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[SSS Review] Add progress visualization feature (#1671)
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docs/api/api_docs/classes/BitWidthConfig.html

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<title>BitWidthConfig &#8212; MCT Documentation: ver 2.6.0</title>
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docs/api/api_docs/classes/DataGenerationConfig.html

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<title>Data Generation Configuration &#8212; MCT Documentation: ver 2.6.0</title>
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docs/api/api_docs/classes/DefaultDict.html

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<title>DefaultDict Class &#8212; MCT Documentation: ver 2.6.0</title>
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<dd><p>Get the value of the inner dictionary by the given key, If key is not in dictionary,
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it uses the default_factory to return a default value.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Return type<span class="colon">:</span></dt>
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<dd class="field-odd"><p><span class="sphinx_autodoc_typehints-type"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></span></p>
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<dt class="field-even">Parameters<span class="colon">:</span></dt>
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<dd class="field-even"><p><strong>key</strong> – Key to use in inner dictionary.</p>
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<dd class="field-odd"><p><strong>key</strong> – Key to use in inner dictionary.</p>
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</dd>
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<dt class="field-odd">Returns<span class="colon">:</span></dt>
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<dd class="field-odd"><p>Value of the inner dictionary by the given key, or a default value if not exist.
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<dt class="field-even">Returns<span class="colon">:</span></dt>
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<dd class="field-even"><p>Value of the inner dictionary by the given key, or a default value if not exist.
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If default_factory was not passed at initialization, it returns None.</p>
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</dd>
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<dt class="field-odd">Return type<span class="colon">:</span></dt>
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<dd class="field-odd"><p><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></p>
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docs/api/api_docs/classes/FrameworkInfo.html

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<title>FrameworkInfo Class &#8212; MCT Documentation: ver 2.6.0</title>
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<p class="rubric">Examples</p>
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<p>When quantizing a Keras model, if we want to quantize the kernels of Conv2D layers only, we can
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set, and we know it’s kernel out/in channel indices are (3, 2) respectivly:</p>
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<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">tensorflow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">tf</span>
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<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
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<span class="gp">&gt;&gt;&gt; </span><span class="n">kernel_ops</span> <span class="o">=</span> <span class="p">[</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">]</span>
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<span class="gp">&gt;&gt;&gt; </span><span class="n">kernel_channels_mapping</span> <span class="o">=</span> <span class="n">DefaultDict</span><span class="p">({</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">:</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">)})</span>
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</pre></div>

docs/api/api_docs/classes/GradientPTQConfig.html

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<title>GradientPTQConfig Class &#8212; MCT Documentation: ver 2.6.0</title>
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docs/api/api_docs/classes/MixedPrecisionQuantizationConfig.html

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<title>MixedPrecisionQuantizationConfig &#8212; MCT Documentation: ver 2.6.0</title>
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docs/api/api_docs/classes/PruningConfig.html

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<title>Pruning Configuration &#8212; MCT Documentation: ver 2.6.0</title>
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docs/api/api_docs/classes/PruningInfo.html

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<title>Pruning Information &#8212; MCT Documentation: ver 2.6.0</title>
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docs/api/api_docs/classes/QuantizationConfig.html

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<title>QuantizationConfig &#8212; MCT Documentation: ver 2.6.0</title>
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activations using thresholds, with weight threshold selection based on MSE and activation threshold selection
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using NOCLIPPING (min/max), while enabling relu_bound_to_power_of_2 and weights_bias_correction,
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you can instantiate a quantization configuration like this:</p>
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<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">model_compression_toolkit</span> <span class="k">as</span> <span class="nn">mct</span>
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<span class="gp">&gt;&gt;&gt; </span><span class="n">qc</span> <span class="o">=</span> <span class="n">mct</span><span class="o">.</span><span class="n">core</span><span class="o">.</span><span class="n">QuantizationConfig</span><span class="p">(</span><span class="n">activation_error_method</span><span class="o">=</span><span class="n">mct</span><span class="o">.</span><span class="n">core</span><span class="o">.</span><span class="n">QuantizationErrorMethod</span><span class="o">.</span><span class="n">NOCLIPPING</span><span class="p">,</span> <span class="n">weights_error_method</span><span class="o">=</span><span class="n">mct</span><span class="o">.</span><span class="n">core</span><span class="o">.</span><span class="n">QuantizationErrorMethod</span><span class="o">.</span><span class="n">MSE</span><span class="p">,</span> <span class="n">relu_bound_to_power_of_2</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weights_bias_correction</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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</pre></div>
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docs/api/api_docs/classes/QuantizationErrorMethod.html

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