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diff --git a/21.02/classarmnn_1_1_quantizer_strategy.xhtml b/21.02/classarmnn_1_1_quantizer_strategy.xhtml new file mode 100644 index 0000000000..d6dfb6c2f8 --- /dev/null +++ b/21.02/classarmnn_1_1_quantizer_strategy.xhtml @@ -0,0 +1,368 @@ +<!-- Copyright (c) 2020 ARM Limited. --> +<!-- --> +<!-- SPDX-License-Identifier: MIT --> +<!-- --> +<!-- HTML header for doxygen 1.8.13--> +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> +<html xmlns="http://www.w3.org/1999/xhtml"> +<head> +<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/> +<meta http-equiv="X-UA-Compatible" content="IE=9"/> +<meta name="generator" content="Doxygen 1.8.13"/> +<meta name="robots" content="NOINDEX, NOFOLLOW" /> +<meta name="viewport" content="width=device-width, initial-scale=1"/> +<title>ArmNN: QuantizerStrategy Class Reference</title> +<link href="tabs.css" rel="stylesheet" type="text/css"/> +<script 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id="side-nav" class="ui-resizable side-nav-resizable"> + <div id="nav-tree"> + <div id="nav-tree-contents"> + <div id="nav-sync" class="sync"></div> + </div> + </div> + <div id="splitbar" style="-moz-user-select:none;" + class="ui-resizable-handle"> + </div> +</div> +<script type="text/javascript"> +$(document).ready(function(){initNavTree('classarmnn_1_1_quantizer_strategy.xhtml','');}); +</script> +<div id="doc-content"> +<!-- window showing the filter options --> +<div id="MSearchSelectWindow" + onmouseover="return searchBox.OnSearchSelectShow()" + onmouseout="return searchBox.OnSearchSelectHide()" + onkeydown="return searchBox.OnSearchSelectKey(event)"> +</div> + +<!-- iframe showing the search results (closed by default) --> +<div id="MSearchResultsWindow"> +<iframe src="javascript:void(0)" frameborder="0" + name="MSearchResults" id="MSearchResults"> +</iframe> +</div> + +<div class="header"> + <div class="summary"> +<a href="#pub-methods">Public Member Functions</a> | +<a href="classarmnn_1_1_quantizer_strategy-members.xhtml">List of all members</a> </div> + <div class="headertitle"> +<div class="title">QuantizerStrategy Class Reference</div> </div> +</div><!--header--> +<div class="contents"> + +<p><code>#include <<a class="el" href="_quantizer_strategy_8hpp_source.xhtml">QuantizerStrategy.hpp</a>></code></p> +<div class="dynheader"> +Inheritance diagram for QuantizerStrategy:</div> +<div class="dyncontent"> + <div class="center"> + <img src="classarmnn_1_1_quantizer_strategy.png" usemap="#QuantizerStrategy_map" alt=""/> + <map id="QuantizerStrategy_map" name="QuantizerStrategy_map"> +<area href="classarmnn_1_1_i_strategy.xhtml" alt="IStrategy" shape="rect" coords="0,0,114,24"/> +</map> + </div></div> +<table class="memberdecls"> +<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a> +Public Member Functions</h2></td></tr> +<tr class="memitem:a7f9b9f65046b39e047e674ab5cd7a18f"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_quantizer_strategy.xhtml#a7f9b9f65046b39e047e674ab5cd7a18f">QuantizerStrategy</a> (const <a class="el" href="classarmnn_1_1_range_tracker.xhtml">RangeTracker</a> &rangeTracker, const <a class="el" href="structarmnn_1_1_i_quantization_scheme.xhtml">IQuantizationScheme</a> *quantizationScheme, bool preserveType)</td></tr> +<tr class="separator:a7f9b9f65046b39e047e674ab5cd7a18f"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:a5869ec621b21f4a3841a08db126d1527"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_quantizer_strategy.xhtml#a5869ec621b21f4a3841a08db126d1527">~QuantizerStrategy</a> ()=default</td></tr> +<tr class="separator:a5869ec621b21f4a3841a08db126d1527"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:ab79e40d3d0c6f4c9e24534f251306add"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_quantizer_strategy.xhtml#ab79e40d3d0c6f4c9e24534f251306add">ExecuteStrategy</a> (const <a class="el" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> *layer, const <a class="el" href="structarmnn_1_1_base_descriptor.xhtml">BaseDescriptor</a> &descriptor, const std::vector< <a class="el" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> > &constants, const char *name, const <a class="el" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> id) override</td></tr> +<tr class="separator:ab79e40d3d0c6f4c9e24534f251306add"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:ae25c71d0c8fceed87f0b84fc032f71c8"><td class="memItemLeft" align="right" valign="top"><a class="el" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_quantizer_strategy.xhtml#ae25c71d0c8fceed87f0b84fc032f71c8">RetrieveFinalNetwork</a> ()</td></tr> +<tr class="memdesc:ae25c71d0c8fceed87f0b84fc032f71c8"><td class="mdescLeft"> </td><td class="mdescRight">Extract the quantized network. <a href="#ae25c71d0c8fceed87f0b84fc032f71c8">More...</a><br /></td></tr> +<tr class="separator:ae25c71d0c8fceed87f0b84fc032f71c8"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="inherit_header pub_methods_classarmnn_1_1_i_strategy"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classarmnn_1_1_i_strategy')"><img src="closed.png" alt="-"/> Public Member Functions inherited from <a class="el" href="classarmnn_1_1_i_strategy.xhtml">IStrategy</a></td></tr> +<tr class="memitem:adf2f7effbf860b32e9a4ef2a73f84190 inherit pub_methods_classarmnn_1_1_i_strategy"><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_i_strategy.xhtml#adf2f7effbf860b32e9a4ef2a73f84190">FinishStrategy</a> ()</td></tr> +<tr class="separator:adf2f7effbf860b32e9a4ef2a73f84190 inherit pub_methods_classarmnn_1_1_i_strategy"><td class="memSeparator" colspan="2"> </td></tr> +</table><table class="memberdecls"> +<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="inherited"></a> +Additional Inherited Members</h2></td></tr> +<tr class="inherit_header pro_methods_classarmnn_1_1_i_strategy"><td colspan="2" onclick="javascript:toggleInherit('pro_methods_classarmnn_1_1_i_strategy')"><img src="closed.png" alt="-"/> Protected Member Functions inherited from <a class="el" href="classarmnn_1_1_i_strategy.xhtml">IStrategy</a></td></tr> +<tr class="memitem:a196ae18353afdcea7f8ab5c4cc7b3a6b inherit pro_methods_classarmnn_1_1_i_strategy"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_i_strategy.xhtml#a196ae18353afdcea7f8ab5c4cc7b3a6b">IStrategy</a> ()</td></tr> +<tr class="separator:a196ae18353afdcea7f8ab5c4cc7b3a6b inherit pro_methods_classarmnn_1_1_i_strategy"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:a6f538e1051453290c8b8bb61e417bce1 inherit pro_methods_classarmnn_1_1_i_strategy"><td class="memItemLeft" align="right" valign="top">virtual </td><td class="memItemRight" valign="bottom"><a class="el" href="classarmnn_1_1_i_strategy.xhtml#a6f538e1051453290c8b8bb61e417bce1">~IStrategy</a> ()</td></tr> +<tr class="separator:a6f538e1051453290c8b8bb61e417bce1 inherit pro_methods_classarmnn_1_1_i_strategy"><td class="memSeparator" colspan="2"> </td></tr> +</table> +<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2> +<div class="textblock"> +<p class="definition">Definition at line <a class="el" href="_quantizer_strategy_8hpp_source.xhtml#l00015">15</a> of file <a class="el" href="_quantizer_strategy_8hpp_source.xhtml">QuantizerStrategy.hpp</a>.</p> +</div><h2 class="groupheader">Constructor & Destructor Documentation</h2> +<a id="a7f9b9f65046b39e047e674ab5cd7a18f"></a> +<h2 class="memtitle"><span class="permalink"><a href="#a7f9b9f65046b39e047e674ab5cd7a18f">◆ </a></span>QuantizerStrategy()</h2> + +<div class="memitem"> +<div class="memproto"> + <table class="memname"> + <tr> + <td class="memname"><a class="el" href="classarmnn_1_1_quantizer_strategy.xhtml">QuantizerStrategy</a> </td> + <td>(</td> + <td class="paramtype">const <a class="el" href="classarmnn_1_1_range_tracker.xhtml">RangeTracker</a> & </td> + <td class="paramname"><em>rangeTracker</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const <a class="el" href="structarmnn_1_1_i_quantization_scheme.xhtml">IQuantizationScheme</a> * </td> + <td class="paramname"><em>quantizationScheme</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">bool </td> + <td class="paramname"><em>preserveType</em> </td> + </tr> + <tr> + <td></td> + <td>)</td> + <td></td><td></td> + </tr> + </table> +</div><div class="memdoc"> + +<p class="definition">Definition at line <a class="el" href="_quantizer_strategy_8cpp_source.xhtml#l00012">12</a> of file <a class="el" href="_quantizer_strategy_8cpp_source.xhtml">QuantizerStrategy.cpp</a>.</p> + +<p class="reference">References <a class="el" href="_assert_8hpp_source.xhtml#l00014">ARMNN_ASSERT</a>, <a class="el" href="_assert_8hpp_source.xhtml#l00015">ARMNN_ASSERT_MSG</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::Boolean</a>, <a class="el" href="_layer_8cpp_source.xhtml#l00130">OutputSlot::CalculateIndexOnOwner()</a>, <a class="el" href="structarmnn_1_1_i_quantization_scheme.xhtml#a6a5561395e9693f02258b49dfcc009b4">IQuantizationScheme::ComputeScheme()</a>, <a class="el" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">IOutputSlot::Connect()</a>, <a class="el" href="_layer_8hpp_source.xhtml#l00055">InputSlot::GetConnectedOutputSlot()</a>, <a class="el" href="structarmnn_1_1_i_quantization_scheme.xhtml#ad23181f9f8fcc85758f62c49fc7ca23f">IQuantizationScheme::GetDataType()</a>, <a class="el" href="_tensor_8hpp_source.xhtml#l00194">TensorInfo::GetDataType()</a>, <a class="el" href="classarmnn_1_1_i_connectable_layer.xhtml#afb5e65c770f6cee222db8af7581541a6">IConnectableLayer::GetGuid()</a>, <a class="el" href="_layer_8hpp_source.xhtml#l00322">Layer::GetGuid()</a>, <a class="el" href="_tensor_8hpp_source.xhtml#l00282">BaseTensor< MemoryType >::GetInfo()</a>, <a class="el" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">IConnectableLayer::GetInputSlot()</a>, <a class="el" href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">IConnectableLayer::GetNumInputSlots()</a>, <a class="el" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">IConnectableLayer::GetOutputSlot()</a>, <a class="el" href="_layer_8hpp_source.xhtml#l00115">OutputSlot::GetOwningLayer()</a>, <a class="el" href="_tensor_8cpp_source.xhtml#l00452">TensorInfo::GetQuantizationScale()</a>, <a class="el" href="_range_tracker_8cpp_source.xhtml#l00029">RangeTracker::GetRange()</a>, <a class="el" href="_layer_8cpp_source.xhtml#l00063">OutputSlot::GetTensorInfo()</a>, <a class="el" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>, <a class="el" href="_numeric_cast_8hpp_source.xhtml#l00035">armnn::numeric_cast()</a>, <a class="el" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">IOutputSlot::SetTensorInfo()</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::Signed32</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6ae1b3c9c6087a93b07c83e0b04f377a8d">armnn::Signed64</a>, and <a class="el" href="_optional_8hpp_source.xhtml#l00146">OptionalReferenceSwitch< std::is_reference< T >::value, T >::value()</a>.</p> +<div class="fragment"><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>  : m_Ranges(rangeTracker)</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>  , m_QuantizedNetwork(<a class="code" href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">INetwork::Create</a>())</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  , m_QuantizationScheme(quantizationScheme)</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  , m_PreserveType(preserveType)</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> {</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> }</div><div class="ttc" id="classarmnn_1_1_i_network_xhtml_a464f0ff87b1aabf71febaa71321dd40b"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a464f0ff87b1aabf71febaa71321dd40b">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create(NetworkOptions networkOptions={})</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00510">Network.cpp:510</a></div></div> +</div><!-- fragment --> +</div> +</div> +<a id="a5869ec621b21f4a3841a08db126d1527"></a> +<h2 class="memtitle"><span class="permalink"><a href="#a5869ec621b21f4a3841a08db126d1527">◆ </a></span>~QuantizerStrategy()</h2> + +<div class="memitem"> +<div class="memproto"> +<table class="mlabels"> + <tr> + <td class="mlabels-left"> + <table class="memname"> + <tr> + <td class="memname">~<a class="el" href="classarmnn_1_1_quantizer_strategy.xhtml">QuantizerStrategy</a> </td> + <td>(</td> + <td class="paramname"></td><td>)</td> + <td></td> + </tr> + </table> + </td> + <td class="mlabels-right"> +<span class="mlabels"><span class="mlabel">default</span></span> </td> + </tr> +</table> +</div><div class="memdoc"> + +</div> +</div> +<h2 class="groupheader">Member Function Documentation</h2> +<a id="ab79e40d3d0c6f4c9e24534f251306add"></a> +<h2 class="memtitle"><span class="permalink"><a href="#ab79e40d3d0c6f4c9e24534f251306add">◆ </a></span>ExecuteStrategy()</h2> + +<div class="memitem"> +<div class="memproto"> +<table class="mlabels"> + <tr> + <td class="mlabels-left"> + <table class="memname"> + <tr> + <td class="memname">void ExecuteStrategy </td> + <td>(</td> + <td class="paramtype">const <a class="el" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> * </td> + <td class="paramname"><em>layer</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const <a class="el" href="structarmnn_1_1_base_descriptor.xhtml">BaseDescriptor</a> & </td> + <td class="paramname"><em>descriptor</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const std::vector< <a class="el" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> > & </td> + <td class="paramname"><em>constants</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const char * </td> + <td class="paramname"><em>name</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">const <a class="el" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> </td> + <td class="paramname"><em>id</em> </td> + </tr> + <tr> + <td></td> + <td>)</td> + <td></td><td></td> + </tr> + </table> + </td> + <td class="mlabels-right"> +<span class="mlabels"><span class="mlabel">override</span><span class="mlabel">virtual</span></span> </td> + </tr> +</table> +</div><div class="memdoc"> + +<p>Implements <a class="el" href="classarmnn_1_1_i_strategy.xhtml#aad5bb4d8050fd428ff03ae6d81e3014c">IStrategy</a>.</p> + +<p class="definition">Definition at line <a class="el" href="_quantizer_strategy_8cpp_source.xhtml#l00118">118</a> of file <a class="el" href="_quantizer_strategy_8cpp_source.xhtml">QuantizerStrategy.cpp</a>.</p> + +<p class="reference">References <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">armnn::Activation</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a7c5531bbefed0945814f874baf9e0e0f">armnn::Addition</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2139684546b147c106b329f41547640c">armnn::ArgMinMax</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae4743c3ec15d1d84169b17264634692e">armnn::BatchNormalization</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a6ee06c6045d0c5b6565a247955ef0fc2">armnn::BatchToSpaceNd</a>, <a class="el" href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f">armnn::Bilinear</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af6c0e3a1c3cfabd32ae8d3ae741fcf0a">armnn::Comparison</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae20f0f2826a6549809f050b86274567f">armnn::Concat</a>, <a class="el" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">IOutputSlot::Connect()</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4acb17869fe51048b5a5c4c6106551a255">armnn::Constant</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4adb033d2f81b68f9a17e8f62de69fed4a">armnn::Convolution2d</a>, <a class="el" href="_network_quantizer_utils_8cpp_source.xhtml#l00015">armnn::CreateQuantizedConst()</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a731729ad1b2c0eb9399b62c770b3482d">armnn::DepthToSpace</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af97adbfc88b7012a0243215b1076e7e7">armnn::DepthwiseConvolution2d</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4abcd30d7ea97ad20c2cddc0f47e6b70c7">armnn::ElementwiseUnary</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4adb3e3f51c9107e26c9bccf9a188ce2ed">armnn::Fill</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::Float16</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4acab78faff25393e9defd1911cb58133e">armnn::FullyConnected</a>, <a class="el" href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">IInputSlot::GetConnection()</a>, <a class="el" href="_tensor_8hpp_source.xhtml#l00194">TensorInfo::GetDataType()</a>, <a class="el" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">IConnectableLayer::GetInputSlot()</a>, <a class="el" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">IConnectableLayer::GetOutputSlot()</a>, <a class="el" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">IOutputSlot::GetTensorInfo()</a>, <a class="el" href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">IConnectableLayer::GetType()</a>, <a class="el" href="_optional_8hpp_source.xhtml#l00053">OptionalBase::has_value()</a>, <a class="el" href="_ignore_unused_8hpp_source.xhtml#l00014">armnn::IgnoreUnused()</a>, <a class="el" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::Input</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a21baa4498161d195f5bb2e3627344ba4">armnn::InstanceNormalization</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ac21dbda57d88c21ec9857f5d1522c488">armnn::LogSoftmax</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00786">ResizeBilinearDescriptor::m_DataLayout</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00823">ResizeDescriptor::m_DataLayout</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00821">ResizeDescriptor::m_Method</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00784">ResizeBilinearDescriptor::m_TargetHeight</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00818">ResizeDescriptor::m_TargetHeight</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00782">ResizeBilinearDescriptor::m_TargetWidth</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00816">ResizeDescriptor::m_TargetWidth</a>, <a class="el" href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0a3d6c9ac08ada31c184094bbc67afe00d">armnn::Mean</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a27d1a1f7b7c2180e5b20ce9e3d00e2dd">armnn::Multiplication</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aac61f2e17250a818dee4d12b112aa88f">armnn::Normalization</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::Output</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ade43468adaf6acb2c38ebc0c1176f82f">armnn::Pad</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7">armnn::Permute</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ad662867a41bfb30b9f75dda2b5849001">armnn::Pooling2d</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a0c5967f09e0669c840ebb1ed0da85e32">armnn::Prelu</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa7c59ccedc6a3bd90c17f3b990afefad">armnn::Reshape</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a9d723d04c40bfd81835c0766a698cf63">armnn::Resize</a>, <a class="el" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">IOutputSlot::SetTensorInfo()</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ad140d37ad98c12ccd8e1c432f548bcdb">armnn::Slice</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a31d953b9d49a6b4378f45097047976d0">armnn::Softmax</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a337c392144dca0d18290c6b4711a2279">armnn::SpaceToBatchNd</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a5e7ff12da912dc79e7e547281823fa4a">armnn::SpaceToDepth</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a41cb9b797ebc6f6f6314e3ded935f4cf">armnn::Splitter</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2187e1021a911b3807cc1bea2eb1a9ca">armnn::Stack</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa31904f2b3479b5a00137fd985974b4d">armnn::StridedSlice</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a6eb8b8b560161603402c0238b3a7d8b0">armnn::Subtraction</a>, <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aaf70b1ac863830a4e1ce6268c8399f54">armnn::Transpose</a>, and <a class="el" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a71b23d26c0f5d20416d6c77754f9806a">armnn::TransposeConvolution2d</a>.</p> +<div class="fragment"><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> {</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(<span class="keywordtype">id</span>);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> </div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  IConnectableLayer* newLayer;</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> </div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordflow">switch</span> (layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">GetType</a>())</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  {</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a7c5531bbefed0945814f874baf9e0e0f">armnn::LayerType::Addition</a> :</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  {</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  newLayer = m_QuantizedNetwork->AddAdditionLayer(name);</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  }</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">armnn::LayerType::Activation</a> :</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  {</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keyword">const</span> ActivationDescriptor& activationDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span>ActivationDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  newLayer = m_QuantizedNetwork->AddActivationLayer(activationDescriptor, name);</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  }</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2139684546b147c106b329f41547640c">armnn::LayerType::ArgMinMax</a> :</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  {</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  ArgMinMaxDescriptor argMinMaxDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span>ArgMinMaxDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  newLayer = m_QuantizedNetwork->AddArgMinMaxLayer(argMinMaxDescriptor, name);</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  }</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae4743c3ec15d1d84169b17264634692e">armnn::LayerType::BatchNormalization</a> :</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  {</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> </div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  BatchNormalizationDescriptor batchNormalizationDescriptor =</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span>BatchNormalizationDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  std::vector<uint8_t> meanBacking;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  ConstTensor qMean = <a class="code" href="namespacearmnn.xhtml#a310dd804fd70eadb1e8854325e63f0bd">CreateQuantizedConst</a>(constants[0], meanBacking);</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> </div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  std::vector<uint8_t> varianceBacking;</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  ConstTensor qVariance = <a class="code" href="namespacearmnn.xhtml#a310dd804fd70eadb1e8854325e63f0bd">CreateQuantizedConst</a>(constants[1], varianceBacking);</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> </div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  std::vector<uint8_t> betaBacking;</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  ConstTensor qBeta = <a class="code" href="namespacearmnn.xhtml#a310dd804fd70eadb1e8854325e63f0bd">CreateQuantizedConst</a>(constants[2], betaBacking);</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> </div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  std::vector<uint8_t> gammaBacking;</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  ConstTensor qGamma = <a class="code" href="namespacearmnn.xhtml#a310dd804fd70eadb1e8854325e63f0bd">CreateQuantizedConst</a>(constants[3], gammaBacking);</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span> </div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  newLayer = m_QuantizedNetwork->AddBatchNormalizationLayer(batchNormalizationDescriptor,</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  qMean,</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  qVariance,</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  qBeta,</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  qGamma,</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  name);</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  }</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a6ee06c6045d0c5b6565a247955ef0fc2">armnn::LayerType::BatchToSpaceNd</a> :</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  {</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  BatchToSpaceNdDescriptor batchToSpaceNdDescriptor =</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span>BatchToSpaceNdDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> </div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  newLayer = m_QuantizedNetwork->AddBatchToSpaceNdLayer(batchToSpaceNdDescriptor, name);</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  }</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af6c0e3a1c3cfabd32ae8d3ae741fcf0a">armnn::LayerType::Comparison</a> :</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  {</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  ComparisonDescriptor comparisonDescriptor =<span class="keyword">static_cast<</span><span class="keyword">const </span>ComparisonDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  newLayer = m_QuantizedNetwork->AddComparisonLayer(comparisonDescriptor, name);</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  }</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae20f0f2826a6549809f050b86274567f">armnn::LayerType::Concat</a> :</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  {</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  OriginsDescriptor originsDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span>OriginsDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  newLayer = m_QuantizedNetwork->AddConcatLayer(originsDescriptor, name);</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  }</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4acb17869fe51048b5a5c4c6106551a255">armnn::LayerType::Constant</a> :</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  {</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  std::vector<uint8_t> inputBacking;</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  ConstTensor qInput = <a class="code" href="namespacearmnn.xhtml#a310dd804fd70eadb1e8854325e63f0bd">CreateQuantizedConst</a>(constants[0], inputBacking);</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> </div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  newLayer = m_QuantizedNetwork->AddConstantLayer(qInput, name);</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  }</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4adb033d2f81b68f9a17e8f62de69fed4a">armnn::LayerType::Convolution2d</a> :</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  {</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a> biases = constants.size() == 1 ?</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a>{} :</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a>(constants[1]);</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> </div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  std::vector<uint8_t> weightsBacking;</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  ConstTensor qWeights = <a class="code" href="namespacearmnn.xhtml#a310dd804fd70eadb1e8854325e63f0bd">CreateQuantizedConst</a>(constants[0], weightsBacking);</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  Optional<ConstTensor> optionalQBiases;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  std::vector<int32_t> biasesBacking;</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> </div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keywordflow">if</span> (biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>())</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  {</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  ConstTensor qBiases = CreateQuantizedBias(layer, qWeights, biases, biasesBacking);</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  optionalQBiases = Optional<ConstTensor>(qBiases);</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  }</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  Convolution2dDescriptor convolution2dDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span>Convolution2dDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> </div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  newLayer = m_QuantizedNetwork->AddConvolution2dLayer(convolution2dDescriptor,</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  qWeights,</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  optionalQBiases,</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  name);</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  }</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a731729ad1b2c0eb9399b62c770b3482d">armnn::LayerType::DepthToSpace</a> :</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  {</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <a class="code" href="namespacearmnn.xhtml#a3647f60510bc8ddaced01c51b0ee8714">DepthToSpaceDescriptor</a> depthToSpaceDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="namespacearmnn.xhtml#a3647f60510bc8ddaced01c51b0ee8714">DepthToSpaceDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> </div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  newLayer = m_QuantizedNetwork->AddDepthToSpaceLayer(depthToSpaceDescriptor, name);</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  }</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af97adbfc88b7012a0243215b1076e7e7">armnn::LayerType::DepthwiseConvolution2d</a> :</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  {</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  DepthwiseConvolution2dDescriptor depthwiseConvolution2dDescriptor =</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span>DepthwiseConvolution2dDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span> </div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a> biases = constants.size() == 1 ?</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a>{} :</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a>(constants[1]);</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> </div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  std::vector<uint8_t> weightsBacking;</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  ConstTensor qWeights = <a class="code" href="namespacearmnn.xhtml#a310dd804fd70eadb1e8854325e63f0bd">CreateQuantizedConst</a>(constants[0], weightsBacking);</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  Optional<ConstTensor> optionalQBiases;</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  std::vector<int32_t> biasesBacking;</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span> </div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="keywordflow">if</span> (biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>())</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  {</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  ConstTensor qBiases = CreateQuantizedBias(layer, qWeights, biases, biasesBacking);</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  optionalQBiases = Optional<ConstTensor>(qBiases);</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  }</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> </div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  newLayer = m_QuantizedNetwork->AddDepthwiseConvolution2dLayer(</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  depthwiseConvolution2dDescriptor,</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  qWeights,</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  optionalQBiases,</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  name);</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  }</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4abcd30d7ea97ad20c2cddc0f47e6b70c7">armnn::LayerType::ElementwiseUnary</a> :</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  {</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  ElementwiseUnaryDescriptor elementwiseUnaryDescriptor =</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span>ElementwiseUnaryDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> </div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  newLayer = m_QuantizedNetwork->AddElementwiseUnaryLayer(elementwiseUnaryDescriptor, name);</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  }</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4adb3e3f51c9107e26c9bccf9a188ce2ed">armnn::LayerType::Fill</a> :</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  {</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  FillDescriptor fillDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span>FillDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> </div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  newLayer = m_QuantizedNetwork->AddFillLayer(fillDescriptor, name);</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  }</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4acab78faff25393e9defd1911cb58133e">armnn::LayerType::FullyConnected</a> :</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  {</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  FullyConnectedDescriptor fullyConnectedDescriptor =</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span>FullyConnectedDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span> </div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a> biases = constants.size() == 1 ?</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a>{} :</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a>(constants[1]);</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> </div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  std::vector<uint8_t> weightsBacking;</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  ConstTensor qWeights = <a class="code" href="namespacearmnn.xhtml#a310dd804fd70eadb1e8854325e63f0bd">CreateQuantizedConst</a>(constants[0], weightsBacking);</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  Optional<ConstTensor> optionalQBiases;</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  std::vector<int32_t> biasesBacking;</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span> </div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="keywordflow">if</span> (biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>())</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  {</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  ConstTensor qBiases = CreateQuantizedBias(layer, qWeights, biases, biasesBacking);</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  optionalQBiases = Optional<ConstTensor>(qBiases);</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  }</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span> </div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  newLayer = m_QuantizedNetwork->AddFullyConnectedLayer(fullyConnectedDescriptor,</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  qWeights,</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  optionalQBiases,</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  name);</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  }</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a> :</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  {</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dataType = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>();</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  IConnectableLayer* inputLayer = m_QuantizedNetwork->AddInputLayer(<span class="keywordtype">id</span>, name);</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> </div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keywordflow">if</span> (m_PreserveType && (dataType == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a> || dataType == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>))</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  {</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  IConnectableLayer* quantizeLayer = m_QuantizedNetwork->AddQuantizeLayer();</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  inputLayer->GetOutputSlot(0).Connect(quantizeLayer->GetInputSlot(0));</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  inputLayer->GetOutputSlot(0).SetTensorInfo(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>());</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  RecordLayer(layer, quantizeLayer);</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  }</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  {</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  RecordLayer(layer, inputLayer);</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  }</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  }</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a21baa4498161d195f5bb2e3627344ba4">armnn::LayerType::InstanceNormalization</a> :</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  {</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  InstanceNormalizationDescriptor instanceNormalizationDescriptor =</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span>InstanceNormalizationDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span> </div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  newLayer =</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  m_QuantizedNetwork->AddInstanceNormalizationLayer(instanceNormalizationDescriptor, name);</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  }</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ac21dbda57d88c21ec9857f5d1522c488">armnn::LayerType::LogSoftmax</a> :</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  {</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <a class="code" href="namespacearmnn.xhtml#ac14705405cbcdd580df613de6766fe65">LogSoftmaxDescriptor</a> logSoftmaxDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="namespacearmnn.xhtml#ac14705405cbcdd580df613de6766fe65">LogSoftmaxDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span> </div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  newLayer = m_QuantizedNetwork->AddLogSoftmaxLayer(logSoftmaxDescriptor, name);</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  }</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0a3d6c9ac08ada31c184094bbc67afe00d">armnn::LayerType::Mean</a> :</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  {</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  MeanDescriptor meanDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span>MeanDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span> </div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  newLayer = m_QuantizedNetwork->AddMeanLayer(meanDescriptor, name);</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  }</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a27d1a1f7b7c2180e5b20ce9e3d00e2dd">armnn::LayerType::Multiplication</a> :</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  {</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  newLayer = m_QuantizedNetwork->AddMultiplicationLayer(name);</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  }</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aac61f2e17250a818dee4d12b112aa88f">armnn::LayerType::Normalization</a> :</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  {</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  NormalizationDescriptor normalizationDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span>NormalizationDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span> </div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  newLayer = m_QuantizedNetwork->AddNormalizationLayer(normalizationDescriptor, name);</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  }</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a> :</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  {</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="keyword">const</span> TensorInfo& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a> = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">GetConnection</a>()-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>& dataType = info.GetDataType();</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  newLayer = m_QuantizedNetwork->AddOutputLayer(<span class="keywordtype">id</span>, name);</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span> </div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <span class="keywordflow">if</span> (m_PreserveType && (dataType == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a> || dataType == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>))</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  {</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  IConnectableLayer* dequantizeLayer = m_QuantizedNetwork->AddDequantizeLayer();</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  RecordLayer(layer, dequantizeLayer);</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  SetQuantizedInputConnections(layer, dequantizeLayer);</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  dequantizeLayer->GetOutputSlot(0).Connect(newLayer->GetInputSlot(0));</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  dequantizeLayer->GetOutputSlot(0).SetTensorInfo(info);</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  }</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  {</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  }</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  }</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ade43468adaf6acb2c38ebc0c1176f82f">armnn::LayerType::Pad</a> :</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  {</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  PadDescriptor padDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span>PadDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span> </div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  newLayer = m_QuantizedNetwork->AddPadLayer(padDescriptor, name);</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  }</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7">armnn::LayerType::Permute</a> :</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  {</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  PermuteDescriptor permuteDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span>PermuteDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span> </div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  newLayer = m_QuantizedNetwork->AddPermuteLayer(permuteDescriptor, name);</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  }</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ad662867a41bfb30b9f75dda2b5849001">armnn::LayerType::Pooling2d</a> :</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  {</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  Pooling2dDescriptor pooling2dDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span>Pooling2dDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span> </div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  newLayer = m_QuantizedNetwork->AddPooling2dLayer(pooling2dDescriptor, name);</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  }</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a0c5967f09e0669c840ebb1ed0da85e32">armnn::LayerType::Prelu</a> :</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  {</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  newLayer = m_QuantizedNetwork->AddPreluLayer(name);</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  }</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa7c59ccedc6a3bd90c17f3b990afefad">armnn::LayerType::Reshape</a> :</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  {</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  ReshapeDescriptor reshapeDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span>ReshapeDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span> </div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  newLayer = m_QuantizedNetwork->AddReshapeLayer(reshapeDescriptor, name);</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  }</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a9d723d04c40bfd81835c0766a698cf63">armnn::LayerType::Resize</a> :</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  {</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span> </div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  ResizeBilinearDescriptor resizeBilinearDescriptor =</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span>ResizeBilinearDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span> </div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  ResizeDescriptor resizeDescriptor;</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  resizeDescriptor.m_Method = <a class="code" href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f">ResizeMethod::Bilinear</a>;</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  resizeDescriptor.m_TargetWidth = resizeBilinearDescriptor.m_TargetWidth;</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  resizeDescriptor.m_TargetHeight = resizeBilinearDescriptor.m_TargetHeight;</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  resizeDescriptor.m_DataLayout = resizeBilinearDescriptor.m_DataLayout;</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span> </div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  newLayer = m_QuantizedNetwork->AddResizeLayer(resizeDescriptor, name);</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  }</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ad140d37ad98c12ccd8e1c432f548bcdb">armnn::LayerType::Slice</a> :</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  {</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  SliceDescriptor sliceDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span>SliceDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span> </div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  newLayer = m_QuantizedNetwork->AddSliceLayer(sliceDescriptor, name);</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  }</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a31d953b9d49a6b4378f45097047976d0">armnn::LayerType::Softmax</a> :</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  {</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  SoftmaxDescriptor softmaxDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span>SoftmaxDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span> </div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  newLayer = m_QuantizedNetwork->AddSoftmaxLayer(softmaxDescriptor, name);</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  }</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a337c392144dca0d18290c6b4711a2279">armnn::LayerType::SpaceToBatchNd</a> :</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  {</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  SpaceToBatchNdDescriptor spaceToBatchNdDescriptor =</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span>SpaceToBatchNdDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span> </div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  newLayer = m_QuantizedNetwork->AddSpaceToBatchNdLayer(spaceToBatchNdDescriptor, name);</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  }</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a5e7ff12da912dc79e7e547281823fa4a">armnn::LayerType::SpaceToDepth</a> :</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  {</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  SpaceToDepthDescriptor spaceToDepthDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span>SpaceToDepthDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  newLayer = m_QuantizedNetwork->AddSpaceToDepthLayer(spaceToDepthDescriptor, name);</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  }</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a41cb9b797ebc6f6f6314e3ded935f4cf">armnn::LayerType::Splitter</a> :</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  {</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <a class="code" href="namespacearmnn.xhtml#a60291543fe872b795e71e05bcd835fd1">SplitterDescriptor</a> splitterDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span><a class="code" href="namespacearmnn.xhtml#a60291543fe872b795e71e05bcd835fd1">SplitterDescriptor</a>&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  newLayer = m_QuantizedNetwork->AddSplitterLayer(splitterDescriptor, name);</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  }</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2187e1021a911b3807cc1bea2eb1a9ca">armnn::LayerType::Stack</a> :</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  {</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  StackDescriptor stackDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span>StackDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span> </div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  newLayer = m_QuantizedNetwork->AddStackLayer(stackDescriptor, name);</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  }</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa31904f2b3479b5a00137fd985974b4d">armnn::LayerType::StridedSlice</a> :</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  {</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  StridedSliceDescriptor stridedSliceDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span>StridedSliceDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span> </div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  newLayer = m_QuantizedNetwork->AddStridedSliceLayer(stridedSliceDescriptor, name);</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  }</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a6eb8b8b560161603402c0238b3a7d8b0">armnn::LayerType::Subtraction</a> :</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  {</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  newLayer = m_QuantizedNetwork->AddSubtractionLayer( name);</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  }</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a71b23d26c0f5d20416d6c77754f9806a">armnn::LayerType::TransposeConvolution2d</a> :</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  {</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span> </div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a> biases = constants.size() == 1 ?</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a>{} :</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<ConstTensor></a>(constants[1]);</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <span class="comment">// quantize weights</span></div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  std::vector<uint8_t> weightsBacking;</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  ConstTensor qWeights = <a class="code" href="namespacearmnn.xhtml#a310dd804fd70eadb1e8854325e63f0bd">CreateQuantizedConst</a>(constants[0], weightsBacking);</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span> </div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="comment">// quantize biases</span></div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  std::vector<int32_t> biasesBacking;</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  Optional<ConstTensor> optionalQBiases;</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <span class="keywordflow">if</span> (biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>())</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  {</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  ConstTensor qBiases = CreateQuantizedBias(layer, qWeights, biases, biasesBacking);</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  optionalQBiases = Optional<ConstTensor>(qBiases);</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  }</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span> </div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  TransposeConvolution2dDescriptor transposeConvolution2dDescriptor =</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  <span class="keyword">static_cast<</span><span class="keyword">const </span>TransposeConvolution2dDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span> </div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  newLayer = m_QuantizedNetwork->AddTransposeConvolution2dLayer(transposeConvolution2dDescriptor,</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  qWeights,</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  optionalQBiases,</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  name);</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  }</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aaf70b1ac863830a4e1ce6268c8399f54">armnn::LayerType::Transpose</a> :</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  {</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  TransposeDescriptor transposeDescriptor = <span class="keyword">static_cast<</span><span class="keyword">const </span>TransposeDescriptor&<span class="keyword">></span>(descriptor);</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span> </div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  newLayer = m_QuantizedNetwork->AddTransposeLayer(transposeDescriptor, name);</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  }</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  {</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <span class="keywordflow">throw</span> UnimplementedException(<span class="stringliteral">"Unimplemented layer encountered"</span>);</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  }</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  }</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  RecordLayer(layer, newLayer);</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  SetQuantizedInputConnections(layer, newLayer);</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span> }</div><div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a71b23d26c0f5d20416d6c77754f9806a"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a71b23d26c0f5d20416d6c77754f9806a">armnn::LayerType::TransposeConvolution2d</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4abcd30d7ea97ad20c2cddc0f47e6b70c7"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4abcd30d7ea97ad20c2cddc0f47e6b70c7">armnn::LayerType::ElementwiseUnary</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4ae20f0f2826a6549809f050b86274567f"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ae20f0f2826a6549809f050b86274567f">armnn::LayerType::Concat</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4af6c0e3a1c3cfabd32ae8d3ae741fcf0a"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af6c0e3a1c3cfabd32ae8d3ae741fcf0a">armnn::LayerType::Comparison</a></div></div> +<div class="ttc" id="classarmnn_1_1_optional_xhtml"><div class="ttname"><a href="classarmnn_1_1_optional.xhtml">armnn::Optional</a></div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00270">Optional.hpp:270</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a6ee06c6045d0c5b6565a247955ef0fc2"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a6ee06c6045d0c5b6565a247955ef0fc2">armnn::LayerType::BatchToSpaceNd</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a2187e1021a911b3807cc1bea2eb1a9ca"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2187e1021a911b3807cc1bea2eb1a9ca">armnn::LayerType::Stack</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aa31904f2b3479b5a00137fd985974b4d"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa31904f2b3479b5a00137fd985974b4d">armnn::LayerType::StridedSlice</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa9a62e70841c4d06dd16306a85700d36">armnn::LayerType::Activation</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a29c2c02a361c9d7028472e5d92cd4a54">armnn::LayerType::Output</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aac61f2e17250a818dee4d12b112aa88f"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aac61f2e17250a818dee4d12b112aa88f">armnn::LayerType::Normalization</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a337c392144dca0d18290c6b4711a2279"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a337c392144dca0d18290c6b4711a2279">armnn::LayerType::SpaceToBatchNd</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4acab78faff25393e9defd1911cb58133e"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4acab78faff25393e9defd1911cb58133e">armnn::LayerType::FullyConnected</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &&...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a27d1a1f7b7c2180e5b20ce9e3d00e2dd"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a27d1a1f7b7c2180e5b20ce9e3d00e2dd">armnn::LayerType::Multiplication</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a21baa4498161d195f5bb2e3627344ba4"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a21baa4498161d195f5bb2e3627344ba4">armnn::LayerType::InstanceNormalization</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a0c5967f09e0669c840ebb1ed0da85e32"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a0c5967f09e0669c840ebb1ed0da85e32">armnn::LayerType::Prelu</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_abc05539fc6e7907f32ef0fb242e3b3b0a3d6c9ac08ada31c184094bbc67afe00d"><div class="ttname"><a href="namespacearmnn.xhtml#abc05539fc6e7907f32ef0fb242e3b3b0a3d6c9ac08ada31c184094bbc67afe00d">armnn::ReduceOperation::Mean</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a6eb8b8b560161603402c0238b3a7d8b0"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a6eb8b8b560161603402c0238b3a7d8b0">armnn::LayerType::Subtraction</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00032">Types.hpp:32</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">armnn::TensorInfo::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00194">Tensor.hpp:194</a></div></div> +<div class="ttc" id="classarmnn_1_1_optional_base_xhtml_a86b749ce2c4bc627fa8a1fcfaf0e314f"><div class="ttname"><a href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">armnn::OptionalBase::has_value</a></div><div class="ttdeci">bool has_value() const noexcept</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00053">Optional.hpp:53</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7">armnn::LayerType::Permute</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a9d723d04c40bfd81835c0766a698cf63"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a9d723d04c40bfd81835c0766a698cf63">armnn::LayerType::Resize</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4adb033d2f81b68f9a17e8f62de69fed4a"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4adb033d2f81b68f9a17e8f62de69fed4a">armnn::LayerType::Convolution2d</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4ade43468adaf6acb2c38ebc0c1176f82f"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ade43468adaf6acb2c38ebc0c1176f82f">armnn::LayerType::Pad</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a31d953b9d49a6b4378f45097047976d0"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a31d953b9d49a6b4378f45097047976d0">armnn::LayerType::Softmax</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aa7c59ccedc6a3bd90c17f3b990afefad"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aa7c59ccedc6a3bd90c17f3b990afefad">armnn::LayerType::Reshape</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4ad662867a41bfb30b9f75dda2b5849001"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ad662867a41bfb30b9f75dda2b5849001">armnn::LayerType::Pooling2d</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a7c5531bbefed0945814f874baf9e0e0f"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a7c5531bbefed0945814f874baf9e0e0f">armnn::LayerType::Addition</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_adceb04ae84c524e4d01881e3754a4d59"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#adceb04ae84c524e4d01881e3754a4d59">armnn::IConnectableLayer::GetType</a></div><div class="ttdeci">virtual LayerType GetType() const =0</div><div class="ttdoc">Returns the armnn::LayerType of this layer. </div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a5e7ff12da912dc79e7e547281823fa4a"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a5e7ff12da912dc79e7e547281823fa4a">armnn::LayerType::SpaceToDepth</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4af97adbfc88b7012a0243215b1076e7e7"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4af97adbfc88b7012a0243215b1076e7e7">armnn::LayerType::DepthwiseConvolution2d</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4acb17869fe51048b5a5c4c6106551a255"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4acb17869fe51048b5a5c4c6106551a255">armnn::LayerType::Constant</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ac14705405cbcdd580df613de6766fe65"><div class="ttname"><a href="namespacearmnn.xhtml#ac14705405cbcdd580df613de6766fe65">armnn::LogSoftmaxDescriptor</a></div><div class="ttdeci">SoftmaxDescriptor LogSoftmaxDescriptor</div><div class="ttdoc">A LogSoftmaxDescriptor for the LogSoftmaxLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00158">Descriptors.hpp:158</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a41cb9b797ebc6f6f6314e3ded935f4cf"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a41cb9b797ebc6f6f6314e3ded935f4cf">armnn::LayerType::Splitter</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f"><div class="ttname"><a href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f">armnn::ResizeMethod::Bilinear</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a2139684546b147c106b329f41547640c"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a2139684546b147c106b329f41547640c">armnn::LayerType::ArgMinMax</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a324118a6721dd6b8a9b9f4e327df2bf5">armnn::LayerType::Input</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a6ec9e0eb66d7d6a01240492a0b18104c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">armnn::IConnectableLayer::GetInputSlot</a></div><div class="ttdeci">virtual const IInputSlot & GetInputSlot(unsigned int index) const =0</div><div class="ttdoc">Get a const input slot handle by slot index. </div></div> +<div class="ttc" id="classarmnn_1_1_i_input_slot_xhtml_a81fbf6103761e55061b62ba989b00f10"><div class="ttname"><a href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">armnn::IInputSlot::GetConnection</a></div><div class="ttdeci">virtual const IOutputSlot * GetConnection() const =0</div></div> 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href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">armnn::IConnectableLayer::GetOutputSlot</a></div><div class="ttdeci">virtual const IOutputSlot & GetOutputSlot(unsigned int index) const =0</div><div class="ttdoc">Get the const output slot handle by slot index. </div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4ac21dbda57d88c21ec9857f5d1522c488"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ac21dbda57d88c21ec9857f5d1522c488">armnn::LayerType::LogSoftmax</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a731729ad1b2c0eb9399b62c770b3482d"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a731729ad1b2c0eb9399b62c770b3482d">armnn::LayerType::DepthToSpace</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4adb3e3f51c9107e26c9bccf9a188ce2ed"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4adb3e3f51c9107e26c9bccf9a188ce2ed">armnn::LayerType::Fill</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a310dd804fd70eadb1e8854325e63f0bd"><div class="ttname"><a href="namespacearmnn.xhtml#a310dd804fd70eadb1e8854325e63f0bd">armnn::CreateQuantizedConst</a></div><div class="ttdeci">ConstTensor CreateQuantizedConst(const ConstTensor &tensor, std::vector< uint8_t > &backing)</div><div class="ttdef"><b>Definition:</b> <a href="_network_quantizer_utils_8cpp_source.xhtml#l00015">NetworkQuantizerUtils.cpp:15</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4aaf70b1ac863830a4e1ce6268c8399f54"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4aaf70b1ac863830a4e1ce6268c8399f54">armnn::LayerType::Transpose</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4ad140d37ad98c12ccd8e1c432f548bcdb"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4ad140d37ad98c12ccd8e1c432f548bcdb">armnn::LayerType::Slice</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a3647f60510bc8ddaced01c51b0ee8714"><div class="ttname"><a href="namespacearmnn.xhtml#a3647f60510bc8ddaced01c51b0ee8714">armnn::DepthToSpaceDescriptor</a></div><div class="ttdeci">SpaceToDepthDescriptor DepthToSpaceDescriptor</div><div class="ttdoc">A DepthToSpaceDescriptor for the DepthToSpaceLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00908">Descriptors.hpp:908</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a60291543fe872b795e71e05bcd835fd1"><div class="ttname"><a href="namespacearmnn.xhtml#a60291543fe872b795e71e05bcd835fd1">armnn::SplitterDescriptor</a></div><div class="ttdeci">ViewsDescriptor SplitterDescriptor</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_fwd_8hpp_source.xhtml#l00057">DescriptorsFwd.hpp:57</a></div></div> +</div><!-- fragment --> +</div> +</div> +<a id="ae25c71d0c8fceed87f0b84fc032f71c8"></a> +<h2 class="memtitle"><span class="permalink"><a href="#ae25c71d0c8fceed87f0b84fc032f71c8">◆ </a></span>RetrieveFinalNetwork()</h2> + +<div class="memitem"> +<div class="memproto"> +<table class="mlabels"> + <tr> + <td class="mlabels-left"> + <table class="memname"> + <tr> + <td class="memname"><a class="el" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> RetrieveFinalNetwork </td> + <td>(</td> + <td class="paramname"></td><td>)</td> + <td></td> + </tr> + </table> + </td> + <td class="mlabels-right"> +<span class="mlabels"><span class="mlabel">inline</span></span> </td> + </tr> +</table> +</div><div class="memdoc"> + +<p>Extract the quantized network. </p> + +<p class="definition">Definition at line <a class="el" href="_quantizer_strategy_8hpp_source.xhtml#l00031">31</a> of file <a class="el" href="_quantizer_strategy_8hpp_source.xhtml">QuantizerStrategy.hpp</a>.</p> +<div class="fragment"><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> { <span class="keywordflow">return</span> std::move(m_QuantizedNetwork); }</div></div><!-- fragment --> +</div> +</div> +<hr/>The documentation for this class was generated from the following files:<ul> +<li>src/armnn/<a class="el" href="_quantizer_strategy_8hpp_source.xhtml">QuantizerStrategy.hpp</a></li> +<li>src/armnn/<a class="el" href="_quantizer_strategy_8cpp_source.xhtml">QuantizerStrategy.cpp</a></li> +</ul> +</div><!-- contents --> +</div><!-- doc-content --> +<!-- start footer part --> +<div id="nav-path" class="navpath"><!-- id is needed for treeview function! --> + <ul> + <li class="navelem"><a class="el" href="namespacearmnn.xhtml">armnn</a></li><li class="navelem"><a class="el" href="classarmnn_1_1_quantizer_strategy.xhtml">QuantizerStrategy</a></li> + <li class="footer">Generated on Thu Feb 25 2021 17:28:00 for ArmNN by + <a href="http://www.doxygen.org/index.html"> + <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.13 </li> + </ul> +</div> +</body> +</html> |