-level-api-for-visualization-of-weight-tensors"]], "Code Examples for Visualization of Weight tensors": [[69, "code-examples-for-visualization-of-weight-tensors"]], "AIMET PyTorch APIs": [[70, "aimet-pytorch-apis"]], "AIMET PyTorch AdaRound API": [[71, "aimet-pytorch-adaround-api"]], "Architecture Checker API": [[72, "architecture-checker-api"]], "HTML report content": [[72, "id1"]], "AIMET PyTorch AutoQuant API": [[73, "aimet-pytorch-autoquant-api"]], "AIMET PyTorch BatchNorm Re-estimation APIs": [[74, "aimet-pytorch-batchnorm-re-estimation-apis"]], "AIMET PyTorch Bias Correction API": [[75, "aimet-pytorch-bias-correction-api"]], "ConvBnInfoType": [[75, "convbninfotype"]], "ActivationType": [[75, "activationtype"]], "Quantization Params": [[75, "quantization-params"]], "Code Example #1 Empirical Bias Correction": [[75, "code-example-1-empirical-bias-correction"]], "Code Example #2 Analytical + Empirical Bias correction": [[75, "code-example-2-analytical-empirical-bias-correction"]], "AIMET PyTorch Compression API": [[76, "aimet-pytorch-compression-api"]], "TAR Selection Parameters": [[76, "tar-selection-parameters"]], "Weight SVD Configuration": [[76, "weight-svd-configuration"]], "AIMET PyTorch Cross Layer Equalization APIs": [[77, "aimet-pytorch-cross-layer-equalization-apis"]], "AIMET PyTorch Layer Output Generation API": [[78, "aimet-pytorch-layer-output-generation-api"]], "PyTorch Model Guidelines": [[79, "pytorch-model-guidelines"]], "Limitations of torch.fx symbolic trace API": [[80, "limitations-of-torch-fx-symbolic-trace-api"]], "Model Validator Utility": [[81, "model-validator-utility"]], "PyTorch Multi-GPU support": [[82, "pytorch-multi-gpu-support"]], "AIMET PyTorch Cross Layer Equalization Primitive API": [[83, "aimet-pytorch-cross-layer-equalization-primitive-api"]], "ClsSetInfo Definition": [[83, "clssetinfo-definition"]], "Code Examples for Lower Level APIs": [[83, "code-examples-for-lower-level-apis"]], "AIMET PyTorch Quant Analyzer API": [[84, "aimet-pytorch-quant-analyzer-api"]], "AIMET PyTorch Quantization APIs": [[85, "aimet-pytorch-quantization-apis"]], "AIMET PyTorch Quantization SIM API": [[86, "aimet-pytorch-quantization-sim-api"]], "Guidelines": [[86, "guidelines"]], "Code Example - Quantization Aware Training (QAT)": [[86, "code-example-quantization-aware-training-qat"]], "AIMET Visualization Compression API": [[87, "aimet-visualization-compression-api"]], "Top-level API Compression": [[87, "top-level-api-compression"]], "AIMET Visualization for Quantization API": [[88, "aimet-visualization-for-quantization-api"]], "Top-level API Quantization": [[88, "top-level-api-quantization"]], "AIMET Installation": [[89, "aimet-installation"]], "Quick Install": [[89, "quick-install"]], "Release Packages": [[89, "release-packages"]], "System Requirements": [[89, "system-requirements"]], "Advanced Installation Instructions": [[89, "advanced-installation-instructions"]], "AIMET Installation in Docker": [[90, "aimet-installation-in-docker"]], "Set variant": [[90, "set-variant"]], "Use prebuilt docker image": [[90, "use-prebuilt-docker-image"]], "Build docker image locally": [[90, "build-docker-image-locally"]], "Start docker container": [[90, "start-docker-container"]], "Install AIMET packages": [[90, "install-aimet-packages"], [91, "install-aimet-packages"]], "From PyPI": [[90, "from-pypi"], [91, "from-pypi"]], "From Release Package": [[90, "from-release-package"], [91, "from-release-package"]], "Environment setup": [[90, "environment-setup"], [91, "environment-setup"]], "AIMET Installation and Setup": [[91, "aimet-installation-and-setup"]], "Install prerequisite packages": [[91, "install-prerequisite-packages"]], "Install GPU packages": [[91, "install-gpu-packages"]], "Install GPU packages for PyTorch 1.13 or ONNX": [[91, "install-gpu-packages-for-pytorch-1-13-or-onnx"]], "Install GPU packages for PyTorch 2.1 or TensorFlow": [[91, "install-gpu-packages-for-pytorch-2-1-or-tensorflow"]], "Install common debian packages": [[91, "install-common-debian-packages"]], "Install tensorflow GPU debian packages": [[91, "install-tensorflow-gpu-debian-packages"]], "Install torch GPU debian packages": [[91, "install-torch-gpu-debian-packages"]], "Install ONNX GPU debian packages": [[91, "install-onnx-gpu-debian-packages"]], "Replace Pillow with Pillow-SIMD": [[91, "replace-pillow-with-pillow-simd"]], "Replace onnxruntime with onnxruntime-gpu": [[91, "replace-onnxruntime-with-onnxruntime-gpu"]], "Post installation steps": [[91, "post-installation-steps"]], "AIMET AdaRound": [[93, "aimet-adaround"]], "AdaRound Use Cases": [[93, "adaround-use-cases"]], "Common terminology": [[93, "common-terminology"]], "Use Cases": [[93, "use-cases"], [104, "use-cases"]], "AIMET AutoQuant": [[94, "aimet-autoquant"]], "Overview": [[94, "overview"], [95, "overview"], [99, "overview"], [100, "overview"], [102, "overview"], [105, "overview"], [106, "overview"], [107, "overview"], [108, "overview"], [110, "overview"], [113, "overview"], [114, "overview"], [116, "overview"]], "Workflow": [[94, "workflow"], [95, "workflow"]], "AIMET BN Re-estimation": [[95, "aimet-bn-re-estimation"]], "AIMET Channel Pruning": [[96, "aimet-channel-pruning"]], "Overall Procedure": [[96, "overall-procedure"]], "Channel Selection": [[96, "channel-selection"]], "Winnowing": [[96, "winnowing"]], "Weight Reconstruction": [[96, "weight-reconstruction"]], "AIMET Compression Features Guidebook": [[97, "aimet-compression-features-guidebook"]], "AIMET Examples": [[98, "aimet-examples"]], "Browse the notebooks": [[98, "browse-the-notebooks"]], "Running the notebooks": [[98, "running-the-notebooks"]], "Install Jupyter": [[98, "install-jupyter"]], "Download the Example notebooks and related code": [[98, "download-the-example-notebooks-and-related-code"]], "Run the notebooks": [[98, "run-the-notebooks"]], "AIMET Greedy Compression Ratio Selection": [[99, "aimet-greedy-compression-ratio-selection"]], "How it works": [[99, "how-it-works"]], "Per-layer Exploration": [[99, "per-layer-exploration"]], "Compression Ratio Selection": [[99, "compression-ratio-selection"]], "AI Model Efficiency Toolkit User Guide": [[100, "ai-model-efficiency-toolkit-user-guide"]], "Features": [[100, "features"]], "Release Information": [[100, "release-information"]], "Installation Guide": [[100, "installation-guide"]], "Getting Started": [[100, "getting-started"]], "toc tree": [[100, "toc-tree"]], "AIMET Known Issues": [[101, "aimet-known-issues"]], "AIMET Model Compression": [[102, "aimet-model-compression"]], "Use Case": [[102, "use-case"]], "Compression ratio selection": [[102, "compression-ratio-selection"]], "Model Compression": [[102, "model-compression"]], "Optional techniques to get better compression results": [[102, "optional-techniques-to-get-better-compression-results"]], "Rank Rounding": [[102, "rank-rounding"]], "Per-layer Fine-tuning": [[102, "per-layer-fine-tuning"]], "FAQs": [[102, "faqs"], [105, "faqs"]], "References": [[102, "references"], [105, "references"]], "Model Guidelines for PyTorch": [[103, "model-guidelines-for-pytorch"]], "AIMET Model Quantization": [[104, "aimet-model-quantization"]], "AIMET Quantization Features": [[104, "aimet-quantization-features"]], "Post-Training Quantization": [[104, "post-training-quantization"]], "Debugging/Analysis Tools": [[104, "debugging-analysis-tools"]], "AIMET Quantization Workflow": [[104, "aimet-quantization-workflow"]], "PyTorch": [[104, "pytorch"], [114, "pytorch"]], "Tensorflow": [[104, "tensorflow"]], "Debugging Guidelines": [[104, "debugging-guidelines"]], "AIMET Post-Training Quantization Techniques": [[105, "aimet-post-training-quantization-techniques"]], "User Flow": [[105, "user-flow"]], "AIMET QuantAnalyzer": [[106, "aimet-quantanalyzer"]], "Requirements": [[106, "requirements"]], "Detailed Analysis Descriptions": [[106, "detailed-analysis-descriptions"]], "AIMET Quantization Aware Training": [[107, "aimet-quantization-aware-training"]], "QAT workflow": [[107, "qat-workflow"]], "QAT modes": [[107, "qat-modes"]], "Recommendations for Quantization-Aware Training": [[107, "recommendations-for-quantization-aware-training"]], "Quantization Simulation Configuration": [[108, "quantization-simulation-configuration"]], "Configuration File Structure": [[108, "configuration-file-structure"]], "How to configure individual Configuration File Sections": [[108, "how-to-configure-individual-configuration-file-sections"]], "AIMET Quantization Features Guidebook": [[109, "aimet-quantization-features-guidebook"]], "AIMET Quantization Simulation": [[110, "aimet-quantization-simulation"]], "QuantSim Workflow": [[110, "quantsim-workflow"]], "Simulating Quantization Noise": [[110, "simulating-quantization-noise"]], "Determining Quantization Parameters (Encodings)": [[110, "determining-quantization-parameters-encodings"]], "Quantization Schemes": [[110, "quantization-schemes"]], "Configuring Quantization Simulation Ops": [[110, "configuring-quantization-simulation-ops"]], "Frequently Asked Questions": [[110, "frequently-asked-questions"]], "AIMET Release Notes": [[111, "aimet-release-notes"]], "1.22.2": [[111, "id1"]], "1.22.1": [[111, "id2"]], "1.22.0": [[111, "id3"]], "1.21.0": [[111, "id4"]], "1.20.0": [[111, "id5"]], "1.19.1.py37": [[111, "py37"]], "1.19.1": [[111, "id6"]], "1.18.0.py37": [[111, "id7"]], "1.18.0": [[111, "id8"]], "1.17.0.py37": [[111, "id9"]], "1.17.0": [[111, "id10"]], "1.16.2.py37": [[111, "id11"]], "1.16.2": [[111, "id12"]], "1.16.1.py37": [[111, "id13"]], "1.16.1": [[111, "id14"]], "1.16.0": [[111, "id15"]], "1.14.0": [[111, "id16"]], "1.13.0": [[111, "id17"]], "AIMET Spatial SVD": [[112, "aimet-spatial-svd"]], "AIMET Visualization": [[113, "aimet-visualization"]], "Design": [[113, "design"]], "Compression": [[113, "compression"]], "Starting a Bokeh Server Session:": [[113, "starting-a-bokeh-server-session"]], "How to use the tool": [[113, "how-to-use-the-tool"]], "AIMET Visualization for Quantization": [[114, "aimet-visualization-for-quantization"]], "Quantization": [[114, "quantization"]], "TensorFlow": [[114, "tensorflow"]], "AIMET Weight SVD": [[115, "aimet-weight-svd"]], "AIMET Winnowing": [[116, "aimet-winnowing"]], "Winnowing Overview": [[116, "winnowing-overview"]], "How Winnowing Works": [[116, "how-winnowing-works"]]}, "indexentries": {"load_keras_model_multi_gpu() (in module aimet_tensorflow.utils.convert_tf_sess_to_keras)": [[33, "aimet_tensorflow.utils.convert_tf_sess_to_keras.load_keras_model_multi_gpu"]], "load_tf_sess_variables_to_keras_single_gpu() (in module aimet_tensorflow.utils.convert_tf_sess_to_keras)": [[33, 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(aimet_torch.auto_quant_v2.autoquant method)": [[73, "aimet_torch.auto_quant_v2.AutoQuant.set_model_preparer_params"]], "set_quant_scheme_candidates() (aimet_torch.auto_quant_v2.autoquant method)": [[73, "aimet_torch.auto_quant_v2.AutoQuant.set_quant_scheme_candidates"]], "fold_all_batch_norms_to_scale() (in module aimet_torch.batch_norm_fold)": [[74, "aimet_torch.batch_norm_fold.fold_all_batch_norms_to_scale"]], "reestimate_bn_stats() (in module aimet_torch.bn_reestimation)": [[74, "aimet_torch.bn_reestimation.reestimate_bn_stats"]], "activationtype (class in aimet_common.defs)": [[75, "aimet_common.defs.ActivationType"]], "convbninfotype (class in aimet_common.bias_correction)": [[75, "aimet_common.bias_correction.ConvBnInfoType"]], "quantparams (class in aimet_torch.quantsim)": [[75, "aimet_torch.quantsim.QuantParams"]], "correct_bias() (in module aimet_torch.bias_correction)": [[75, "aimet_torch.bias_correction.correct_bias"]], "no_activation (aimet_common.defs.activationtype attribute)": [[75, "aimet_common.defs.ActivationType.no_activation"]], "relu (aimet_common.defs.activationtype attribute)": [[75, "aimet_common.defs.ActivationType.relu"]], "relu6 (aimet_common.defs.activationtype attribute)": [[75, "aimet_common.defs.ActivationType.relu6"]], "channelpruningparameters (class in aimet_torch.defs)": [[76, "aimet_torch.defs.ChannelPruningParameters"]], "channelpruningparameters.automodeparams (class in aimet_torch.defs)": [[76, "aimet_torch.defs.ChannelPruningParameters.AutoModeParams"]], "channelpruningparameters.manualmodeparams (class in aimet_torch.defs)": [[76, "aimet_torch.defs.ChannelPruningParameters.ManualModeParams"]], "channelpruningparameters.mode (class in aimet_torch.defs)": [[76, "aimet_torch.defs.ChannelPruningParameters.Mode"]], "greedyselectionparameters (class in aimet_common.defs)": [[76, "aimet_common.defs.GreedySelectionParameters"]], "modelcompressor (class in aimet_torch.compress)": [[76, "aimet_torch.compress.ModelCompressor"]], "modulecompratiopair (class in aimet_torch.defs)": [[76, "aimet_torch.defs.ModuleCompRatioPair"]], "spatialsvdparameters (class in aimet_torch.defs)": [[76, "aimet_torch.defs.SpatialSvdParameters"]], "spatialsvdparameters.automodeparams (class in aimet_torch.defs)": [[76, "aimet_torch.defs.SpatialSvdParameters.AutoModeParams"]], "spatialsvdparameters.manualmodeparams (class in aimet_torch.defs)": [[76, "aimet_torch.defs.SpatialSvdParameters.ManualModeParams"]], "spatialsvdparameters.mode (class in aimet_torch.defs)": [[76, "aimet_torch.defs.SpatialSvdParameters.Mode"]], "tarrankselectionparameters (class in aimet_torch.defs)": [[76, "aimet_torch.defs.TarRankSelectionParameters"]], "weightsvdparameters (class in aimet_torch.defs)": [[76, "aimet_torch.defs.WeightSvdParameters"]], "weightsvdparameters.automodeparams (class in aimet_torch.defs)": [[76, "aimet_torch.defs.WeightSvdParameters.AutoModeParams"]], "weightsvdparameters.manualmodeparams (class in aimet_torch.defs)": [[76, "aimet_torch.defs.WeightSvdParameters.ManualModeParams"]], "weightsvdparameters.mode (class in aimet_torch.defs)": [[76, "aimet_torch.defs.WeightSvdParameters.Mode"]], "auto (aimet_torch.defs.channelpruningparameters.mode attribute)": [[76, "aimet_torch.defs.ChannelPruningParameters.Mode.auto"]], "auto (aimet_torch.defs.spatialsvdparameters.mode attribute)": [[76, "aimet_torch.defs.SpatialSvdParameters.Mode.auto"]], "auto (aimet_torch.defs.weightsvdparameters.mode attribute)": [[76, "aimet_torch.defs.WeightSvdParameters.Mode.auto"]], "compress_model() (aimet_torch.compress.modelcompressor static method)": [[76, "aimet_torch.compress.ModelCompressor.compress_model"]], "manual (aimet_torch.defs.channelpruningparameters.mode attribute)": [[76, "aimet_torch.defs.ChannelPruningParameters.Mode.manual"]], "manual (aimet_torch.defs.spatialsvdparameters.mode attribute)": [[76, "aimet_torch.defs.SpatialSvdParameters.Mode.manual"]], "manual (aimet_torch.defs.weightsvdparameters.mode attribute)": [[76, "aimet_torch.defs.WeightSvdParameters.Mode.manual"]], "equalize_model() (in module aimet_torch.cross_layer_equalization)": [[77, "aimet_torch.cross_layer_equalization.equalize_model"]], "layeroutpututil (class in aimet_torch.layer_output_utils)": [[78, "aimet_torch.layer_output_utils.LayerOutputUtil"]], "namingscheme (class in aimet_torch.layer_output_utils)": [[78, "aimet_torch.layer_output_utils.NamingScheme"]], "onnx (aimet_torch.layer_output_utils.namingscheme attribute)": [[78, "aimet_torch.layer_output_utils.NamingScheme.ONNX"]], "pytorch (aimet_torch.layer_output_utils.namingscheme attribute)": [[78, "aimet_torch.layer_output_utils.NamingScheme.PYTORCH"]], "torchscript (aimet_torch.layer_output_utils.namingscheme attribute)": [[78, "aimet_torch.layer_output_utils.NamingScheme.TORCHSCRIPT"]], "generate_layer_outputs() (aimet_torch.layer_output_utils.layeroutpututil method)": [[78, "aimet_torch.layer_output_utils.LayerOutputUtil.generate_layer_outputs"]], "prepare_model() (in module aimet_torch.model_preparer)": [[80, "aimet_torch.model_preparer.prepare_model"]], "clssetinfo (class in aimet_torch.cross_layer_equalization)": [[83, "aimet_torch.cross_layer_equalization.ClsSetInfo"]], "clssetinfo.clssetlayerpairinfo (class in aimet_torch.cross_layer_equalization)": [[83, "aimet_torch.cross_layer_equalization.ClsSetInfo.ClsSetLayerPairInfo"]], "bias_fold() (in module aimet_torch.cross_layer_equalization.highbiasfold)": [[83, "aimet_torch.cross_layer_equalization.HighBiasFold.bias_fold"], [83, "id0"]], "fold_all_batch_norms() (in module aimet_torch.batch_norm_fold)": [[83, "aimet_torch.batch_norm_fold.fold_all_batch_norms"]], "fold_given_batch_norms() (in module aimet_torch.batch_norm_fold)": [[83, "aimet_torch.batch_norm_fold.fold_given_batch_norms"]], "scale_cls_sets() (in module aimet_torch.cross_layer_equalization.crosslayerscaling)": [[83, "aimet_torch.cross_layer_equalization.CrossLayerScaling.scale_cls_sets"]], "scale_model() (in module aimet_torch.cross_layer_equalization.crosslayerscaling)": [[83, "aimet_torch.cross_layer_equalization.CrossLayerScaling.scale_model"]], "quantanalyzer (class in aimet_torch.quant_analyzer)": [[84, "aimet_torch.quant_analyzer.QuantAnalyzer"]], "analyze() (aimet_torch.quant_analyzer.quantanalyzer method)": [[84, "aimet_torch.quant_analyzer.QuantAnalyzer.analyze"]], "enable_per_layer_mse_loss() (aimet_torch.quant_analyzer.quantanalyzer method)": [[84, "aimet_torch.quant_analyzer.QuantAnalyzer.enable_per_layer_mse_loss"]], "quantizationsimmodel (class in aimet_torch.quantsim)": [[86, "aimet_torch.quantsim.QuantizationSimModel"]], "compute_encodings() (aimet_torch.quantsim.quantizationsimmodel method)": [[86, "aimet_torch.quantsim.QuantizationSimModel.compute_encodings"]], "export() (aimet_torch.quantsim.quantizationsimmodel method)": [[86, "aimet_torch.quantsim.QuantizationSimModel.export"]], "load_checkpoint() (aimet_torch.quantsim method)": [[86, "aimet_torch.quantsim.load_checkpoint"]], "save_checkpoint() (aimet_torch.quantsim method)": [[86, "aimet_torch.quantsim.save_checkpoint"]], "visualizecompression (class in aimet_torch.visualize_serialized_data)": [[87, "aimet_torch.visualize_serialized_data.VisualizeCompression"]], "display_comp_ratio_plot() (aimet_torch.visualize_serialized_data.visualizecompression method)": [[87, "aimet_torch.visualize_serialized_data.VisualizeCompression.display_comp_ratio_plot"]], "display_eval_scores() (aimet_torch.visualize_serialized_data.visualizecompression method)": [[87, "aimet_torch.visualize_serialized_data.VisualizeCompression.display_eval_scores"]], "visualize_changes_after_optimization() (in module aimet_torch.visualize_model)": [[88, "aimet_torch.visualize_model.visualize_changes_after_optimization"]], "visualize_relative_weight_ranges_to_identify_problematic_layers() (in module aimet_torch.visualize_model)": [[88, "aimet_torch.visualize_model.visualize_relative_weight_ranges_to_identify_problematic_layers"]], "visualize_weight_ranges() (in module aimet_torch.visualize_model)": [[88, "aimet_torch.visualize_model.visualize_weight_ranges"]]}})
\ No newline at end of file
diff --git a/releases/1.32.0/toplevelhidden.html b/releases/1.32.0/toplevelhidden.html
index 4a18229..231425a 100644
--- a/releases/1.32.0/toplevelhidden.html
+++ b/releases/1.32.0/toplevelhidden.html
@@ -1020,9 +1020,10 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="install/index.html"> Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="install/index.html#release-packages">Release packages</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install/index.html#quick-install">Quick Install</a></li>
+<li class="toctree-l2"><a class="reference internal" href="install/index.html#release-packages">Release Packages</a></li>
 <li class="toctree-l2"><a class="reference internal" href="install/index.html#system-requirements">System Requirements</a></li>
-<li class="toctree-l2"><a class="reference internal" href="install/index.html#installation-instructions">Installation Instructions</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="install/index.html#advanced-installation-instructions">Advanced Installation Instructions</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="install/install_host.html">Install in Host Machine</a><ul>
 <li class="toctree-l4"><a class="reference internal" href="install/install_host.html#install-prerequisite-packages">Install prerequisite packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="install/install_host.html#install-gpu-packages">Install GPU packages</a><ul>
@@ -1030,7 +1031,11 @@
 <li class="toctree-l5"><a class="reference internal" href="install/install_host.html#install-gpu-packages-for-pytorch-2-1-or-tensorflow">Install GPU packages for PyTorch 2.1 or TensorFlow</a></li>
 </ul>
 </li>
-<li class="toctree-l4"><a class="reference internal" href="install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
+<li class="toctree-l4"><a class="reference internal" href="install/install_host.html#install-aimet-packages">Install AIMET packages</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="install/install_host.html#from-pypi">From PyPI</a></li>
+<li class="toctree-l5"><a class="reference internal" href="install/install_host.html#from-release-package">From Release Package</a></li>
+</ul>
+</li>
 <li class="toctree-l4"><a class="reference internal" href="install/install_host.html#install-common-debian-packages">Install common debian packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="install/install_host.html#install-tensorflow-gpu-debian-packages">Install tensorflow GPU debian packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="install/install_host.html#install-torch-gpu-debian-packages">Install torch GPU debian packages</a></li>
diff --git a/releases/1.32.0/torch_v2/install/index.html b/releases/1.32.0/torch_v2/install/index.html
index 4c3c830..0a334f4 100644
--- a/releases/1.32.0/torch_v2/install/index.html
+++ b/releases/1.32.0/torch_v2/install/index.html
@@ -121,30 +121,47 @@
              
   <div class="section" id="aimet-installation">
 <span id="ug-installation"></span><h1>AIMET Installation<a class="headerlink" href="#aimet-installation" title="Permalink to this heading"></a></h1>
-<div class="section" id="release-packages">
-<h2>Release packages<a class="headerlink" href="#release-packages" title="Permalink to this heading"></a></h2>
-<p>AIMET release packages are hosted at <a class="reference external" href="https://github.com/quic/aimet/releases">https://github.com/quic/aimet/releases</a>. Each release includes multiple python packages of the following format:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">&lt;</span><span class="n">PACKAGE_PREFIX</span><span class="o">&gt;-&lt;</span><span class="n">VARIANT</span><span class="o">&gt;</span><span class="n">_</span><span class="o">&lt;</span><span class="n">VERSION</span><span class="o">&gt;-</span><span class="n">cp310</span><span class="o">-</span><span class="n">cp310</span><span class="o">-</span><span class="n">linux_x86_64</span><span class="o">.</span><span class="n">whl</span>
+<div class="section" id="quick-install">
+<h2>Quick Install<a class="headerlink" href="#quick-install" title="Permalink to this heading"></a></h2>
+<p>The AIMET PyTorch GPU PyPI packages are available for environments that meet the following requirements:</p>
+<ul class="simple">
+<li><p>64-bit Intel x86-compatible processor</p></li>
+<li><p>Linux Ubuntu 22.04 LTS [Python 3.10] or Linux Ubuntu 20.04 LTS [Python 3.8]</p></li>
+<li><p>Torch 1.13+cu117</p></li>
+</ul>
+<p><strong>Pip install:</strong></p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">apt</span><span class="o">-</span><span class="n">get</span> <span class="n">install</span> <span class="n">liblapacke</span>
+<span class="n">python3</span> <span class="o">-</span><span class="n">m</span> <span class="n">pip</span> <span class="n">install</span> <span class="n">aimet</span><span class="o">-</span><span class="n">torch</span>
 </pre></div>
 </div>
-<p>Please find more information below about each <em>VARIANT</em>.</p>
-<p>PyTorch</p>
-<ol class="arabic simple">
-<li><p><strong>torch-gpu</strong> for PyTorch 1.13 GPU package with Python 3.10 and CUDA 11.x - <em>Recommended for use</em> with PyTorch models</p></li>
-<li><p><strong>torch-cpu</strong> for PyTorch 1.13 CPU package with Python 3.10 - If installing on a machine without CUDA</p></li>
-<li><p><strong>torch-gpu-pt21</strong> for PyTorch 2.1 GPU package with Python 3.10 and CUDA 11.x</p></li>
-<li><p><strong>torch-cpu-pt21</strong> for PyTorch 2.1 CPU package with Python 3.10 - If installing on a machine without CUDA</p></li>
-</ol>
-<p>TensorFlow</p>
-<ol class="arabic simple">
-<li><p><strong>tf-gpu</strong> for TensorFlow 2.10 GPU package with Python 3.10 - <em>Recommended for use</em> with TensorFlow models</p></li>
-<li><p><strong>tf-cpu</strong> for TensorFlow 2.10 CPU package with Python 3.10 - If installing on a machine without CUDA</p></li>
-</ol>
-<p>ONNX</p>
-<ol class="arabic simple">
-<li><p><strong>onnx-gpu</strong> for ONNX 1.14 GPU package with Python 3.10 - <em>Recommended for use</em> with ONNX models</p></li>
-<li><p><strong>onnx-cpu</strong> for ONNX 1.14 CPU package with Python 3.10 - If installing on a machine without CUDA</p></li>
-</ol>
+</div>
+<div class="section" id="release-packages">
+<h2>Release Packages<a class="headerlink" href="#release-packages" title="Permalink to this heading"></a></h2>
+<p>For other aimet variants, install the latest version from the .whl files hosted at <a class="reference external" href="https://github.com/quic/aimet/releases">https://github.com/quic/aimet/releases</a></p>
+<p><strong>PyTorch</strong></p>
+<pre class="literal-block"># Pytorch 1.13 with CUDA 11.x
+python3 -m pip install https://github.com/quic/aimet/releases/download/1.31.0/aimet_torch_gpu_1.31.0-cp38-cp38-manylinux_2_34_x86_64.whl -f <a class="reference external" href="https://download.pytorch.org/whl/torch_stable.html">https://download.pytorch.org/whl/torch_stable.html</a>
+# Pytorch 1.13 CPU only
+python3 -m pip install https://github.com/quic/aimet/releases/download/1.31.0/aimet_torch_cpu_1.31.0-cp38-cp38-manylinux_2_34_x86_64.whl
+
+# Pytorch 2.1 with CUDA 11.x
+python3 -m pip install https://github.com/quic/aimet/releases/download/1.31.0/aimet_torch_gpu_pt21_1.31.0-cp38-cp38-manylinux_2_34_x86_64.whl -f <a class="reference external" href="https://download.pytorch.org/whl/torch_stable.html">https://download.pytorch.org/whl/torch_stable.html</a>
+# Pytorch 2.1 CPU only
+python3 -m pip install https://github.com/quic/aimet/releases/download/1.31.0/aimet_torch_cpu_pt21_1.31.0-cp38-cp38-manylinux_2_34_x86_64.whl</pre>
+<p><strong>TensorFlow</strong></p>
+<pre class="literal-block"># Tensorflow 2.10 GPU with CUDA 11.x
+python3 -m pip install https://github.com/quic/aimet/releases/download/1.31.0/aimet_tensorflow_gpu_1.31.0-cp38-cp38-manylinux_2_34_x86_64.whl
+# Tensorflow 2.10 CPU only
+python3 -m pip install https://github.com/quic/aimet/releases/download/1.31.0/aimet_tensorflow_cpu_1.31.0-cp38-cp38-manylinux_2_34_x86_64.whl</pre>
+<p><strong>Onnx</strong></p>
+<pre class="literal-block"># ONNX 1.14 GPU
+python3 -m pip install https://github.com/quic/aimet/releases/download/1.31.0/aimet_onnx_gpu_1.31.0-cp38-cp38-manylinux_2_34_x86_64.whl
+# ONNX 1.14 CPU
+python3 -m pip install https://github.com/quic/aimet/releases/download/1.31.0/aimet_onnx_cpu_1.31.0-cp38-cp38-manylinux_2_34_x86_64.whl</pre>
+<p>For previous AIMET releases, browse packages at <a class="reference external" href="https://github.com/quic/aimet/releases">https://github.com/quic/aimet/releases</a>. Each release includes multiple python packages of the following format:</p>
+<pre class="literal-block"># VARIANT in {torch_gpu, torch_cpu, tf_gpu, tf_cpu, onnx_gpu, onnx_cpu}
+# PACKAGE_PREFIX in {aimet_torch, aimet_tensorflow, aimet_onnx}
+&lt;PACKAGE_PREFIX&gt;-&lt;VARIANT&gt;_&lt;VERSION&gt;-cp38-cp38-manylinux_2_34_x86_64.whl</pre>
 </div>
 <div class="section" id="system-requirements">
 <h2>System Requirements<a class="headerlink" href="#system-requirements" title="Permalink to this heading"></a></h2>
@@ -178,8 +195,8 @@ <h2>System Requirements<a class="headerlink" href="#system-requirements" title="
 </ul>
 <p>While these are not minimum requirements, they are recommended for good performance when training large networks.</p>
 </div>
-<div class="section" id="installation-instructions">
-<h2>Installation Instructions<a class="headerlink" href="#installation-instructions" title="Permalink to this heading"></a></h2>
+<div class="section" id="advanced-installation-instructions">
+<h2>Advanced Installation Instructions<a class="headerlink" href="#advanced-installation-instructions" title="Permalink to this heading"></a></h2>
 <dl class="simple">
 <dt>There are two ways to setup and install AIMET:</dt><dd><ul class="simple">
 <li><p>On your host machine</p></li>
diff --git a/releases/1.32.0/torch_v2/install/install_docker.html b/releases/1.32.0/torch_v2/install/install_docker.html
index 029a893..3dd280b 100644
--- a/releases/1.32.0/torch_v2/install/install_docker.html
+++ b/releases/1.32.0/torch_v2/install/install_docker.html
@@ -219,8 +219,8 @@ <h3>From Release Package<a class="headerlink" href="#from-release-package" title
 <ol class="arabic simple">
 <li><p>For the PyTorch 1.13 GPU variant, use “torch_gpu”</p></li>
 <li><p>For the PyTorch 1.13 CPU variant, use “torch_cpu”</p></li>
-<li><p>For the PyTorch 2.1 GPU variant, use “torch_gpu-pt21”</p></li>
-<li><p>For the PyTorch 2.1 CPU variant, use “torch_cpu-pt21”</p></li>
+<li><p>For the PyTorch 2.1 GPU variant, use “torch_gpu_pt21”</p></li>
+<li><p>For the PyTorch 2.1 CPU variant, use “torch_cpu_pt21”</p></li>
 <li><p>For the TensorFlow GPU variant, use “tf_gpu”</p></li>
 <li><p>For the TensorFlow CPU variant, use “tf_cpu”</p></li>
 <li><p>For the ONNX GPU variant, use “onnx_gpu”</p></li>
@@ -238,7 +238,7 @@ <h3>From Release Package<a class="headerlink" href="#from-release-package" title
 </pre></div>
 </div>
 <p>Set the common suffix for the package files as follows:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">wheel_file_suffix</span><span class="o">=</span><span class="s2">&quot;cp310-cp310-linux_x86_64.whl&quot;</span>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">wheel_file_suffix</span><span class="o">=</span><span class="s2">&quot;cp310-cp310-manylinux_2_34_x86_64.whl&quot;</span>
 </pre></div>
 </div>
 <p>Install the AIMET packages in the order specified below:</p>
@@ -251,11 +251,11 @@ <h3>From Release Package<a class="headerlink" href="#from-release-package" title
 </dd>
 </dl>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install ONE of the following depending on the variant</span>
-python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_torch-<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span><span class="w"> </span>-f<span class="w"> </span>https://download.pytorch.org/whl/torch_stable.html
+python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span><span class="w"> </span>-f<span class="w"> </span>https://download.pytorch.org/whl/torch_stable.html
 <span class="c1"># OR</span>
-python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_tensorflow-<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span>
+python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span>
 <span class="c1"># OR</span>
-python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_onnx-<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span>
+python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span>
 </pre></div>
 </div>
 </div>
diff --git a/releases/1.32.0/torch_v2/install/install_host.html b/releases/1.32.0/torch_v2/install/install_host.html
index 9c9cd7e..86be6cb 100644
--- a/releases/1.32.0/torch_v2/install/install_host.html
+++ b/releases/1.32.0/torch_v2/install/install_host.html
@@ -203,6 +203,25 @@ <h3>Install GPU packages for PyTorch 2.1 or TensorFlow<a class="headerlink" href
 </div>
 <div class="section" id="install-aimet-packages">
 <h2>Install AIMET packages<a class="headerlink" href="#install-aimet-packages" title="Permalink to this heading"></a></h2>
+<div class="section" id="from-pypi">
+<h3>From PyPI<a class="headerlink" href="#from-pypi" title="Permalink to this heading"></a></h3>
+<p>Aimet Torch GPU can install from pypi through the following method:</p>
+<p>Go to <a class="reference external" href="https://pypi.org/project/aimet-torch">https://pypi.org/project/aimet-torch</a> to identify a version you wish to install</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>For PyTorch 1.13 GPU you should use aimet-torch==1.31.1</p></li>
+<li><p>For Pytorch 2.1.2 GPU you should use aimet-torch &gt;= 1.32.0</p></li>
+</ul>
+</div></blockquote>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>sudo<span class="w"> </span>apt-get<span class="w"> </span>install<span class="w"> </span>liblapacke<span class="w"> </span>-y
+pip<span class="w"> </span>install<span class="w"> </span>aimet-torch
+</pre></div>
+</div>
+</div>
+<div class="section" id="from-release-package">
+<h3>From Release Package<a class="headerlink" href="#from-release-package" title="Permalink to this heading"></a></h3>
+<p>Alternatively, we host .whl packages for each release at <a class="reference external" href="https://github.com/quic/aimet/releases">https://github.com/quic/aimet/releases</a>. Identify the release tag
+of the package you wish to install, then follow the instructions below to install AIMET from the .whl file.</p>
 <p>Go to <a class="reference external" href="https://github.com/quic/aimet/releases">https://github.com/quic/aimet/releases</a> and identify the release tag of the package you want to install.</p>
 <p>Set the &lt;variant_string&gt; to ONE of the following depending on your desired variant</p>
 <ol class="arabic simple">
@@ -234,14 +253,15 @@ <h2>Install AIMET packages<a class="headerlink" href="#install-aimet-packages" t
 <p>Install the AIMET packages in the order specified below:</p>
 <p><strong>NOTE:</strong> Python dependencies will automatically get installed.</p>
 <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install ONE of the following depending on the variant</span>
-python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_torch-<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span><span class="w"> </span>-f<span class="w"> </span>https://download.pytorch.org/whl/torch_stable.html
+python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span><span class="w"> </span>-f<span class="w"> </span>https://download.pytorch.org/whl/torch_stable.html
 <span class="c1"># OR</span>
-python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_tensorflow-<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span>
+python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span>
 <span class="c1"># OR</span>
-python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_onnx-<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span>
+python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="si">${</span><span class="nv">download_url</span><span class="si">}</span>/aimet_<span class="si">${</span><span class="nv">AIMET_VARIANT</span><span class="si">}</span>_<span class="si">${</span><span class="nv">release_tag</span><span class="si">}</span>-<span class="si">${</span><span class="nv">wheel_file_suffix</span><span class="si">}</span>
 </pre></div>
 </div>
 </div>
+</div>
 <div class="section" id="install-common-debian-packages">
 <h2>Install common debian packages<a class="headerlink" href="#install-common-debian-packages" title="Permalink to this heading"></a></h2>
 <p><strong>NOTE:</strong> Do the following ONLY for the TensorFlow variant packages.</p>
diff --git a/releases/1.32.0/torch_v2/objects.inv b/releases/1.32.0/torch_v2/objects.inv
index b21a47f..8bd98a7 100644
Binary files a/releases/1.32.0/torch_v2/objects.inv and b/releases/1.32.0/torch_v2/objects.inv differ
diff --git a/releases/1.32.0/torch_v2/searchindex.js b/releases/1.32.0/torch_v2/searchindex.js
index 9741db3..250b548 100644
--- a/releases/1.32.0/torch_v2/searchindex.js
+++ b/releases/1.32.0/torch_v2/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["install/index", "install/install_docker", "install/install_host", "toplevelhidden", "torch_docs/api/nn.fake_quantization_mixin", "torch_docs/api/nn.quantization_mixin", "torch_docs/api/q.quantized_tensor", "torch_docs/encoding_analyzer", "torch_docs/examples/ptq", "torch_docs/index", "torch_docs/quantized_modules", "torch_docs/quantizer", "torch_docs/tutorials/quickstart", "user_guide/adaround", "user_guide/auto_quant", "user_guide/bn_reestimation", "user_guide/channel_pruning", "user_guide/compression_feature_guidebook", "user_guide/greedy_compression_ratio_selection", "user_guide/index", "user_guide/known_issues", "user_guide/model_compression", "user_guide/model_guidelines", "user_guide/model_quantization", "user_guide/post_training_quant_techniques", "user_guide/quant_analyzer", "user_guide/quantization_aware_training", "user_guide/quantization_configuration", "user_guide/quantization_feature_guidebook", "user_guide/quantization_sim", "user_guide/release_notes", "user_guide/spatial_svd", "user_guide/visualization_compression", "user_guide/visualization_quant", "user_guide/weight_svd", "user_guide/winnowing"], "filenames": ["install/index.rst", "install/install_docker.rst", "install/install_host.rst", "toplevelhidden.rst", "torch_docs/api/nn.fake_quantization_mixin.rst", "torch_docs/api/nn.quantization_mixin.rst", "torch_docs/api/q.quantized_tensor.rst", "torch_docs/encoding_analyzer.rst", "torch_docs/examples/ptq.rst", "torch_docs/index.rst", "torch_docs/quantized_modules.rst", "torch_docs/quantizer.rst", "torch_docs/tutorials/quickstart.rst", "user_guide/adaround.rst", "user_guide/auto_quant.rst", "user_guide/bn_reestimation.rst", "user_guide/channel_pruning.rst", "user_guide/compression_feature_guidebook.rst", "user_guide/greedy_compression_ratio_selection.rst", "user_guide/index.rst", "user_guide/known_issues.rst", "user_guide/model_compression.rst", "user_guide/model_guidelines.rst", "user_guide/model_quantization.rst", "user_guide/post_training_quant_techniques.rst", "user_guide/quant_analyzer.rst", "user_guide/quantization_aware_training.rst", "user_guide/quantization_configuration.rst", "user_guide/quantization_feature_guidebook.rst", "user_guide/quantization_sim.rst", "user_guide/release_notes.rst", "user_guide/spatial_svd.rst", "user_guide/visualization_compression.rst", "user_guide/visualization_quant.rst", "user_guide/weight_svd.rst", "user_guide/winnowing.rst"], "titles": ["AIMET Installation", "AIMET Installation in Docker", "AIMET Installation and Setup", "&lt;no title&gt;", "nn.FakeQuantizationMixin", "nn.QuantizationMixin", "quantization.QuantizedTensor", "Encoding Analyzers", "Post-Training Quantization", "Welcome to AI Model Efficiency Toolkit PyTorch API Docs!", "Quantized Modules", "Quantizers", "Quickstart Guide", "AIMET AdaRound", "AIMET AutoQuant", "AIMET BN Re-estimation", "AIMET Channel Pruning", "AIMET Compression Features Guidebook", "AIMET 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-<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
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+<li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#from-pypi">From PyPI</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#from-release-package">From Release Package</a></li>
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diff --git a/releases/1.32.0/user_guide/bn_reestimation.html b/releases/1.32.0/user_guide/bn_reestimation.html
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-<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
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+<li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#from-pypi">From PyPI</a></li>
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 <li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#install-gpu-packages-for-pytorch-2-1-or-tensorflow">Install GPU packages for PyTorch 2.1 or TensorFlow</a></li>
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 </li>
-<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#from-pypi">From PyPI</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#from-release-package">From Release Package</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-common-debian-packages">Install common debian packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-tensorflow-gpu-debian-packages">Install tensorflow GPU debian packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-torch-gpu-debian-packages">Install torch GPU debian packages</a></li>
diff --git a/releases/1.32.0/user_guide/compression_feature_guidebook.html b/releases/1.32.0/user_guide/compression_feature_guidebook.html
index 58af5eb..f0d0251 100644
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-<li class="toctree-l2"><a class="reference internal" href="../install/index.html#installation-instructions">Installation Instructions</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../install/index.html#advanced-installation-instructions">Advanced Installation Instructions</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install/install_host.html">Install in Host Machine</a><ul>
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 <li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#install-gpu-packages-for-pytorch-2-1-or-tensorflow">Install GPU packages for PyTorch 2.1 or TensorFlow</a></li>
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-<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#from-pypi">From PyPI</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#from-release-package">From Release Package</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../install/index.html#installation-instructions">Installation Instructions</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
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+<li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#from-pypi">From PyPI</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../install/index.html#installation-instructions">Installation Instructions</a><ul>
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-<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#from-pypi">From PyPI</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-tensorflow-gpu-debian-packages">Install tensorflow GPU debian packages</a></li>
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diff --git a/releases/1.32.0/user_guide/quant_analyzer.html b/releases/1.32.0/user_guide/quant_analyzer.html
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-<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
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+<li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#from-pypi">From PyPI</a></li>
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diff --git a/releases/1.32.0/user_guide/visualization_quant.html b/releases/1.32.0/user_guide/visualization_quant.html
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-<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#from-pypi">From PyPI</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#from-release-package">From Release Package</a></li>
+</ul>
+</li>
 <li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-common-debian-packages">Install common debian packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-tensorflow-gpu-debian-packages">Install tensorflow GPU debian packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-torch-gpu-debian-packages">Install torch GPU debian packages</a></li>
diff --git a/releases/1.32.0/user_guide/weight_svd.html b/releases/1.32.0/user_guide/weight_svd.html
index f3818d9..1464c74 100644
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+<li class="toctree-l2"><a class="reference internal" href="../install/index.html#release-packages">Release Packages</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../install/index.html#system-requirements">System Requirements</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../install/index.html#installation-instructions">Installation Instructions</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../install/index.html#advanced-installation-instructions">Advanced Installation Instructions</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install/install_host.html">Install in Host Machine</a><ul>
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@@ -1032,7 +1033,11 @@
 <li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#install-gpu-packages-for-pytorch-2-1-or-tensorflow">Install GPU packages for PyTorch 2.1 or TensorFlow</a></li>
 </ul>
 </li>
-<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#from-pypi">From PyPI</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#from-release-package">From Release Package</a></li>
+</ul>
+</li>
 <li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-common-debian-packages">Install common debian packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-tensorflow-gpu-debian-packages">Install tensorflow GPU debian packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-torch-gpu-debian-packages">Install torch GPU debian packages</a></li>
diff --git a/releases/1.32.0/user_guide/winnowing.html b/releases/1.32.0/user_guide/winnowing.html
index dc21fe9..5ed8aa7 100644
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+<li class="toctree-l2"><a class="reference internal" href="../install/index.html#quick-install">Quick Install</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../install/index.html#release-packages">Release Packages</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../install/index.html#system-requirements">System Requirements</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../install/index.html#installation-instructions">Installation Instructions</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../install/index.html#advanced-installation-instructions">Advanced Installation Instructions</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../install/install_host.html">Install in Host Machine</a><ul>
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 <li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-gpu-packages">Install GPU packages</a><ul>
@@ -1032,7 +1033,11 @@
 <li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#install-gpu-packages-for-pytorch-2-1-or-tensorflow">Install GPU packages for PyTorch 2.1 or TensorFlow</a></li>
 </ul>
 </li>
-<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a></li>
+<li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-aimet-packages">Install AIMET packages</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#from-pypi">From PyPI</a></li>
+<li class="toctree-l5"><a class="reference internal" href="../install/install_host.html#from-release-package">From Release Package</a></li>
+</ul>
+</li>
 <li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-common-debian-packages">Install common debian packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-tensorflow-gpu-debian-packages">Install tensorflow GPU debian packages</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../install/install_host.html#install-torch-gpu-debian-packages">Install torch GPU debian packages</a></li>