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classedge_1_1Model.html

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@@ -169,6 +169,10 @@ <h2><a href="#pub-methods">Public functions</a></h2>
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<span class="m-doc-wrap-bumper">auto <a href="#a2a78a15d2354411e1c613b762ee5c660" class="m-doc">getCreationStatus</a>(</span><span class="m-doc-wrap">) const -&gt; STATUS</span>
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</dt>
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<dd>Get the status of model creation.</dd>
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<dt>
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<span class="m-doc-wrap-bumper">auto <a href="#a95b1d52fd30acc8017605ef0fd29f9f9" class="m-doc">getPrecision</a>(</span><span class="m-doc-wrap">) const -&gt; TensorType</span>
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</dt>
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<dd>Get the precision used for model execution.</dd>
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</dl>
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</section>
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<section id="pro-methods">
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<span class="m-doc-wrap-bumper">void <a href="#ab4148431cd28c1cbd839f6b981bef700" class="m-doc">setDelegate</a>(</span><span class="m-doc-wrap">const <a href="namespaceedge.html#a2a339162b70a461c879db12d3832592d" class="m-doc">DELEGATE</a>&amp; delegate)</span>
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</dt>
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<dd>Set the delegate for model execution.</dd>
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<dt>
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<span class="m-doc-wrap-bumper">void <a href="#a4d58bbffeb8d0044109dad15c48bd7a1" class="m-doc">setPrecision</a>(</span><span class="m-doc-wrap">const <a href="namespaceedge.html#a35bf76c8f50658546b6dc23d80b9798c" class="m-doc">TensorType</a>&amp; precision)</span>
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</dt>
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<dd>Set the precision for model execution.</dd>
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<dt>
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<span class="m-doc-wrap-bumper">void <a href="#a72470f3781e6fbbfe160961ad68e265d" class="m-doc">setCreationStatus</a>(</span><span class="m-doc-wrap">const <a href="namespaceedge.html#adbc82abb7981e8ce0228aa51fdd879a3" class="m-doc">STATUS</a>&amp; status)</span>
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</dt>
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</table>
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<p>Verify that the model was created successfully</p>
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</div></section>
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<section class="m-doc-details" id="a95b1d52fd30acc8017605ef0fd29f9f9"><div>
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<h3>
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<span class="m-doc-wrap-bumper">auto edge::<wbr />Model::<wbr /></span><span class="m-doc-wrap"><span class="m-doc-wrap-bumper"><a href="#a95b1d52fd30acc8017605ef0fd29f9f9" class="m-doc-self">getPrecision</a>(</span><span class="m-doc-wrap">) const -&gt; TensorType</span></span>
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</h3>
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<p>Get the precision used for model execution.</p>
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<table class="m-table m-fullwidth m-flat">
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<tfoot>
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<tr>
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<th style="width: 1%">Returns</th>
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<td>The pricsion used model execution</td>
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</tr>
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</tfoot>
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</table>
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</div></section>
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<section class="m-doc-details" id="ab4148431cd28c1cbd839f6b981bef700"><div>
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<h3>
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<span class="m-doc-wrap-bumper">void edge::<wbr />Model::<wbr /></span><span class="m-doc-wrap"><span class="m-doc-wrap-bumper"><a href="#ab4148431cd28c1cbd839f6b981bef700" class="m-doc-self">setDelegate</a>(</span><span class="m-doc-wrap">const <a href="namespaceedge.html#a2a339162b70a461c879db12d3832592d" class="m-doc">DELEGATE</a>&amp; delegate) <span class="m-label m-warning">protected</span></span></span>
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</tbody>
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</table>
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<p>This method is used by derivatives to allow users to query the currently set delegate</p>
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</div></section>
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<section class="m-doc-details" id="a4d58bbffeb8d0044109dad15c48bd7a1"><div>
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<h3>
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<span class="m-doc-wrap-bumper">void edge::<wbr />Model::<wbr /></span><span class="m-doc-wrap"><span class="m-doc-wrap-bumper"><a href="#a4d58bbffeb8d0044109dad15c48bd7a1" class="m-doc-self">setPrecision</a>(</span><span class="m-doc-wrap">const <a href="namespaceedge.html#a35bf76c8f50658546b6dc23d80b9798c" class="m-doc">TensorType</a>&amp; precision) <span class="m-label m-warning">protected</span></span></span>
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</h3>
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<p>Set the precision for model execution.</p>
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<p>This method is used by derivatives to allow users to query the execution precision</p>
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</div></section>
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<section class="m-doc-details" id="a72470f3781e6fbbfe160961ad68e265d"><div>
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<h3>

classedge_1_1qnn_1_1TensorImpl.html

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@@ -80,7 +80,8 @@ <h2><a href="#base-classes">Base classes</a></h2>
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<h2><a href="#typeless-methods">Constructors, destructors, conversion operators</a></h2>
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<dl class="m-doc">
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<dt>
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<span class="m-doc-wrap-bumper"><a href="#a28d79aa5ea92d126fc8aea0baca66311" class="m-doc">TensorImpl</a>(</span><span class="m-doc-wrap">Qnn_Tensor_t* qnnTensor = nullptr) <span class="m-label m-flat m-info">explicit</span> </span>
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<span class="m-doc-wrap-bumper"><a href="#a66e73fe2c83a0884dab507aed9755a37" class="m-doc">TensorImpl</a>(</span><span class="m-doc-wrap">Qnn_Tensor_t* qnnTensor = nullptr,
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bool allocate = true) <span class="m-label m-flat m-info">explicit</span> </span>
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</dt>
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<dd>Constructor for <a href="classedge_1_1qnn_1_1TensorImpl.html" class="m-doc">TensorImpl</a>.</dd>
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<dt id="a6605f21dc5bb51834534c45b93a6433a">
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</section>
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<section>
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<h2>Function documentation</h2>
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<section class="m-doc-details" id="a28d79aa5ea92d126fc8aea0baca66311"><div>
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<section class="m-doc-details" id="a66e73fe2c83a0884dab507aed9755a37"><div>
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<h3>
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<span class="m-doc-wrap-bumper"> edge::<wbr />qnn::<wbr />TensorImpl::<wbr /></span><span class="m-doc-wrap"><span class="m-doc-wrap-bumper"><a href="#a28d79aa5ea92d126fc8aea0baca66311" class="m-doc-self">TensorImpl</a>(</span><span class="m-doc-wrap">Qnn_Tensor_t* qnnTensor = nullptr) <span class="m-label m-info">explicit</span> </span></span>
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<span class="m-doc-wrap-bumper"> edge::<wbr />qnn::<wbr />TensorImpl::<wbr /></span><span class="m-doc-wrap"><span class="m-doc-wrap-bumper"><a href="#a66e73fe2c83a0884dab507aed9755a37" class="m-doc-self">TensorImpl</a>(</span><span class="m-doc-wrap">Qnn_Tensor_t* qnnTensor = nullptr,
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bool allocate = true) <span class="m-label m-info">explicit</span> </span></span>
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</h3>
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<p>Constructor for <a href="classedge_1_1qnn_1_1TensorImpl.html" class="m-doc">TensorImpl</a>.</p>
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<table class="m-table m-fullwidth m-flat">
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<td style="width: 1%">qnnTensor</td>
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<td>Pointer to the QnnTensor object.</td>
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</tr>
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<tr>
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<td>allocate</td>
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<td>Whether to allocate underlying memory for the <a href="classedge_1_1Tensor.html" class="m-doc">Tensor</a>.</td>
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</tr>
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</tbody>
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</table>
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</div></section>

md_example_README.html

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<span class="w"> </span><span class="nt">&quot;jobs&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;&lt;num threads&gt;&quot;</span>
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<span class="w"> </span><span class="p">}</span>
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<span class="w"> </span><span class="p">]</span>
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<span class="p">}</span></pre><p>For MacOS, replace &quot;Unix Makefiles&quot; with &quot;Xcode&quot;.</p><blockquote><p>[!NOTE] Examples require additional dependencies to the main library. As such, it is</p></blockquote><p>required to supply <code>-o examples=True</code> to the <code>conan install</code> command.</p><p>In the examples below, for GPU support add <code>-o gpu=True</code> to the <code>conan install</code> command.</p><blockquote><p>[!NOTE] The tensorflow-lite conan package disables GPU by default and as such these</p></blockquote><p>steps will not work currently. I have patched the recipe locally to enable GPU support and will make this available on Conan Center or another repository soon. In the mean time, my custom recipe can be be used as outlined <a href="https://github.com/neuralize-ai/tensorflow-lite-conan">here</a>. If you have previously <code>conan install</code>ed, remove the existing TFLite package(s) using <code>conan remove &quot;tensorflow-lite&quot;</code>. Make sure to create the TFLite package version that is required in <a href="/conanfile.py">conanfile</a>.</p><section id="autotoc_md9"><h2><a href="#autotoc_md9">Unix</a></h2><p>Run all examples using one of the following methods from the project root directory.</p><p>For <code>Debug</code>:</p><pre class="m-code">conan<span class="w"> </span>install<span class="w"> </span>.<span class="w"> </span>-b<span class="w"> </span>missing<span class="w"> </span>-s<span class="w"> </span><span class="nv">build_type</span><span class="o">=</span>Debug<span class="w"> </span>-o<span class="w"> </span><span class="nv">examples</span><span class="o">=</span>True
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<span class="p">}</span></pre><p>For MacOS, replace &quot;Unix Makefiles&quot; with &quot;Xcode&quot;.</p><blockquote><p>[!NOTE] Examples require additional dependencies to the main library. As such, it is</p></blockquote><p>required to supply <code>-o examples=True</code> to the <code>conan install</code> command.</p><p>In the examples below, for GPU support add <code>-o gpu=True</code> to the <code>conan install</code> command.</p><blockquote><p>[!NOTE] The tensorflow-lite conan package disables GPU by default and as such these</p></blockquote><p>steps will not work currently. I have patched the recipe locally to enable GPU support and will make this available on Conan Center or another repository soon. In the mean time, my custom recipe can be be used as outlined <a href="https://github.com/neuralize-ai/tensorflow-lite-conan">here</a>. If you have previously <code>conan install</code>ed, remove the existing TFLite package(s) using <code>conan remove &quot;tensorflow-lite&quot;</code>. Make sure to create the TFLite package version that is required in <a href="/conanfile.py">conanfile</a>.</p><p>GPU support requires a functioning OpenCL installation. Refer to your OS documentation for the steps for setting this up correctly for your GPU vendor.</p><section id="autotoc_md9"><h2><a href="#autotoc_md9">Unix</a></h2><p>Run all examples using one of the following methods from the project root directory.</p><p>For <code>Debug</code>:</p><pre class="m-code">conan<span class="w"> </span>install<span class="w"> </span>.<span class="w"> </span>-b<span class="w"> </span>missing<span class="w"> </span>-s<span class="w"> </span><span class="nv">build_type</span><span class="o">=</span>Debug<span class="w"> </span>-o<span class="w"> </span><span class="nv">examples</span><span class="o">=</span>True
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cmake<span class="w"> </span>--preset<span class="o">=</span>dev
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cmake<span class="w"> </span>--build<span class="w"> </span>--preset<span class="o">=</span>dev<span class="w"> </span>-t<span class="w"> </span>run-examples</pre><p>For <code>Release</code>:</p><pre class="m-code">conan<span class="w"> </span>install<span class="w"> </span>.<span class="w"> </span>-b<span class="w"> </span>missing<span class="w"> </span>-o<span class="w"> </span><span class="nv">examples</span><span class="o">=</span>True
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cmake<span class="w"> </span>--preset<span class="o">=</span>rel

namespaceedge.html

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<dt>
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<span class="m-doc-wrap-bumper">enum class <a href="#a35bf76c8f50658546b6dc23d80b9798c" class="m-doc">TensorType</a>: uint8_t { </span><span class="m-doc-wrap"><a href="#a35bf76c8f50658546b6dc23d80b9798ca40aa75f8e8cfdf7b660c5620e953229f" class="m-doc">UNSUPPORTED</a>,
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<a href="#a35bf76c8f50658546b6dc23d80b9798ca0323d2829f046f18b7dbcc0f58f941bc" class="m-doc">NOTYPE</a>,
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<a href="#a35bf76c8f50658546b6dc23d80b9798ca967d280b5c16d95f2947647dd2ca6cc2" class="m-doc">FLOAT32</a>,
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<a href="#a35bf76c8f50658546b6dc23d80b9798cac49f280a5ad551ccc77be0b01a2f386a" class="m-doc">FLOAT16</a>,
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<a href="#a35bf76c8f50658546b6dc23d80b9798ca6495adba09844fac8eeb0aba86e6f1bf" class="m-doc">INT32</a>,
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<a href="#a35bf76c8f50658546b6dc23d80b9798ca17266551181f69a1b4a3ad5c9e270afc" class="m-doc">UINT32</a>,
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<a href="#a35bf76c8f50658546b6dc23d80b9798ca967d280b5c16d95f2947647dd2ca6cc2" class="m-doc">FLOAT32</a>,
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<a href="#a35bf76c8f50658546b6dc23d80b9798caee9d73311ff0658494edfff14c3ec1e3" class="m-doc">INT8</a>,
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<a href="#a35bf76c8f50658546b6dc23d80b9798caecfc091ed2a607335524c8389cfa41b5" class="m-doc">UINT8</a> }</span>
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<a href="#a35bf76c8f50658546b6dc23d80b9798ca5f90af42814c0a419d715d43ae54fd7a" class="m-doc">INT16</a>,
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<a href="#a35bf76c8f50658546b6dc23d80b9798ca6495adba09844fac8eeb0aba86e6f1bf" class="m-doc">INT32</a>,
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<a href="#a35bf76c8f50658546b6dc23d80b9798caecfc091ed2a607335524c8389cfa41b5" class="m-doc">UINT8</a>,
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<a href="#a35bf76c8f50658546b6dc23d80b9798ca48d8f1a723d44ff4a87db1bb6c551c62" class="m-doc">UINT16</a>,
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<a href="#a35bf76c8f50658546b6dc23d80b9798ca17266551181f69a1b4a3ad5c9e270afc" class="m-doc">UINT32</a> }</span>
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</dt>
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<dd>Enum class representing different types of tensors.</dd>
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</dl>

searchdata-v1.js

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