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diff --git a/20.02/_serializer_tests_8cpp_source.xhtml b/20.02/_serializer_tests_8cpp_source.xhtml new file mode 100644 index 0000000000..c246a290ce --- /dev/null +++ b/20.02/_serializer_tests_8cpp_source.xhtml @@ -0,0 +1,339 @@ +<!-- 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: src/armnnSerializer/test/SerializerTests.cpp Source File</title> +<link href="tabs.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="jquery.js"></script> +<script type="text/javascript" src="dynsections.js"></script> +<link href="navtree.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="resize.js"></script> +<script type="text/javascript" src="navtreedata.js"></script> +<script type="text/javascript" src="navtree.js"></script> +<script type="text/javascript"> + $(document).ready(initResizable); +</script> +<link href="search/search.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="search/searchdata.js"></script> +<script type="text/javascript" src="search/search.js"></script> +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + extensions: ["tex2jax.js"], + jax: ["input/TeX","output/HTML-CSS"], +}); +</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script> +<link href="doxygen.css" rel="stylesheet" type="text/css" /> +<link href="stylesheet.css" rel="stylesheet" type="text/css"/> +</head> +<body> +<div id="top"><!-- do not remove this div, it is closed by doxygen! --> +<div id="titlearea"> +<table cellspacing="0" cellpadding="0"> + <tbody> + <tr style="height: 56px;"> + <img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 10rem; margin-top: .5rem; margin-left 10px"/> + <td style="padding-left: 0.5em;"> + <div id="projectname"> +  <span id="projectnumber">20.02</span> + </div> + </td> + </tr> + </tbody> +</table> +</div> +<!-- end header part --> +<!-- Generated by Doxygen 1.8.13 --> +<script type="text/javascript"> +var searchBox = new SearchBox("searchBox", "search",false,'Search'); +</script> +<script type="text/javascript" src="menudata.js"></script> +<script type="text/javascript" src="menu.js"></script> +<script type="text/javascript"> +$(function() { + initMenu('',true,false,'search.php','Search'); + $(document).ready(function() { init_search(); }); +}); +</script> +<div id="main-nav"></div> +</div><!-- top --> +<div id="side-nav" class="ui-resizable side-nav-resizable"> + <div id="nav-tree"> + <div id="nav-tree-contents"> + <div id="nav-sync" class="sync"></div> + </div> + </div> + <div id="splitbar" style="-moz-user-select:none;" + class="ui-resizable-handle"> + </div> +</div> +<script type="text/javascript"> +$(document).ready(function(){initNavTree('_serializer_tests_8cpp_source.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="headertitle"> +<div class="title">SerializerTests.cpp</div> </div> +</div><!--header--> +<div class="contents"> +<a href="_serializer_tests_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "../Serializer.hpp"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <<a class="code" href="_descriptors_8hpp.xhtml">armnn/Descriptors.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_i_network_8hpp.xhtml">armnn/INetwork.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_types_utils_8hpp.xhtml">armnn/TypesUtils.hpp</a>></span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_lstm_params_8hpp.xhtml">armnn/LstmParams.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <<a class="code" href="_quantized_lstm_params_8hpp.xhtml">armnn/QuantizedLstmParams.hpp</a>></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <<a class="code" href="_i_deserializer_8hpp.xhtml">armnnDeserializer/IDeserializer.hpp</a>></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <random></span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <vector></span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> </div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include <boost/test/unit_test.hpp></span></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> <span class="keyword">using</span> <a class="code" href="classarmnn_deserializer_1_1_i_deserializer.xhtml">armnnDeserializer::IDeserializer</a>;</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> {</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> </div><div class="line"><a name="l00025"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196"> 25</a></span> <span class="preprocessor">#define DECLARE_LAYER_VERIFIER_CLASS(name) \</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">class name##LayerVerifier : public LayerVerifierBase \</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">{ \</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">public: \</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor"> name##LayerVerifier(const std::string& layerName, \</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor"> const std::vector<armnn::TensorInfo>& inputInfos, \</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor"> const std::vector<armnn::TensorInfo>& outputInfos) \</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor"> : LayerVerifierBase(layerName, inputInfos, outputInfos) {} \</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">\</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor"> void Visit##name##Layer(const armnn::IConnectableLayer* layer, const char* name) override \</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor"> { \</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="preprocessor"> VerifyNameAndConnections(layer, name); \</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor"> } \</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="preprocessor">};</span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> </div><div class="line"><a name="l00040"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1"> 40</a></span> <span class="preprocessor">#define DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(name) \</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="preprocessor">class name##LayerVerifier : public LayerVerifierBaseWithDescriptor<armnn::name##Descriptor> \</span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="preprocessor">{ \</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="preprocessor">public: \</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="preprocessor"> name##LayerVerifier(const std::string& layerName, \</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="preprocessor"> const std::vector<armnn::TensorInfo>& inputInfos, \</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="preprocessor"> const std::vector<armnn::TensorInfo>& outputInfos, \</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="preprocessor"> const armnn::name##Descriptor& descriptor) \</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="preprocessor"> : LayerVerifierBaseWithDescriptor<armnn::name##Descriptor>( \</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> <span class="preprocessor"> layerName, inputInfos, outputInfos, descriptor) {} \</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> <span class="preprocessor">\</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <span class="preprocessor"> void Visit##name##Layer(const armnn::IConnectableLayer* layer, \</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <span class="preprocessor"> const armnn::name##Descriptor& descriptor, \</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> <span class="preprocessor"> const char* name) override \</span></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> <span class="preprocessor"> { \</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> <span class="preprocessor"> VerifyNameAndConnections(layer, name); \</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> <span class="preprocessor"> VerifyDescriptor(descriptor); \</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> <span class="preprocessor"> } \</span></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> <span class="preprocessor">};</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> </div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> <span class="keyword">struct </span>DefaultLayerVerifierPolicy</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> {</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keyword">static</span> <span class="keywordtype">void</span> Apply(<span class="keyword">const</span> std::string)</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  {</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  BOOST_TEST_MESSAGE(<span class="stringliteral">"Unexpected layer found in network"</span>);</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  BOOST_TEST(<span class="keyword">false</span>);</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  }</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> };</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> </div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> <span class="keyword">class </span>LayerVerifierBase : <span class="keyword">public</span> <a class="code" href="classarmnn_1_1_layer_visitor_base.xhtml">armnn::LayerVisitorBase</a><DefaultLayerVerifierPolicy></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> {</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  LayerVerifierBase(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos)</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  : m_LayerName(layerName)</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  , m_InputTensorInfos(inputInfos)</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  , m_OutputTensorInfos(outputInfos) {}</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keywordtype">void</span> VisitInputLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>*, <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a>, <span class="keyword">const</span> <span class="keywordtype">char</span>*)<span class="keyword"> override </span>{}</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> </div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keywordtype">void</span> VisitOutputLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>*, <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a>, <span class="keyword">const</span> <span class="keywordtype">char</span>*)<span class="keyword"> override </span>{}</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> </div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> <span class="keyword">protected</span>:</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keywordtype">void</span> VerifyNameAndConnections(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer, <span class="keyword">const</span> <span class="keywordtype">char</span>* name)</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  {</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  BOOST_TEST(name == m_LayerName.c_str());</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> </div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  BOOST_TEST(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">GetNumInputSlots</a>() == m_InputTensorInfos.size());</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  BOOST_TEST(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>() == m_OutputTensorInfos.size());</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < m_InputTensorInfos.size(); i++)</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  {</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>* connectedOutput = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(i).<a class="code" href="classarmnn_1_1_i_input_slot.xhtml#a81fbf6103761e55061b62ba989b00f10">GetConnection</a>();</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(connectedOutput);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> </div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& connectedInfo = connectedOutput-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  BOOST_TEST(connectedInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>() == m_InputTensorInfos[i].GetShape());</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  BOOST_TEST(</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(connectedInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()) == <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(m_InputTensorInfos[i].GetDataType()));</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> </div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  BOOST_TEST(connectedInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>() == m_InputTensorInfos[i].GetQuantizationScale());</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  BOOST_TEST(connectedInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>() == m_InputTensorInfos[i].GetQuantizationOffset());</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  }</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> </div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < m_OutputTensorInfos.size(); i++)</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  {</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& outputInfo = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(i).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  BOOST_TEST(outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>() == m_OutputTensorInfos[i].GetShape());</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  BOOST_TEST(</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()) == <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(m_OutputTensorInfos[i].GetDataType()));</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> </div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  BOOST_TEST(outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>() == m_OutputTensorInfos[i].GetQuantizationScale());</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  BOOST_TEST(outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>() == m_OutputTensorInfos[i].GetQuantizationOffset());</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  }</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  }</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> </div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keywordtype">void</span> VerifyConstTensors(<span class="keyword">const</span> std::string& tensorName,</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>* expectedPtr,</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>* actualPtr)</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  {</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keywordflow">if</span> (expectedPtr == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  {</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  BOOST_CHECK_MESSAGE(actualPtr == <span class="keyword">nullptr</span>, tensorName + <span class="stringliteral">" should not exist"</span>);</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  }</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  {</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  BOOST_CHECK_MESSAGE(actualPtr != <span class="keyword">nullptr</span>, tensorName + <span class="stringliteral">" should have been set"</span>);</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordflow">if</span> (actualPtr != <span class="keyword">nullptr</span>)</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="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& expectedInfo = expectedPtr-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>();</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& actualInfo = actualPtr-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">GetInfo</a>();</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> </div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  BOOST_CHECK_MESSAGE(expectedInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>() == actualInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(),</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  tensorName + <span class="stringliteral">" shapes don't match"</span>);</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  BOOST_CHECK_MESSAGE(</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(expectedInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()) == <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(actualInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()),</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  tensorName + <span class="stringliteral">" data types don't match"</span>);</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> </div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  BOOST_CHECK_MESSAGE(expectedPtr-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>() == actualPtr-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>(),</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  tensorName + <span class="stringliteral">" (GetNumBytes) data sizes do not match"</span>);</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keywordflow">if</span> (expectedPtr-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>() == actualPtr-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</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>  <span class="comment">//check the data is identical</span></div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* expectedData = <span class="keyword">static_cast<</span><span class="keyword">const </span><span class="keywordtype">char</span>*<span class="keyword">></span>(expectedPtr-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>());</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* actualData = <span class="keyword">static_cast<</span><span class="keyword">const </span><span class="keywordtype">char</span>*<span class="keyword">></span>(actualPtr-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>());</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keywordtype">bool</span> same = <span class="keyword">true</span>;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < expectedPtr-><a class="code" href="classarmnn_1_1_base_tensor.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>(); ++i)</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>  same = expectedData[i] == actualData[i];</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keywordflow">if</span> (!same)</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  {</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  }</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>  BOOST_CHECK_MESSAGE(same, tensorName + <span class="stringliteral">" data does not match"</span>);</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  }</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>  }</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  }</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> <span class="keyword">private</span>:</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  std::string m_LayerName;</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  std::vector<armnn::TensorInfo> m_InputTensorInfos;</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  std::vector<armnn::TensorInfo> m_OutputTensorInfos;</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> };</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> </div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> <span class="keyword">template</span><<span class="keyword">typename</span> Descriptor></div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> <span class="keyword">class </span>LayerVerifierBaseWithDescriptor : <span class="keyword">public</span> LayerVerifierBase</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> {</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  LayerVerifierBaseWithDescriptor(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keyword">const</span> Descriptor& descriptor)</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  : LayerVerifierBase(layerName, inputInfos, outputInfos)</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  , m_Descriptor(descriptor) {}</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> </div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> <span class="keyword">protected</span>:</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keywordtype">void</span> VerifyDescriptor(<span class="keyword">const</span> Descriptor& descriptor)</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  {</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(descriptor == m_Descriptor);</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  }</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> </div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  Descriptor m_Descriptor;</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> </div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> <span class="keyword">template</span><<span class="keyword">typename</span> T></div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> <span class="keywordtype">void</span> CompareConstTensorData(<span class="keyword">const</span> <span class="keywordtype">void</span>* data1, <span class="keyword">const</span> <span class="keywordtype">void</span>* data2, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numElements)</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> {</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  T typedData1 = <span class="keyword">static_cast<</span>T<span class="keyword">></span>(data1);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  T typedData2 = <span class="keyword">static_cast<</span>T<span class="keyword">></span>(data2);</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(typedData1);</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(typedData2);</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> </div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < numElements; i++)</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>  BOOST_TEST(typedData1[i] == typedData2[i]);</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</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> </div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> <span class="keywordtype">void</span> CompareConstTensor(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& tensor1, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& tensor2)</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> {</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  BOOST_TEST(tensor1.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>() == tensor2.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>());</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  BOOST_TEST(<a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(tensor1.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()) == <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(tensor2.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()));</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>  <span class="keywordflow">switch</span> (tensor1.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>())</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  {</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>:</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  CompareConstTensorData<const float*>(</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  tensor1.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>(), tensor2.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>(), tensor1.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>:</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>:</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  CompareConstTensorData<const uint8_t*>(</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  tensor1.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>(), tensor2.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>(), tensor1.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>:</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  CompareConstTensorData<const int32_t*>(</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  tensor1.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>(), tensor2.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">GetMemoryArea</a>(), tensor1.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="comment">// Note that Float16 is not yet implemented</span></div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  BOOST_TEST_MESSAGE(<span class="stringliteral">"Unexpected datatype"</span>);</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  BOOST_TEST(<span class="keyword">false</span>);</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> }</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> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> DeserializeNetwork(<span class="keyword">const</span> std::string& serializerString)</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span> {</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  std::vector<std::uint8_t> <span class="keyword">const</span> serializerVector{serializerString.begin(), serializerString.end()};</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keywordflow">return</span> IDeserializer::Create()->CreateNetworkFromBinary(serializerVector);</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> </div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span> std::string SerializeNetwork(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_network.xhtml">armnn::INetwork</a>& network)</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>  <a class="code" href="classarmnn_serializer_1_1_serializer.xhtml">armnnSerializer::Serializer</a> <a class="code" href="namespacearmnn_serializer.xhtml">serializer</a>;</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  serializer.<a class="code" href="classarmnn_serializer_1_1_serializer.xhtml#a62dbb19d4776b489161b699f608f0150">Serialize</a>(network);</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span> </div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  std::stringstream stream;</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  serializer.<a class="code" href="classarmnn_serializer_1_1_serializer.xhtml#af21f36069c661c4afa7221a305de80e0">SaveSerializedToStream</a>(stream);</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> </div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  std::string serializerString{stream.str()};</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="keywordflow">return</span> serializerString;</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> </div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span> <span class="keyword">template</span><<span class="keyword">typename</span> DataType></div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span> <span class="keyword">static</span> std::vector<DataType> GenerateRandomData(<span class="keywordtype">size_t</span> size)</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> {</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  constexpr <span class="keywordtype">bool</span> isIntegerType = std::is_integral<DataType>::value;</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="keyword">using</span> Distribution =</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keyword">typename</span> std::conditional<isIntegerType,</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  std::uniform_int_distribution<DataType>,</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  std::uniform_real_distribution<DataType>>::type;</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span> </div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="keyword">static</span> constexpr <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> lowerLimit = std::numeric_limits<DataType>::min();</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keyword">static</span> constexpr <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> upperLimit = std::numeric_limits<DataType>::max();</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="keyword">static</span> Distribution distribution(lowerLimit, upperLimit);</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keyword">static</span> std::default_random_engine generator;</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> </div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  std::vector<DataType> randomData(size);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  std::generate(randomData.begin(), randomData.end(), []() { <span class="keywordflow">return</span> distribution(generator); });</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span> </div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="keywordflow">return</span> randomData;</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> </div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> } <span class="comment">// anonymous namespace</span></div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span> </div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> <a class="code" href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a>(SerializerTests)</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> </div><div class="line"><a name="l00271"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e"> 271</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeAddition)</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>  <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(Addition)</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>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"addition"</span>);</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo({1, 2, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> additionLayer = network->AddAdditionLayer(layerName.c_str());</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> </div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  inputLayer0-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(additionLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  inputLayer1-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(additionLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  additionLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span> </div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  inputLayer0-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  inputLayer1-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  additionLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span> </div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  AdditionLayerVerifier verifier(layerName, {tensorInfo, tensorInfo}, {tensorInfo});</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</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"><a class="line" href="_serializer_tests_8cpp.xhtml#a0e876ee76e1b7b55c4a24cea29ee70ac"> 299</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeArgMinMax)</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>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#a374120de442fe42c26536bb4f1e2a5a1">ArgMinMax</a>)</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span> </div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"argminmax"</span>);</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({1, 2, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({1, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>);</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span> </div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml">armnn::ArgMinMaxDescriptor</a> descriptor;</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  descriptor.<a class="code" href="structarmnn_1_1_arg_min_max_descriptor.xhtml#ab1ae6f520bb1a4da191a0ae907477f23">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">armnn::ArgMinMaxFunction::Max</a>;</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  descriptor.m_Axis = 1;</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span> </div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> argMinMaxLayer = network->AddArgMinMaxLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span> </div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(argMinMaxLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  argMinMaxLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span> </div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  argMinMaxLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span> </div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span> </div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  ArgMinMaxLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span> }</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span> </div><div class="line"><a name="l00329"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#add359ae172212d256d7024a16b577fa8"> 329</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeBatchNormalization)</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>  <span class="keyword">using</span> Descriptor = <a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml">armnn::BatchNormalizationDescriptor</a>;</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="keyword">class </span>BatchNormalizationLayerVerifier : <span class="keyword">public</span> LayerVerifierBaseWithDescriptor<Descriptor></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="keyword">public</span>:</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  BatchNormalizationLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="keyword">const</span> Descriptor& descriptor,</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& mean,</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& variance,</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& beta,</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& gamma)</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  , m_Mean(mean)</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  , m_Variance(variance)</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  , m_Beta(beta)</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  , m_Gamma(gamma) {}</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span> </div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="keywordtype">void</span> VisitBatchNormalizationLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="keyword">const</span> Descriptor& descriptor,</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& mean,</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& variance,</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& beta,</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& gamma,</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  VerifyDescriptor(descriptor);</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span> </div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  CompareConstTensor(mean, m_Mean);</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  CompareConstTensor(variance, m_Variance);</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  CompareConstTensor(beta, m_Beta);</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  CompareConstTensor(gamma, m_Gamma);</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  }</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span> </div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> m_Mean;</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> m_Variance;</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> m_Beta;</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> m_Gamma;</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="keyword">const</span> std::string layerName(<span class="stringliteral">"batchNormalization"</span>);</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</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>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> meanInfo({1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> varianceInfo({1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> betaInfo({1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> gammaInfo({1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</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>  <a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml">armnn::BatchNormalizationDescriptor</a> descriptor;</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  descriptor.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">m_Eps</a> = 0.0010000000475f;</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  descriptor.m_DataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span> </div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  std::vector<float> meanData({5.0});</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  std::vector<float> varianceData({2.0});</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  std::vector<float> betaData({1.0});</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  std::vector<float> gammaData({0.0});</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>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> mean(meanInfo, meanData);</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> variance(varianceInfo, varianceData);</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> beta(betaInfo, betaData);</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> gamma(gammaInfo, gammaData);</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> batchNormalizationLayer =</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  network->AddBatchNormalizationLayer(descriptor, mean, variance, beta, gamma, layerName.c_str());</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span> </div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(batchNormalizationLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  batchNormalizationLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span> </div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  batchNormalizationLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span> </div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  BatchNormalizationLayerVerifier verifier(</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  layerName, {inputInfo}, {outputInfo}, descriptor, mean, variance, beta, gamma);</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span> }</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span> </div><div class="line"><a name="l00416"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a51de9ca6ac8a186f48cca59f392e4b50"> 416</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeBatchToSpaceNd)</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>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#a8746512fab5ec10c2c57800c66311ba7">BatchToSpaceNd</a>)</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> </div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"spaceToBatchNd"</span>);</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({4, 1, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({1, 1, 4, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span> </div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">armnn::BatchToSpaceNdDescriptor</a> desc;</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  desc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>;</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  desc.m_BlockShape = {2, 2};</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  desc.m_Crops = {{0, 0}, {0, 0}};</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span> </div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> batchToSpaceNdLayer = network->AddBatchToSpaceNdLayer(desc, layerName.c_str());</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span> </div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(batchToSpaceNdLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  batchToSpaceNdLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</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>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  batchToSpaceNdLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</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>  BatchToSpaceNdLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span> }</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span> </div><div class="line"><a name="l00447"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a16dc6220342037f5a890ad7a912594e7"> 447</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeComparison)</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>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(Comparison)</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>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"comparison"</span>);</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span> </div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> shape{2, 1, 2, 4};</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="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>);</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span> </div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <a class="code" href="structarmnn_1_1_comparison_descriptor.xhtml">armnn::ComparisonDescriptor</a> descriptor(<a class="code" href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58ca19bb0af2c3c530538cb41aff7f235b96">armnn::ComparisonOperation::NotEqual</a>);</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span> </div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> comparisonLayer = network->AddComparisonLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</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>  inputLayer0-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(comparisonLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  inputLayer1-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(comparisonLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  comparisonLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span> </div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  inputLayer0-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  inputLayer1-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  comparisonLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</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>  ComparisonLayerVerifier verifier(layerName, { inputInfo, inputInfo }, { outputInfo }, descriptor);</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span> }</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span> </div><div class="line"><a name="l00481"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a3c47c5c535712035fb962c91fffc3447"> 481</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeConstant)</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span> {</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  <span class="keyword">class </span>ConstantLayerVerifier : <span class="keyword">public</span> LayerVerifierBase</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  {</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  ConstantLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& layerInput)</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  : LayerVerifierBase(layerName, inputInfos, outputInfos)</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  , m_LayerInput(layerInput) {}</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>  <span class="keywordtype">void</span> VisitConstantLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& input,</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  CompareConstTensor(input, m_LayerInput);</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  }</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span> </div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <span class="keywordtype">void</span> VisitAdditionLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>*, <span class="keyword">const</span> <span class="keywordtype">char</span>*)<span class="keyword"> override </span>{}</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span> </div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> m_LayerInput;</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> </div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"constant"</span>);</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span> </div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  std::vector<float> constantData = GenerateRandomData<float>(info.GetNumElements());</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> constTensor(info, constantData);</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>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network(<a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>());</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* input = network->AddInputLayer(0);</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* constant = network->AddConstantLayer(constTensor, layerName.c_str());</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* add = network->AddAdditionLayer();</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* output = network->AddOutputLayer(0);</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span> </div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  input-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  constant-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  add-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span> </div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  input-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  constant-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  add-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span> </div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span> </div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  ConstantLayerVerifier verifier(layerName, {}, {info}, constTensor);</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span> }</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span> </div><div class="line"><a name="l00534"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a5356190bf530f061bfad94d3b5842e07"> 534</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeConvolution2d)</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span> {</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  <span class="keyword">using</span> Descriptor = <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a>;</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <span class="keyword">class </span>Convolution2dLayerVerifier : <span class="keyword">public</span> LayerVerifierBaseWithDescriptor<Descriptor></div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  {</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  Convolution2dLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <span class="keyword">const</span> Descriptor& descriptor,</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& weights,</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a>& biases)</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  , m_Weights(weights)</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  , m_Biases(biases) {}</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span> </div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  <span class="keywordtype">void</span> VisitConvolution2dLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  <span class="keyword">const</span> Descriptor& descriptor,</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& weights,</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a>& biases,</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  VerifyDescriptor(descriptor);</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span> </div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  <span class="comment">// check weights</span></div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  CompareConstTensor(weights, m_Weights);</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span> </div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  <span class="comment">// check biases</span></div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() == descriptor.m_BiasEnabled);</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() == m_Biases.has_value());</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span> </div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  <span class="keywordflow">if</span> (biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() && m_Biases.has_value())</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  {</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  CompareConstTensor(biases.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>(), m_Biases.value());</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  }</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  }</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span> </div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> m_Weights;</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a> m_Biases;</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  };</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span> </div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"convolution2d"</span>);</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 5, 5, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span> </div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span> </div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements());</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span> </div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements());</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasesInfo, biasesData);</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span> </div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 2;</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 2;</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = 2;</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = 2;</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span> </div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> convLayer =</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  network->AddConvolution2dLayer(descriptor,</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  weights,</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a>(biases),</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  layerName.c_str());</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span> </div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  convLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span> </div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  convLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span> </div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span> </div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  Convolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span> }</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span> </div><div class="line"><a name="l00624"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a38de5ca76565a5326549aa88153f5aec"> 624</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeDepthToSpace)</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span> {</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#ab023d9a7687e35c0f108458a094c1f56">DepthToSpace</a>)</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span> </div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"depthToSpace"</span>);</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span> </div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 8, 4, 12 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 16, 8, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span> </div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  <a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml">armnn::DepthToSpaceDescriptor</a> desc;</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  desc.<a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml#a6c6b8957f1e176867e5fb05b1a1a1486">m_BlockSize</a> = 2;</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  desc.m_DataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span> </div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> depthToSpaceLayer = network->AddDepthToSpaceLayer(desc, layerName.c_str());</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span> </div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(depthToSpaceLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  depthToSpaceLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span> </div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  depthToSpaceLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span> </div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span> </div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  DepthToSpaceLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span> }</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span> </div><div class="line"><a name="l00655"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a119be6a507d98bdd38a54db9f7036139"> 655</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeDepthwiseConvolution2d)</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span> {</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  <span class="keyword">using</span> Descriptor = <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">armnn::DepthwiseConvolution2dDescriptor</a>;</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  <span class="keyword">class </span>DepthwiseConvolution2dLayerVerifier : <span class="keyword">public</span> LayerVerifierBaseWithDescriptor<Descriptor></div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  {</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  DepthwiseConvolution2dLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  <span class="keyword">const</span> Descriptor& descriptor,</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& weights,</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a>& biases) :</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor),</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  m_Weights(weights),</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  m_Biases(biases) {}</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span> </div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  <span class="keywordtype">void</span> VisitDepthwiseConvolution2dLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  <span class="keyword">const</span> Descriptor& descriptor,</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& weights,</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a>& biases,</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  VerifyDescriptor(descriptor);</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span> </div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  <span class="comment">// check weights</span></div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  CompareConstTensor(weights, m_Weights);</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span> </div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  <span class="comment">// check biases</span></div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() == descriptor.m_BiasEnabled);</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() == m_Biases.has_value());</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span> </div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  <span class="keywordflow">if</span> (biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() && m_Biases.has_value())</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  {</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  CompareConstTensor(biases.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>(), m_Biases.value());</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  }</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  }</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span> </div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> m_Weights;</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a> m_Biases;</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  };</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span> </div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"depwiseConvolution2d"</span>);</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 5, 5, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 3, 3, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span> </div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 1, 3, 3, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span> </div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements());</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span> </div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  std::vector<int32_t> biasesData = GenerateRandomData<int32_t>(biasesInfo.GetNumElements());</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasesInfo, biasesData);</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span> </div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">armnn::DepthwiseConvolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 2;</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 2;</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = 2;</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = 2;</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span> </div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> depthwiseConvLayer =</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  network->AddDepthwiseConvolution2dLayer(descriptor,</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  weights,</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a>(biases),</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  layerName.c_str());</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span> </div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(depthwiseConvLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  depthwiseConvLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span> </div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  depthwiseConvLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span> </div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span> </div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  DepthwiseConvolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span> }</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span> </div><div class="line"><a name="l00745"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a20970cfb9b49e5080b90f605ae840761"> 745</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeDequantize)</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span> {</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(<a class="code" href="namespacearmnn.xhtml#a855293b1be0581fb61ef6a1c5b027d0f">Dequantize</a>)</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span> </div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"dequantize"</span>);</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({ 1, 5, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>, 0.5f, 1);</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 5, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span> </div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> dequantizeLayer = network->AddDequantizeLayer(layerName.c_str());</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span> </div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(dequantizeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  dequantizeLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span> </div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  dequantizeLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span> </div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span> </div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  DequantizeLayerVerifier verifier(layerName, {inputInfo}, {outputInfo});</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span> }</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span> </div><div class="line"><a name="l00771"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a855eea3f4f96815bb7a4cefde6791a3a"> 771</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeDetectionPostProcess)</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span> {</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  <span class="keyword">using</span> Descriptor = <a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml">armnn::DetectionPostProcessDescriptor</a>;</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  <span class="keyword">class </span>DetectionPostProcessLayerVerifier : <span class="keyword">public</span> LayerVerifierBaseWithDescriptor<Descriptor></div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  {</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  DetectionPostProcessLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  <span class="keyword">const</span> Descriptor& descriptor,</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& <a class="code" href="_neon_end_to_end_tests_8cpp.xhtml#ac0981848e4ae57729f14f72bd4caa9f8">anchors</a>)</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  , m_Anchors(anchors) {}</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span> </div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  <span class="keywordtype">void</span> VisitDetectionPostProcessLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  <span class="keyword">const</span> Descriptor& descriptor,</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& anchors,</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  VerifyDescriptor(descriptor);</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span> </div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  CompareConstTensor(anchors, m_Anchors);</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  }</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span> </div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> m_Anchors;</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  };</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span> </div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"detectionPostProcess"</span>);</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span> </div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo> inputInfos({</div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 6, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>),</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 6, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>)</div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  });</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span> </div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo> outputInfos({</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 3, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>),</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>),</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>),</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>)</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>  });</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span> </div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  <a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml">armnn::DetectionPostProcessDescriptor</a> descriptor;</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7ed9bc7c26df67d274d5dd4cd83adf0f">m_UseRegularNms</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae72089bcab60ac175557f4241b16a014">m_MaxDetections</a> = 3;</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a9ae2c9796692ebeafe19a4d3f09c8ea8">m_MaxClassesPerDetection</a> = 1;</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7e2f87544b8bc7e497e1dec8d3ca4055">m_DetectionsPerClass</a> =1;</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a4392dd6b4862cc9cf95ae8f1001ba592">m_NmsScoreThreshold</a> = 0.0;</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a53c8a7f33a40e1e240256bcfcf41b101">m_NmsIouThreshold</a> = 0.5;</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a3a04b0ccee4bb2f21721ee5045e83df4">m_NumClasses</a> = 2;</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7a2156ec7d9c012ce00bbcc6afcb9028">m_ScaleY</a> = 10.0;</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae64523937ea910030ad66fee6fddd51f">m_ScaleX</a> = 10.0;</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#aa61510cbd529870182e918ac6e8b9d72">m_ScaleH</a> = 5.0;</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  descriptor.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ab509802c659de19929f18bad14a35c58">m_ScaleW</a> = 5.0;</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span> </div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="_neon_end_to_end_tests_8cpp.xhtml#a358cb7cd3c0647b25be049fd734b8c22">anchorsInfo</a>({ 6, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  <span class="keyword">const</span> std::vector<float> anchorsData({</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  0.5f, 0.5f, 1.0f, 1.0f,</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  0.5f, 0.5f, 1.0f, 1.0f,</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  0.5f, 0.5f, 1.0f, 1.0f,</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  0.5f, 10.5f, 1.0f, 1.0f,</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  0.5f, 10.5f, 1.0f, 1.0f,</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  0.5f, 100.5f, 1.0f, 1.0f</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  });</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> <a class="code" href="_neon_end_to_end_tests_8cpp.xhtml#ac0981848e4ae57729f14f72bd4caa9f8">anchors</a>(<a class="code" href="_neon_end_to_end_tests_8cpp.xhtml#a358cb7cd3c0647b25be049fd734b8c22">anchorsInfo</a>, anchorsData);</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span> </div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> detectionLayer =</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  network->AddDetectionPostProcessLayer(descriptor, anchors, layerName.c_str());</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span> </div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < 2; i++)</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  {</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(static_cast<int>(i));</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(detectionLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(i));</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfos[i]);</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>  }</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span> </div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < 4; i++)</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  {</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(static_cast<int>(i));</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  detectionLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(i).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  detectionLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(i).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfos[i]);</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  }</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span> </div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span> </div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  DetectionPostProcessLayerVerifier verifier(layerName, inputInfos, outputInfos, descriptor, anchors);</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span> }</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span> </div><div class="line"><a name="l00863"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a3472d2c268b61fb7d623163c7d828c80"> 863</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeDivision)</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span> {</div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(Division)</div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span> </div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"division"</span>);</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 1, 5, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span> </div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> divisionLayer = network->AddDivisionLayer(layerName.c_str());</div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span> </div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  inputLayer0-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(divisionLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  inputLayer1-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(divisionLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  divisionLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span> </div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  inputLayer0-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  inputLayer1-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  divisionLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span> </div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span> </div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  DivisionLayerVerifier verifier(layerName, {info, info}, {info});</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span> }</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span> </div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span> <span class="keyword">class </span>EqualLayerVerifier : <span class="keyword">public</span> LayerVerifierBase</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span> {</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span>  EqualLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos)</div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>  : LayerVerifierBase(layerName, inputInfos, outputInfos) {}</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span> </div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  <span class="keywordtype">void</span> VisitComparisonLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_comparison_descriptor.xhtml">armnn::ComparisonDescriptor</a>& descriptor,</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(descriptor.<a class="code" href="structarmnn_1_1_comparison_descriptor.xhtml#a865dc4f43cb0ff01a1dcf78036912fd1">m_Operation</a> == <a class="code" href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58caf5f286e73bda105e538310b3190f75c5">armnn::ComparisonOperation::Equal</a>);</div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span>  }</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span> </div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  <span class="keywordtype">void</span> VisitEqualLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>*, <span class="keyword">const</span> <span class="keywordtype">char</span>*)<span class="keyword"> override</span></div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(<span class="stringliteral">"EqualLayer should have translated to ComparisonLayer"</span>);</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span>  }</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span> };</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span> </div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span> <span class="comment">// NOTE: Until the deprecated AddEqualLayer disappears this test checks that calling</span></div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span> <span class="comment">// AddEqualLayer places a ComparisonLayer into the serialized format and that</span></div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span> <span class="comment">// when this deserialises we have a ComparisonLayer</span></div><div class="line"><a name="l00916"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a6a14fa535c1751752fdbd0725bc7ad3e"> 916</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeEqual)</div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span> {</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"equal"</span>);</div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span> </div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> shape{2, 1, 2, 4};</div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span> </div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>);</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span> </div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span>  <a class="code" href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> equalLayer = network->AddEqualLayer(layerName.c_str());</div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span>  <a class="code" href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span> </div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span>  inputLayer0-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(equalLayer->GetInputSlot(0));</div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  inputLayer1-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(equalLayer->GetInputSlot(1));</div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  equalLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));</div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span> </div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  inputLayer0-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  inputLayer1-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span>  equalLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);</div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span> </div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span> </div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span>  EqualLayerVerifier verifier(layerName, { inputInfo, inputInfo }, { outputInfo });</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span> }</div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span> </div><div class="line"><a name="l00948"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a50830e0b70c8a84af0b27f0dcf4b3389"> 948</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(EnsureEqualBackwardCompatibility)</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span> {</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  <span class="comment">// The hex data below is a flat buffer containing a simple network with two inputs,</span></div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  <span class="comment">// an EqualLayer (now deprecated) and an output</span></div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  <span class="comment">//</span></div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span>  <span class="comment">// This test verifies that we can still deserialize this old-style model by replacing</span></div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  <span class="comment">// the EqualLayer with an equivalent ComparisonLayer</span></div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  <span class="keyword">const</span> std::vector<uint8_t> equalModel =</div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  {</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span>  0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span>  0xCC, 0x01, 0x00, 0x00, 0x20, 0x01, 0x00, 0x00, 0x70, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x02, 0x00,</div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  0x60, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0xFE, 0xFE, 0xFF, 0xFF, 0x04, 0x00,</div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  0x00, 0x00, 0x06, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xEA, 0xFE, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span>  0x64, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB4, 0xFE, 0xFF, 0xFF, 0x00, 0x00,</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span>  0x00, 0x13, 0x04, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x36, 0xFF, 0xFF, 0xFF,</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1C, 0x00,</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x65, 0x71, 0x75, 0x61, 0x6C, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span>  0x5C, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x34, 0xFF,</div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x92, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x04, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00,</div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span>  0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x08, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span>  0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span>  0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00,</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00,</div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span>  0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span>  0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0E, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00,</div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00,</div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00,</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,</div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  0x04, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x14, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span>  0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00,</div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span>  0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span>  0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x08, 0x00,</div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  0x07, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span>  0x04, 0x00, 0x00, 0x00</div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span>  };</div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span> </div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(std::string(equalModel.begin(), equalModel.end()));</div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span> </div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> shape{ 2, 1, 2, 4 };</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span> </div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>);</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span> </div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>  EqualLayerVerifier verifier(<span class="stringliteral">"equal"</span>, { inputInfo, inputInfo }, { outputInfo });</div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span> }</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span> </div><div class="line"><a name="l01008"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a9e151842e28471a4e04ee9c5f5c05a74"> 1008</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeFloor)</div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span> {</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(Floor)</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span> </div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"floor"</span>);</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({4,4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span> </div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> floorLayer = network->AddFloorLayer(layerName.c_str());</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span> </div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(floorLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>  floorLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span> </div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>  floorLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span> </div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span> </div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  FloorLayerVerifier verifier(layerName, {info}, {info});</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span> }</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span> </div><div class="line"><a name="l01033"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a0d3a0736e9d014001bd37e232c54ff48"> 1033</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeFullyConnected)</div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span> {</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>  <span class="keyword">using</span> Descriptor = <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a>;</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>  <span class="keyword">class </span>FullyConnectedLayerVerifier : <span class="keyword">public</span> LayerVerifierBaseWithDescriptor<Descriptor></div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>  {</div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span>  FullyConnectedLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>  <span class="keyword">const</span> Descriptor& descriptor,</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& weight,</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a>& bias)</div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>  : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)</div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>  , m_Weight(weight)</div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>  , m_Bias(bias) {}</div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span> </div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>  <span class="keywordtype">void</span> VisitFullyConnectedLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>  <span class="keyword">const</span> Descriptor& descriptor,</div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& weight,</div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a>& bias,</div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span> <span class="keyword"> </span>{</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>  VerifyDescriptor(descriptor);</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span> </div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>  CompareConstTensor(weight, m_Weight);</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span> </div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>  BOOST_TEST(bias.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() == descriptor.m_BiasEnabled);</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span>  BOOST_TEST(bias.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() == m_Bias.has_value());</div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span> </div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>  <span class="keywordflow">if</span> (bias.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() && m_Bias.has_value())</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  {</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  CompareConstTensor(bias.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>(), m_Bias.value());</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span>  }</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>  }</div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span> </div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> m_Weight;</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a> m_Bias;</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>  };</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span> </div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"fullyConnected"</span>);</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 2, 5, 1, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span> </div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 5, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>  std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements());</div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>  std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements());</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasesInfo, biasesData);</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span> </div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a> descriptor;</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>  descriptor.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">m_TransposeWeightMatrix</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span> </div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> fullyConnectedLayer =</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>  network->AddFullyConnectedLayer(descriptor,</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>  weights,</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a>(biases),</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>  layerName.c_str());</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span> </div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(fullyConnectedLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>  fullyConnectedLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span> </div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>  fullyConnectedLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span> </div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span> </div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>  FullyConnectedLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span> }</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span> </div><div class="line"><a name="l01111"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a5174c1e8962eda5aab37f17d72506c75"> 1111</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeGather)</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span> {</div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>  <span class="keyword">class </span>GatherLayerVerifier : <span class="keyword">public</span> LayerVerifierBase</div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>  {</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>  GatherLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos)</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>  : LayerVerifierBase(layerName, inputInfos, outputInfos) {}</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span> </div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>  <span class="keywordtype">void</span> VisitGatherLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer, <span class="keyword">const</span> <span class="keywordtype">char</span> *name)<span class="keyword"> override</span></div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span> <span class="keyword"> </span>{</div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>  }</div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span> </div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>  <span class="keywordtype">void</span> VisitConstantLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>*,</div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&,</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>*)<span class="keyword"> override </span>{}</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span>  };</div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span> </div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"gather"</span>);</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> paramsInfo({ 8 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>);</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>);</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> indicesInfo({ 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>);</div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span> </div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  paramsInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(1.0f);</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>  paramsInfo.SetQuantizationOffset(0);</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>  outputInfo.SetQuantizationScale(1.0f);</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span>  outputInfo.SetQuantizationOffset(0);</div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span> </div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>  <span class="keyword">const</span> std::vector<int32_t>& indicesData = {7, 6, 5};</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span> </div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> *<span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> *<span class="keyword">const</span> constantLayer =</div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>  network->AddConstantLayer(<a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>(indicesInfo, indicesData));</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> *<span class="keyword">const</span> gatherLayer = network->AddGatherLayer(layerName.c_str());</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> *<span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span> </div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(gatherLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>  constantLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(gatherLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>  gatherLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span> </div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(paramsInfo);</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  constantLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(indicesInfo);</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  gatherLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span> </div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span> </div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>  GatherLayerVerifier verifier(layerName, {paramsInfo, indicesInfo}, {outputInfo});</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span> }</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span> </div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span> <span class="keyword">class </span>GreaterLayerVerifier : <span class="keyword">public</span> LayerVerifierBase</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span> {</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span> <span class="keyword">public</span>:</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>  GreaterLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos)</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>  : LayerVerifierBase(layerName, inputInfos, outputInfos) {}</div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span> </div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>  <span class="keywordtype">void</span> VisitComparisonLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_comparison_descriptor.xhtml">armnn::ComparisonDescriptor</a>& descriptor,</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span> <span class="keyword"> </span>{</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(descriptor.<a class="code" href="structarmnn_1_1_comparison_descriptor.xhtml#a865dc4f43cb0ff01a1dcf78036912fd1">m_Operation</a> == <a class="code" href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58ca8768a6821cd735aea4f5b0df88c1fc6a">armnn::ComparisonOperation::Greater</a>);</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>  }</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span> </div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  <span class="keywordtype">void</span> VisitGreaterLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>*, <span class="keyword">const</span> <span class="keywordtype">char</span>*)<span class="keyword"> override</span></div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span> <span class="keyword"> </span>{</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(<span class="stringliteral">"GreaterLayer should have translated to ComparisonLayer"</span>);</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  }</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span> };</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span> </div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span> <span class="comment">// NOTE: Until the deprecated AddGreaterLayer disappears this test checks that calling</span></div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span> <span class="comment">// AddGreaterLayer places a ComparisonLayer into the serialized format and that</span></div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span> <span class="comment">// when this deserialises we have a ComparisonLayer</span></div><div class="line"><a name="l01190"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a461a321aa5b80473430a18a899c801f7"> 1190</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeGreater)</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span> {</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"greater"</span>);</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span> </div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> shape{2, 1, 2, 4};</div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span> </div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>);</div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span> </div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>  <a class="code" href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> equalLayer = network->AddGreaterLayer(layerName.c_str());</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  <a class="code" href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span> </div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>  inputLayer0-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(equalLayer->GetInputSlot(0));</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>  inputLayer1-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(equalLayer->GetInputSlot(1));</div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>  equalLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span> </div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span>  inputLayer0-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>  inputLayer1-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>  equalLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span> </div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span> </div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span>  GreaterLayerVerifier verifier(layerName, { inputInfo, inputInfo }, { outputInfo });</div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span> }</div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span> </div><div class="line"><a name="l01222"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a61ae7cc03a04c0e69c1acccf62507e3e"> 1222</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(EnsureGreaterBackwardCompatibility)</div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span> {</div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>  <span class="comment">// The hex data below is a flat buffer containing a simple network with two inputs,</span></div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>  <span class="comment">// an GreaterLayer (now deprecated) and an output</span></div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>  <span class="comment">//</span></div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>  <span class="comment">// This test verifies that we can still deserialize this old-style model by replacing</span></div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>  <span class="comment">// the GreaterLayer with an equivalent ComparisonLayer</span></div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span>  <span class="keyword">const</span> std::vector<uint8_t> greaterModel =</div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span>  {</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>  0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>  0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>  0xCC, 0x01, 0x00, 0x00, 0x20, 0x01, 0x00, 0x00, 0x70, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x02, 0x00,</div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>  0x60, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0xFE, 0xFE, 0xFF, 0xFF, 0x04, 0x00,</div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>  0x00, 0x00, 0x06, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xEA, 0xFE, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>  0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>  0x64, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB4, 0xFE, 0xFF, 0xFF, 0x00, 0x00,</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  0x00, 0x19, 0x04, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x36, 0xFF, 0xFF, 0xFF,</div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>  0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1C, 0x00,</div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>  0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x67, 0x72, 0x65, 0x61, 0x74, 0x65, 0x72, 0x00, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  0x5C, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x34, 0xFF,</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x92, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x04, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>  0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00,</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>  0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x08, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>  0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span>  0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00,</div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>  0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>  0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 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0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00,</div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>  0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>  0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>  0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x08, 0x00,</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>  0x07, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span>  0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>  0x02, 0x00, 0x00, 0x00</div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span>  };</div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span> </div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(std::string(greaterModel.begin(), greaterModel.end()));</div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span> </div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> shape{ 1, 2, 2, 2 };</div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span> </div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a>);</div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span> </div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>  GreaterLayerVerifier verifier(<span class="stringliteral">"greater"</span>, { inputInfo, inputInfo }, { outputInfo });</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span> }</div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span> </div><div class="line"><a name="l01282"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a49a39142f8899f9220b03c20ee3fe7db"> 1282</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeInstanceNormalization)</div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span> {</div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(InstanceNormalization)</div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span> </div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"instanceNormalization"</span>);</div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 1, 2, 1, 5 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span> </div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>  <a class="code" href="structarmnn_1_1_instance_normalization_descriptor.xhtml">armnn::InstanceNormalizationDescriptor</a> descriptor;</div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>  descriptor.<a class="code" href="structarmnn_1_1_instance_normalization_descriptor.xhtml#a5e078fd505aef7bccaa05c8058e096cc">m_Gamma</a> = 1.1f;</div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>  descriptor.m_Beta = 0.1f;</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>  descriptor.m_Eps = 0.0001f;</div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>  descriptor.m_DataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span> </div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> instanceNormLayer =</div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>  network->AddInstanceNormalizationLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span> </div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(instanceNormLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span>  instanceNormLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span> </div><div class="line"><a name="l01304"></a><span class="lineno"> 1304</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span>  instanceNormLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span> </div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span> </div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>  InstanceNormalizationLayerVerifier verifier(layerName, {info}, {info}, descriptor);</div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span> }</div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span> </div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span> <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(L2Normalization)</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span> </div><div class="line"><a name="l01316"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#ad946ab635806bc9fde5e841aea2a9d23"> 1316</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeL2Normalization)</div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span> {</div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>  <span class="keyword">const</span> std::string l2NormLayerName(<span class="stringliteral">"l2Normalization"</span>);</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({1, 2, 1, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span> </div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>  <a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml">armnn::L2NormalizationDescriptor</a> desc;</div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>  desc.<a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>;</div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>  desc.m_Eps = 0.0001f;</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span> </div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> l2NormLayer = network->AddL2NormalizationLayer(desc, l2NormLayerName.c_str());</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span> </div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>  inputLayer0-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(l2NormLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span>  l2NormLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span> </div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>  inputLayer0-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>  l2NormLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span> </div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span> </div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>  L2NormalizationLayerVerifier verifier(l2NormLayerName, {info}, {info}, desc);</div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span> }</div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span> </div><div class="line"><a name="l01343"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a9ca28c0e25dfe0ca2810fc1ef33a5e6c"> 1343</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(EnsureL2NormalizationBackwardCompatibility)</div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span> {</div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span>  <span class="comment">// The hex data below is a flat buffer containing a simple network with one input</span></div><div class="line"><a name="l01346"></a><span class="lineno"> 1346</span>  <span class="comment">// a L2Normalization layer and an output layer with dimensions as per the tensor infos below.</span></div><div class="line"><a name="l01347"></a><span class="lineno"> 1347</span>  <span class="comment">//</span></div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span>  <span class="comment">// This test verifies that we can still read back these old style</span></div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span>  <span class="comment">// models without the normalization epsilon value.</span></div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>  <span class="keyword">const</span> std::vector<uint8_t> l2NormalizationModel =</div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>  {</div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>  0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span>  0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span>  0x3C, 0x01, 0x00, 0x00, 0x74, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>  0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0xE8, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B,</div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>  0x04, 0x00, 0x00, 0x00, 0xD6, 0xFE, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x08, 0x00,</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>  0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>  0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span>  0x4C, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x44, 0xFF, 0xFF, 0xFF, 0x00, 0x00,</div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>  0x00, 0x20, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>  0x20, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x06, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span>  0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0E, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span>  0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x1F, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x20, 0x00,</div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span>  0x00, 0x00, 0x0F, 0x00, 0x00, 0x00, 0x6C, 0x32, 0x4E, 0x6F, 0x72, 0x6D, 0x61, 0x6C, 0x69, 0x7A, 0x61, 0x74,</div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>  0x69, 0x6F, 0x6E, 0x00, 0x01, 0x00, 0x00, 0x00, 0x48, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00,</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>  0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span>  0x52, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x00,</div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span>  0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>  0x08, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span>  0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,</div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span>  0x04, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l01373"></a><span class="lineno"> 1373</span>  0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x14, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</span>  0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00,</div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span>  0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span>  0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span>  0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x08, 0x00,</div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>  0x07, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>  0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>  0x05, 0x00, 0x00, 0x00, 0x00</div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span>  };</div><div class="line"><a name="l01382"></a><span class="lineno"> 1382</span> </div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork =</div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</span>  DeserializeNetwork(std::string(l2NormalizationModel.begin(), l2NormalizationModel.end()));</div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01386"></a><span class="lineno"> 1386</span> </div><div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"l2Normalization"</span>);</div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 2, 1, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01389"></a><span class="lineno"> 1389</span> </div><div class="line"><a name="l01390"></a><span class="lineno"> 1390</span>  <a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml">armnn::L2NormalizationDescriptor</a> desc;</div><div class="line"><a name="l01391"></a><span class="lineno"> 1391</span>  desc.<a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>;</div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span>  <span class="comment">// Since this variable does not exist in the l2NormalizationModel dump, the default value will be loaded</span></div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>  desc.m_Eps = 1e-12f;</div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span> </div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>  L2NormalizationLayerVerifier verifier(layerName, {inputInfo}, {inputInfo}, desc);</div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span> }</div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span> </div><div class="line"><a name="l01399"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a6f57bfeb8cd67cdf480f23030b374331"> 1399</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeLogSoftmax)</div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</span> {</div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#ac52e04c0e349e25bcdaa72c27395ef8f">LogSoftmax</a>)</div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span> </div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"log_softmax"</span>);</div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({1, 10}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span> </div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>  <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">armnn::LogSoftmaxDescriptor</a> descriptor;</div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>  descriptor.<a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = 1.0f;</div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>  descriptor.m_Axis = -1;</div><div class="line"><a name="l01409"></a><span class="lineno"> 1409</span> </div><div class="line"><a name="l01410"></a><span class="lineno"> 1410</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> logSoftmaxLayer = network->AddLogSoftmaxLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l01413"></a><span class="lineno"> 1413</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01414"></a><span class="lineno"> 1414</span> </div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(logSoftmaxLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span>  logSoftmaxLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span> </div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>  logSoftmaxLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span> </div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span> </div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>  LogSoftmaxLayerVerifier verifier(layerName, {info}, {info}, descriptor);</div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span> }</div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span> </div><div class="line"><a name="l01428"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#aeedeb69141be5280c53e327a2ac76320"> 1428</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeMaximum)</div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span> {</div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(Maximum)</div><div class="line"><a name="l01431"></a><span class="lineno"> 1431</span> </div><div class="line"><a name="l01432"></a><span class="lineno"> 1432</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"maximum"</span>);</div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 1, 2, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span> </div><div class="line"><a name="l01435"></a><span class="lineno"> 1435</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01436"></a><span class="lineno"> 1436</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l01438"></a><span class="lineno"> 1438</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> maximumLayer = network->AddMaximumLayer(layerName.c_str());</div><div class="line"><a name="l01439"></a><span class="lineno"> 1439</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span> </div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</span>  inputLayer0-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(maximumLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01442"></a><span class="lineno"> 1442</span>  inputLayer1-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(maximumLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span>  maximumLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span> </div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span>  inputLayer0-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01446"></a><span class="lineno"> 1446</span>  inputLayer1-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01447"></a><span class="lineno"> 1447</span>  maximumLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01448"></a><span class="lineno"> 1448</span> </div><div class="line"><a name="l01449"></a><span class="lineno"> 1449</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span> </div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>  MaximumLayerVerifier verifier(layerName, {info, info}, {info});</div><div class="line"><a name="l01453"></a><span class="lineno"> 1453</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01454"></a><span class="lineno"> 1454</span> }</div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span> </div><div class="line"><a name="l01456"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a4da2b73764b4e976afb82b8864b99be8"> 1456</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeMean)</div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span> {</div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#a165ae372a7f67cad64ef3395d30122ce">Mean</a>)</div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span> </div><div class="line"><a name="l01460"></a><span class="lineno"> 1460</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"mean"</span>);</div><div class="line"><a name="l01461"></a><span class="lineno"> 1461</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({1, 1, 3, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01462"></a><span class="lineno"> 1462</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({1, 1, 1, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01463"></a><span class="lineno"> 1463</span> </div><div class="line"><a name="l01464"></a><span class="lineno"> 1464</span>  <a class="code" href="structarmnn_1_1_mean_descriptor.xhtml">armnn::MeanDescriptor</a> descriptor;</div><div class="line"><a name="l01465"></a><span class="lineno"> 1465</span>  descriptor.<a class="code" href="structarmnn_1_1_mean_descriptor.xhtml#a1f0d67b087c491248bd1cde3ff995a95">m_Axis</a> = { 2 };</div><div class="line"><a name="l01466"></a><span class="lineno"> 1466</span>  descriptor.m_KeepDims = <span class="keyword">true</span>;</div><div class="line"><a name="l01467"></a><span class="lineno"> 1467</span> </div><div class="line"><a name="l01468"></a><span class="lineno"> 1468</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01470"></a><span class="lineno"> 1470</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> meanLayer = network->AddMeanLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l01471"></a><span class="lineno"> 1471</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01472"></a><span class="lineno"> 1472</span> </div><div class="line"><a name="l01473"></a><span class="lineno"> 1473</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(meanLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</span>  meanLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span> </div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01477"></a><span class="lineno"> 1477</span>  meanLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l01478"></a><span class="lineno"> 1478</span> </div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01481"></a><span class="lineno"> 1481</span> </div><div class="line"><a name="l01482"></a><span class="lineno"> 1482</span>  MeanLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l01483"></a><span class="lineno"> 1483</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01484"></a><span class="lineno"> 1484</span> }</div><div class="line"><a name="l01485"></a><span class="lineno"> 1485</span> </div><div class="line"><a name="l01486"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#aa5b91e9c6d7ba20294ff5416969a85cf"> 1486</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeMerge)</div><div class="line"><a name="l01487"></a><span class="lineno"> 1487</span> {</div><div class="line"><a name="l01488"></a><span class="lineno"> 1488</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(Merge)</div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span> </div><div class="line"><a name="l01490"></a><span class="lineno"> 1490</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"merge"</span>);</div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 1, 2, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span> </div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01494"></a><span class="lineno"> 1494</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l01495"></a><span class="lineno"> 1495</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> mergeLayer = network->AddMergeLayer(layerName.c_str());</div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span> </div><div class="line"><a name="l01499"></a><span class="lineno"> 1499</span>  inputLayer0-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(mergeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01500"></a><span class="lineno"> 1500</span>  inputLayer1-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(mergeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01501"></a><span class="lineno"> 1501</span>  mergeLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01502"></a><span class="lineno"> 1502</span> </div><div class="line"><a name="l01503"></a><span class="lineno"> 1503</span>  inputLayer0-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01504"></a><span class="lineno"> 1504</span>  inputLayer1-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01505"></a><span class="lineno"> 1505</span>  mergeLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01506"></a><span class="lineno"> 1506</span> </div><div class="line"><a name="l01507"></a><span class="lineno"> 1507</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01508"></a><span class="lineno"> 1508</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01509"></a><span class="lineno"> 1509</span> </div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</span>  MergeLayerVerifier verifier(layerName, {info, info}, {info});</div><div class="line"><a name="l01511"></a><span class="lineno"> 1511</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span> }</div><div class="line"><a name="l01513"></a><span class="lineno"> 1513</span> </div><div class="line"><a name="l01514"></a><span class="lineno"> 1514</span> <span class="keyword">class </span>MergerLayerVerifier : <span class="keyword">public</span> LayerVerifierBaseWithDescriptor<armnn::OriginsDescriptor></div><div class="line"><a name="l01515"></a><span class="lineno"> 1515</span> {</div><div class="line"><a name="l01516"></a><span class="lineno"> 1516</span> <span class="keyword">public</span>:</div><div class="line"><a name="l01517"></a><span class="lineno"> 1517</span>  MergerLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l01518"></a><span class="lineno"> 1518</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l01519"></a><span class="lineno"> 1519</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l01520"></a><span class="lineno"> 1520</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a>& descriptor)</div><div class="line"><a name="l01521"></a><span class="lineno"> 1521</span>  : LayerVerifierBaseWithDescriptor<armnn::OriginsDescriptor>(layerName, inputInfos, outputInfos, descriptor) {}</div><div class="line"><a name="l01522"></a><span class="lineno"> 1522</span> </div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span>  <span class="keywordtype">void</span> VisitMergerLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>*,</div><div class="line"><a name="l01524"></a><span class="lineno"> 1524</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a>&,</div><div class="line"><a name="l01525"></a><span class="lineno"> 1525</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>*)<span class="keyword"> override</span></div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span> <span class="keyword"> </span>{</div><div class="line"><a name="l01527"></a><span class="lineno"> 1527</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(<span class="stringliteral">"MergerLayer should have translated to ConcatLayer"</span>);</div><div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>  }</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span> </div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span>  <span class="keywordtype">void</span> VisitConcatLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a>& descriptor,</div><div class="line"><a name="l01532"></a><span class="lineno"> 1532</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l01533"></a><span class="lineno"> 1533</span> <span class="keyword"> </span>{</div><div class="line"><a name="l01534"></a><span class="lineno"> 1534</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l01535"></a><span class="lineno"> 1535</span>  VerifyDescriptor(descriptor);</div><div class="line"><a name="l01536"></a><span class="lineno"> 1536</span>  }</div><div class="line"><a name="l01537"></a><span class="lineno"> 1537</span> };</div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span> </div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span> <span class="comment">// NOTE: Until the deprecated AddMergerLayer disappears this test checks that calling</span></div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span> <span class="comment">// AddMergerLayer places a ConcatLayer into the serialized format and that</span></div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span> <span class="comment">// when this deserialises we have a ConcatLayer</span></div><div class="line"><a name="l01542"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#ac5c00e890d80662b6fe3fea6c898b66f"> 1542</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeMerger)</div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span> {</div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"merger"</span>);</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({2, 3, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({4, 3, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span> </div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span>  <span class="keyword">const</span> std::vector<armnn::TensorShape> shapes({inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()});</div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span> </div><div class="line"><a name="l01550"></a><span class="lineno"> 1550</span>  <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a> descriptor =</div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</span>  <a class="code" href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">armnn::CreateDescriptorForConcatenation</a>(shapes.begin(), shapes.end(), 0);</div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span> </div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01554"></a><span class="lineno"> 1554</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayerOne = network->AddInputLayer(0);</div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayerTwo = network->AddInputLayer(1);</div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span>  <a class="code" href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> mergerLayer = network->AddMergerLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l01558"></a><span class="lineno"> 1558</span>  <a class="code" href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span> </div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>  inputLayerOne-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(mergerLayer->GetInputSlot(0));</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span>  inputLayerTwo-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(mergerLayer->GetInputSlot(1));</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span>  mergerLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));</div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span> </div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span>  inputLayerOne-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span>  inputLayerTwo-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span>  mergerLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);</div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span> </div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span>  std::string mergerLayerNetwork = SerializeNetwork(*network);</div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(mergerLayerNetwork);</div><div class="line"><a name="l01571"></a><span class="lineno"> 1571</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span> </div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span>  MergerLayerVerifier verifier(layerName, {inputInfo, inputInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01575"></a><span class="lineno"> 1575</span> }</div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span> </div><div class="line"><a name="l01577"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a78a526878db0d0d2217044621194552f"> 1577</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(EnsureMergerLayerBackwardCompatibility)</div><div class="line"><a name="l01578"></a><span class="lineno"> 1578</span> {</div><div class="line"><a name="l01579"></a><span class="lineno"> 1579</span>  <span class="comment">// The hex data below is a flat buffer containing a simple network with two inputs</span></div><div class="line"><a name="l01580"></a><span class="lineno"> 1580</span>  <span class="comment">// a merger layer (now deprecated) and an output layer with dimensions as per the tensor infos below.</span></div><div class="line"><a name="l01581"></a><span class="lineno"> 1581</span>  <span class="comment">//</span></div><div class="line"><a name="l01582"></a><span class="lineno"> 1582</span>  <span class="comment">// This test verifies that we can still read back these old style</span></div><div class="line"><a name="l01583"></a><span class="lineno"> 1583</span>  <span class="comment">// models replacing the MergerLayers with ConcatLayers with the same parameters.</span></div><div class="line"><a name="l01584"></a><span class="lineno"> 1584</span>  <span class="keyword">const</span> std::vector<uint8_t> mergerModel =</div><div class="line"><a name="l01585"></a><span class="lineno"> 1585</span>  {</div><div class="line"><a name="l01586"></a><span class="lineno"> 1586</span>  0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l01587"></a><span class="lineno"> 1587</span>  0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</span>  0x38, 0x02, 0x00, 0x00, 0x8C, 0x01, 0x00, 0x00, 0x70, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x02, 0x00,</div><div class="line"><a name="l01589"></a><span class="lineno"> 1589</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01590"></a><span class="lineno"> 1590</span>  0xF4, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x92, 0xFE, 0xFF, 0xFF, 0x04, 0x00,</div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>  0x00, 0x00, 0x9A, 0xFE, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x7E, 0xFE, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span>  0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>  0xF8, 0xFE, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x48, 0xFE, 0xFF, 0xFF, 0x00, 0x00,</div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</span>  0x00, 0x1F, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01596"></a><span class="lineno"> 1596</span>  0x68, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,</div><div class="line"><a name="l01597"></a><span class="lineno"> 1597</span>  0x0C, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01598"></a><span class="lineno"> 1598</span>  0x02, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x22, 0xFF, 0xFF, 0xFF, 0x04, 0x00,</div><div class="line"><a name="l01599"></a><span class="lineno"> 1599</span>  0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01600"></a><span class="lineno"> 1600</span>  0x00, 0x00, 0x00, 0x00, 0x3E, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01601"></a><span class="lineno"> 1601</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x36, 0xFF, 0xFF, 0xFF,</div><div class="line"><a name="l01602"></a><span class="lineno"> 1602</span>  0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x1E, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1C, 0x00,</div><div class="line"><a name="l01603"></a><span class="lineno"> 1603</span>  0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x6D, 0x65, 0x72, 0x67, 0x65, 0x72, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01604"></a><span class="lineno"> 1604</span>  0x5C, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x34, 0xFF,</div><div class="line"><a name="l01605"></a><span class="lineno"> 1605</span>  0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x92, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01606"></a><span class="lineno"> 1606</span>  0x00, 0x00, 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0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00,</div><div class="line"><a name="l01623"></a><span class="lineno"> 1623</span>  0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01624"></a><span class="lineno"> 1624</span>  0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01625"></a><span class="lineno"> 1625</span>  0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x08, 0x00,</div><div class="line"><a name="l01626"></a><span class="lineno"> 1626</span>  0x07, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01627"></a><span class="lineno"> 1627</span>  0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01628"></a><span class="lineno"> 1628</span>  0x02, 0x00, 0x00, 0x00</div><div class="line"><a name="l01629"></a><span class="lineno"> 1629</span>  };</div><div class="line"><a name="l01630"></a><span class="lineno"> 1630</span> </div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(std::string(mergerModel.begin(), mergerModel.end()));</div><div class="line"><a name="l01632"></a><span class="lineno"> 1632</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01633"></a><span class="lineno"> 1633</span> </div><div class="line"><a name="l01634"></a><span class="lineno"> 1634</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 2, 3, 2, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01635"></a><span class="lineno"> 1635</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 4, 3, 2, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01636"></a><span class="lineno"> 1636</span> </div><div class="line"><a name="l01637"></a><span class="lineno"> 1637</span>  <span class="keyword">const</span> std::vector<armnn::TensorShape> shapes({inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()});</div><div class="line"><a name="l01638"></a><span class="lineno"> 1638</span> </div><div class="line"><a name="l01639"></a><span class="lineno"> 1639</span>  <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a> descriptor =</div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span>  <a class="code" href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">armnn::CreateDescriptorForConcatenation</a>(shapes.begin(), shapes.end(), 0);</div><div class="line"><a name="l01641"></a><span class="lineno"> 1641</span> </div><div class="line"><a name="l01642"></a><span class="lineno"> 1642</span>  MergerLayerVerifier verifier(<span class="stringliteral">"merger"</span>, { inputInfo, inputInfo }, { outputInfo }, descriptor);</div><div class="line"><a name="l01643"></a><span class="lineno"> 1643</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01644"></a><span class="lineno"> 1644</span> }</div><div class="line"><a name="l01645"></a><span class="lineno"> 1645</span> </div><div class="line"><a name="l01646"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a8560bbe4155b6f639feea42ca759ceb8"> 1646</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeConcat)</div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span> {</div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"concat"</span>);</div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({2, 3, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({4, 3, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span> </div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>  <span class="keyword">const</span> std::vector<armnn::TensorShape> shapes({inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()});</div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span> </div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span>  <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a> descriptor =</div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span>  <a class="code" href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">armnn::CreateDescriptorForConcatenation</a>(shapes.begin(), shapes.end(), 0);</div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</span> </div><div class="line"><a name="l01657"></a><span class="lineno"> 1657</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayerOne = network->AddInputLayer(0);</div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayerTwo = network->AddInputLayer(1);</div><div class="line"><a name="l01660"></a><span class="lineno"> 1660</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> concatLayer = network->AddConcatLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01662"></a><span class="lineno"> 1662</span> </div><div class="line"><a name="l01663"></a><span class="lineno"> 1663</span>  inputLayerOne-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01664"></a><span class="lineno"> 1664</span>  inputLayerTwo-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(concatLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01665"></a><span class="lineno"> 1665</span>  concatLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span> </div><div class="line"><a name="l01667"></a><span class="lineno"> 1667</span>  inputLayerOne-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01668"></a><span class="lineno"> 1668</span>  inputLayerTwo-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01669"></a><span class="lineno"> 1669</span>  concatLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l01670"></a><span class="lineno"> 1670</span> </div><div class="line"><a name="l01671"></a><span class="lineno"> 1671</span>  std::string concatLayerNetwork = SerializeNetwork(*network);</div><div class="line"><a name="l01672"></a><span class="lineno"> 1672</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(concatLayerNetwork);</div><div class="line"><a name="l01673"></a><span class="lineno"> 1673</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01674"></a><span class="lineno"> 1674</span> </div><div class="line"><a name="l01675"></a><span class="lineno"> 1675</span>  <span class="comment">// NOTE: using the MergerLayerVerifier to ensure that it is a concat layer and not a</span></div><div class="line"><a name="l01676"></a><span class="lineno"> 1676</span>  <span class="comment">// merger layer that gets placed into the graph.</span></div><div class="line"><a name="l01677"></a><span class="lineno"> 1677</span>  MergerLayerVerifier verifier(layerName, {inputInfo, inputInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l01678"></a><span class="lineno"> 1678</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01679"></a><span class="lineno"> 1679</span> }</div><div class="line"><a name="l01680"></a><span class="lineno"> 1680</span> </div><div class="line"><a name="l01681"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a44fce4d0e871f0ef2f4751f8ba3d8162"> 1681</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeMinimum)</div><div class="line"><a name="l01682"></a><span class="lineno"> 1682</span> {</div><div class="line"><a name="l01683"></a><span class="lineno"> 1683</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(Minimum)</div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span> </div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"minimum"</span>);</div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 1, 2, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01687"></a><span class="lineno"> 1687</span> </div><div class="line"><a name="l01688"></a><span class="lineno"> 1688</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01689"></a><span class="lineno"> 1689</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l01690"></a><span class="lineno"> 1690</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l01691"></a><span class="lineno"> 1691</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> minimumLayer = network->AddMinimumLayer(layerName.c_str());</div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span> </div><div class="line"><a name="l01694"></a><span class="lineno"> 1694</span>  inputLayer0-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(minimumLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01695"></a><span class="lineno"> 1695</span>  inputLayer1-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(minimumLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01696"></a><span class="lineno"> 1696</span>  minimumLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01697"></a><span class="lineno"> 1697</span> </div><div class="line"><a name="l01698"></a><span class="lineno"> 1698</span>  inputLayer0-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01699"></a><span class="lineno"> 1699</span>  inputLayer1-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01700"></a><span class="lineno"> 1700</span>  minimumLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01701"></a><span class="lineno"> 1701</span> </div><div class="line"><a name="l01702"></a><span class="lineno"> 1702</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01703"></a><span class="lineno"> 1703</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01704"></a><span class="lineno"> 1704</span> </div><div class="line"><a name="l01705"></a><span class="lineno"> 1705</span>  MinimumLayerVerifier verifier(layerName, {info, info}, {info});</div><div class="line"><a name="l01706"></a><span class="lineno"> 1706</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01707"></a><span class="lineno"> 1707</span> }</div><div class="line"><a name="l01708"></a><span class="lineno"> 1708</span> </div><div class="line"><a name="l01709"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a64531a96adf17b0fda1da04c5233a6b0"> 1709</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeMultiplication)</div><div class="line"><a name="l01710"></a><span class="lineno"> 1710</span> {</div><div class="line"><a name="l01711"></a><span class="lineno"> 1711</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(Multiplication)</div><div class="line"><a name="l01712"></a><span class="lineno"> 1712</span> </div><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"multiplication"</span>);</div><div class="line"><a name="l01714"></a><span class="lineno"> 1714</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 1, 5, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01715"></a><span class="lineno"> 1715</span> </div><div class="line"><a name="l01716"></a><span class="lineno"> 1716</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l01718"></a><span class="lineno"> 1718</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l01719"></a><span class="lineno"> 1719</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> multiplicationLayer = network->AddMultiplicationLayer(layerName.c_str());</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span> </div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span>  inputLayer0-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(multiplicationLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>  inputLayer1-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(multiplicationLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span>  multiplicationLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span> </div><div class="line"><a name="l01726"></a><span class="lineno"> 1726</span>  inputLayer0-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01727"></a><span class="lineno"> 1727</span>  inputLayer1-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span>  multiplicationLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span> </div><div class="line"><a name="l01730"></a><span class="lineno"> 1730</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01731"></a><span class="lineno"> 1731</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01732"></a><span class="lineno"> 1732</span> </div><div class="line"><a name="l01733"></a><span class="lineno"> 1733</span>  MultiplicationLayerVerifier verifier(layerName, {info, info}, {info});</div><div class="line"><a name="l01734"></a><span class="lineno"> 1734</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01735"></a><span class="lineno"> 1735</span> }</div><div class="line"><a name="l01736"></a><span class="lineno"> 1736</span> </div><div class="line"><a name="l01737"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a2bb05baf0128ccdc37d28da84f8d5986"> 1737</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializePrelu)</div><div class="line"><a name="l01738"></a><span class="lineno"> 1738</span> {</div><div class="line"><a name="l01739"></a><span class="lineno"> 1739</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(Prelu)</div><div class="line"><a name="l01740"></a><span class="lineno"> 1740</span> </div><div class="line"><a name="l01741"></a><span class="lineno"> 1741</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"prelu"</span>);</div><div class="line"><a name="l01742"></a><span class="lineno"> 1742</span> </div><div class="line"><a name="l01743"></a><span class="lineno"> 1743</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo ({ 4, 1, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01744"></a><span class="lineno"> 1744</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> alphaTensorInfo ({ 5, 4, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01745"></a><span class="lineno"> 1745</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 5, 4, 3, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01746"></a><span class="lineno"> 1746</span> </div><div class="line"><a name="l01747"></a><span class="lineno"> 1747</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01748"></a><span class="lineno"> 1748</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01749"></a><span class="lineno"> 1749</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> alphaLayer = network->AddInputLayer(1);</div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> preluLayer = network->AddPreluLayer(layerName.c_str());</div><div class="line"><a name="l01751"></a><span class="lineno"> 1751</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01752"></a><span class="lineno"> 1752</span> </div><div class="line"><a name="l01753"></a><span class="lineno"> 1753</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(preluLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01754"></a><span class="lineno"> 1754</span>  alphaLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(preluLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l01755"></a><span class="lineno"> 1755</span>  preluLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01756"></a><span class="lineno"> 1756</span> </div><div class="line"><a name="l01757"></a><span class="lineno"> 1757</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l01758"></a><span class="lineno"> 1758</span>  alphaLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(alphaTensorInfo);</div><div class="line"><a name="l01759"></a><span class="lineno"> 1759</span>  preluLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01760"></a><span class="lineno"> 1760</span> </div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span> </div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span>  PreluLayerVerifier verifier(layerName, {inputTensorInfo, alphaTensorInfo}, {outputTensorInfo});</div><div class="line"><a name="l01765"></a><span class="lineno"> 1765</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01766"></a><span class="lineno"> 1766</span> }</div><div class="line"><a name="l01767"></a><span class="lineno"> 1767</span> </div><div class="line"><a name="l01768"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#afa5c407820579e1e5c0c21a5e189fc15"> 1768</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeNormalization)</div><div class="line"><a name="l01769"></a><span class="lineno"> 1769</span> {</div><div class="line"><a name="l01770"></a><span class="lineno"> 1770</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(Normalization)</div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span> </div><div class="line"><a name="l01772"></a><span class="lineno"> 1772</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"normalization"</span>);</div><div class="line"><a name="l01773"></a><span class="lineno"> 1773</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({2, 1, 2, 2}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01774"></a><span class="lineno"> 1774</span> </div><div class="line"><a name="l01775"></a><span class="lineno"> 1775</span>  <a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml">armnn::NormalizationDescriptor</a> desc;</div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>  desc.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>;</div><div class="line"><a name="l01777"></a><span class="lineno"> 1777</span>  desc.m_NormSize = 3;</div><div class="line"><a name="l01778"></a><span class="lineno"> 1778</span>  desc.m_Alpha = 1;</div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span>  desc.m_Beta = 1;</div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span>  desc.m_K = 1;</div><div class="line"><a name="l01781"></a><span class="lineno"> 1781</span> </div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01784"></a><span class="lineno"> 1784</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> normalizationLayer = network->AddNormalizationLayer(desc, layerName.c_str());</div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span> </div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(normalizationLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span>  normalizationLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01789"></a><span class="lineno"> 1789</span> </div><div class="line"><a name="l01790"></a><span class="lineno"> 1790</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01791"></a><span class="lineno"> 1791</span>  normalizationLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span> </div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01794"></a><span class="lineno"> 1794</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01795"></a><span class="lineno"> 1795</span> </div><div class="line"><a name="l01796"></a><span class="lineno"> 1796</span>  NormalizationLayerVerifier verifier(layerName, {info}, {info}, desc);</div><div class="line"><a name="l01797"></a><span class="lineno"> 1797</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01798"></a><span class="lineno"> 1798</span> }</div><div class="line"><a name="l01799"></a><span class="lineno"> 1799</span> </div><div class="line"><a name="l01800"></a><span class="lineno"> 1800</span> <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#a28e115f5d28500324b53fae9e6c00b77">Pad</a>)</div><div class="line"><a name="l01801"></a><span class="lineno"> 1801</span> </div><div class="line"><a name="l01802"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a3f3fdd97607d0410c150efc859bb0492"> 1802</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializePad)</div><div class="line"><a name="l01803"></a><span class="lineno"> 1803</span> {</div><div class="line"><a name="l01804"></a><span class="lineno"> 1804</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"pad"</span>);</div><div class="line"><a name="l01805"></a><span class="lineno"> 1805</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 2, 3, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01806"></a><span class="lineno"> 1806</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 3, 5, 7}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01807"></a><span class="lineno"> 1807</span> </div><div class="line"><a name="l01808"></a><span class="lineno"> 1808</span>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">armnn::PadDescriptor</a> desc({{0, 0}, {1, 0}, {1, 1}, {1, 2}});</div><div class="line"><a name="l01809"></a><span class="lineno"> 1809</span> </div><div class="line"><a name="l01810"></a><span class="lineno"> 1810</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01811"></a><span class="lineno"> 1811</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01812"></a><span class="lineno"> 1812</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> padLayer = network->AddPadLayer(desc, layerName.c_str());</div><div class="line"><a name="l01813"></a><span class="lineno"> 1813</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01814"></a><span class="lineno"> 1814</span> </div><div class="line"><a name="l01815"></a><span class="lineno"> 1815</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(padLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01816"></a><span class="lineno"> 1816</span>  padLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01817"></a><span class="lineno"> 1817</span> </div><div class="line"><a name="l01818"></a><span class="lineno"> 1818</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l01819"></a><span class="lineno"> 1819</span>  padLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span> </div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01822"></a><span class="lineno"> 1822</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01823"></a><span class="lineno"> 1823</span> </div><div class="line"><a name="l01824"></a><span class="lineno"> 1824</span>  PadLayerVerifier verifier(layerName, {inputTensorInfo}, {outputTensorInfo}, desc);</div><div class="line"><a name="l01825"></a><span class="lineno"> 1825</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01826"></a><span class="lineno"> 1826</span> }</div><div class="line"><a name="l01827"></a><span class="lineno"> 1827</span> </div><div class="line"><a name="l01828"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a11e96613a35dabe725a61196e90d93e3"> 1828</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(EnsurePadBackwardCompatibility)</div><div class="line"><a name="l01829"></a><span class="lineno"> 1829</span> {</div><div class="line"><a name="l01830"></a><span class="lineno"> 1830</span>  <span class="comment">// The PadDescriptor is being extended with a float PadValue (so a value other than 0</span></div><div class="line"><a name="l01831"></a><span class="lineno"> 1831</span>  <span class="comment">// can be used to pad the tensor.</span></div><div class="line"><a name="l01832"></a><span class="lineno"> 1832</span>  <span class="comment">//</span></div><div class="line"><a name="l01833"></a><span class="lineno"> 1833</span>  <span class="comment">// This test contains a binary representation of a simple input->pad->output network</span></div><div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>  <span class="comment">// prior to this change to test that the descriptor has been updated in a backward</span></div><div class="line"><a name="l01835"></a><span class="lineno"> 1835</span>  <span class="comment">// compatible way with respect to Deserialization of older binary dumps</span></div><div class="line"><a name="l01836"></a><span class="lineno"> 1836</span>  <span class="keyword">const</span> std::vector<uint8_t> padModel =</div><div class="line"><a name="l01837"></a><span class="lineno"> 1837</span>  {</div><div class="line"><a name="l01838"></a><span class="lineno"> 1838</span>  0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l01839"></a><span class="lineno"> 1839</span>  0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01840"></a><span class="lineno"> 1840</span>  0x54, 0x01, 0x00, 0x00, 0x6C, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01841"></a><span class="lineno"> 1841</span>  0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0xD0, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B,</div><div class="line"><a name="l01842"></a><span class="lineno"> 1842</span>  0x04, 0x00, 0x00, 0x00, 0x96, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF, 0xFF, 0x04, 0x00,</div><div class="line"><a name="l01843"></a><span class="lineno"> 1843</span>  0x00, 0x00, 0x72, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01844"></a><span class="lineno"> 1844</span>  0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00,</div><div class="line"><a name="l01845"></a><span class="lineno"> 1845</span>  0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2C, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01846"></a><span class="lineno"> 1846</span>  0x00, 0x00, 0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x16, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00,</div><div class="line"><a name="l01847"></a><span class="lineno"> 1847</span>  0x0E, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x4C, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01848"></a><span class="lineno"> 1848</span>  0x00, 0x00, 0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x08, 0x00,</div><div class="line"><a name="l01849"></a><span class="lineno"> 1849</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01850"></a><span class="lineno"> 1850</span>  0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01851"></a><span class="lineno"> 1851</span>  0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0E, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01852"></a><span class="lineno"> 1852</span>  0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00,</div><div class="line"><a name="l01853"></a><span class="lineno"> 1853</span>  0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x70, 0x61, 0x64, 0x00, 0x01, 0x00, 0x00, 0x00, 0x48, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01854"></a><span class="lineno"> 1854</span>  0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,</div><div class="line"><a name="l01855"></a><span class="lineno"> 1855</span>  0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01856"></a><span class="lineno"> 1856</span>  0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x05, 0x00,</div><div class="line"><a name="l01857"></a><span class="lineno"> 1857</span>  0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01858"></a><span class="lineno"> 1858</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00,</div><div class="line"><a name="l01859"></a><span class="lineno"> 1859</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x04, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01860"></a><span class="lineno"> 1860</span>  0x00, 0x00, 0x06, 0x00, 0x0A, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01861"></a><span class="lineno"> 1861</span>  0x0E, 0x00, 0x14, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01862"></a><span class="lineno"> 1862</span>  0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01863"></a><span class="lineno"> 1863</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01864"></a><span class="lineno"> 1864</span>  0x08, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l01865"></a><span class="lineno"> 1865</span>  0x0A, 0x00, 0x10, 0x00, 0x08, 0x00, 0x07, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01,</div><div class="line"><a name="l01866"></a><span class="lineno"> 1866</span>  0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00,</div><div class="line"><a name="l01867"></a><span class="lineno"> 1867</span>  0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00</div><div class="line"><a name="l01868"></a><span class="lineno"> 1868</span>  };</div><div class="line"><a name="l01869"></a><span class="lineno"> 1869</span> </div><div class="line"><a name="l01870"></a><span class="lineno"> 1870</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(std::string(padModel.begin(), padModel.end()));</div><div class="line"><a name="l01871"></a><span class="lineno"> 1871</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01872"></a><span class="lineno"> 1872</span> </div><div class="line"><a name="l01873"></a><span class="lineno"> 1873</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 2, 3, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01874"></a><span class="lineno"> 1874</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ 1, 3, 5, 7 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01875"></a><span class="lineno"> 1875</span> </div><div class="line"><a name="l01876"></a><span class="lineno"> 1876</span>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">armnn::PadDescriptor</a> descriptor({{ 0, 0 }, { 1, 0 }, { 1, 1 }, { 1, 2 }});</div><div class="line"><a name="l01877"></a><span class="lineno"> 1877</span> </div><div class="line"><a name="l01878"></a><span class="lineno"> 1878</span>  PadLayerVerifier verifier(<span class="stringliteral">"pad"</span>, { inputInfo }, { outputInfo }, descriptor);</div><div class="line"><a name="l01879"></a><span class="lineno"> 1879</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01880"></a><span class="lineno"> 1880</span> }</div><div class="line"><a name="l01881"></a><span class="lineno"> 1881</span> </div><div class="line"><a name="l01882"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a4959da03b99bcdf40acb442ca9b1752f"> 1882</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializePermute)</div><div class="line"><a name="l01883"></a><span class="lineno"> 1883</span> {</div><div class="line"><a name="l01884"></a><span class="lineno"> 1884</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">Permute</a>)</div><div class="line"><a name="l01885"></a><span class="lineno"> 1885</span> </div><div class="line"><a name="l01886"></a><span class="lineno"> 1886</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"permute"</span>);</div><div class="line"><a name="l01887"></a><span class="lineno"> 1887</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({4, 3, 2, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01888"></a><span class="lineno"> 1888</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({1, 2, 3, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01889"></a><span class="lineno"> 1889</span> </div><div class="line"><a name="l01890"></a><span class="lineno"> 1890</span>  <a class="code" href="structarmnn_1_1_permute_descriptor.xhtml">armnn::PermuteDescriptor</a> descriptor(<a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a>({3, 2, 1, 0}));</div><div class="line"><a name="l01891"></a><span class="lineno"> 1891</span> </div><div class="line"><a name="l01892"></a><span class="lineno"> 1892</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01893"></a><span class="lineno"> 1893</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01894"></a><span class="lineno"> 1894</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> permuteLayer = network->AddPermuteLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l01895"></a><span class="lineno"> 1895</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01896"></a><span class="lineno"> 1896</span> </div><div class="line"><a name="l01897"></a><span class="lineno"> 1897</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(permuteLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01898"></a><span class="lineno"> 1898</span>  permuteLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01899"></a><span class="lineno"> 1899</span> </div><div class="line"><a name="l01900"></a><span class="lineno"> 1900</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l01901"></a><span class="lineno"> 1901</span>  permuteLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01902"></a><span class="lineno"> 1902</span> </div><div class="line"><a name="l01903"></a><span class="lineno"> 1903</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01904"></a><span class="lineno"> 1904</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01905"></a><span class="lineno"> 1905</span> </div><div class="line"><a name="l01906"></a><span class="lineno"> 1906</span>  PermuteLayerVerifier verifier(layerName, {inputTensorInfo}, {outputTensorInfo}, descriptor);</div><div class="line"><a name="l01907"></a><span class="lineno"> 1907</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01908"></a><span class="lineno"> 1908</span> }</div><div class="line"><a name="l01909"></a><span class="lineno"> 1909</span> </div><div class="line"><a name="l01910"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a1684386a1de76091bfb6407d82db04f7"> 1910</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializePooling2d)</div><div class="line"><a name="l01911"></a><span class="lineno"> 1911</span> {</div><div class="line"><a name="l01912"></a><span class="lineno"> 1912</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#ae2e93e304cf516841c521e3eaee025cd">Pooling2d</a>)</div><div class="line"><a name="l01913"></a><span class="lineno"> 1913</span> </div><div class="line"><a name="l01914"></a><span class="lineno"> 1914</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"pooling2d"</span>);</div><div class="line"><a name="l01915"></a><span class="lineno"> 1915</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({1, 2, 2, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01916"></a><span class="lineno"> 1916</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({1, 1, 1, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01917"></a><span class="lineno"> 1917</span> </div><div class="line"><a name="l01918"></a><span class="lineno"> 1918</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">armnn::Pooling2dDescriptor</a> desc;</div><div class="line"><a name="l01919"></a><span class="lineno"> 1919</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l01920"></a><span class="lineno"> 1920</span>  desc.m_PadTop = 0;</div><div class="line"><a name="l01921"></a><span class="lineno"> 1921</span>  desc.m_PadBottom = 0;</div><div class="line"><a name="l01922"></a><span class="lineno"> 1922</span>  desc.m_PadLeft = 0;</div><div class="line"><a name="l01923"></a><span class="lineno"> 1923</span>  desc.m_PadRight = 0;</div><div class="line"><a name="l01924"></a><span class="lineno"> 1924</span>  desc.m_PoolType = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l01925"></a><span class="lineno"> 1925</span>  desc.m_OutputShapeRounding = <a class="code" href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</a>;</div><div class="line"><a name="l01926"></a><span class="lineno"> 1926</span>  desc.m_PaddingMethod = <a class="code" href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a>;</div><div class="line"><a name="l01927"></a><span class="lineno"> 1927</span>  desc.m_PoolHeight = 2;</div><div class="line"><a name="l01928"></a><span class="lineno"> 1928</span>  desc.m_PoolWidth = 2;</div><div class="line"><a name="l01929"></a><span class="lineno"> 1929</span>  desc.m_StrideX = 2;</div><div class="line"><a name="l01930"></a><span class="lineno"> 1930</span>  desc.m_StrideY = 2;</div><div class="line"><a name="l01931"></a><span class="lineno"> 1931</span> </div><div class="line"><a name="l01932"></a><span class="lineno"> 1932</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01933"></a><span class="lineno"> 1933</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01934"></a><span class="lineno"> 1934</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> pooling2dLayer = network->AddPooling2dLayer(desc, layerName.c_str());</div><div class="line"><a name="l01935"></a><span class="lineno"> 1935</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01936"></a><span class="lineno"> 1936</span> </div><div class="line"><a name="l01937"></a><span class="lineno"> 1937</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(pooling2dLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01938"></a><span class="lineno"> 1938</span>  pooling2dLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01939"></a><span class="lineno"> 1939</span> </div><div class="line"><a name="l01940"></a><span class="lineno"> 1940</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01941"></a><span class="lineno"> 1941</span>  pooling2dLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l01942"></a><span class="lineno"> 1942</span> </div><div class="line"><a name="l01943"></a><span class="lineno"> 1943</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01945"></a><span class="lineno"> 1945</span> </div><div class="line"><a name="l01946"></a><span class="lineno"> 1946</span>  Pooling2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l01947"></a><span class="lineno"> 1947</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01948"></a><span class="lineno"> 1948</span> }</div><div class="line"><a name="l01949"></a><span class="lineno"> 1949</span> </div><div class="line"><a name="l01950"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#aba0c861593907248e1d293a588383bb1"> 1950</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeQuantize)</div><div class="line"><a name="l01951"></a><span class="lineno"> 1951</span> {</div><div class="line"><a name="l01952"></a><span class="lineno"> 1952</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(<a class="code" href="namespacearmnn.xhtml#ad773a034fb9983e15f3094b4c5c7c30c">Quantize</a>)</div><div class="line"><a name="l01953"></a><span class="lineno"> 1953</span> </div><div class="line"><a name="l01954"></a><span class="lineno"> 1954</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"quantize"</span>);</div><div class="line"><a name="l01955"></a><span class="lineno"> 1955</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 1, 2, 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01956"></a><span class="lineno"> 1956</span> </div><div class="line"><a name="l01957"></a><span class="lineno"> 1957</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01958"></a><span class="lineno"> 1958</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01959"></a><span class="lineno"> 1959</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> quantizeLayer = network->AddQuantizeLayer(layerName.c_str());</div><div class="line"><a name="l01960"></a><span class="lineno"> 1960</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01961"></a><span class="lineno"> 1961</span> </div><div class="line"><a name="l01962"></a><span class="lineno"> 1962</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(quantizeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01963"></a><span class="lineno"> 1963</span>  quantizeLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01964"></a><span class="lineno"> 1964</span> </div><div class="line"><a name="l01965"></a><span class="lineno"> 1965</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01966"></a><span class="lineno"> 1966</span>  quantizeLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l01967"></a><span class="lineno"> 1967</span> </div><div class="line"><a name="l01968"></a><span class="lineno"> 1968</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01969"></a><span class="lineno"> 1969</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01970"></a><span class="lineno"> 1970</span> </div><div class="line"><a name="l01971"></a><span class="lineno"> 1971</span>  QuantizeLayerVerifier verifier(layerName, {info}, {info});</div><div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l01973"></a><span class="lineno"> 1973</span> }</div><div class="line"><a name="l01974"></a><span class="lineno"> 1974</span> </div><div class="line"><a name="l01975"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a6e0afb5f057cb45dbbcf7c5efad61f20"> 1975</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeReshape)</div><div class="line"><a name="l01976"></a><span class="lineno"> 1976</span> {</div><div class="line"><a name="l01977"></a><span class="lineno"> 1977</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(Reshape)</div><div class="line"><a name="l01978"></a><span class="lineno"> 1978</span> </div><div class="line"><a name="l01979"></a><span class="lineno"> 1979</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"reshape"</span>);</div><div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({1, 9}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01981"></a><span class="lineno"> 1981</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({3, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01982"></a><span class="lineno"> 1982</span> </div><div class="line"><a name="l01983"></a><span class="lineno"> 1983</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">armnn::ReshapeDescriptor</a> descriptor({3, 3});</div><div class="line"><a name="l01984"></a><span class="lineno"> 1984</span> </div><div class="line"><a name="l01985"></a><span class="lineno"> 1985</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l01986"></a><span class="lineno"> 1986</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l01987"></a><span class="lineno"> 1987</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> reshapeLayer = network->AddReshapeLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l01988"></a><span class="lineno"> 1988</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l01989"></a><span class="lineno"> 1989</span> </div><div class="line"><a name="l01990"></a><span class="lineno"> 1990</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(reshapeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01991"></a><span class="lineno"> 1991</span>  reshapeLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01992"></a><span class="lineno"> 1992</span> </div><div class="line"><a name="l01993"></a><span class="lineno"> 1993</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l01994"></a><span class="lineno"> 1994</span>  reshapeLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l01995"></a><span class="lineno"> 1995</span> </div><div class="line"><a name="l01996"></a><span class="lineno"> 1996</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l01997"></a><span class="lineno"> 1997</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l01998"></a><span class="lineno"> 1998</span> </div><div class="line"><a name="l01999"></a><span class="lineno"> 1999</span>  ReshapeLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l02000"></a><span class="lineno"> 2000</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l02001"></a><span class="lineno"> 2001</span> }</div><div class="line"><a name="l02002"></a><span class="lineno"> 2002</span> </div><div class="line"><a name="l02003"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a66859a4301a19650149508458d36d5d6"> 2003</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeResize)</div><div class="line"><a name="l02004"></a><span class="lineno"> 2004</span> {</div><div class="line"><a name="l02005"></a><span class="lineno"> 2005</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#a25dc224be48103343302b5a6fd588fe7">Resize</a>)</div><div class="line"><a name="l02006"></a><span class="lineno"> 2006</span> </div><div class="line"><a name="l02007"></a><span class="lineno"> 2007</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"resize"</span>);</div><div class="line"><a name="l02008"></a><span class="lineno"> 2008</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 3, 5, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02009"></a><span class="lineno"> 2009</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 3, 2, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02010"></a><span class="lineno"> 2010</span> </div><div class="line"><a name="l02011"></a><span class="lineno"> 2011</span>  <a class="code" href="structarmnn_1_1_resize_descriptor.xhtml">armnn::ResizeDescriptor</a> desc;</div><div class="line"><a name="l02012"></a><span class="lineno"> 2012</span>  desc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">m_TargetWidth</a> = 4;</div><div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>  desc.m_TargetHeight = 2;</div><div class="line"><a name="l02014"></a><span class="lineno"> 2014</span>  desc.m_Method = <a class="code" href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaa020331bb30d2fa2ecf7c3a0777823f">armnn::ResizeMethod::NearestNeighbor</a>;</div><div class="line"><a name="l02015"></a><span class="lineno"> 2015</span> </div><div class="line"><a name="l02016"></a><span class="lineno"> 2016</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02017"></a><span class="lineno"> 2017</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02018"></a><span class="lineno"> 2018</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> resizeLayer = network->AddResizeLayer(desc, layerName.c_str());</div><div class="line"><a name="l02019"></a><span class="lineno"> 2019</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02020"></a><span class="lineno"> 2020</span> </div><div class="line"><a name="l02021"></a><span class="lineno"> 2021</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(resizeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02022"></a><span class="lineno"> 2022</span>  resizeLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02023"></a><span class="lineno"> 2023</span> </div><div class="line"><a name="l02024"></a><span class="lineno"> 2024</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l02025"></a><span class="lineno"> 2025</span>  resizeLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l02026"></a><span class="lineno"> 2026</span> </div><div class="line"><a name="l02027"></a><span class="lineno"> 2027</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02028"></a><span class="lineno"> 2028</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02029"></a><span class="lineno"> 2029</span> </div><div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>  ResizeLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l02031"></a><span class="lineno"> 2031</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l02032"></a><span class="lineno"> 2032</span> }</div><div class="line"><a name="l02033"></a><span class="lineno"> 2033</span> </div><div class="line"><a name="l02034"></a><span class="lineno"> 2034</span> <span class="keyword">class </span>ResizeBilinearLayerVerifier : <span class="keyword">public</span> LayerVerifierBaseWithDescriptor<armnn::ResizeBilinearDescriptor></div><div class="line"><a name="l02035"></a><span class="lineno"> 2035</span> {</div><div class="line"><a name="l02036"></a><span class="lineno"> 2036</span> <span class="keyword">public</span>:</div><div class="line"><a name="l02037"></a><span class="lineno"> 2037</span>  ResizeBilinearLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l02038"></a><span class="lineno"> 2038</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l02039"></a><span class="lineno"> 2039</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l02040"></a><span class="lineno"> 2040</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_resize_bilinear_descriptor.xhtml">armnn::ResizeBilinearDescriptor</a>& descriptor)</div><div class="line"><a name="l02041"></a><span class="lineno"> 2041</span>  : LayerVerifierBaseWithDescriptor<armnn::ResizeBilinearDescriptor>(</div><div class="line"><a name="l02042"></a><span class="lineno"> 2042</span>  layerName, inputInfos, outputInfos, descriptor) {}</div><div class="line"><a name="l02043"></a><span class="lineno"> 2043</span> </div><div class="line"><a name="l02044"></a><span class="lineno"> 2044</span>  <span class="keywordtype">void</span> VisitResizeLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l02045"></a><span class="lineno"> 2045</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_resize_descriptor.xhtml">armnn::ResizeDescriptor</a>& descriptor,</div><div class="line"><a name="l02046"></a><span class="lineno"> 2046</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l02047"></a><span class="lineno"> 2047</span> <span class="keyword"> </span>{</div><div class="line"><a name="l02048"></a><span class="lineno"> 2048</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l02049"></a><span class="lineno"> 2049</span> </div><div class="line"><a name="l02050"></a><span class="lineno"> 2050</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(descriptor.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a869254cb56968986a78a79e1d6d4a86b">m_Method</a> == <a class="code" href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f">armnn::ResizeMethod::Bilinear</a>);</div><div class="line"><a name="l02051"></a><span class="lineno"> 2051</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(descriptor.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">m_TargetWidth</a> == m_Descriptor.m_TargetWidth);</div><div class="line"><a name="l02052"></a><span class="lineno"> 2052</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(descriptor.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a46c3fa15c46fb0d1dcdc24d0ea5cb5cd">m_TargetHeight</a> == m_Descriptor.m_TargetHeight);</div><div class="line"><a name="l02053"></a><span class="lineno"> 2053</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(descriptor.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> == m_Descriptor.m_DataLayout);</div><div class="line"><a name="l02054"></a><span class="lineno"> 2054</span>  }</div><div class="line"><a name="l02055"></a><span class="lineno"> 2055</span> </div><div class="line"><a name="l02056"></a><span class="lineno"> 2056</span>  <span class="keywordtype">void</span> VisitResizeBilinearLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>*,</div><div class="line"><a name="l02057"></a><span class="lineno"> 2057</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_resize_bilinear_descriptor.xhtml">armnn::ResizeBilinearDescriptor</a>&,</div><div class="line"><a name="l02058"></a><span class="lineno"> 2058</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>*)<span class="keyword"> override</span></div><div class="line"><a name="l02059"></a><span class="lineno"> 2059</span> <span class="keyword"> </span>{</div><div class="line"><a name="l02060"></a><span class="lineno"> 2060</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_exception.xhtml">armnn::Exception</a>(<span class="stringliteral">"ResizeBilinearLayer should have translated to ResizeLayer"</span>);</div><div class="line"><a name="l02061"></a><span class="lineno"> 2061</span>  }</div><div class="line"><a name="l02062"></a><span class="lineno"> 2062</span> };</div><div class="line"><a name="l02063"></a><span class="lineno"> 2063</span> </div><div class="line"><a name="l02064"></a><span class="lineno"> 2064</span> <span class="comment">// NOTE: Until the deprecated AddResizeBilinearLayer disappears this test checks that</span></div><div class="line"><a name="l02065"></a><span class="lineno"> 2065</span> <span class="comment">// calling AddResizeBilinearLayer places a ResizeLayer into the serialized format</span></div><div class="line"><a name="l02066"></a><span class="lineno"> 2066</span> <span class="comment">// and that when this deserialises we have a ResizeLayer</span></div><div class="line"><a name="l02067"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a245e67bfc92e7d3ebc671f58b01ef9a7"> 2067</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeResizeBilinear)</div><div class="line"><a name="l02068"></a><span class="lineno"> 2068</span> {</div><div class="line"><a name="l02069"></a><span class="lineno"> 2069</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"resizeBilinear"</span>);</div><div class="line"><a name="l02070"></a><span class="lineno"> 2070</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 3, 5, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02071"></a><span class="lineno"> 2071</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 3, 2, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02072"></a><span class="lineno"> 2072</span> </div><div class="line"><a name="l02073"></a><span class="lineno"> 2073</span>  <a class="code" href="structarmnn_1_1_resize_bilinear_descriptor.xhtml">armnn::ResizeBilinearDescriptor</a> desc;</div><div class="line"><a name="l02074"></a><span class="lineno"> 2074</span>  desc.<a class="code" href="structarmnn_1_1_resize_bilinear_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">m_TargetWidth</a> = 4u;</div><div class="line"><a name="l02075"></a><span class="lineno"> 2075</span>  desc.m_TargetHeight = 2u;</div><div class="line"><a name="l02076"></a><span class="lineno"> 2076</span> </div><div class="line"><a name="l02077"></a><span class="lineno"> 2077</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02078"></a><span class="lineno"> 2078</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02079"></a><span class="lineno"> 2079</span>  <a class="code" href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="line"><a name="l02080"></a><span class="lineno"> 2080</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> resizeLayer = network->AddResizeBilinearLayer(desc, layerName.c_str());</div><div class="line"><a name="l02081"></a><span class="lineno"> 2081</span>  <a class="code" href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="line"><a name="l02082"></a><span class="lineno"> 2082</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02083"></a><span class="lineno"> 2083</span> </div><div class="line"><a name="l02084"></a><span class="lineno"> 2084</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(resizeLayer->GetInputSlot(0));</div><div class="line"><a name="l02085"></a><span class="lineno"> 2085</span>  resizeLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));</div><div class="line"><a name="l02086"></a><span class="lineno"> 2086</span> </div><div class="line"><a name="l02087"></a><span class="lineno"> 2087</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l02088"></a><span class="lineno"> 2088</span>  resizeLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);</div><div class="line"><a name="l02089"></a><span class="lineno"> 2089</span> </div><div class="line"><a name="l02090"></a><span class="lineno"> 2090</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02091"></a><span class="lineno"> 2091</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02092"></a><span class="lineno"> 2092</span> </div><div class="line"><a name="l02093"></a><span class="lineno"> 2093</span>  ResizeBilinearLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l02094"></a><span class="lineno"> 2094</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l02095"></a><span class="lineno"> 2095</span> }</div><div class="line"><a name="l02096"></a><span class="lineno"> 2096</span> </div><div class="line"><a name="l02097"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a9366f4798611b3505474586402acab33"> 2097</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(EnsureResizeBilinearBackwardCompatibility)</div><div class="line"><a name="l02098"></a><span class="lineno"> 2098</span> {</div><div class="line"><a name="l02099"></a><span class="lineno"> 2099</span>  <span class="comment">// The hex data below is a flat buffer containing a simple network with an input,</span></div><div class="line"><a name="l02100"></a><span class="lineno"> 2100</span>  <span class="comment">// a ResizeBilinearLayer (now deprecated) and an output</span></div><div class="line"><a name="l02101"></a><span class="lineno"> 2101</span>  <span class="comment">//</span></div><div class="line"><a name="l02102"></a><span class="lineno"> 2102</span>  <span class="comment">// This test verifies that we can still deserialize this old-style model by replacing</span></div><div class="line"><a name="l02103"></a><span class="lineno"> 2103</span>  <span class="comment">// the ResizeBilinearLayer with an equivalent ResizeLayer</span></div><div class="line"><a name="l02104"></a><span class="lineno"> 2104</span>  <span class="keyword">const</span> std::vector<uint8_t> resizeBilinearModel =</div><div class="line"><a name="l02105"></a><span class="lineno"> 2105</span>  {</div><div class="line"><a name="l02106"></a><span class="lineno"> 2106</span>  0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l02107"></a><span class="lineno"> 2107</span>  0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02108"></a><span class="lineno"> 2108</span>  0x50, 0x01, 0x00, 0x00, 0x74, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02109"></a><span class="lineno"> 2109</span>  0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0xD4, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B,</div><div class="line"><a name="l02110"></a><span class="lineno"> 2110</span>  0x04, 0x00, 0x00, 0x00, 0xC2, 0xFE, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x08, 0x00,</div><div class="line"><a name="l02111"></a><span class="lineno"> 2111</span>  0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x8A, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02112"></a><span class="lineno"> 2112</span>  0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02113"></a><span class="lineno"> 2113</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02114"></a><span class="lineno"> 2114</span>  0x38, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x30, 0xFF, 0xFF, 0xFF, 0x00, 0x00,</div><div class="line"><a name="l02115"></a><span class="lineno"> 2115</span>  0x00, 0x1A, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02116"></a><span class="lineno"> 2116</span>  0x34, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x12, 0x00, 0x08, 0x00, 0x0C, 0x00,</div><div class="line"><a name="l02117"></a><span class="lineno"> 2117</span>  0x07, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02118"></a><span class="lineno"> 2118</span>  0x00, 0x00, 0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0E, 0x00,</div><div class="line"><a name="l02119"></a><span class="lineno"> 2119</span>  0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x19, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02120"></a><span class="lineno"> 2120</span>  0x20, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x72, 0x65, 0x73, 0x69, 0x7A, 0x65, 0x42, 0x69, 0x6C, 0x69,</div><div class="line"><a name="l02121"></a><span class="lineno"> 2121</span>  0x6E, 0x65, 0x61, 0x72, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x48, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02122"></a><span class="lineno"> 2122</span>  0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00,</div><div class="line"><a name="l02123"></a><span class="lineno"> 2123</span>  0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02124"></a><span class="lineno"> 2124</span>  0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00,</div><div class="line"><a name="l02125"></a><span class="lineno"> 2125</span>  0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02126"></a><span class="lineno"> 2126</span>  0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02127"></a><span class="lineno"> 2127</span>  0x00, 0x09, 0x04, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00,</div><div class="line"><a name="l02128"></a><span class="lineno"> 2128</span>  0x0A, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x14, 0x00,</div><div class="line"><a name="l02129"></a><span class="lineno"> 2129</span>  0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02130"></a><span class="lineno"> 2130</span>  0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02131"></a><span class="lineno"> 2131</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l02132"></a><span class="lineno"> 2132</span>  0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00,</div><div class="line"><a name="l02133"></a><span class="lineno"> 2133</span>  0x08, 0x00, 0x07, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l02134"></a><span class="lineno"> 2134</span>  0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x05, 0x00,</div><div class="line"><a name="l02135"></a><span class="lineno"> 2135</span>  0x00, 0x00, 0x05, 0x00, 0x00, 0x00</div><div class="line"><a name="l02136"></a><span class="lineno"> 2136</span>  };</div><div class="line"><a name="l02137"></a><span class="lineno"> 2137</span> </div><div class="line"><a name="l02138"></a><span class="lineno"> 2138</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork =</div><div class="line"><a name="l02139"></a><span class="lineno"> 2139</span>  DeserializeNetwork(std::string(resizeBilinearModel.begin(), resizeBilinearModel.end()));</div><div class="line"><a name="l02140"></a><span class="lineno"> 2140</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02141"></a><span class="lineno"> 2141</span> </div><div class="line"><a name="l02142"></a><span class="lineno"> 2142</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 3, 5, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02143"></a><span class="lineno"> 2143</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({1, 3, 2, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02144"></a><span class="lineno"> 2144</span> </div><div class="line"><a name="l02145"></a><span class="lineno"> 2145</span>  <a class="code" href="structarmnn_1_1_resize_bilinear_descriptor.xhtml">armnn::ResizeBilinearDescriptor</a> descriptor;</div><div class="line"><a name="l02146"></a><span class="lineno"> 2146</span>  descriptor.<a class="code" href="structarmnn_1_1_resize_bilinear_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">m_TargetWidth</a> = 4u;</div><div class="line"><a name="l02147"></a><span class="lineno"> 2147</span>  descriptor.m_TargetHeight = 2u;</div><div class="line"><a name="l02148"></a><span class="lineno"> 2148</span> </div><div class="line"><a name="l02149"></a><span class="lineno"> 2149</span>  ResizeBilinearLayerVerifier verifier(<span class="stringliteral">"resizeBilinear"</span>, { inputInfo }, { outputInfo }, descriptor);</div><div class="line"><a name="l02150"></a><span class="lineno"> 2150</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l02151"></a><span class="lineno"> 2151</span> }</div><div class="line"><a name="l02152"></a><span class="lineno"> 2152</span> </div><div class="line"><a name="l02153"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#af6e91a50d2eb6d34cb2067b030f96f49"> 2153</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeSlice)</div><div class="line"><a name="l02154"></a><span class="lineno"> 2154</span> {</div><div class="line"><a name="l02155"></a><span class="lineno"> 2155</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#a044ea0cc993d4d1fbe4ec877b17b8d39">Slice</a>)</div><div class="line"><a name="l02156"></a><span class="lineno"> 2156</span> </div><div class="line"><a name="l02157"></a><span class="lineno"> 2157</span>  <span class="keyword">const</span> std::string layerName{<span class="stringliteral">"slice"</span>};</div><div class="line"><a name="l02158"></a><span class="lineno"> 2158</span> </div><div class="line"><a name="l02159"></a><span class="lineno"> 2159</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({3, 2, 3, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02160"></a><span class="lineno"> 2160</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({2, 2, 2, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02161"></a><span class="lineno"> 2161</span> </div><div class="line"><a name="l02162"></a><span class="lineno"> 2162</span>  <a class="code" href="structarmnn_1_1_slice_descriptor.xhtml">armnn::SliceDescriptor</a> descriptor({ 0, 0, 1, 0}, {2, 2, 2, 1});</div><div class="line"><a name="l02163"></a><span class="lineno"> 2163</span> </div><div class="line"><a name="l02164"></a><span class="lineno"> 2164</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02165"></a><span class="lineno"> 2165</span> </div><div class="line"><a name="l02166"></a><span class="lineno"> 2166</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02167"></a><span class="lineno"> 2167</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> sliceLayer = network->AddSliceLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l02168"></a><span class="lineno"> 2168</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02169"></a><span class="lineno"> 2169</span> </div><div class="line"><a name="l02170"></a><span class="lineno"> 2170</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(sliceLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02171"></a><span class="lineno"> 2171</span>  sliceLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02172"></a><span class="lineno"> 2172</span> </div><div class="line"><a name="l02173"></a><span class="lineno"> 2173</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l02174"></a><span class="lineno"> 2174</span>  sliceLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l02175"></a><span class="lineno"> 2175</span> </div><div class="line"><a name="l02176"></a><span class="lineno"> 2176</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02177"></a><span class="lineno"> 2177</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02178"></a><span class="lineno"> 2178</span> </div><div class="line"><a name="l02179"></a><span class="lineno"> 2179</span>  SliceLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);</div><div class="line"><a name="l02180"></a><span class="lineno"> 2180</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l02181"></a><span class="lineno"> 2181</span> }</div><div class="line"><a name="l02182"></a><span class="lineno"> 2182</span> </div><div class="line"><a name="l02183"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a4a4bf71c1ff0da50e564ef2514b4ffbb"> 2183</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeSoftmax)</div><div class="line"><a name="l02184"></a><span class="lineno"> 2184</span> {</div><div class="line"><a name="l02185"></a><span class="lineno"> 2185</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#aa999ff2585ad75b95954a9323f63c32b">Softmax</a>)</div><div class="line"><a name="l02186"></a><span class="lineno"> 2186</span> </div><div class="line"><a name="l02187"></a><span class="lineno"> 2187</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"softmax"</span>);</div><div class="line"><a name="l02188"></a><span class="lineno"> 2188</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({1, 10}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02189"></a><span class="lineno"> 2189</span> </div><div class="line"><a name="l02190"></a><span class="lineno"> 2190</span>  <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">armnn::SoftmaxDescriptor</a> descriptor;</div><div class="line"><a name="l02191"></a><span class="lineno"> 2191</span>  descriptor.<a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = 1.0f;</div><div class="line"><a name="l02192"></a><span class="lineno"> 2192</span> </div><div class="line"><a name="l02193"></a><span class="lineno"> 2193</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02194"></a><span class="lineno"> 2194</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02195"></a><span class="lineno"> 2195</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> softmaxLayer = network->AddSoftmaxLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l02196"></a><span class="lineno"> 2196</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02197"></a><span class="lineno"> 2197</span> </div><div class="line"><a name="l02198"></a><span class="lineno"> 2198</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(softmaxLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02199"></a><span class="lineno"> 2199</span>  softmaxLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02200"></a><span class="lineno"> 2200</span> </div><div class="line"><a name="l02201"></a><span class="lineno"> 2201</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02202"></a><span class="lineno"> 2202</span>  softmaxLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02203"></a><span class="lineno"> 2203</span> </div><div class="line"><a name="l02204"></a><span class="lineno"> 2204</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02205"></a><span class="lineno"> 2205</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02206"></a><span class="lineno"> 2206</span> </div><div class="line"><a name="l02207"></a><span class="lineno"> 2207</span>  SoftmaxLayerVerifier verifier(layerName, {info}, {info}, descriptor);</div><div class="line"><a name="l02208"></a><span class="lineno"> 2208</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l02209"></a><span class="lineno"> 2209</span> }</div><div class="line"><a name="l02210"></a><span class="lineno"> 2210</span> </div><div class="line"><a name="l02211"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a798088dcd410d5c4e70e619986a19fa1"> 2211</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeSpaceToBatchNd)</div><div class="line"><a name="l02212"></a><span class="lineno"> 2212</span> {</div><div class="line"><a name="l02213"></a><span class="lineno"> 2213</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#a4a180e425d4c19b2cdea4ce5760180e1">SpaceToBatchNd</a>)</div><div class="line"><a name="l02214"></a><span class="lineno"> 2214</span> </div><div class="line"><a name="l02215"></a><span class="lineno"> 2215</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"spaceToBatchNd"</span>);</div><div class="line"><a name="l02216"></a><span class="lineno"> 2216</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({2, 1, 2, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02217"></a><span class="lineno"> 2217</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({8, 1, 1, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02218"></a><span class="lineno"> 2218</span> </div><div class="line"><a name="l02219"></a><span class="lineno"> 2219</span>  <a class="code" href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml">armnn::SpaceToBatchNdDescriptor</a> desc;</div><div class="line"><a name="l02220"></a><span class="lineno"> 2220</span>  desc.<a class="code" href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>;</div><div class="line"><a name="l02221"></a><span class="lineno"> 2221</span>  desc.m_BlockShape = {2, 2};</div><div class="line"><a name="l02222"></a><span class="lineno"> 2222</span>  desc.m_PadList = {{0, 0}, {2, 0}};</div><div class="line"><a name="l02223"></a><span class="lineno"> 2223</span> </div><div class="line"><a name="l02224"></a><span class="lineno"> 2224</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02225"></a><span class="lineno"> 2225</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02226"></a><span class="lineno"> 2226</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> spaceToBatchNdLayer = network->AddSpaceToBatchNdLayer(desc, layerName.c_str());</div><div class="line"><a name="l02227"></a><span class="lineno"> 2227</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02228"></a><span class="lineno"> 2228</span> </div><div class="line"><a name="l02229"></a><span class="lineno"> 2229</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(spaceToBatchNdLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02230"></a><span class="lineno"> 2230</span>  spaceToBatchNdLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02231"></a><span class="lineno"> 2231</span> </div><div class="line"><a name="l02232"></a><span class="lineno"> 2232</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l02233"></a><span class="lineno"> 2233</span>  spaceToBatchNdLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l02234"></a><span class="lineno"> 2234</span> </div><div class="line"><a name="l02235"></a><span class="lineno"> 2235</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02236"></a><span class="lineno"> 2236</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02237"></a><span class="lineno"> 2237</span> </div><div class="line"><a name="l02238"></a><span class="lineno"> 2238</span>  SpaceToBatchNdLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l02239"></a><span class="lineno"> 2239</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l02240"></a><span class="lineno"> 2240</span> }</div><div class="line"><a name="l02241"></a><span class="lineno"> 2241</span> </div><div class="line"><a name="l02242"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a46f46381910339f4154d071b074df35e"> 2242</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeSpaceToDepth)</div><div class="line"><a name="l02243"></a><span class="lineno"> 2243</span> {</div><div class="line"><a name="l02244"></a><span class="lineno"> 2244</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#a5e1dc69443b64ad16b669388a6023f7a">SpaceToDepth</a>)</div><div class="line"><a name="l02245"></a><span class="lineno"> 2245</span> </div><div class="line"><a name="l02246"></a><span class="lineno"> 2246</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"spaceToDepth"</span>);</div><div class="line"><a name="l02247"></a><span class="lineno"> 2247</span> </div><div class="line"><a name="l02248"></a><span class="lineno"> 2248</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 16, 8, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02249"></a><span class="lineno"> 2249</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 8, 4, 12 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02250"></a><span class="lineno"> 2250</span> </div><div class="line"><a name="l02251"></a><span class="lineno"> 2251</span>  <a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml">armnn::SpaceToDepthDescriptor</a> desc;</div><div class="line"><a name="l02252"></a><span class="lineno"> 2252</span>  desc.<a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml#a6c6b8957f1e176867e5fb05b1a1a1486">m_BlockSize</a> = 2;</div><div class="line"><a name="l02253"></a><span class="lineno"> 2253</span>  desc.m_DataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l02254"></a><span class="lineno"> 2254</span> </div><div class="line"><a name="l02255"></a><span class="lineno"> 2255</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02256"></a><span class="lineno"> 2256</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02257"></a><span class="lineno"> 2257</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> spaceToDepthLayer = network->AddSpaceToDepthLayer(desc, layerName.c_str());</div><div class="line"><a name="l02258"></a><span class="lineno"> 2258</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02259"></a><span class="lineno"> 2259</span> </div><div class="line"><a name="l02260"></a><span class="lineno"> 2260</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(spaceToDepthLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02261"></a><span class="lineno"> 2261</span>  spaceToDepthLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02262"></a><span class="lineno"> 2262</span> </div><div class="line"><a name="l02263"></a><span class="lineno"> 2263</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l02264"></a><span class="lineno"> 2264</span>  spaceToDepthLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l02265"></a><span class="lineno"> 2265</span> </div><div class="line"><a name="l02266"></a><span class="lineno"> 2266</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02267"></a><span class="lineno"> 2267</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02268"></a><span class="lineno"> 2268</span> </div><div class="line"><a name="l02269"></a><span class="lineno"> 2269</span>  SpaceToDepthLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l02270"></a><span class="lineno"> 2270</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l02271"></a><span class="lineno"> 2271</span> }</div><div class="line"><a name="l02272"></a><span class="lineno"> 2272</span> </div><div class="line"><a name="l02273"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#aad7569c190fcdb4901e8665c80df013b"> 2273</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeSplitter)</div><div class="line"><a name="l02274"></a><span class="lineno"> 2274</span> {</div><div class="line"><a name="l02275"></a><span class="lineno"> 2275</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#a427c3d26d05b518b1ace407035f5920e">Splitter</a>)</div><div class="line"><a name="l02276"></a><span class="lineno"> 2276</span> </div><div class="line"><a name="l02277"></a><span class="lineno"> 2277</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numViews = 3;</div><div class="line"><a name="l02278"></a><span class="lineno"> 2278</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDimensions = 4;</div><div class="line"><a name="l02279"></a><span class="lineno"> 2279</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = {1, 18, 4, 4};</div><div class="line"><a name="l02280"></a><span class="lineno"> 2280</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = {1, 6, 4, 4};</div><div class="line"><a name="l02281"></a><span class="lineno"> 2281</span> </div><div class="line"><a name="l02282"></a><span class="lineno"> 2282</span>  <span class="comment">// This is modelled on how the caffe parser sets up a splitter layer to partition an input along dimension one.</span></div><div class="line"><a name="l02283"></a><span class="lineno"> 2283</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitterDimSizes[4] = {<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(inputShape[0]),</div><div class="line"><a name="l02284"></a><span class="lineno"> 2284</span>  static_cast<unsigned int>(inputShape[1]),</div><div class="line"><a name="l02285"></a><span class="lineno"> 2285</span>  <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(inputShape[2]),</div><div class="line"><a name="l02286"></a><span class="lineno"> 2286</span>  static_cast<unsigned int>(inputShape[3])};</div><div class="line"><a name="l02287"></a><span class="lineno"> 2287</span>  splitterDimSizes[1] /= numViews;</div><div class="line"><a name="l02288"></a><span class="lineno"> 2288</span>  <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">armnn::ViewsDescriptor</a> desc(numViews, numDimensions);</div><div class="line"><a name="l02289"></a><span class="lineno"> 2289</span> </div><div class="line"><a name="l02290"></a><span class="lineno"> 2290</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> g = 0; g < numViews; ++g)</div><div class="line"><a name="l02291"></a><span class="lineno"> 2291</span>  {</div><div class="line"><a name="l02292"></a><span class="lineno"> 2292</span>  desc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(g, 1, splitterDimSizes[1] * g);</div><div class="line"><a name="l02293"></a><span class="lineno"> 2293</span> </div><div class="line"><a name="l02294"></a><span class="lineno"> 2294</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIdx=0; dimIdx < 4; dimIdx++)</div><div class="line"><a name="l02295"></a><span class="lineno"> 2295</span>  {</div><div class="line"><a name="l02296"></a><span class="lineno"> 2296</span>  desc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">SetViewSize</a>(g, dimIdx, splitterDimSizes[dimIdx]);</div><div class="line"><a name="l02297"></a><span class="lineno"> 2297</span>  }</div><div class="line"><a name="l02298"></a><span class="lineno"> 2298</span>  }</div><div class="line"><a name="l02299"></a><span class="lineno"> 2299</span> </div><div class="line"><a name="l02300"></a><span class="lineno"> 2300</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"splitter"</span>);</div><div class="line"><a name="l02301"></a><span class="lineno"> 2301</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo(numDimensions, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02302"></a><span class="lineno"> 2302</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo(numDimensions, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02303"></a><span class="lineno"> 2303</span> </div><div class="line"><a name="l02304"></a><span class="lineno"> 2304</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02305"></a><span class="lineno"> 2305</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02306"></a><span class="lineno"> 2306</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> splitterLayer = network->AddSplitterLayer(desc, layerName.c_str());</div><div class="line"><a name="l02307"></a><span class="lineno"> 2307</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer0 = network->AddOutputLayer(0);</div><div class="line"><a name="l02308"></a><span class="lineno"> 2308</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer1 = network->AddOutputLayer(1);</div><div class="line"><a name="l02309"></a><span class="lineno"> 2309</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer2 = network->AddOutputLayer(2);</div><div class="line"><a name="l02310"></a><span class="lineno"> 2310</span> </div><div class="line"><a name="l02311"></a><span class="lineno"> 2311</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(splitterLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02312"></a><span class="lineno"> 2312</span>  splitterLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02313"></a><span class="lineno"> 2313</span>  splitterLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02314"></a><span class="lineno"> 2314</span>  splitterLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer2-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02315"></a><span class="lineno"> 2315</span> </div><div class="line"><a name="l02316"></a><span class="lineno"> 2316</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l02317"></a><span class="lineno"> 2317</span>  splitterLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l02318"></a><span class="lineno"> 2318</span>  splitterLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l02319"></a><span class="lineno"> 2319</span>  splitterLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l02320"></a><span class="lineno"> 2320</span> </div><div class="line"><a name="l02321"></a><span class="lineno"> 2321</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02322"></a><span class="lineno"> 2322</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02323"></a><span class="lineno"> 2323</span> </div><div class="line"><a name="l02324"></a><span class="lineno"> 2324</span>  SplitterLayerVerifier verifier(layerName, {inputInfo}, {outputInfo, outputInfo, outputInfo}, desc);</div><div class="line"><a name="l02325"></a><span class="lineno"> 2325</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l02326"></a><span class="lineno"> 2326</span> }</div><div class="line"><a name="l02327"></a><span class="lineno"> 2327</span> </div><div class="line"><a name="l02328"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a9e3547d945fb7ee85e09cfd3423780a9"> 2328</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeStack)</div><div class="line"><a name="l02329"></a><span class="lineno"> 2329</span> {</div><div class="line"><a name="l02330"></a><span class="lineno"> 2330</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#a6ef2dcac2ec0683d52df1b051404e7d6">Stack</a>)</div><div class="line"><a name="l02331"></a><span class="lineno"> 2331</span> </div><div class="line"><a name="l02332"></a><span class="lineno"> 2332</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"stack"</span>);</div><div class="line"><a name="l02333"></a><span class="lineno"> 2333</span> </div><div class="line"><a name="l02334"></a><span class="lineno"> 2334</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo ({4, 3, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02335"></a><span class="lineno"> 2335</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({4, 3, 2, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02336"></a><span class="lineno"> 2336</span> </div><div class="line"><a name="l02337"></a><span class="lineno"> 2337</span>  <a class="code" href="structarmnn_1_1_stack_descriptor.xhtml">armnn::StackDescriptor</a> descriptor(2, 2, {4, 3, 5});</div><div class="line"><a name="l02338"></a><span class="lineno"> 2338</span> </div><div class="line"><a name="l02339"></a><span class="lineno"> 2339</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02340"></a><span class="lineno"> 2340</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(0);</div><div class="line"><a name="l02341"></a><span class="lineno"> 2341</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer2 = network->AddInputLayer(1);</div><div class="line"><a name="l02342"></a><span class="lineno"> 2342</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> stackLayer = network->AddStackLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l02343"></a><span class="lineno"> 2343</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02344"></a><span class="lineno"> 2344</span> </div><div class="line"><a name="l02345"></a><span class="lineno"> 2345</span>  inputLayer1-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(stackLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02346"></a><span class="lineno"> 2346</span>  inputLayer2-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(stackLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02347"></a><span class="lineno"> 2347</span>  stackLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02348"></a><span class="lineno"> 2348</span> </div><div class="line"><a name="l02349"></a><span class="lineno"> 2349</span>  inputLayer1-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l02350"></a><span class="lineno"> 2350</span>  inputLayer2-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l02351"></a><span class="lineno"> 2351</span>  stackLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02352"></a><span class="lineno"> 2352</span> </div><div class="line"><a name="l02353"></a><span class="lineno"> 2353</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02354"></a><span class="lineno"> 2354</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02355"></a><span class="lineno"> 2355</span> </div><div class="line"><a name="l02356"></a><span class="lineno"> 2356</span>  StackLayerVerifier verifier(layerName, {inputTensorInfo, inputTensorInfo}, {outputTensorInfo}, descriptor);</div><div class="line"><a name="l02357"></a><span class="lineno"> 2357</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l02358"></a><span class="lineno"> 2358</span> }</div><div class="line"><a name="l02359"></a><span class="lineno"> 2359</span> </div><div class="line"><a name="l02360"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a865de239f4cf854e65f8b61ebbbb7fbd"> 2360</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeStandIn)</div><div class="line"><a name="l02361"></a><span class="lineno"> 2361</span> {</div><div class="line"><a name="l02362"></a><span class="lineno"> 2362</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(StandIn)</div><div class="line"><a name="l02363"></a><span class="lineno"> 2363</span> </div><div class="line"><a name="l02364"></a><span class="lineno"> 2364</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"standIn"</span>);</div><div class="line"><a name="l02365"></a><span class="lineno"> 2365</span> </div><div class="line"><a name="l02366"></a><span class="lineno"> 2366</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo({ 1u }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02367"></a><span class="lineno"> 2367</span>  <a class="code" href="structarmnn_1_1_stand_in_descriptor.xhtml">armnn::StandInDescriptor</a> descriptor(2u, 2u);</div><div class="line"><a name="l02368"></a><span class="lineno"> 2368</span> </div><div class="line"><a name="l02369"></a><span class="lineno"> 2369</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02370"></a><span class="lineno"> 2370</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l02371"></a><span class="lineno"> 2371</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l02372"></a><span class="lineno"> 2372</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> standInLayer = network->AddStandInLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l02373"></a><span class="lineno"> 2373</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer0 = network->AddOutputLayer(0);</div><div class="line"><a name="l02374"></a><span class="lineno"> 2374</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer1 = network->AddOutputLayer(1);</div><div class="line"><a name="l02375"></a><span class="lineno"> 2375</span> </div><div class="line"><a name="l02376"></a><span class="lineno"> 2376</span>  inputLayer0-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(standInLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02377"></a><span class="lineno"> 2377</span>  inputLayer0-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l02378"></a><span class="lineno"> 2378</span> </div><div class="line"><a name="l02379"></a><span class="lineno"> 2379</span>  inputLayer1-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(standInLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02380"></a><span class="lineno"> 2380</span>  inputLayer1-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l02381"></a><span class="lineno"> 2381</span> </div><div class="line"><a name="l02382"></a><span class="lineno"> 2382</span>  standInLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer0-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02383"></a><span class="lineno"> 2383</span>  standInLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l02384"></a><span class="lineno"> 2384</span> </div><div class="line"><a name="l02385"></a><span class="lineno"> 2385</span>  standInLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer1-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02386"></a><span class="lineno"> 2386</span>  standInLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l02387"></a><span class="lineno"> 2387</span> </div><div class="line"><a name="l02388"></a><span class="lineno"> 2388</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02389"></a><span class="lineno"> 2389</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02390"></a><span class="lineno"> 2390</span> </div><div class="line"><a name="l02391"></a><span class="lineno"> 2391</span>  StandInLayerVerifier verifier(layerName, { tensorInfo, tensorInfo }, { tensorInfo, tensorInfo }, descriptor);</div><div class="line"><a name="l02392"></a><span class="lineno"> 2392</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l02393"></a><span class="lineno"> 2393</span> }</div><div class="line"><a name="l02394"></a><span class="lineno"> 2394</span> </div><div class="line"><a name="l02395"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a2db8caccb8225bbef2368872aa9355d1"> 2395</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeStridedSlice)</div><div class="line"><a name="l02396"></a><span class="lineno"> 2396</span> {</div><div class="line"><a name="l02397"></a><span class="lineno"> 2397</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn.xhtml#a86d7a7168ac00b75b4971f9aad623698">StridedSlice</a>)</div><div class="line"><a name="l02398"></a><span class="lineno"> 2398</span> </div><div class="line"><a name="l02399"></a><span class="lineno"> 2399</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"stridedSlice"</span>);</div><div class="line"><a name="l02400"></a><span class="lineno"> 2400</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({3, 2, 3, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02401"></a><span class="lineno"> 2401</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({3, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02402"></a><span class="lineno"> 2402</span> </div><div class="line"><a name="l02403"></a><span class="lineno"> 2403</span>  <a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml">armnn::StridedSliceDescriptor</a> desc({0, 0, 1, 0}, {1, 1, 1, 1}, {1, 1, 1, 1});</div><div class="line"><a name="l02404"></a><span class="lineno"> 2404</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#ac37e49c0d6e6e54f9d2015d0f11f8ee7">m_EndMask</a> = (1 << 4) - 1;</div><div class="line"><a name="l02405"></a><span class="lineno"> 2405</span>  desc.m_ShrinkAxisMask = (1 << 1) | (1 << 2);</div><div class="line"><a name="l02406"></a><span class="lineno"> 2406</span>  desc.m_DataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>;</div><div class="line"><a name="l02407"></a><span class="lineno"> 2407</span> </div><div class="line"><a name="l02408"></a><span class="lineno"> 2408</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02409"></a><span class="lineno"> 2409</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02410"></a><span class="lineno"> 2410</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> stridedSliceLayer = network->AddStridedSliceLayer(desc, layerName.c_str());</div><div class="line"><a name="l02411"></a><span class="lineno"> 2411</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02412"></a><span class="lineno"> 2412</span> </div><div class="line"><a name="l02413"></a><span class="lineno"> 2413</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(stridedSliceLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02414"></a><span class="lineno"> 2414</span>  stridedSliceLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02415"></a><span class="lineno"> 2415</span> </div><div class="line"><a name="l02416"></a><span class="lineno"> 2416</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l02417"></a><span class="lineno"> 2417</span>  stridedSliceLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l02418"></a><span class="lineno"> 2418</span> </div><div class="line"><a name="l02419"></a><span class="lineno"> 2419</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02420"></a><span class="lineno"> 2420</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02421"></a><span class="lineno"> 2421</span> </div><div class="line"><a name="l02422"></a><span class="lineno"> 2422</span>  StridedSliceLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);</div><div class="line"><a name="l02423"></a><span class="lineno"> 2423</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l02424"></a><span class="lineno"> 2424</span> }</div><div class="line"><a name="l02425"></a><span class="lineno"> 2425</span> </div><div class="line"><a name="l02426"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a1ceee00de3ecf9d68bdebed498cd049a"> 2426</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeSubtraction)</div><div class="line"><a name="l02427"></a><span class="lineno"> 2427</span> {</div><div class="line"><a name="l02428"></a><span class="lineno"> 2428</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a>(Subtraction)</div><div class="line"><a name="l02429"></a><span class="lineno"> 2429</span> </div><div class="line"><a name="l02430"></a><span class="lineno"> 2430</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"subtraction"</span>);</div><div class="line"><a name="l02431"></a><span class="lineno"> 2431</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 1, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02432"></a><span class="lineno"> 2432</span> </div><div class="line"><a name="l02433"></a><span class="lineno"> 2433</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02434"></a><span class="lineno"> 2434</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer0 = network->AddInputLayer(0);</div><div class="line"><a name="l02435"></a><span class="lineno"> 2435</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer1 = network->AddInputLayer(1);</div><div class="line"><a name="l02436"></a><span class="lineno"> 2436</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> subtractionLayer = network->AddSubtractionLayer(layerName.c_str());</div><div class="line"><a name="l02437"></a><span class="lineno"> 2437</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02438"></a><span class="lineno"> 2438</span> </div><div class="line"><a name="l02439"></a><span class="lineno"> 2439</span>  inputLayer0-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(subtractionLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02440"></a><span class="lineno"> 2440</span>  inputLayer1-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(subtractionLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02441"></a><span class="lineno"> 2441</span>  subtractionLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02442"></a><span class="lineno"> 2442</span> </div><div class="line"><a name="l02443"></a><span class="lineno"> 2443</span>  inputLayer0-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02444"></a><span class="lineno"> 2444</span>  inputLayer1-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02445"></a><span class="lineno"> 2445</span>  subtractionLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02446"></a><span class="lineno"> 2446</span> </div><div class="line"><a name="l02447"></a><span class="lineno"> 2447</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02448"></a><span class="lineno"> 2448</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02449"></a><span class="lineno"> 2449</span> </div><div class="line"><a name="l02450"></a><span class="lineno"> 2450</span>  SubtractionLayerVerifier verifier(layerName, {info, info}, {info});</div><div class="line"><a name="l02451"></a><span class="lineno"> 2451</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l02452"></a><span class="lineno"> 2452</span> }</div><div class="line"><a name="l02453"></a><span class="lineno"> 2453</span> </div><div class="line"><a name="l02454"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a2ae604a52ed7b47ffe458eaff5675476"> 2454</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeSwitch)</div><div class="line"><a name="l02455"></a><span class="lineno"> 2455</span> {</div><div class="line"><a name="l02456"></a><span class="lineno"> 2456</span>  <span class="keyword">class </span>SwitchLayerVerifier : <span class="keyword">public</span> LayerVerifierBase</div><div class="line"><a name="l02457"></a><span class="lineno"> 2457</span>  {</div><div class="line"><a name="l02458"></a><span class="lineno"> 2458</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l02459"></a><span class="lineno"> 2459</span>  SwitchLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l02460"></a><span class="lineno"> 2460</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l02461"></a><span class="lineno"> 2461</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos)</div><div class="line"><a name="l02462"></a><span class="lineno"> 2462</span>  : LayerVerifierBase(layerName, inputInfos, outputInfos) {}</div><div class="line"><a name="l02463"></a><span class="lineno"> 2463</span> </div><div class="line"><a name="l02464"></a><span class="lineno"> 2464</span>  <span class="keywordtype">void</span> VisitSwitchLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer, <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l02465"></a><span class="lineno"> 2465</span> <span class="keyword"> </span>{</div><div class="line"><a name="l02466"></a><span class="lineno"> 2466</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l02467"></a><span class="lineno"> 2467</span>  }</div><div class="line"><a name="l02468"></a><span class="lineno"> 2468</span> </div><div class="line"><a name="l02469"></a><span class="lineno"> 2469</span>  <span class="keywordtype">void</span> VisitConstantLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>*,</div><div class="line"><a name="l02470"></a><span class="lineno"> 2470</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>&,</div><div class="line"><a name="l02471"></a><span class="lineno"> 2471</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>*)<span class="keyword"> override </span>{}</div><div class="line"><a name="l02472"></a><span class="lineno"> 2472</span>  };</div><div class="line"><a name="l02473"></a><span class="lineno"> 2473</span> </div><div class="line"><a name="l02474"></a><span class="lineno"> 2474</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"switch"</span>);</div><div class="line"><a name="l02475"></a><span class="lineno"> 2475</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 1, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02476"></a><span class="lineno"> 2476</span> </div><div class="line"><a name="l02477"></a><span class="lineno"> 2477</span>  std::vector<float> constantData = GenerateRandomData<float>(info.GetNumElements());</div><div class="line"><a name="l02478"></a><span class="lineno"> 2478</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> constTensor(info, constantData);</div><div class="line"><a name="l02479"></a><span class="lineno"> 2479</span> </div><div class="line"><a name="l02480"></a><span class="lineno"> 2480</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02481"></a><span class="lineno"> 2481</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02482"></a><span class="lineno"> 2482</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> constantLayer = network->AddConstantLayer(constTensor, <span class="stringliteral">"constant"</span>);</div><div class="line"><a name="l02483"></a><span class="lineno"> 2483</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> switchLayer = network->AddSwitchLayer(layerName.c_str());</div><div class="line"><a name="l02484"></a><span class="lineno"> 2484</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> trueOutputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02485"></a><span class="lineno"> 2485</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> falseOutputLayer = network->AddOutputLayer(1);</div><div class="line"><a name="l02486"></a><span class="lineno"> 2486</span> </div><div class="line"><a name="l02487"></a><span class="lineno"> 2487</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(switchLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02488"></a><span class="lineno"> 2488</span>  constantLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(switchLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02489"></a><span class="lineno"> 2489</span>  switchLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(trueOutputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02490"></a><span class="lineno"> 2490</span>  switchLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(falseOutputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02491"></a><span class="lineno"> 2491</span> </div><div class="line"><a name="l02492"></a><span class="lineno"> 2492</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02493"></a><span class="lineno"> 2493</span>  constantLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02494"></a><span class="lineno"> 2494</span>  switchLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02495"></a><span class="lineno"> 2495</span>  switchLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02496"></a><span class="lineno"> 2496</span> </div><div class="line"><a name="l02497"></a><span class="lineno"> 2497</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02498"></a><span class="lineno"> 2498</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02499"></a><span class="lineno"> 2499</span> </div><div class="line"><a name="l02500"></a><span class="lineno"> 2500</span>  SwitchLayerVerifier verifier(layerName, {info, info}, {info, info});</div><div class="line"><a name="l02501"></a><span class="lineno"> 2501</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l02502"></a><span class="lineno"> 2502</span> }</div><div class="line"><a name="l02503"></a><span class="lineno"> 2503</span> </div><div class="line"><a name="l02504"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a11f5fe3da18636059c4d8c21e11ac3f5"> 2504</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeTranspose)</div><div class="line"><a name="l02505"></a><span class="lineno"> 2505</span> {</div><div class="line"><a name="l02506"></a><span class="lineno"> 2506</span>  <a class="code" href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a>(<a class="code" href="namespacearmnn_utils.xhtml#a405d5f966ec992d1717711e5a2d7909d">Transpose</a>)</div><div class="line"><a name="l02507"></a><span class="lineno"> 2507</span> </div><div class="line"><a name="l02508"></a><span class="lineno"> 2508</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"transpose"</span>);</div><div class="line"><a name="l02509"></a><span class="lineno"> 2509</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({4, 3, 2, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02510"></a><span class="lineno"> 2510</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({1, 2, 3, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02511"></a><span class="lineno"> 2511</span> </div><div class="line"><a name="l02512"></a><span class="lineno"> 2512</span>  <a class="code" href="structarmnn_1_1_transpose_descriptor.xhtml">armnn::TransposeDescriptor</a> descriptor(<a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a>({3, 2, 1, 0}));</div><div class="line"><a name="l02513"></a><span class="lineno"> 2513</span> </div><div class="line"><a name="l02514"></a><span class="lineno"> 2514</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02515"></a><span class="lineno"> 2515</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02516"></a><span class="lineno"> 2516</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> transposeLayer = network->AddTransposeLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l02517"></a><span class="lineno"> 2517</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02518"></a><span class="lineno"> 2518</span> </div><div class="line"><a name="l02519"></a><span class="lineno"> 2519</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(transposeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02520"></a><span class="lineno"> 2520</span>  transposeLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02521"></a><span class="lineno"> 2521</span> </div><div class="line"><a name="l02522"></a><span class="lineno"> 2522</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l02523"></a><span class="lineno"> 2523</span>  transposeLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02524"></a><span class="lineno"> 2524</span> </div><div class="line"><a name="l02525"></a><span class="lineno"> 2525</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02526"></a><span class="lineno"> 2526</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02527"></a><span class="lineno"> 2527</span> </div><div class="line"><a name="l02528"></a><span class="lineno"> 2528</span>  TransposeLayerVerifier verifier(layerName, {inputTensorInfo}, {outputTensorInfo}, descriptor);</div><div class="line"><a name="l02529"></a><span class="lineno"> 2529</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l02530"></a><span class="lineno"> 2530</span> }</div><div class="line"><a name="l02531"></a><span class="lineno"> 2531</span> </div><div class="line"><a name="l02532"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#acb977ca4fb62419f89117fa33c5a4d4a"> 2532</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeTransposeConvolution2d)</div><div class="line"><a name="l02533"></a><span class="lineno"> 2533</span> {</div><div class="line"><a name="l02534"></a><span class="lineno"> 2534</span>  <span class="keyword">using</span> Descriptor = <a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">armnn::TransposeConvolution2dDescriptor</a>;</div><div class="line"><a name="l02535"></a><span class="lineno"> 2535</span>  <span class="keyword">class </span>TransposeConvolution2dLayerVerifier : <span class="keyword">public</span> LayerVerifierBaseWithDescriptor<Descriptor></div><div class="line"><a name="l02536"></a><span class="lineno"> 2536</span>  {</div><div class="line"><a name="l02537"></a><span class="lineno"> 2537</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l02538"></a><span class="lineno"> 2538</span>  TransposeConvolution2dLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l02539"></a><span class="lineno"> 2539</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l02540"></a><span class="lineno"> 2540</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l02541"></a><span class="lineno"> 2541</span>  <span class="keyword">const</span> Descriptor& descriptor,</div><div class="line"><a name="l02542"></a><span class="lineno"> 2542</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& weights,</div><div class="line"><a name="l02543"></a><span class="lineno"> 2543</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a>& biases)</div><div class="line"><a name="l02544"></a><span class="lineno"> 2544</span>  : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)</div><div class="line"><a name="l02545"></a><span class="lineno"> 2545</span>  , m_Weights(weights)</div><div class="line"><a name="l02546"></a><span class="lineno"> 2546</span>  , m_Biases(biases)</div><div class="line"><a name="l02547"></a><span class="lineno"> 2547</span>  {}</div><div class="line"><a name="l02548"></a><span class="lineno"> 2548</span> </div><div class="line"><a name="l02549"></a><span class="lineno"> 2549</span>  <span class="keywordtype">void</span> VisitTransposeConvolution2dLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l02550"></a><span class="lineno"> 2550</span>  <span class="keyword">const</span> Descriptor& descriptor,</div><div class="line"><a name="l02551"></a><span class="lineno"> 2551</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& weights,</div><div class="line"><a name="l02552"></a><span class="lineno"> 2552</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a>& biases,</div><div class="line"><a name="l02553"></a><span class="lineno"> 2553</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l02554"></a><span class="lineno"> 2554</span> <span class="keyword"> </span>{</div><div class="line"><a name="l02555"></a><span class="lineno"> 2555</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l02556"></a><span class="lineno"> 2556</span>  VerifyDescriptor(descriptor);</div><div class="line"><a name="l02557"></a><span class="lineno"> 2557</span> </div><div class="line"><a name="l02558"></a><span class="lineno"> 2558</span>  <span class="comment">// check weights</span></div><div class="line"><a name="l02559"></a><span class="lineno"> 2559</span>  CompareConstTensor(weights, m_Weights);</div><div class="line"><a name="l02560"></a><span class="lineno"> 2560</span> </div><div class="line"><a name="l02561"></a><span class="lineno"> 2561</span>  <span class="comment">// check biases</span></div><div class="line"><a name="l02562"></a><span class="lineno"> 2562</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() == descriptor.m_BiasEnabled);</div><div class="line"><a name="l02563"></a><span class="lineno"> 2563</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() == m_Biases.has_value());</div><div class="line"><a name="l02564"></a><span class="lineno"> 2564</span> </div><div class="line"><a name="l02565"></a><span class="lineno"> 2565</span>  <span class="keywordflow">if</span> (biases.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() && m_Biases.has_value())</div><div class="line"><a name="l02566"></a><span class="lineno"> 2566</span>  {</div><div class="line"><a name="l02567"></a><span class="lineno"> 2567</span>  CompareConstTensor(biases.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>(), m_Biases.value());</div><div class="line"><a name="l02568"></a><span class="lineno"> 2568</span>  }</div><div class="line"><a name="l02569"></a><span class="lineno"> 2569</span>  }</div><div class="line"><a name="l02570"></a><span class="lineno"> 2570</span> </div><div class="line"><a name="l02571"></a><span class="lineno"> 2571</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l02572"></a><span class="lineno"> 2572</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> m_Weights;</div><div class="line"><a name="l02573"></a><span class="lineno"> 2573</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a> m_Biases;</div><div class="line"><a name="l02574"></a><span class="lineno"> 2574</span>  };</div><div class="line"><a name="l02575"></a><span class="lineno"> 2575</span> </div><div class="line"><a name="l02576"></a><span class="lineno"> 2576</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"transposeConvolution2d"</span>);</div><div class="line"><a name="l02577"></a><span class="lineno"> 2577</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo ({ 1, 7, 7, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02578"></a><span class="lineno"> 2578</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({ 1, 9, 9, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02579"></a><span class="lineno"> 2579</span> </div><div class="line"><a name="l02580"></a><span class="lineno"> 2580</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsInfo({ 1, 3, 3, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02581"></a><span class="lineno"> 2581</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesInfo ({ 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02582"></a><span class="lineno"> 2582</span> </div><div class="line"><a name="l02583"></a><span class="lineno"> 2583</span>  std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements());</div><div class="line"><a name="l02584"></a><span class="lineno"> 2584</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(weightsInfo, weightsData);</div><div class="line"><a name="l02585"></a><span class="lineno"> 2585</span> </div><div class="line"><a name="l02586"></a><span class="lineno"> 2586</span>  std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements());</div><div class="line"><a name="l02587"></a><span class="lineno"> 2587</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasesInfo, biasesData);</div><div class="line"><a name="l02588"></a><span class="lineno"> 2588</span> </div><div class="line"><a name="l02589"></a><span class="lineno"> 2589</span>  <a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">armnn::TransposeConvolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l02590"></a><span class="lineno"> 2590</span>  descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l02591"></a><span class="lineno"> 2591</span>  descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l02592"></a><span class="lineno"> 2592</span>  descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l02593"></a><span class="lineno"> 2593</span>  descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l02594"></a><span class="lineno"> 2594</span>  descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l02595"></a><span class="lineno"> 2595</span>  descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l02596"></a><span class="lineno"> 2596</span>  descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l02597"></a><span class="lineno"> 2597</span>  descriptor.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l02598"></a><span class="lineno"> 2598</span> </div><div class="line"><a name="l02599"></a><span class="lineno"> 2599</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02600"></a><span class="lineno"> 2600</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02601"></a><span class="lineno"> 2601</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> convLayer =</div><div class="line"><a name="l02602"></a><span class="lineno"> 2602</span>  network->AddTransposeConvolution2dLayer(descriptor,</div><div class="line"><a name="l02603"></a><span class="lineno"> 2603</span>  weights,</div><div class="line"><a name="l02604"></a><span class="lineno"> 2604</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::ConstTensor></a>(biases),</div><div class="line"><a name="l02605"></a><span class="lineno"> 2605</span>  layerName.c_str());</div><div class="line"><a name="l02606"></a><span class="lineno"> 2606</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l02607"></a><span class="lineno"> 2607</span> </div><div class="line"><a name="l02608"></a><span class="lineno"> 2608</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02609"></a><span class="lineno"> 2609</span>  convLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02610"></a><span class="lineno"> 2610</span> </div><div class="line"><a name="l02611"></a><span class="lineno"> 2611</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l02612"></a><span class="lineno"> 2612</span>  convLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l02613"></a><span class="lineno"> 2613</span> </div><div class="line"><a name="l02614"></a><span class="lineno"> 2614</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02615"></a><span class="lineno"> 2615</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02616"></a><span class="lineno"> 2616</span> </div><div class="line"><a name="l02617"></a><span class="lineno"> 2617</span>  TransposeConvolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);</div><div class="line"><a name="l02618"></a><span class="lineno"> 2618</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l02619"></a><span class="lineno"> 2619</span> }</div><div class="line"><a name="l02620"></a><span class="lineno"> 2620</span> </div><div class="line"><a name="l02621"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#aca4adebc92f5a0afc5969f5be06ec2b4"> 2621</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeNonLinearNetwork)</div><div class="line"><a name="l02622"></a><span class="lineno"> 2622</span> {</div><div class="line"><a name="l02623"></a><span class="lineno"> 2623</span>  <span class="keyword">class </span>ConstantLayerVerifier : <span class="keyword">public</span> LayerVerifierBase</div><div class="line"><a name="l02624"></a><span class="lineno"> 2624</span>  {</div><div class="line"><a name="l02625"></a><span class="lineno"> 2625</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l02626"></a><span class="lineno"> 2626</span>  ConstantLayerVerifier(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l02627"></a><span class="lineno"> 2627</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l02628"></a><span class="lineno"> 2628</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l02629"></a><span class="lineno"> 2629</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& layerInput)</div><div class="line"><a name="l02630"></a><span class="lineno"> 2630</span>  : LayerVerifierBase(layerName, inputInfos, outputInfos)</div><div class="line"><a name="l02631"></a><span class="lineno"> 2631</span>  , m_LayerInput(layerInput) {}</div><div class="line"><a name="l02632"></a><span class="lineno"> 2632</span> </div><div class="line"><a name="l02633"></a><span class="lineno"> 2633</span>  <span class="keywordtype">void</span> VisitConstantLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l02634"></a><span class="lineno"> 2634</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a>& input,</div><div class="line"><a name="l02635"></a><span class="lineno"> 2635</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)<span class="keyword"> override</span></div><div class="line"><a name="l02636"></a><span class="lineno"> 2636</span> <span class="keyword"> </span>{</div><div class="line"><a name="l02637"></a><span class="lineno"> 2637</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l02638"></a><span class="lineno"> 2638</span>  CompareConstTensor(input, m_LayerInput);</div><div class="line"><a name="l02639"></a><span class="lineno"> 2639</span>  }</div><div class="line"><a name="l02640"></a><span class="lineno"> 2640</span> </div><div class="line"><a name="l02641"></a><span class="lineno"> 2641</span>  <span class="keywordtype">void</span> VisitAdditionLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>*, <span class="keyword">const</span> <span class="keywordtype">char</span>*)<span class="keyword"> override </span>{}</div><div class="line"><a name="l02642"></a><span class="lineno"> 2642</span> </div><div class="line"><a name="l02643"></a><span class="lineno"> 2643</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l02644"></a><span class="lineno"> 2644</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> m_LayerInput;</div><div class="line"><a name="l02645"></a><span class="lineno"> 2645</span>  };</div><div class="line"><a name="l02646"></a><span class="lineno"> 2646</span> </div><div class="line"><a name="l02647"></a><span class="lineno"> 2647</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"constant"</span>);</div><div class="line"><a name="l02648"></a><span class="lineno"> 2648</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> info({ 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02649"></a><span class="lineno"> 2649</span> </div><div class="line"><a name="l02650"></a><span class="lineno"> 2650</span>  std::vector<float> constantData = GenerateRandomData<float>(info.GetNumElements());</div><div class="line"><a name="l02651"></a><span class="lineno"> 2651</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> constTensor(info, constantData);</div><div class="line"><a name="l02652"></a><span class="lineno"> 2652</span> </div><div class="line"><a name="l02653"></a><span class="lineno"> 2653</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network(<a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>());</div><div class="line"><a name="l02654"></a><span class="lineno"> 2654</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* input = network->AddInputLayer(0);</div><div class="line"><a name="l02655"></a><span class="lineno"> 2655</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* add = network->AddAdditionLayer();</div><div class="line"><a name="l02656"></a><span class="lineno"> 2656</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* constant = network->AddConstantLayer(constTensor, layerName.c_str());</div><div class="line"><a name="l02657"></a><span class="lineno"> 2657</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* output = network->AddOutputLayer(0);</div><div class="line"><a name="l02658"></a><span class="lineno"> 2658</span> </div><div class="line"><a name="l02659"></a><span class="lineno"> 2659</span>  input-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02660"></a><span class="lineno"> 2660</span>  constant-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(add-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02661"></a><span class="lineno"> 2661</span>  add-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02662"></a><span class="lineno"> 2662</span> </div><div class="line"><a name="l02663"></a><span class="lineno"> 2663</span>  input-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02664"></a><span class="lineno"> 2664</span>  constant-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02665"></a><span class="lineno"> 2665</span>  add-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(info);</div><div class="line"><a name="l02666"></a><span class="lineno"> 2666</span> </div><div class="line"><a name="l02667"></a><span class="lineno"> 2667</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02668"></a><span class="lineno"> 2668</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02669"></a><span class="lineno"> 2669</span> </div><div class="line"><a name="l02670"></a><span class="lineno"> 2670</span>  ConstantLayerVerifier verifier(layerName, {}, {info}, constTensor);</div><div class="line"><a name="l02671"></a><span class="lineno"> 2671</span>  deserializedNetwork->Accept(verifier);</div><div class="line"><a name="l02672"></a><span class="lineno"> 2672</span> }</div><div class="line"><a name="l02673"></a><span class="lineno"> 2673</span> </div><div class="line"><a name="l02674"></a><span class="lineno"> 2674</span> <span class="keyword">class </span>VerifyLstmLayer : <span class="keyword">public</span> LayerVerifierBaseWithDescriptor<armnn::LstmDescriptor></div><div class="line"><a name="l02675"></a><span class="lineno"> 2675</span> {</div><div class="line"><a name="l02676"></a><span class="lineno"> 2676</span> <span class="keyword">public</span>:</div><div class="line"><a name="l02677"></a><span class="lineno"> 2677</span>  VerifyLstmLayer(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l02678"></a><span class="lineno"> 2678</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l02679"></a><span class="lineno"> 2679</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l02680"></a><span class="lineno"> 2680</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a>& descriptor,</div><div class="line"><a name="l02681"></a><span class="lineno"> 2681</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a>& inputParams)</div><div class="line"><a name="l02682"></a><span class="lineno"> 2682</span>  : LayerVerifierBaseWithDescriptor<armnn::LstmDescriptor>(layerName, inputInfos, outputInfos, descriptor)</div><div class="line"><a name="l02683"></a><span class="lineno"> 2683</span>  , m_InputParams(inputParams) {}</div><div class="line"><a name="l02684"></a><span class="lineno"> 2684</span> </div><div class="line"><a name="l02685"></a><span class="lineno"> 2685</span>  <span class="keywordtype">void</span> VisitLstmLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l02686"></a><span class="lineno"> 2686</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a>& descriptor,</div><div class="line"><a name="l02687"></a><span class="lineno"> 2687</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a>& params,</div><div class="line"><a name="l02688"></a><span class="lineno"> 2688</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)</div><div class="line"><a name="l02689"></a><span class="lineno"> 2689</span>  {</div><div class="line"><a name="l02690"></a><span class="lineno"> 2690</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l02691"></a><span class="lineno"> 2691</span>  VerifyDescriptor(descriptor);</div><div class="line"><a name="l02692"></a><span class="lineno"> 2692</span>  VerifyInputParameters(params);</div><div class="line"><a name="l02693"></a><span class="lineno"> 2693</span>  }</div><div class="line"><a name="l02694"></a><span class="lineno"> 2694</span> </div><div class="line"><a name="l02695"></a><span class="lineno"> 2695</span> <span class="keyword">protected</span>:</div><div class="line"><a name="l02696"></a><span class="lineno"> 2696</span>  <span class="keywordtype">void</span> VerifyInputParameters(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a>& params)</div><div class="line"><a name="l02697"></a><span class="lineno"> 2697</span>  {</div><div class="line"><a name="l02698"></a><span class="lineno"> 2698</span>  VerifyConstTensors(</div><div class="line"><a name="l02699"></a><span class="lineno"> 2699</span>  <span class="stringliteral">"m_InputToInputWeights"</span>, m_InputParams.m_InputToInputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a>);</div><div class="line"><a name="l02700"></a><span class="lineno"> 2700</span>  VerifyConstTensors(</div><div class="line"><a name="l02701"></a><span class="lineno"> 2701</span>  <span class="stringliteral">"m_InputToForgetWeights"</span>, m_InputParams.m_InputToForgetWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a>);</div><div class="line"><a name="l02702"></a><span class="lineno"> 2702</span>  VerifyConstTensors(</div><div class="line"><a name="l02703"></a><span class="lineno"> 2703</span>  <span class="stringliteral">"m_InputToCellWeights"</span>, m_InputParams.m_InputToCellWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a>);</div><div class="line"><a name="l02704"></a><span class="lineno"> 2704</span>  VerifyConstTensors(</div><div class="line"><a name="l02705"></a><span class="lineno"> 2705</span>  <span class="stringliteral">"m_InputToOutputWeights"</span>, m_InputParams.m_InputToOutputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a>);</div><div class="line"><a name="l02706"></a><span class="lineno"> 2706</span>  VerifyConstTensors(</div><div class="line"><a name="l02707"></a><span class="lineno"> 2707</span>  <span class="stringliteral">"m_RecurrentToInputWeights"</span>, m_InputParams.m_RecurrentToInputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a>);</div><div class="line"><a name="l02708"></a><span class="lineno"> 2708</span>  VerifyConstTensors(</div><div class="line"><a name="l02709"></a><span class="lineno"> 2709</span>  <span class="stringliteral">"m_RecurrentToForgetWeights"</span>, m_InputParams.m_RecurrentToForgetWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a>);</div><div class="line"><a name="l02710"></a><span class="lineno"> 2710</span>  VerifyConstTensors(</div><div class="line"><a name="l02711"></a><span class="lineno"> 2711</span>  <span class="stringliteral">"m_RecurrentToCellWeights"</span>, m_InputParams.m_RecurrentToCellWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a>);</div><div class="line"><a name="l02712"></a><span class="lineno"> 2712</span>  VerifyConstTensors(</div><div class="line"><a name="l02713"></a><span class="lineno"> 2713</span>  <span class="stringliteral">"m_RecurrentToOutputWeights"</span>, m_InputParams.m_RecurrentToOutputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a>);</div><div class="line"><a name="l02714"></a><span class="lineno"> 2714</span>  VerifyConstTensors(</div><div class="line"><a name="l02715"></a><span class="lineno"> 2715</span>  <span class="stringliteral">"m_CellToInputWeights"</span>, m_InputParams.m_CellToInputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">m_CellToInputWeights</a>);</div><div class="line"><a name="l02716"></a><span class="lineno"> 2716</span>  VerifyConstTensors(</div><div class="line"><a name="l02717"></a><span class="lineno"> 2717</span>  <span class="stringliteral">"m_CellToForgetWeights"</span>, m_InputParams.m_CellToForgetWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a>);</div><div class="line"><a name="l02718"></a><span class="lineno"> 2718</span>  VerifyConstTensors(</div><div class="line"><a name="l02719"></a><span class="lineno"> 2719</span>  <span class="stringliteral">"m_CellToOutputWeights"</span>, m_InputParams.m_CellToOutputWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a>);</div><div class="line"><a name="l02720"></a><span class="lineno"> 2720</span>  VerifyConstTensors(</div><div class="line"><a name="l02721"></a><span class="lineno"> 2721</span>  <span class="stringliteral">"m_InputGateBias"</span>, m_InputParams.m_InputGateBias, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a>);</div><div class="line"><a name="l02722"></a><span class="lineno"> 2722</span>  VerifyConstTensors(</div><div class="line"><a name="l02723"></a><span class="lineno"> 2723</span>  <span class="stringliteral">"m_ForgetGateBias"</span>, m_InputParams.m_ForgetGateBias, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a>);</div><div class="line"><a name="l02724"></a><span class="lineno"> 2724</span>  VerifyConstTensors(</div><div class="line"><a name="l02725"></a><span class="lineno"> 2725</span>  <span class="stringliteral">"m_CellBias"</span>, m_InputParams.m_CellBias, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a>);</div><div class="line"><a name="l02726"></a><span class="lineno"> 2726</span>  VerifyConstTensors(</div><div class="line"><a name="l02727"></a><span class="lineno"> 2727</span>  <span class="stringliteral">"m_OutputGateBias"</span>, m_InputParams.m_OutputGateBias, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a>);</div><div class="line"><a name="l02728"></a><span class="lineno"> 2728</span>  VerifyConstTensors(</div><div class="line"><a name="l02729"></a><span class="lineno"> 2729</span>  <span class="stringliteral">"m_ProjectionWeights"</span>, m_InputParams.m_ProjectionWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a>);</div><div class="line"><a name="l02730"></a><span class="lineno"> 2730</span>  VerifyConstTensors(</div><div class="line"><a name="l02731"></a><span class="lineno"> 2731</span>  <span class="stringliteral">"m_ProjectionBias"</span>, m_InputParams.m_ProjectionBias, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">m_ProjectionBias</a>);</div><div class="line"><a name="l02732"></a><span class="lineno"> 2732</span>  VerifyConstTensors(</div><div class="line"><a name="l02733"></a><span class="lineno"> 2733</span>  <span class="stringliteral">"m_InputLayerNormWeights"</span>, m_InputParams.m_InputLayerNormWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a0cd848f65ec31778d708852f0042fe37">m_InputLayerNormWeights</a>);</div><div class="line"><a name="l02734"></a><span class="lineno"> 2734</span>  VerifyConstTensors(</div><div class="line"><a name="l02735"></a><span class="lineno"> 2735</span>  <span class="stringliteral">"m_ForgetLayerNormWeights"</span>, m_InputParams.m_ForgetLayerNormWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#af0f796fba1a2be9c56b4c9ee534577ee">m_ForgetLayerNormWeights</a>);</div><div class="line"><a name="l02736"></a><span class="lineno"> 2736</span>  VerifyConstTensors(</div><div class="line"><a name="l02737"></a><span class="lineno"> 2737</span>  <span class="stringliteral">"m_CellLayerNormWeights"</span>, m_InputParams.m_CellLayerNormWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ad0b8c32bb5381f4cc999093ba3b98b43">m_CellLayerNormWeights</a>);</div><div class="line"><a name="l02738"></a><span class="lineno"> 2738</span>  VerifyConstTensors(</div><div class="line"><a name="l02739"></a><span class="lineno"> 2739</span>  <span class="stringliteral">"m_OutputLayerNormWeights"</span>, m_InputParams.m_OutputLayerNormWeights, params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9b18daea2e9f42386055326fd016519a">m_OutputLayerNormWeights</a>);</div><div class="line"><a name="l02740"></a><span class="lineno"> 2740</span>  }</div><div class="line"><a name="l02741"></a><span class="lineno"> 2741</span> </div><div class="line"><a name="l02742"></a><span class="lineno"> 2742</span> <span class="keyword">private</span>:</div><div class="line"><a name="l02743"></a><span class="lineno"> 2743</span>  <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> m_InputParams;</div><div class="line"><a name="l02744"></a><span class="lineno"> 2744</span> };</div><div class="line"><a name="l02745"></a><span class="lineno"> 2745</span> </div><div class="line"><a name="l02746"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a6aa1ada80d8a67ff3212b2dcab708960"> 2746</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeLstmCifgPeepholeNoProjection)</div><div class="line"><a name="l02747"></a><span class="lineno"> 2747</span> {</div><div class="line"><a name="l02748"></a><span class="lineno"> 2748</span>  <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a> descriptor;</div><div class="line"><a name="l02749"></a><span class="lineno"> 2749</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a> = 4;</div><div class="line"><a name="l02750"></a><span class="lineno"> 2750</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a> = 0.0f;</div><div class="line"><a name="l02751"></a><span class="lineno"> 2751</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a> = 0.0f;</div><div class="line"><a name="l02752"></a><span class="lineno"> 2752</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> = <span class="keyword">true</span>; <span class="comment">// if this is true then we DON'T need to set the OptCifgParams</span></div><div class="line"><a name="l02753"></a><span class="lineno"> 2753</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l02754"></a><span class="lineno"> 2754</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l02755"></a><span class="lineno"> 2755</span> </div><div class="line"><a name="l02756"></a><span class="lineno"> 2756</span>  <span class="keyword">const</span> uint32_t batchSize = 1;</div><div class="line"><a name="l02757"></a><span class="lineno"> 2757</span>  <span class="keyword">const</span> uint32_t inputSize = 2;</div><div class="line"><a name="l02758"></a><span class="lineno"> 2758</span>  <span class="keyword">const</span> uint32_t numUnits = 4;</div><div class="line"><a name="l02759"></a><span class="lineno"> 2759</span>  <span class="keyword">const</span> uint32_t outputSize = numUnits;</div><div class="line"><a name="l02760"></a><span class="lineno"> 2760</span> </div><div class="line"><a name="l02761"></a><span class="lineno"> 2761</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputWeightsInfo1({numUnits, inputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02762"></a><span class="lineno"> 2762</span>  std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());</div><div class="line"><a name="l02763"></a><span class="lineno"> 2763</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(inputWeightsInfo1, inputToForgetWeightsData);</div><div class="line"><a name="l02764"></a><span class="lineno"> 2764</span> </div><div class="line"><a name="l02765"></a><span class="lineno"> 2765</span>  std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());</div><div class="line"><a name="l02766"></a><span class="lineno"> 2766</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(inputWeightsInfo1, inputToCellWeightsData);</div><div class="line"><a name="l02767"></a><span class="lineno"> 2767</span> </div><div class="line"><a name="l02768"></a><span class="lineno"> 2768</span>  std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());</div><div class="line"><a name="l02769"></a><span class="lineno"> 2769</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToOutputWeights(inputWeightsInfo1, inputToOutputWeightsData);</div><div class="line"><a name="l02770"></a><span class="lineno"> 2770</span> </div><div class="line"><a name="l02771"></a><span class="lineno"> 2771</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputWeightsInfo2({numUnits, outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02772"></a><span class="lineno"> 2772</span>  std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());</div><div class="line"><a name="l02773"></a><span class="lineno"> 2773</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToForgetWeights(inputWeightsInfo2, recurrentToForgetWeightsData);</div><div class="line"><a name="l02774"></a><span class="lineno"> 2774</span> </div><div class="line"><a name="l02775"></a><span class="lineno"> 2775</span>  std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());</div><div class="line"><a name="l02776"></a><span class="lineno"> 2776</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToCellWeights(inputWeightsInfo2, recurrentToCellWeightsData);</div><div class="line"><a name="l02777"></a><span class="lineno"> 2777</span> </div><div class="line"><a name="l02778"></a><span class="lineno"> 2778</span>  std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());</div><div class="line"><a name="l02779"></a><span class="lineno"> 2779</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToOutputWeights(inputWeightsInfo2, recurrentToOutputWeightsData);</div><div class="line"><a name="l02780"></a><span class="lineno"> 2780</span> </div><div class="line"><a name="l02781"></a><span class="lineno"> 2781</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputWeightsInfo3({numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02782"></a><span class="lineno"> 2782</span>  std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements());</div><div class="line"><a name="l02783"></a><span class="lineno"> 2783</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToForgetWeights(inputWeightsInfo3, cellToForgetWeightsData);</div><div class="line"><a name="l02784"></a><span class="lineno"> 2784</span> </div><div class="line"><a name="l02785"></a><span class="lineno"> 2785</span>  std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements());</div><div class="line"><a name="l02786"></a><span class="lineno"> 2786</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToOutputWeights(inputWeightsInfo3, cellToOutputWeightsData);</div><div class="line"><a name="l02787"></a><span class="lineno"> 2787</span> </div><div class="line"><a name="l02788"></a><span class="lineno"> 2788</span>  std::vector<float> forgetGateBiasData(numUnits, 1.0f);</div><div class="line"><a name="l02789"></a><span class="lineno"> 2789</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetGateBias(inputWeightsInfo3, forgetGateBiasData);</div><div class="line"><a name="l02790"></a><span class="lineno"> 2790</span> </div><div class="line"><a name="l02791"></a><span class="lineno"> 2791</span>  std::vector<float> cellBiasData(numUnits, 0.0f);</div><div class="line"><a name="l02792"></a><span class="lineno"> 2792</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellBias(inputWeightsInfo3, cellBiasData);</div><div class="line"><a name="l02793"></a><span class="lineno"> 2793</span> </div><div class="line"><a name="l02794"></a><span class="lineno"> 2794</span>  std::vector<float> outputGateBiasData(numUnits, 0.0f);</div><div class="line"><a name="l02795"></a><span class="lineno"> 2795</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputGateBias(inputWeightsInfo3, outputGateBiasData);</div><div class="line"><a name="l02796"></a><span class="lineno"> 2796</span> </div><div class="line"><a name="l02797"></a><span class="lineno"> 2797</span>  <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> params;</div><div class="line"><a name="l02798"></a><span class="lineno"> 2798</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a> = &inputToForgetWeights;</div><div class="line"><a name="l02799"></a><span class="lineno"> 2799</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a> = &inputToCellWeights;</div><div class="line"><a name="l02800"></a><span class="lineno"> 2800</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &inputToOutputWeights;</div><div class="line"><a name="l02801"></a><span class="lineno"> 2801</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeights;</div><div class="line"><a name="l02802"></a><span class="lineno"> 2802</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a> = &recurrentToCellWeights;</div><div class="line"><a name="l02803"></a><span class="lineno"> 2803</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeights;</div><div class="line"><a name="l02804"></a><span class="lineno"> 2804</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &forgetGateBias;</div><div class="line"><a name="l02805"></a><span class="lineno"> 2805</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a> = &cellBias;</div><div class="line"><a name="l02806"></a><span class="lineno"> 2806</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &outputGateBias;</div><div class="line"><a name="l02807"></a><span class="lineno"> 2807</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a> = &cellToForgetWeights;</div><div class="line"><a name="l02808"></a><span class="lineno"> 2808</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a> = &cellToOutputWeights;</div><div class="line"><a name="l02809"></a><span class="lineno"> 2809</span> </div><div class="line"><a name="l02810"></a><span class="lineno"> 2810</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02811"></a><span class="lineno"> 2811</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02812"></a><span class="lineno"> 2812</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateIn = network->AddInputLayer(1);</div><div class="line"><a name="l02813"></a><span class="lineno"> 2813</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateIn = network->AddInputLayer(2);</div><div class="line"><a name="l02814"></a><span class="lineno"> 2814</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"lstm"</span>);</div><div class="line"><a name="l02815"></a><span class="lineno"> 2815</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> lstmLayer = network->AddLstmLayer(descriptor, params, layerName.c_str());</div><div class="line"><a name="l02816"></a><span class="lineno"> 2816</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> scratchBuffer = network->AddOutputLayer(0);</div><div class="line"><a name="l02817"></a><span class="lineno"> 2817</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateOut = network->AddOutputLayer(1);</div><div class="line"><a name="l02818"></a><span class="lineno"> 2818</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateOut = network->AddOutputLayer(2);</div><div class="line"><a name="l02819"></a><span class="lineno"> 2819</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(3);</div><div class="line"><a name="l02820"></a><span class="lineno"> 2820</span> </div><div class="line"><a name="l02821"></a><span class="lineno"> 2821</span>  <span class="comment">// connect up</span></div><div class="line"><a name="l02822"></a><span class="lineno"> 2822</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ batchSize, inputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02823"></a><span class="lineno"> 2823</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateTensorInfo({ batchSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02824"></a><span class="lineno"> 2824</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateTensorInfo({ batchSize, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02825"></a><span class="lineno"> 2825</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> lstmTensorInfoScratchBuff({ batchSize, numUnits * 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02826"></a><span class="lineno"> 2826</span> </div><div class="line"><a name="l02827"></a><span class="lineno"> 2827</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02828"></a><span class="lineno"> 2828</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l02829"></a><span class="lineno"> 2829</span> </div><div class="line"><a name="l02830"></a><span class="lineno"> 2830</span>  outputStateIn-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02831"></a><span class="lineno"> 2831</span>  outputStateIn-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l02832"></a><span class="lineno"> 2832</span> </div><div class="line"><a name="l02833"></a><span class="lineno"> 2833</span>  cellStateIn-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l02834"></a><span class="lineno"> 2834</span>  cellStateIn-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l02835"></a><span class="lineno"> 2835</span> </div><div class="line"><a name="l02836"></a><span class="lineno"> 2836</span>  lstmLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(scratchBuffer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02837"></a><span class="lineno"> 2837</span>  lstmLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(lstmTensorInfoScratchBuff);</div><div class="line"><a name="l02838"></a><span class="lineno"> 2838</span> </div><div class="line"><a name="l02839"></a><span class="lineno"> 2839</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputStateOut-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02840"></a><span class="lineno"> 2840</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l02841"></a><span class="lineno"> 2841</span> </div><div class="line"><a name="l02842"></a><span class="lineno"> 2842</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(cellStateOut-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02843"></a><span class="lineno"> 2843</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l02844"></a><span class="lineno"> 2844</span> </div><div class="line"><a name="l02845"></a><span class="lineno"> 2845</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(3).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02846"></a><span class="lineno"> 2846</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(3).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l02847"></a><span class="lineno"> 2847</span> </div><div class="line"><a name="l02848"></a><span class="lineno"> 2848</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02849"></a><span class="lineno"> 2849</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02850"></a><span class="lineno"> 2850</span> </div><div class="line"><a name="l02851"></a><span class="lineno"> 2851</span>  VerifyLstmLayer checker(</div><div class="line"><a name="l02852"></a><span class="lineno"> 2852</span>  layerName,</div><div class="line"><a name="l02853"></a><span class="lineno"> 2853</span>  {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},</div><div class="line"><a name="l02854"></a><span class="lineno"> 2854</span>  {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l02855"></a><span class="lineno"> 2855</span>  descriptor,</div><div class="line"><a name="l02856"></a><span class="lineno"> 2856</span>  params);</div><div class="line"><a name="l02857"></a><span class="lineno"> 2857</span>  deserializedNetwork->Accept(checker);</div><div class="line"><a name="l02858"></a><span class="lineno"> 2858</span> }</div><div class="line"><a name="l02859"></a><span class="lineno"> 2859</span> </div><div class="line"><a name="l02860"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a884784964d5b3e12dd1a0b76e63a85f9"> 2860</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeLstmNoCifgWithPeepholeAndProjection)</div><div class="line"><a name="l02861"></a><span class="lineno"> 2861</span> {</div><div class="line"><a name="l02862"></a><span class="lineno"> 2862</span>  <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a> descriptor;</div><div class="line"><a name="l02863"></a><span class="lineno"> 2863</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a> = 4;</div><div class="line"><a name="l02864"></a><span class="lineno"> 2864</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a> = 0.0f;</div><div class="line"><a name="l02865"></a><span class="lineno"> 2865</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a> = 0.0f;</div><div class="line"><a name="l02866"></a><span class="lineno"> 2866</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> = <span class="keyword">false</span>; <span class="comment">// if this is true then we DON'T need to set the OptCifgParams</span></div><div class="line"><a name="l02867"></a><span class="lineno"> 2867</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l02868"></a><span class="lineno"> 2868</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l02869"></a><span class="lineno"> 2869</span> </div><div class="line"><a name="l02870"></a><span class="lineno"> 2870</span>  <span class="keyword">const</span> uint32_t batchSize = 2;</div><div class="line"><a name="l02871"></a><span class="lineno"> 2871</span>  <span class="keyword">const</span> uint32_t inputSize = 5;</div><div class="line"><a name="l02872"></a><span class="lineno"> 2872</span>  <span class="keyword">const</span> uint32_t numUnits = 20;</div><div class="line"><a name="l02873"></a><span class="lineno"> 2873</span>  <span class="keyword">const</span> uint32_t outputSize = 16;</div><div class="line"><a name="l02874"></a><span class="lineno"> 2874</span> </div><div class="line"><a name="l02875"></a><span class="lineno"> 2875</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20x5({numUnits, inputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02876"></a><span class="lineno"> 2876</span>  std::vector<float> inputToInputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l02877"></a><span class="lineno"> 2877</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);</div><div class="line"><a name="l02878"></a><span class="lineno"> 2878</span> </div><div class="line"><a name="l02879"></a><span class="lineno"> 2879</span>  std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l02880"></a><span class="lineno"> 2880</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);</div><div class="line"><a name="l02881"></a><span class="lineno"> 2881</span> </div><div class="line"><a name="l02882"></a><span class="lineno"> 2882</span>  std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l02883"></a><span class="lineno"> 2883</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);</div><div class="line"><a name="l02884"></a><span class="lineno"> 2884</span> </div><div class="line"><a name="l02885"></a><span class="lineno"> 2885</span>  std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l02886"></a><span class="lineno"> 2886</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData);</div><div class="line"><a name="l02887"></a><span class="lineno"> 2887</span> </div><div class="line"><a name="l02888"></a><span class="lineno"> 2888</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20({numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02889"></a><span class="lineno"> 2889</span>  std::vector<float> inputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l02890"></a><span class="lineno"> 2890</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputGateBias(tensorInfo20, inputGateBiasData);</div><div class="line"><a name="l02891"></a><span class="lineno"> 2891</span> </div><div class="line"><a name="l02892"></a><span class="lineno"> 2892</span>  std::vector<float> forgetGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l02893"></a><span class="lineno"> 2893</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetGateBias(tensorInfo20, forgetGateBiasData);</div><div class="line"><a name="l02894"></a><span class="lineno"> 2894</span> </div><div class="line"><a name="l02895"></a><span class="lineno"> 2895</span>  std::vector<float> cellBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l02896"></a><span class="lineno"> 2896</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellBias(tensorInfo20, cellBiasData);</div><div class="line"><a name="l02897"></a><span class="lineno"> 2897</span> </div><div class="line"><a name="l02898"></a><span class="lineno"> 2898</span>  std::vector<float> outputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l02899"></a><span class="lineno"> 2899</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputGateBias(tensorInfo20, outputGateBiasData);</div><div class="line"><a name="l02900"></a><span class="lineno"> 2900</span> </div><div class="line"><a name="l02901"></a><span class="lineno"> 2901</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20x16({numUnits, outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02902"></a><span class="lineno"> 2902</span>  std::vector<float> recurrentToInputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l02903"></a><span class="lineno"> 2903</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);</div><div class="line"><a name="l02904"></a><span class="lineno"> 2904</span> </div><div class="line"><a name="l02905"></a><span class="lineno"> 2905</span>  std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l02906"></a><span class="lineno"> 2906</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);</div><div class="line"><a name="l02907"></a><span class="lineno"> 2907</span> </div><div class="line"><a name="l02908"></a><span class="lineno"> 2908</span>  std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l02909"></a><span class="lineno"> 2909</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);</div><div class="line"><a name="l02910"></a><span class="lineno"> 2910</span> </div><div class="line"><a name="l02911"></a><span class="lineno"> 2911</span>  std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l02912"></a><span class="lineno"> 2912</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);</div><div class="line"><a name="l02913"></a><span class="lineno"> 2913</span> </div><div class="line"><a name="l02914"></a><span class="lineno"> 2914</span>  std::vector<float> cellToInputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l02915"></a><span class="lineno"> 2915</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToInputWeights(tensorInfo20, cellToInputWeightsData);</div><div class="line"><a name="l02916"></a><span class="lineno"> 2916</span> </div><div class="line"><a name="l02917"></a><span class="lineno"> 2917</span>  std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l02918"></a><span class="lineno"> 2918</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);</div><div class="line"><a name="l02919"></a><span class="lineno"> 2919</span> </div><div class="line"><a name="l02920"></a><span class="lineno"> 2920</span>  std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l02921"></a><span class="lineno"> 2921</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToOutputWeights(tensorInfo20, cellToOutputWeightsData);</div><div class="line"><a name="l02922"></a><span class="lineno"> 2922</span> </div><div class="line"><a name="l02923"></a><span class="lineno"> 2923</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo16x20({outputSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02924"></a><span class="lineno"> 2924</span>  std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements());</div><div class="line"><a name="l02925"></a><span class="lineno"> 2925</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> projectionWeights(tensorInfo16x20, projectionWeightsData);</div><div class="line"><a name="l02926"></a><span class="lineno"> 2926</span> </div><div class="line"><a name="l02927"></a><span class="lineno"> 2927</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo16({outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02928"></a><span class="lineno"> 2928</span>  std::vector<float> projectionBiasData(outputSize, 0.f);</div><div class="line"><a name="l02929"></a><span class="lineno"> 2929</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> projectionBias(tensorInfo16, projectionBiasData);</div><div class="line"><a name="l02930"></a><span class="lineno"> 2930</span> </div><div class="line"><a name="l02931"></a><span class="lineno"> 2931</span>  <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> params;</div><div class="line"><a name="l02932"></a><span class="lineno"> 2932</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a> = &inputToForgetWeights;</div><div class="line"><a name="l02933"></a><span class="lineno"> 2933</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a> = &inputToCellWeights;</div><div class="line"><a name="l02934"></a><span class="lineno"> 2934</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &inputToOutputWeights;</div><div class="line"><a name="l02935"></a><span class="lineno"> 2935</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeights;</div><div class="line"><a name="l02936"></a><span class="lineno"> 2936</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a> = &recurrentToCellWeights;</div><div class="line"><a name="l02937"></a><span class="lineno"> 2937</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeights;</div><div class="line"><a name="l02938"></a><span class="lineno"> 2938</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &forgetGateBias;</div><div class="line"><a name="l02939"></a><span class="lineno"> 2939</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a> = &cellBias;</div><div class="line"><a name="l02940"></a><span class="lineno"> 2940</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &outputGateBias;</div><div class="line"><a name="l02941"></a><span class="lineno"> 2941</span> </div><div class="line"><a name="l02942"></a><span class="lineno"> 2942</span>  <span class="comment">// additional params because: descriptor.m_CifgEnabled = false</span></div><div class="line"><a name="l02943"></a><span class="lineno"> 2943</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a> = &inputToInputWeights;</div><div class="line"><a name="l02944"></a><span class="lineno"> 2944</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a> = &recurrentToInputWeights;</div><div class="line"><a name="l02945"></a><span class="lineno"> 2945</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">m_CellToInputWeights</a> = &cellToInputWeights;</div><div class="line"><a name="l02946"></a><span class="lineno"> 2946</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a> = &inputGateBias;</div><div class="line"><a name="l02947"></a><span class="lineno"> 2947</span> </div><div class="line"><a name="l02948"></a><span class="lineno"> 2948</span>  <span class="comment">// additional params because: descriptor.m_ProjectionEnabled = true</span></div><div class="line"><a name="l02949"></a><span class="lineno"> 2949</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a> = &projectionWeights;</div><div class="line"><a name="l02950"></a><span class="lineno"> 2950</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">m_ProjectionBias</a> = &projectionBias;</div><div class="line"><a name="l02951"></a><span class="lineno"> 2951</span> </div><div class="line"><a name="l02952"></a><span class="lineno"> 2952</span>  <span class="comment">// additional params because: descriptor.m_PeepholeEnabled = true</span></div><div class="line"><a name="l02953"></a><span class="lineno"> 2953</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a> = &cellToForgetWeights;</div><div class="line"><a name="l02954"></a><span class="lineno"> 2954</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a> = &cellToOutputWeights;</div><div class="line"><a name="l02955"></a><span class="lineno"> 2955</span> </div><div class="line"><a name="l02956"></a><span class="lineno"> 2956</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l02957"></a><span class="lineno"> 2957</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l02958"></a><span class="lineno"> 2958</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateIn = network->AddInputLayer(1);</div><div class="line"><a name="l02959"></a><span class="lineno"> 2959</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateIn = network->AddInputLayer(2);</div><div class="line"><a name="l02960"></a><span class="lineno"> 2960</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"lstm"</span>);</div><div class="line"><a name="l02961"></a><span class="lineno"> 2961</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> lstmLayer = network->AddLstmLayer(descriptor, params, layerName.c_str());</div><div class="line"><a name="l02962"></a><span class="lineno"> 2962</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> scratchBuffer = network->AddOutputLayer(0);</div><div class="line"><a name="l02963"></a><span class="lineno"> 2963</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateOut = network->AddOutputLayer(1);</div><div class="line"><a name="l02964"></a><span class="lineno"> 2964</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateOut = network->AddOutputLayer(2);</div><div class="line"><a name="l02965"></a><span class="lineno"> 2965</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(3);</div><div class="line"><a name="l02966"></a><span class="lineno"> 2966</span> </div><div class="line"><a name="l02967"></a><span class="lineno"> 2967</span>  <span class="comment">// connect up</span></div><div class="line"><a name="l02968"></a><span class="lineno"> 2968</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ batchSize, inputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02969"></a><span class="lineno"> 2969</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateTensorInfo({ batchSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02970"></a><span class="lineno"> 2970</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateTensorInfo({ batchSize, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02971"></a><span class="lineno"> 2971</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02972"></a><span class="lineno"> 2972</span> </div><div class="line"><a name="l02973"></a><span class="lineno"> 2973</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02974"></a><span class="lineno"> 2974</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l02975"></a><span class="lineno"> 2975</span> </div><div class="line"><a name="l02976"></a><span class="lineno"> 2976</span>  outputStateIn-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l02977"></a><span class="lineno"> 2977</span>  outputStateIn-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l02978"></a><span class="lineno"> 2978</span> </div><div class="line"><a name="l02979"></a><span class="lineno"> 2979</span>  cellStateIn-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l02980"></a><span class="lineno"> 2980</span>  cellStateIn-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l02981"></a><span class="lineno"> 2981</span> </div><div class="line"><a name="l02982"></a><span class="lineno"> 2982</span>  lstmLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(scratchBuffer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02983"></a><span class="lineno"> 2983</span>  lstmLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(lstmTensorInfoScratchBuff);</div><div class="line"><a name="l02984"></a><span class="lineno"> 2984</span> </div><div class="line"><a name="l02985"></a><span class="lineno"> 2985</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputStateOut-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02986"></a><span class="lineno"> 2986</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l02987"></a><span class="lineno"> 2987</span> </div><div class="line"><a name="l02988"></a><span class="lineno"> 2988</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(cellStateOut-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02989"></a><span class="lineno"> 2989</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l02990"></a><span class="lineno"> 2990</span> </div><div class="line"><a name="l02991"></a><span class="lineno"> 2991</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(3).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02992"></a><span class="lineno"> 2992</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(3).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l02993"></a><span class="lineno"> 2993</span> </div><div class="line"><a name="l02994"></a><span class="lineno"> 2994</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l02995"></a><span class="lineno"> 2995</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l02996"></a><span class="lineno"> 2996</span> </div><div class="line"><a name="l02997"></a><span class="lineno"> 2997</span>  VerifyLstmLayer checker(</div><div class="line"><a name="l02998"></a><span class="lineno"> 2998</span>  layerName,</div><div class="line"><a name="l02999"></a><span class="lineno"> 2999</span>  {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},</div><div class="line"><a name="l03000"></a><span class="lineno"> 3000</span>  {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l03001"></a><span class="lineno"> 3001</span>  descriptor,</div><div class="line"><a name="l03002"></a><span class="lineno"> 3002</span>  params);</div><div class="line"><a name="l03003"></a><span class="lineno"> 3003</span>  deserializedNetwork->Accept(checker);</div><div class="line"><a name="l03004"></a><span class="lineno"> 3004</span> }</div><div class="line"><a name="l03005"></a><span class="lineno"> 3005</span> </div><div class="line"><a name="l03006"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#ab6ffd7bf1455358bc87321974530cc58"> 3006</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeLstmNoCifgWithPeepholeWithProjectionWithLayerNorm)</div><div class="line"><a name="l03007"></a><span class="lineno"> 3007</span> {</div><div class="line"><a name="l03008"></a><span class="lineno"> 3008</span>  <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a> descriptor;</div><div class="line"><a name="l03009"></a><span class="lineno"> 3009</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a> = 4;</div><div class="line"><a name="l03010"></a><span class="lineno"> 3010</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a> = 0.0f;</div><div class="line"><a name="l03011"></a><span class="lineno"> 3011</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a> = 0.0f;</div><div class="line"><a name="l03012"></a><span class="lineno"> 3012</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> = <span class="keyword">false</span>; <span class="comment">// if this is true then we DON'T need to set the OptCifgParams</span></div><div class="line"><a name="l03013"></a><span class="lineno"> 3013</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l03014"></a><span class="lineno"> 3014</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l03015"></a><span class="lineno"> 3015</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">m_LayerNormEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l03016"></a><span class="lineno"> 3016</span> </div><div class="line"><a name="l03017"></a><span class="lineno"> 3017</span>  <span class="keyword">const</span> uint32_t batchSize = 2;</div><div class="line"><a name="l03018"></a><span class="lineno"> 3018</span>  <span class="keyword">const</span> uint32_t inputSize = 5;</div><div class="line"><a name="l03019"></a><span class="lineno"> 3019</span>  <span class="keyword">const</span> uint32_t numUnits = 20;</div><div class="line"><a name="l03020"></a><span class="lineno"> 3020</span>  <span class="keyword">const</span> uint32_t outputSize = 16;</div><div class="line"><a name="l03021"></a><span class="lineno"> 3021</span> </div><div class="line"><a name="l03022"></a><span class="lineno"> 3022</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20x5({numUnits, inputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03023"></a><span class="lineno"> 3023</span>  std::vector<float> inputToInputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l03024"></a><span class="lineno"> 3024</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);</div><div class="line"><a name="l03025"></a><span class="lineno"> 3025</span> </div><div class="line"><a name="l03026"></a><span class="lineno"> 3026</span>  std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l03027"></a><span class="lineno"> 3027</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);</div><div class="line"><a name="l03028"></a><span class="lineno"> 3028</span> </div><div class="line"><a name="l03029"></a><span class="lineno"> 3029</span>  std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l03030"></a><span class="lineno"> 3030</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);</div><div class="line"><a name="l03031"></a><span class="lineno"> 3031</span> </div><div class="line"><a name="l03032"></a><span class="lineno"> 3032</span>  std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());</div><div class="line"><a name="l03033"></a><span class="lineno"> 3033</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData);</div><div class="line"><a name="l03034"></a><span class="lineno"> 3034</span> </div><div class="line"><a name="l03035"></a><span class="lineno"> 3035</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20({numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03036"></a><span class="lineno"> 3036</span>  std::vector<float> inputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l03037"></a><span class="lineno"> 3037</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputGateBias(tensorInfo20, inputGateBiasData);</div><div class="line"><a name="l03038"></a><span class="lineno"> 3038</span> </div><div class="line"><a name="l03039"></a><span class="lineno"> 3039</span>  std::vector<float> forgetGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l03040"></a><span class="lineno"> 3040</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetGateBias(tensorInfo20, forgetGateBiasData);</div><div class="line"><a name="l03041"></a><span class="lineno"> 3041</span> </div><div class="line"><a name="l03042"></a><span class="lineno"> 3042</span>  std::vector<float> cellBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l03043"></a><span class="lineno"> 3043</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellBias(tensorInfo20, cellBiasData);</div><div class="line"><a name="l03044"></a><span class="lineno"> 3044</span> </div><div class="line"><a name="l03045"></a><span class="lineno"> 3045</span>  std::vector<float> outputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l03046"></a><span class="lineno"> 3046</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputGateBias(tensorInfo20, outputGateBiasData);</div><div class="line"><a name="l03047"></a><span class="lineno"> 3047</span> </div><div class="line"><a name="l03048"></a><span class="lineno"> 3048</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20x16({numUnits, outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03049"></a><span class="lineno"> 3049</span>  std::vector<float> recurrentToInputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l03050"></a><span class="lineno"> 3050</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);</div><div class="line"><a name="l03051"></a><span class="lineno"> 3051</span> </div><div class="line"><a name="l03052"></a><span class="lineno"> 3052</span>  std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l03053"></a><span class="lineno"> 3053</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);</div><div class="line"><a name="l03054"></a><span class="lineno"> 3054</span> </div><div class="line"><a name="l03055"></a><span class="lineno"> 3055</span>  std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l03056"></a><span class="lineno"> 3056</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);</div><div class="line"><a name="l03057"></a><span class="lineno"> 3057</span> </div><div class="line"><a name="l03058"></a><span class="lineno"> 3058</span>  std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());</div><div class="line"><a name="l03059"></a><span class="lineno"> 3059</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);</div><div class="line"><a name="l03060"></a><span class="lineno"> 3060</span> </div><div class="line"><a name="l03061"></a><span class="lineno"> 3061</span>  std::vector<float> cellToInputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l03062"></a><span class="lineno"> 3062</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToInputWeights(tensorInfo20, cellToInputWeightsData);</div><div class="line"><a name="l03063"></a><span class="lineno"> 3063</span> </div><div class="line"><a name="l03064"></a><span class="lineno"> 3064</span>  std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l03065"></a><span class="lineno"> 3065</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);</div><div class="line"><a name="l03066"></a><span class="lineno"> 3066</span> </div><div class="line"><a name="l03067"></a><span class="lineno"> 3067</span>  std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l03068"></a><span class="lineno"> 3068</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToOutputWeights(tensorInfo20, cellToOutputWeightsData);</div><div class="line"><a name="l03069"></a><span class="lineno"> 3069</span> </div><div class="line"><a name="l03070"></a><span class="lineno"> 3070</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo16x20({outputSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03071"></a><span class="lineno"> 3071</span>  std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements());</div><div class="line"><a name="l03072"></a><span class="lineno"> 3072</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> projectionWeights(tensorInfo16x20, projectionWeightsData);</div><div class="line"><a name="l03073"></a><span class="lineno"> 3073</span> </div><div class="line"><a name="l03074"></a><span class="lineno"> 3074</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo16({outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03075"></a><span class="lineno"> 3075</span>  std::vector<float> projectionBiasData(outputSize, 0.f);</div><div class="line"><a name="l03076"></a><span class="lineno"> 3076</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> projectionBias(tensorInfo16, projectionBiasData);</div><div class="line"><a name="l03077"></a><span class="lineno"> 3077</span> </div><div class="line"><a name="l03078"></a><span class="lineno"> 3078</span>  std::vector<float> inputLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l03079"></a><span class="lineno"> 3079</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputLayerNormWeights(tensorInfo20, forgetGateBiasData);</div><div class="line"><a name="l03080"></a><span class="lineno"> 3080</span> </div><div class="line"><a name="l03081"></a><span class="lineno"> 3081</span>  std::vector<float> forgetLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l03082"></a><span class="lineno"> 3082</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetLayerNormWeights(tensorInfo20, forgetGateBiasData);</div><div class="line"><a name="l03083"></a><span class="lineno"> 3083</span> </div><div class="line"><a name="l03084"></a><span class="lineno"> 3084</span>  std::vector<float> cellLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l03085"></a><span class="lineno"> 3085</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellLayerNormWeights(tensorInfo20, forgetGateBiasData);</div><div class="line"><a name="l03086"></a><span class="lineno"> 3086</span> </div><div class="line"><a name="l03087"></a><span class="lineno"> 3087</span>  std::vector<float> outLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());</div><div class="line"><a name="l03088"></a><span class="lineno"> 3088</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outLayerNormWeights(tensorInfo20, forgetGateBiasData);</div><div class="line"><a name="l03089"></a><span class="lineno"> 3089</span> </div><div class="line"><a name="l03090"></a><span class="lineno"> 3090</span>  <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> params;</div><div class="line"><a name="l03091"></a><span class="lineno"> 3091</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a> = &inputToForgetWeights;</div><div class="line"><a name="l03092"></a><span class="lineno"> 3092</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a> = &inputToCellWeights;</div><div class="line"><a name="l03093"></a><span class="lineno"> 3093</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &inputToOutputWeights;</div><div class="line"><a name="l03094"></a><span class="lineno"> 3094</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeights;</div><div class="line"><a name="l03095"></a><span class="lineno"> 3095</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a> = &recurrentToCellWeights;</div><div class="line"><a name="l03096"></a><span class="lineno"> 3096</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeights;</div><div class="line"><a name="l03097"></a><span class="lineno"> 3097</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &forgetGateBias;</div><div class="line"><a name="l03098"></a><span class="lineno"> 3098</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a> = &cellBias;</div><div class="line"><a name="l03099"></a><span class="lineno"> 3099</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &outputGateBias;</div><div class="line"><a name="l03100"></a><span class="lineno"> 3100</span> </div><div class="line"><a name="l03101"></a><span class="lineno"> 3101</span>  <span class="comment">// additional params because: descriptor.m_CifgEnabled = false</span></div><div class="line"><a name="l03102"></a><span class="lineno"> 3102</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a> = &inputToInputWeights;</div><div class="line"><a name="l03103"></a><span class="lineno"> 3103</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a> = &recurrentToInputWeights;</div><div class="line"><a name="l03104"></a><span class="lineno"> 3104</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">m_CellToInputWeights</a> = &cellToInputWeights;</div><div class="line"><a name="l03105"></a><span class="lineno"> 3105</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a> = &inputGateBias;</div><div class="line"><a name="l03106"></a><span class="lineno"> 3106</span> </div><div class="line"><a name="l03107"></a><span class="lineno"> 3107</span>  <span class="comment">// additional params because: descriptor.m_ProjectionEnabled = true</span></div><div class="line"><a name="l03108"></a><span class="lineno"> 3108</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a> = &projectionWeights;</div><div class="line"><a name="l03109"></a><span class="lineno"> 3109</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">m_ProjectionBias</a> = &projectionBias;</div><div class="line"><a name="l03110"></a><span class="lineno"> 3110</span> </div><div class="line"><a name="l03111"></a><span class="lineno"> 3111</span>  <span class="comment">// additional params because: descriptor.m_PeepholeEnabled = true</span></div><div class="line"><a name="l03112"></a><span class="lineno"> 3112</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a> = &cellToForgetWeights;</div><div class="line"><a name="l03113"></a><span class="lineno"> 3113</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a> = &cellToOutputWeights;</div><div class="line"><a name="l03114"></a><span class="lineno"> 3114</span> </div><div class="line"><a name="l03115"></a><span class="lineno"> 3115</span>  <span class="comment">// additional params because: despriptor.m_LayerNormEnabled = true</span></div><div class="line"><a name="l03116"></a><span class="lineno"> 3116</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a0cd848f65ec31778d708852f0042fe37">m_InputLayerNormWeights</a> = &inputLayerNormWeights;</div><div class="line"><a name="l03117"></a><span class="lineno"> 3117</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#af0f796fba1a2be9c56b4c9ee534577ee">m_ForgetLayerNormWeights</a> = &forgetLayerNormWeights;</div><div class="line"><a name="l03118"></a><span class="lineno"> 3118</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ad0b8c32bb5381f4cc999093ba3b98b43">m_CellLayerNormWeights</a> = &cellLayerNormWeights;</div><div class="line"><a name="l03119"></a><span class="lineno"> 3119</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9b18daea2e9f42386055326fd016519a">m_OutputLayerNormWeights</a> = &outLayerNormWeights;</div><div class="line"><a name="l03120"></a><span class="lineno"> 3120</span> </div><div class="line"><a name="l03121"></a><span class="lineno"> 3121</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l03122"></a><span class="lineno"> 3122</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l03123"></a><span class="lineno"> 3123</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateIn = network->AddInputLayer(1);</div><div class="line"><a name="l03124"></a><span class="lineno"> 3124</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateIn = network->AddInputLayer(2);</div><div class="line"><a name="l03125"></a><span class="lineno"> 3125</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"lstm"</span>);</div><div class="line"><a name="l03126"></a><span class="lineno"> 3126</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> lstmLayer = network->AddLstmLayer(descriptor, params, layerName.c_str());</div><div class="line"><a name="l03127"></a><span class="lineno"> 3127</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> scratchBuffer = network->AddOutputLayer(0);</div><div class="line"><a name="l03128"></a><span class="lineno"> 3128</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateOut = network->AddOutputLayer(1);</div><div class="line"><a name="l03129"></a><span class="lineno"> 3129</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateOut = network->AddOutputLayer(2);</div><div class="line"><a name="l03130"></a><span class="lineno"> 3130</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(3);</div><div class="line"><a name="l03131"></a><span class="lineno"> 3131</span> </div><div class="line"><a name="l03132"></a><span class="lineno"> 3132</span>  <span class="comment">// connect up</span></div><div class="line"><a name="l03133"></a><span class="lineno"> 3133</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ batchSize, inputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03134"></a><span class="lineno"> 3134</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateTensorInfo({ batchSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03135"></a><span class="lineno"> 3135</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateTensorInfo({ batchSize, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03136"></a><span class="lineno"> 3136</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03137"></a><span class="lineno"> 3137</span> </div><div class="line"><a name="l03138"></a><span class="lineno"> 3138</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l03139"></a><span class="lineno"> 3139</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l03140"></a><span class="lineno"> 3140</span> </div><div class="line"><a name="l03141"></a><span class="lineno"> 3141</span>  outputStateIn-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l03142"></a><span class="lineno"> 3142</span>  outputStateIn-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l03143"></a><span class="lineno"> 3143</span> </div><div class="line"><a name="l03144"></a><span class="lineno"> 3144</span>  cellStateIn-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l03145"></a><span class="lineno"> 3145</span>  cellStateIn-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l03146"></a><span class="lineno"> 3146</span> </div><div class="line"><a name="l03147"></a><span class="lineno"> 3147</span>  lstmLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(scratchBuffer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l03148"></a><span class="lineno"> 3148</span>  lstmLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(lstmTensorInfoScratchBuff);</div><div class="line"><a name="l03149"></a><span class="lineno"> 3149</span> </div><div class="line"><a name="l03150"></a><span class="lineno"> 3150</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputStateOut-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l03151"></a><span class="lineno"> 3151</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l03152"></a><span class="lineno"> 3152</span> </div><div class="line"><a name="l03153"></a><span class="lineno"> 3153</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(cellStateOut-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l03154"></a><span class="lineno"> 3154</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(2).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l03155"></a><span class="lineno"> 3155</span> </div><div class="line"><a name="l03156"></a><span class="lineno"> 3156</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(3).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l03157"></a><span class="lineno"> 3157</span>  lstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(3).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l03158"></a><span class="lineno"> 3158</span> </div><div class="line"><a name="l03159"></a><span class="lineno"> 3159</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l03160"></a><span class="lineno"> 3160</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l03161"></a><span class="lineno"> 3161</span> </div><div class="line"><a name="l03162"></a><span class="lineno"> 3162</span>  VerifyLstmLayer checker(</div><div class="line"><a name="l03163"></a><span class="lineno"> 3163</span>  layerName,</div><div class="line"><a name="l03164"></a><span class="lineno"> 3164</span>  {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},</div><div class="line"><a name="l03165"></a><span class="lineno"> 3165</span>  {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l03166"></a><span class="lineno"> 3166</span>  descriptor,</div><div class="line"><a name="l03167"></a><span class="lineno"> 3167</span>  params);</div><div class="line"><a name="l03168"></a><span class="lineno"> 3168</span>  deserializedNetwork->Accept(checker);</div><div class="line"><a name="l03169"></a><span class="lineno"> 3169</span> }</div><div class="line"><a name="l03170"></a><span class="lineno"> 3170</span> </div><div class="line"><a name="l03171"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#ab1042b9567ba6e028411498f0387fbbd"> 3171</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(EnsureLstmLayersBackwardCompatibility)</div><div class="line"><a name="l03172"></a><span class="lineno"> 3172</span> {</div><div class="line"><a name="l03173"></a><span class="lineno"> 3173</span>  <span class="comment">// The hex data below is a flat buffer containing a lstm layer with no Cifg, with peephole and projection</span></div><div class="line"><a name="l03174"></a><span class="lineno"> 3174</span>  <span class="comment">// enabled. That data was obtained before additional layer normalization parameters where added to the</span></div><div class="line"><a name="l03175"></a><span class="lineno"> 3175</span>  <span class="comment">// lstm serializer. That way it can be tested if a lstm model with the old parameter configuration can</span></div><div class="line"><a name="l03176"></a><span class="lineno"> 3176</span>  <span class="comment">// still be loaded</span></div><div class="line"><a name="l03177"></a><span class="lineno"> 3177</span>  <span class="keyword">const</span> std::vector<uint8_t> lstmNoCifgWithPeepholeAndProjectionModel =</div><div class="line"><a name="l03178"></a><span class="lineno"> 3178</span>  {</div><div class="line"><a name="l03179"></a><span class="lineno"> 3179</span>  0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l03180"></a><span class="lineno"> 3180</span>  0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x2C, 0x00, 0x00, 0x00, 0x38, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03181"></a><span class="lineno"> 3181</span>  0xDC, 0x29, 0x00, 0x00, 0x38, 0x29, 0x00, 0x00, 0xB4, 0x28, 0x00, 0x00, 0x94, 0x01, 0x00, 0x00, 0x3C, 0x01,</div><div class="line"><a name="l03182"></a><span class="lineno"> 3182</span>  0x00, 0x00, 0xE0, 0x00, 0x00, 0x00, 0x84, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03183"></a><span class="lineno"> 3183</span>  0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00,</div><div class="line"><a name="l03184"></a><span class="lineno"> 3184</span>  0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x70, 0xD6, 0xFF, 0xFF,</div><div class="line"><a name="l03185"></a><span class="lineno"> 3185</span>  0x00, 0x00, 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x06, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x88, 0xD7,</div><div class="line"><a name="l03186"></a><span class="lineno"> 3186</span>  0xFF, 0xFF, 0x08, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xF6, 0xD6, 0xFF, 0xFF, 0x07, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03187"></a><span class="lineno"> 3187</span>  0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03188"></a><span class="lineno"> 3188</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03189"></a><span class="lineno"> 3189</span>  0xE8, 0xD7, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xC8, 0xD6, 0xFF, 0xFF, 0x00, 0x00,</div><div class="line"><a name="l03190"></a><span class="lineno"> 3190</span>  0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x5E, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xE0, 0xD7, 0xFF, 0xFF,</div><div class="line"><a name="l03191"></a><span class="lineno"> 3191</span>  0x08, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x4E, 0xD7, 0xFF, 0xFF, 0x06, 0x00, 0x00, 0x00, 0x10, 0x00,</div><div class="line"><a name="l03192"></a><span class="lineno"> 3192</span>  0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03193"></a><span class="lineno"> 3193</span>  0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0xD8,</div><div class="line"><a name="l03194"></a><span class="lineno"> 3194</span>  0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x20, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B,</div><div class="line"><a name="l03195"></a><span class="lineno"> 3195</span>  0x04, 0x00, 0x00, 0x00, 0xB6, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x38, 0xD8, 0xFF, 0xFF, 0x08, 0x00,</div><div class="line"><a name="l03196"></a><span class="lineno"> 3196</span>  0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0xA6, 0xD7, 0xFF, 0xFF, 0x05, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03197"></a><span class="lineno"> 3197</span>  0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03198"></a><span class="lineno"> 3198</span>  0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x98, 0xD8, 0xFF, 0xFF,</div><div class="line"><a name="l03199"></a><span class="lineno"> 3199</span>  0x03, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x78, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B, 0x04, 0x00,</div><div class="line"><a name="l03200"></a><span class="lineno"> 3200</span>  0x00, 0x00, 0x0E, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x16, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03201"></a><span class="lineno"> 3201</span>  0xFA, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00,</div><div class="line"><a name="l03202"></a><span class="lineno"> 3202</span>  0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03203"></a><span class="lineno"> 3203</span>  0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xEC, 0xD8, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03204"></a><span class="lineno"> 3204</span>  0x00, 0x00, 0x6C, 0xD8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x23, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00,</div><div class="line"><a name="l03205"></a><span class="lineno"> 3205</span>  0x12, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0xE0, 0x25, 0x00, 0x00, 0xD0, 0x25,</div><div class="line"><a name="l03206"></a><span class="lineno"> 3206</span>  0x00, 0x00, 0x2C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x26, 0x00, 0x48, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00,</div><div class="line"><a name="l03207"></a><span class="lineno"> 3207</span>  0x10, 0x00, 0x14, 0x00, 0x18, 0x00, 0x1C, 0x00, 0x20, 0x00, 0x24, 0x00, 0x28, 0x00, 0x2C, 0x00, 0x30, 0x00,</div><div class="line"><a name="l03208"></a><span class="lineno"> 3208</span>  0x34, 0x00, 0x38, 0x00, 0x3C, 0x00, 0x40, 0x00, 0x44, 0x00, 0x26, 0x00, 0x00, 0x00, 0xC4, 0x23, 0x00, 0x00,</div><div class="line"><a name="l03209"></a><span class="lineno"> 3209</span>  0xF8, 0x21, 0x00, 0x00, 0x2C, 0x20, 0x00, 0x00, 0xF0, 0x1A, 0x00, 0x00, 0xB4, 0x15, 0x00, 0x00, 0x78, 0x10,</div><div class="line"><a name="l03210"></a><span class="lineno"> 3210</span>  0x00, 0x00, 0xF0, 0x0F, 0x00, 0x00, 0x68, 0x0F, 0x00, 0x00, 0xE0, 0x0E, 0x00, 0x00, 0x14, 0x0D, 0x00, 0x00,</div><div class="line"><a name="l03211"></a><span class="lineno"> 3211</span>  0xD8, 0x07, 0x00, 0x00, 0x50, 0x07, 0x00, 0x00, 0xC8, 0x06, 0x00, 0x00, 0x8C, 0x01, 0x00, 0x00, 0x14, 0x01,</div><div class="line"><a name="l03212"></a><span class="lineno"> 3212</span>  0x00, 0x00, 0x8C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xEE, 0xD7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03,</div><div class="line"><a name="l03213"></a><span class="lineno"> 3213</span>  0x64, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xFE, 0xD8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x14, 0x00,</div><div class="line"><a name="l03214"></a><span class="lineno"> 3214</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03215"></a><span class="lineno"> 3215</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03216"></a><span class="lineno"> 3216</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03217"></a><span class="lineno"> 3217</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03218"></a><span class="lineno"> 3218</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x5A, 0xD8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01,</div><div class="line"><a name="l03219"></a><span class="lineno"> 3219</span>  0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x72, 0xD8,</div><div class="line"><a name="l03220"></a><span class="lineno"> 3220</span>  0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x64, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x82, 0xD9, 0xFF, 0xFF,</div><div class="line"><a name="l03221"></a><span class="lineno"> 3221</span>  0x04, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03222"></a><span class="lineno"> 3222</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03223"></a><span class="lineno"> 3223</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03224"></a><span class="lineno"> 3224</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03225"></a><span class="lineno"> 3225</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xDE, 0xD8,</div><div class="line"><a name="l03226"></a><span class="lineno"> 3226</span>  0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03227"></a><span class="lineno"> 3227</span>  0x14, 0x00, 0x00, 0x00, 0xF6, 0xD8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x54, 0x00, 0x00, 0x00, 0x04, 0x00,</div><div class="line"><a name="l03228"></a><span class="lineno"> 3228</span>  0x00, 0x00, 0x06, 0xDA, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03229"></a><span class="lineno"> 3229</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03230"></a><span class="lineno"> 3230</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03231"></a><span class="lineno"> 3231</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03232"></a><span class="lineno"> 3232</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xD9, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03233"></a><span class="lineno"> 3233</span>  0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x6A, 0xD9, 0xFF, 0xFF, 0x00, 0x00,</div><div class="line"><a name="l03234"></a><span class="lineno"> 3234</span>  0x00, 0x03, 0x14, 0x05, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x7A, 0xDA, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03235"></a><span class="lineno"> 3235</span>  0x40, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03236"></a><span class="lineno"> 3236</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03237"></a><span class="lineno"> 3237</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03238"></a><span class="lineno"> 3238</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03239"></a><span class="lineno"> 3239</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03240"></a><span class="lineno"> 3240</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03241"></a><span class="lineno"> 3241</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03242"></a><span class="lineno"> 3242</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03243"></a><span class="lineno"> 3243</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03244"></a><span class="lineno"> 3244</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03245"></a><span class="lineno"> 3245</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03246"></a><span class="lineno"> 3246</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03247"></a><span class="lineno"> 3247</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03248"></a><span class="lineno"> 3248</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03249"></a><span class="lineno"> 3249</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03250"></a><span class="lineno"> 3250</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03251"></a><span class="lineno"> 3251</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03252"></a><span class="lineno"> 3252</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03253"></a><span class="lineno"> 3253</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03254"></a><span class="lineno"> 3254</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03255"></a><span class="lineno"> 3255</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03256"></a><span class="lineno"> 3256</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03257"></a><span class="lineno"> 3257</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03258"></a><span class="lineno"> 3258</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03259"></a><span class="lineno"> 3259</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03260"></a><span class="lineno"> 3260</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03261"></a><span class="lineno"> 3261</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03262"></a><span class="lineno"> 3262</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03263"></a><span class="lineno"> 3263</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03264"></a><span class="lineno"> 3264</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03265"></a><span class="lineno"> 3265</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03266"></a><span class="lineno"> 3266</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03267"></a><span class="lineno"> 3267</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03268"></a><span class="lineno"> 3268</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03269"></a><span class="lineno"> 3269</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03270"></a><span class="lineno"> 3270</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03271"></a><span class="lineno"> 3271</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03272"></a><span class="lineno"> 3272</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03273"></a><span class="lineno"> 3273</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03274"></a><span class="lineno"> 3274</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03275"></a><span class="lineno"> 3275</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03276"></a><span class="lineno"> 3276</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03277"></a><span class="lineno"> 3277</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03278"></a><span class="lineno"> 3278</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03279"></a><span class="lineno"> 3279</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03280"></a><span class="lineno"> 3280</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03281"></a><span class="lineno"> 3281</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03282"></a><span class="lineno"> 3282</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03283"></a><span class="lineno"> 3283</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03284"></a><span class="lineno"> 3284</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03285"></a><span class="lineno"> 3285</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03286"></a><span class="lineno"> 3286</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03287"></a><span class="lineno"> 3287</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03288"></a><span class="lineno"> 3288</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03289"></a><span class="lineno"> 3289</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03290"></a><span class="lineno"> 3290</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03291"></a><span class="lineno"> 3291</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03292"></a><span class="lineno"> 3292</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03293"></a><span class="lineno"> 3293</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03294"></a><span class="lineno"> 3294</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03295"></a><span class="lineno"> 3295</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03296"></a><span class="lineno"> 3296</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03297"></a><span class="lineno"> 3297</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03298"></a><span class="lineno"> 3298</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03299"></a><span class="lineno"> 3299</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03300"></a><span class="lineno"> 3300</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03301"></a><span class="lineno"> 3301</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03302"></a><span class="lineno"> 3302</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03303"></a><span class="lineno"> 3303</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03304"></a><span class="lineno"> 3304</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03305"></a><span class="lineno"> 3305</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03306"></a><span class="lineno"> 3306</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x86, 0xDE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03307"></a><span class="lineno"> 3307</span>  0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0xA2, 0xDE,</div><div class="line"><a name="l03308"></a><span class="lineno"> 3308</span>  0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x64, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xB2, 0xDF, 0xFF, 0xFF,</div><div class="line"><a name="l03309"></a><span class="lineno"> 3309</span>  0x04, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03310"></a><span class="lineno"> 3310</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03311"></a><span class="lineno"> 3311</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03312"></a><span class="lineno"> 3312</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03313"></a><span class="lineno"> 3313</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0xDF,</div><div class="line"><a name="l03314"></a><span class="lineno"> 3314</span>  0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03315"></a><span class="lineno"> 3315</span>  0x14, 0x00, 0x00, 0x00, 0x26, 0xDF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x64, 0x00, 0x00, 0x00, 0x04, 0x00,</div><div class="line"><a name="l03316"></a><span class="lineno"> 3316</span>  0x00, 0x00, 0x36, 0xE0, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03317"></a><span class="lineno"> 3317</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03318"></a><span class="lineno"> 3318</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03319"></a><span class="lineno"> 3319</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03320"></a><span class="lineno"> 3320</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03321"></a><span class="lineno"> 3321</span>  0x00, 0x00, 0x00, 0x00, 0x92, 0xDF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03322"></a><span class="lineno"> 3322</span>  0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0xAA, 0xDF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03,</div><div class="line"><a name="l03323"></a><span class="lineno"> 3323</span>  0x14, 0x05, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xBA, 0xE0, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x40, 0x01,</div><div class="line"><a name="l03324"></a><span class="lineno"> 3324</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03325"></a><span class="lineno"> 3325</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03326"></a><span class="lineno"> 3326</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03327"></a><span class="lineno"> 3327</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03328"></a><span class="lineno"> 3328</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03329"></a><span class="lineno"> 3329</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03330"></a><span class="lineno"> 3330</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03331"></a><span class="lineno"> 3331</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03332"></a><span class="lineno"> 3332</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03333"></a><span class="lineno"> 3333</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03334"></a><span class="lineno"> 3334</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03335"></a><span class="lineno"> 3335</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03336"></a><span class="lineno"> 3336</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03337"></a><span class="lineno"> 3337</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03338"></a><span class="lineno"> 3338</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03339"></a><span class="lineno"> 3339</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03340"></a><span class="lineno"> 3340</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03341"></a><span class="lineno"> 3341</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03342"></a><span class="lineno"> 3342</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03343"></a><span class="lineno"> 3343</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03344"></a><span class="lineno"> 3344</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03345"></a><span class="lineno"> 3345</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03346"></a><span class="lineno"> 3346</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03347"></a><span class="lineno"> 3347</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03348"></a><span class="lineno"> 3348</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03349"></a><span class="lineno"> 3349</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03350"></a><span class="lineno"> 3350</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03351"></a><span class="lineno"> 3351</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03352"></a><span class="lineno"> 3352</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03353"></a><span class="lineno"> 3353</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03354"></a><span class="lineno"> 3354</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03355"></a><span class="lineno"> 3355</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03356"></a><span class="lineno"> 3356</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03357"></a><span class="lineno"> 3357</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03358"></a><span class="lineno"> 3358</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03359"></a><span class="lineno"> 3359</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03360"></a><span class="lineno"> 3360</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03361"></a><span class="lineno"> 3361</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03362"></a><span class="lineno"> 3362</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03363"></a><span class="lineno"> 3363</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03364"></a><span class="lineno"> 3364</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03365"></a><span class="lineno"> 3365</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03366"></a><span class="lineno"> 3366</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03367"></a><span class="lineno"> 3367</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03368"></a><span class="lineno"> 3368</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03369"></a><span class="lineno"> 3369</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03370"></a><span class="lineno"> 3370</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03371"></a><span class="lineno"> 3371</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03372"></a><span class="lineno"> 3372</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03373"></a><span class="lineno"> 3373</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03374"></a><span class="lineno"> 3374</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03375"></a><span class="lineno"> 3375</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03376"></a><span class="lineno"> 3376</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03377"></a><span class="lineno"> 3377</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03378"></a><span class="lineno"> 3378</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03379"></a><span class="lineno"> 3379</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03380"></a><span class="lineno"> 3380</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03381"></a><span class="lineno"> 3381</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03382"></a><span class="lineno"> 3382</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03383"></a><span class="lineno"> 3383</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03384"></a><span class="lineno"> 3384</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03385"></a><span class="lineno"> 3385</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03386"></a><span class="lineno"> 3386</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03387"></a><span class="lineno"> 3387</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03388"></a><span class="lineno"> 3388</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03389"></a><span class="lineno"> 3389</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03390"></a><span class="lineno"> 3390</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03391"></a><span class="lineno"> 3391</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03392"></a><span class="lineno"> 3392</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03393"></a><span class="lineno"> 3393</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03394"></a><span class="lineno"> 3394</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03395"></a><span class="lineno"> 3395</span>  0x00, 0x00, 0x00, 0x00, 0xC6, 0xE4, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03396"></a><span class="lineno"> 3396</span>  0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0xE2, 0xE4, 0xFF, 0xFF,</div><div class="line"><a name="l03397"></a><span class="lineno"> 3397</span>  0x00, 0x00, 0x00, 0x03, 0xA4, 0x01, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xF2, 0xE5, 0xFF, 0xFF, 0x04, 0x00,</div><div class="line"><a name="l03398"></a><span class="lineno"> 3398</span>  0x00, 0x00, 0x64, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03399"></a><span class="lineno"> 3399</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03400"></a><span class="lineno"> 3400</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03401"></a><span class="lineno"> 3401</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03402"></a><span class="lineno"> 3402</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03403"></a><span class="lineno"> 3403</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03404"></a><span class="lineno"> 3404</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03405"></a><span class="lineno"> 3405</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03406"></a><span class="lineno"> 3406</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03407"></a><span class="lineno"> 3407</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03408"></a><span class="lineno"> 3408</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03409"></a><span class="lineno"> 3409</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03410"></a><span class="lineno"> 3410</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03411"></a><span class="lineno"> 3411</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03412"></a><span class="lineno"> 3412</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03413"></a><span class="lineno"> 3413</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03414"></a><span class="lineno"> 3414</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03415"></a><span class="lineno"> 3415</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03416"></a><span class="lineno"> 3416</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03417"></a><span class="lineno"> 3417</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03418"></a><span class="lineno"> 3418</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03419"></a><span class="lineno"> 3419</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03420"></a><span class="lineno"> 3420</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8E, 0xE6, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01,</div><div class="line"><a name="l03421"></a><span class="lineno"> 3421</span>  0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x05, 0x00,</div><div class="line"><a name="l03422"></a><span class="lineno"> 3422</span>  0x00, 0x00, 0xAA, 0xE6, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x64, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03423"></a><span class="lineno"> 3423</span>  0xBA, 0xE7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03424"></a><span class="lineno"> 3424</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03425"></a><span class="lineno"> 3425</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03426"></a><span class="lineno"> 3426</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03427"></a><span class="lineno"> 3427</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03428"></a><span class="lineno"> 3428</span>  0x00, 0x00, 0x16, 0xE7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03429"></a><span class="lineno"> 3429</span>  0x01, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x2E, 0xE7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x64, 0x00,</div><div class="line"><a name="l03430"></a><span class="lineno"> 3430</span>  0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x3E, 0xE8, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03431"></a><span class="lineno"> 3431</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03432"></a><span class="lineno"> 3432</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03433"></a><span class="lineno"> 3433</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03434"></a><span class="lineno"> 3434</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03435"></a><span class="lineno"> 3435</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x9A, 0xE7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00,</div><div class="line"><a name="l03436"></a><span class="lineno"> 3436</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0xB2, 0xE7, 0xFF, 0xFF,</div><div class="line"><a name="l03437"></a><span class="lineno"> 3437</span>  0x00, 0x00, 0x00, 0x03, 0x64, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xC2, 0xE8, 0xFF, 0xFF, 0x04, 0x00,</div><div class="line"><a name="l03438"></a><span class="lineno"> 3438</span>  0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03439"></a><span class="lineno"> 3439</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03440"></a><span class="lineno"> 3440</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03441"></a><span class="lineno"> 3441</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03442"></a><span class="lineno"> 3442</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1E, 0xE8, 0xFF, 0xFF,</div><div class="line"><a name="l03443"></a><span class="lineno"> 3443</span>  0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x14, 0x00,</div><div class="line"><a name="l03444"></a><span class="lineno"> 3444</span>  0x00, 0x00, 0x36, 0xE8, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x14, 0x05, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03445"></a><span class="lineno"> 3445</span>  0x46, 0xE9, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x40, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03446"></a><span class="lineno"> 3446</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03447"></a><span class="lineno"> 3447</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03448"></a><span class="lineno"> 3448</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03449"></a><span class="lineno"> 3449</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03450"></a><span class="lineno"> 3450</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03451"></a><span class="lineno"> 3451</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03452"></a><span class="lineno"> 3452</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03453"></a><span class="lineno"> 3453</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03454"></a><span class="lineno"> 3454</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03455"></a><span class="lineno"> 3455</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03456"></a><span class="lineno"> 3456</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03457"></a><span class="lineno"> 3457</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03458"></a><span class="lineno"> 3458</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03459"></a><span class="lineno"> 3459</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03460"></a><span class="lineno"> 3460</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03461"></a><span class="lineno"> 3461</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03462"></a><span class="lineno"> 3462</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03463"></a><span class="lineno"> 3463</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03464"></a><span class="lineno"> 3464</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03465"></a><span class="lineno"> 3465</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03466"></a><span class="lineno"> 3466</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03467"></a><span class="lineno"> 3467</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03468"></a><span class="lineno"> 3468</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03469"></a><span class="lineno"> 3469</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03470"></a><span class="lineno"> 3470</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03471"></a><span class="lineno"> 3471</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03472"></a><span class="lineno"> 3472</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03473"></a><span class="lineno"> 3473</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03474"></a><span class="lineno"> 3474</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03475"></a><span class="lineno"> 3475</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03476"></a><span class="lineno"> 3476</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03477"></a><span class="lineno"> 3477</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03478"></a><span class="lineno"> 3478</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03479"></a><span class="lineno"> 3479</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03480"></a><span class="lineno"> 3480</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03481"></a><span class="lineno"> 3481</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03482"></a><span class="lineno"> 3482</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03483"></a><span class="lineno"> 3483</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03484"></a><span class="lineno"> 3484</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03485"></a><span class="lineno"> 3485</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03486"></a><span class="lineno"> 3486</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03487"></a><span class="lineno"> 3487</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03488"></a><span class="lineno"> 3488</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03489"></a><span class="lineno"> 3489</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03490"></a><span class="lineno"> 3490</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03491"></a><span class="lineno"> 3491</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03492"></a><span class="lineno"> 3492</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03493"></a><span class="lineno"> 3493</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03494"></a><span class="lineno"> 3494</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03495"></a><span class="lineno"> 3495</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03496"></a><span class="lineno"> 3496</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03497"></a><span class="lineno"> 3497</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03498"></a><span class="lineno"> 3498</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03499"></a><span class="lineno"> 3499</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03500"></a><span class="lineno"> 3500</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03501"></a><span class="lineno"> 3501</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03502"></a><span class="lineno"> 3502</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03503"></a><span class="lineno"> 3503</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03504"></a><span class="lineno"> 3504</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03505"></a><span class="lineno"> 3505</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03506"></a><span class="lineno"> 3506</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03507"></a><span class="lineno"> 3507</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03508"></a><span class="lineno"> 3508</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03509"></a><span class="lineno"> 3509</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03510"></a><span class="lineno"> 3510</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03511"></a><span class="lineno"> 3511</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03512"></a><span class="lineno"> 3512</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03513"></a><span class="lineno"> 3513</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03514"></a><span class="lineno"> 3514</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03515"></a><span class="lineno"> 3515</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03516"></a><span class="lineno"> 3516</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xED, 0xFF, 0xFF,</div><div class="line"><a name="l03517"></a><span class="lineno"> 3517</span>  0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00,</div><div class="line"><a name="l03518"></a><span class="lineno"> 3518</span>  0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x6E, 0xED, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0x14, 0x05, 0x00, 0x00,</div><div class="line"><a name="l03519"></a><span class="lineno"> 3519</span>  0x04, 0x00, 0x00, 0x00, 0x7E, 0xEE, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x40, 0x01, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03520"></a><span class="lineno"> 3520</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03521"></a><span class="lineno"> 3521</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03522"></a><span class="lineno"> 3522</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03523"></a><span class="lineno"> 3523</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03524"></a><span class="lineno"> 3524</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03525"></a><span class="lineno"> 3525</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03526"></a><span class="lineno"> 3526</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03527"></a><span class="lineno"> 3527</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03528"></a><span class="lineno"> 3528</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03529"></a><span class="lineno"> 3529</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03530"></a><span class="lineno"> 3530</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03531"></a><span class="lineno"> 3531</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03532"></a><span class="lineno"> 3532</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03533"></a><span class="lineno"> 3533</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03534"></a><span class="lineno"> 3534</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03535"></a><span class="lineno"> 3535</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03536"></a><span class="lineno"> 3536</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03537"></a><span class="lineno"> 3537</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03538"></a><span class="lineno"> 3538</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03539"></a><span class="lineno"> 3539</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03540"></a><span class="lineno"> 3540</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03541"></a><span class="lineno"> 3541</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03542"></a><span class="lineno"> 3542</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03543"></a><span class="lineno"> 3543</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03544"></a><span class="lineno"> 3544</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03545"></a><span class="lineno"> 3545</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03546"></a><span class="lineno"> 3546</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03547"></a><span class="lineno"> 3547</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03548"></a><span class="lineno"> 3548</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03549"></a><span class="lineno"> 3549</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03550"></a><span class="lineno"> 3550</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03551"></a><span class="lineno"> 3551</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03552"></a><span class="lineno"> 3552</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03553"></a><span class="lineno"> 3553</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03554"></a><span class="lineno"> 3554</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03555"></a><span class="lineno"> 3555</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03556"></a><span class="lineno"> 3556</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03557"></a><span class="lineno"> 3557</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03558"></a><span class="lineno"> 3558</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03559"></a><span class="lineno"> 3559</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03560"></a><span class="lineno"> 3560</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03561"></a><span class="lineno"> 3561</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03562"></a><span class="lineno"> 3562</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03563"></a><span class="lineno"> 3563</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03564"></a><span class="lineno"> 3564</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03565"></a><span class="lineno"> 3565</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03566"></a><span class="lineno"> 3566</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03567"></a><span class="lineno"> 3567</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03568"></a><span class="lineno"> 3568</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03569"></a><span class="lineno"> 3569</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03570"></a><span class="lineno"> 3570</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03571"></a><span class="lineno"> 3571</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03572"></a><span class="lineno"> 3572</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03573"></a><span class="lineno"> 3573</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03574"></a><span class="lineno"> 3574</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03575"></a><span class="lineno"> 3575</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03576"></a><span class="lineno"> 3576</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03577"></a><span class="lineno"> 3577</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03578"></a><span class="lineno"> 3578</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03579"></a><span class="lineno"> 3579</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03580"></a><span class="lineno"> 3580</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03581"></a><span class="lineno"> 3581</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03582"></a><span class="lineno"> 3582</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03583"></a><span class="lineno"> 3583</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03584"></a><span class="lineno"> 3584</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03585"></a><span class="lineno"> 3585</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03586"></a><span class="lineno"> 3586</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03587"></a><span class="lineno"> 3587</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03588"></a><span class="lineno"> 3588</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03589"></a><span class="lineno"> 3589</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03590"></a><span class="lineno"> 3590</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03591"></a><span class="lineno"> 3591</span>  0x8A, 0xF2, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00,</div><div class="line"><a name="l03592"></a><span class="lineno"> 3592</span>  0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0xA6, 0xF2, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03,</div><div class="line"><a name="l03593"></a><span class="lineno"> 3593</span>  0x14, 0x05, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xB6, 0xF3, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x40, 0x01,</div><div class="line"><a name="l03594"></a><span class="lineno"> 3594</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03595"></a><span class="lineno"> 3595</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03596"></a><span class="lineno"> 3596</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03597"></a><span class="lineno"> 3597</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03598"></a><span class="lineno"> 3598</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03599"></a><span class="lineno"> 3599</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03600"></a><span class="lineno"> 3600</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03601"></a><span class="lineno"> 3601</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03602"></a><span class="lineno"> 3602</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03603"></a><span class="lineno"> 3603</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03604"></a><span class="lineno"> 3604</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03605"></a><span class="lineno"> 3605</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03606"></a><span class="lineno"> 3606</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03607"></a><span class="lineno"> 3607</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03608"></a><span class="lineno"> 3608</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03609"></a><span class="lineno"> 3609</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03610"></a><span class="lineno"> 3610</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03611"></a><span class="lineno"> 3611</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03612"></a><span class="lineno"> 3612</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03613"></a><span class="lineno"> 3613</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03614"></a><span class="lineno"> 3614</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03615"></a><span class="lineno"> 3615</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03616"></a><span class="lineno"> 3616</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03617"></a><span class="lineno"> 3617</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03618"></a><span class="lineno"> 3618</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03619"></a><span class="lineno"> 3619</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03620"></a><span class="lineno"> 3620</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03621"></a><span class="lineno"> 3621</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03622"></a><span class="lineno"> 3622</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03623"></a><span class="lineno"> 3623</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03624"></a><span class="lineno"> 3624</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03625"></a><span class="lineno"> 3625</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03626"></a><span class="lineno"> 3626</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03627"></a><span class="lineno"> 3627</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03628"></a><span class="lineno"> 3628</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03629"></a><span class="lineno"> 3629</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03630"></a><span class="lineno"> 3630</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03631"></a><span class="lineno"> 3631</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03632"></a><span class="lineno"> 3632</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03633"></a><span class="lineno"> 3633</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03634"></a><span class="lineno"> 3634</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03635"></a><span class="lineno"> 3635</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03636"></a><span class="lineno"> 3636</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03637"></a><span class="lineno"> 3637</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03638"></a><span class="lineno"> 3638</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03639"></a><span class="lineno"> 3639</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03640"></a><span class="lineno"> 3640</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03641"></a><span class="lineno"> 3641</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03642"></a><span class="lineno"> 3642</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03643"></a><span class="lineno"> 3643</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03644"></a><span class="lineno"> 3644</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03645"></a><span class="lineno"> 3645</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03646"></a><span class="lineno"> 3646</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03647"></a><span class="lineno"> 3647</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03648"></a><span class="lineno"> 3648</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03649"></a><span class="lineno"> 3649</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03650"></a><span class="lineno"> 3650</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03651"></a><span class="lineno"> 3651</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03652"></a><span class="lineno"> 3652</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03653"></a><span class="lineno"> 3653</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03654"></a><span class="lineno"> 3654</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03655"></a><span class="lineno"> 3655</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03656"></a><span class="lineno"> 3656</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03657"></a><span class="lineno"> 3657</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03658"></a><span class="lineno"> 3658</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03659"></a><span class="lineno"> 3659</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03660"></a><span class="lineno"> 3660</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03661"></a><span class="lineno"> 3661</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03662"></a><span class="lineno"> 3662</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03663"></a><span class="lineno"> 3663</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03664"></a><span class="lineno"> 3664</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03665"></a><span class="lineno"> 3665</span>  0x00, 0x00, 0x00, 0x00, 0xC2, 0xF7, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03666"></a><span class="lineno"> 3666</span>  0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0xDE, 0xF7, 0xFF, 0xFF,</div><div class="line"><a name="l03667"></a><span class="lineno"> 3667</span>  0x00, 0x00, 0x00, 0x03, 0xA4, 0x01, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xEE, 0xF8, 0xFF, 0xFF, 0x04, 0x00,</div><div class="line"><a name="l03668"></a><span class="lineno"> 3668</span>  0x00, 0x00, 0x64, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03669"></a><span class="lineno"> 3669</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03670"></a><span class="lineno"> 3670</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03671"></a><span class="lineno"> 3671</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03672"></a><span class="lineno"> 3672</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03673"></a><span class="lineno"> 3673</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03674"></a><span class="lineno"> 3674</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03675"></a><span class="lineno"> 3675</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03676"></a><span class="lineno"> 3676</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03677"></a><span class="lineno"> 3677</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03678"></a><span class="lineno"> 3678</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03679"></a><span class="lineno"> 3679</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03680"></a><span class="lineno"> 3680</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03681"></a><span class="lineno"> 3681</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03682"></a><span class="lineno"> 3682</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03683"></a><span class="lineno"> 3683</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03684"></a><span class="lineno"> 3684</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03685"></a><span class="lineno"> 3685</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03686"></a><span class="lineno"> 3686</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03687"></a><span class="lineno"> 3687</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03688"></a><span class="lineno"> 3688</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03689"></a><span class="lineno"> 3689</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03690"></a><span class="lineno"> 3690</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8A, 0xF9, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01,</div><div class="line"><a name="l03691"></a><span class="lineno"> 3691</span>  0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x05, 0x00,</div><div class="line"><a name="l03692"></a><span class="lineno"> 3692</span>  0x00, 0x00, 0xA6, 0xF9, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0xA4, 0x01, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03693"></a><span class="lineno"> 3693</span>  0xB6, 0xFA, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x64, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03694"></a><span class="lineno"> 3694</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03695"></a><span class="lineno"> 3695</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03696"></a><span class="lineno"> 3696</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03697"></a><span class="lineno"> 3697</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03698"></a><span class="lineno"> 3698</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03699"></a><span class="lineno"> 3699</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03700"></a><span class="lineno"> 3700</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03701"></a><span class="lineno"> 3701</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03702"></a><span class="lineno"> 3702</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03703"></a><span class="lineno"> 3703</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03704"></a><span class="lineno"> 3704</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03705"></a><span class="lineno"> 3705</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03706"></a><span class="lineno"> 3706</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03707"></a><span class="lineno"> 3707</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03708"></a><span class="lineno"> 3708</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03709"></a><span class="lineno"> 3709</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03710"></a><span class="lineno"> 3710</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03711"></a><span class="lineno"> 3711</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03712"></a><span class="lineno"> 3712</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03713"></a><span class="lineno"> 3713</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03714"></a><span class="lineno"> 3714</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03715"></a><span class="lineno"> 3715</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x52, 0xFB,</div><div class="line"><a name="l03716"></a><span class="lineno"> 3716</span>  0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03717"></a><span class="lineno"> 3717</span>  0x14, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x6E, 0xFB, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x03, 0xA4, 0x01,</div><div class="line"><a name="l03718"></a><span class="lineno"> 3718</span>  0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x7E, 0xFC, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x64, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03719"></a><span class="lineno"> 3719</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03720"></a><span class="lineno"> 3720</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03721"></a><span class="lineno"> 3721</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03722"></a><span class="lineno"> 3722</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03723"></a><span class="lineno"> 3723</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03724"></a><span class="lineno"> 3724</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03725"></a><span class="lineno"> 3725</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03726"></a><span class="lineno"> 3726</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03727"></a><span class="lineno"> 3727</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03728"></a><span class="lineno"> 3728</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03729"></a><span class="lineno"> 3729</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03730"></a><span class="lineno"> 3730</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03731"></a><span class="lineno"> 3731</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03732"></a><span class="lineno"> 3732</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03733"></a><span class="lineno"> 3733</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03734"></a><span class="lineno"> 3734</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03735"></a><span class="lineno"> 3735</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03736"></a><span class="lineno"> 3736</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03737"></a><span class="lineno"> 3737</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03738"></a><span class="lineno"> 3738</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03739"></a><span class="lineno"> 3739</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03740"></a><span class="lineno"> 3740</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03741"></a><span class="lineno"> 3741</span>  0x00, 0x00, 0x00, 0x00, 0x1A, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03742"></a><span class="lineno"> 3742</span>  0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x10, 0x00, 0x0C, 0x00,</div><div class="line"><a name="l03743"></a><span class="lineno"> 3743</span>  0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x06, 0x00, 0x07, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03744"></a><span class="lineno"> 3744</span>  0x01, 0x01, 0x04, 0x00, 0x00, 0x00, 0x2E, 0xFE, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03745"></a><span class="lineno"> 3745</span>  0x22, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6C, 0x73,</div><div class="line"><a name="l03746"></a><span class="lineno"> 3746</span>  0x74, 0x6D, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xEC, 0x00, 0x00, 0x00, 0xD0, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03747"></a><span class="lineno"> 3747</span>  0xB4, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x88, 0x00, 0x00, 0x00, 0x5C, 0x00, 0x00, 0x00, 0x30, 0x00,</div><div class="line"><a name="l03748"></a><span class="lineno"> 3748</span>  0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x14, 0xFF, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03749"></a><span class="lineno"> 3749</span>  0xA6, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00,</div><div class="line"><a name="l03750"></a><span class="lineno"> 3750</span>  0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x3C, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03751"></a><span class="lineno"> 3751</span>  0x04, 0x00, 0x00, 0x00, 0xCE, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03752"></a><span class="lineno"> 3752</span>  0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x64, 0xFF, 0xFF, 0xFF,</div><div class="line"><a name="l03753"></a><span class="lineno"> 3753</span>  0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xF6, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00,</div><div class="line"><a name="l03754"></a><span class="lineno"> 3754</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03755"></a><span class="lineno"> 3755</span>  0xB4, 0xFE, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x1A, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00,</div><div class="line"><a name="l03756"></a><span class="lineno"> 3756</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x50, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03757"></a><span class="lineno"> 3757</span>  0xF0, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00,</div><div class="line"><a name="l03758"></a><span class="lineno"> 3758</span>  0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03759"></a><span class="lineno"> 3759</span>  0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03760"></a><span class="lineno"> 3760</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE8, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x09, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03761"></a><span class="lineno"> 3761</span>  0x7E, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00,</div><div class="line"><a name="l03762"></a><span class="lineno"> 3762</span>  0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03763"></a><span class="lineno"> 3763</span>  0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03764"></a><span class="lineno"> 3764</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03765"></a><span class="lineno"> 3765</span>  0x68, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xCE, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00,</div><div class="line"><a name="l03766"></a><span class="lineno"> 3766</span>  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03767"></a><span class="lineno"> 3767</span>  0x08, 0x00, 0x0E, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x0C, 0x00,</div><div class="line"><a name="l03768"></a><span class="lineno"> 3768</span>  0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03769"></a><span class="lineno"> 3769</span>  0x08, 0x00, 0x0E, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x01, 0x00,</div><div class="line"><a name="l03770"></a><span class="lineno"> 3770</span>  0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00,</div><div class="line"><a name="l03771"></a><span class="lineno"> 3771</span>  0x0E, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00,</div><div class="line"><a name="l03772"></a><span class="lineno"> 3772</span>  0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03773"></a><span class="lineno"> 3773</span>  0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,</div><div class="line"><a name="l03774"></a><span class="lineno"> 3774</span>  0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03775"></a><span class="lineno"> 3775</span>  0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x08, 0x00,</div><div class="line"><a name="l03776"></a><span class="lineno"> 3776</span>  0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x04, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03777"></a><span class="lineno"> 3777</span>  0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x0A, 0x00, 0x04, 0x00, 0x06, 0x00,</div><div class="line"><a name="l03778"></a><span class="lineno"> 3778</span>  0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x14, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,</div><div class="line"><a name="l03779"></a><span class="lineno"> 3779</span>  0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00,</div><div class="line"><a name="l03780"></a><span class="lineno"> 3780</span>  0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,</div><div class="line"><a name="l03781"></a><span class="lineno"> 3781</span>  0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,</div><div class="line"><a name="l03782"></a><span class="lineno"> 3782</span>  0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x08, 0x00, 0x07, 0x00, 0x0C, 0x00,</div><div class="line"><a name="l03783"></a><span class="lineno"> 3783</span>  0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00,</div><div class="line"><a name="l03784"></a><span class="lineno"> 3784</span>  0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x00</div><div class="line"><a name="l03785"></a><span class="lineno"> 3785</span>  };</div><div class="line"><a name="l03786"></a><span class="lineno"> 3786</span> </div><div class="line"><a name="l03787"></a><span class="lineno"> 3787</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork =</div><div class="line"><a name="l03788"></a><span class="lineno"> 3788</span>  DeserializeNetwork(std::string(lstmNoCifgWithPeepholeAndProjectionModel.begin(),</div><div class="line"><a name="l03789"></a><span class="lineno"> 3789</span>  lstmNoCifgWithPeepholeAndProjectionModel.end()));</div><div class="line"><a name="l03790"></a><span class="lineno"> 3790</span> </div><div class="line"><a name="l03791"></a><span class="lineno"> 3791</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l03792"></a><span class="lineno"> 3792</span> </div><div class="line"><a name="l03793"></a><span class="lineno"> 3793</span>  <span class="comment">// generating the same model parameters which where used to serialize the model (Layer norm is not specified)</span></div><div class="line"><a name="l03794"></a><span class="lineno"> 3794</span>  <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a> descriptor;</div><div class="line"><a name="l03795"></a><span class="lineno"> 3795</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a> = 4;</div><div class="line"><a name="l03796"></a><span class="lineno"> 3796</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a> = 0.0f;</div><div class="line"><a name="l03797"></a><span class="lineno"> 3797</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a> = 0.0f;</div><div class="line"><a name="l03798"></a><span class="lineno"> 3798</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l03799"></a><span class="lineno"> 3799</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l03800"></a><span class="lineno"> 3800</span>  descriptor.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l03801"></a><span class="lineno"> 3801</span> </div><div class="line"><a name="l03802"></a><span class="lineno"> 3802</span>  <span class="keyword">const</span> uint32_t batchSize = 2u;</div><div class="line"><a name="l03803"></a><span class="lineno"> 3803</span>  <span class="keyword">const</span> uint32_t inputSize = 5u;</div><div class="line"><a name="l03804"></a><span class="lineno"> 3804</span>  <span class="keyword">const</span> uint32_t numUnits = 20u;</div><div class="line"><a name="l03805"></a><span class="lineno"> 3805</span>  <span class="keyword">const</span> uint32_t outputSize = 16u;</div><div class="line"><a name="l03806"></a><span class="lineno"> 3806</span> </div><div class="line"><a name="l03807"></a><span class="lineno"> 3807</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20x5({numUnits, inputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03808"></a><span class="lineno"> 3808</span>  std::vector<float> inputToInputWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);</div><div class="line"><a name="l03809"></a><span class="lineno"> 3809</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);</div><div class="line"><a name="l03810"></a><span class="lineno"> 3810</span> </div><div class="line"><a name="l03811"></a><span class="lineno"> 3811</span>  std::vector<float> inputToForgetWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);</div><div class="line"><a name="l03812"></a><span class="lineno"> 3812</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);</div><div class="line"><a name="l03813"></a><span class="lineno"> 3813</span> </div><div class="line"><a name="l03814"></a><span class="lineno"> 3814</span>  std::vector<float> inputToCellWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);</div><div class="line"><a name="l03815"></a><span class="lineno"> 3815</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);</div><div class="line"><a name="l03816"></a><span class="lineno"> 3816</span> </div><div class="line"><a name="l03817"></a><span class="lineno"> 3817</span>  std::vector<float> inputToOutputWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);</div><div class="line"><a name="l03818"></a><span class="lineno"> 3818</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData);</div><div class="line"><a name="l03819"></a><span class="lineno"> 3819</span> </div><div class="line"><a name="l03820"></a><span class="lineno"> 3820</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20({numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03821"></a><span class="lineno"> 3821</span>  std::vector<float> inputGateBiasData(tensorInfo20.GetNumElements(), 0.0f);</div><div class="line"><a name="l03822"></a><span class="lineno"> 3822</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputGateBias(tensorInfo20, inputGateBiasData);</div><div class="line"><a name="l03823"></a><span class="lineno"> 3823</span> </div><div class="line"><a name="l03824"></a><span class="lineno"> 3824</span>  std::vector<float> forgetGateBiasData(tensorInfo20.GetNumElements(), 0.0f);</div><div class="line"><a name="l03825"></a><span class="lineno"> 3825</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetGateBias(tensorInfo20, forgetGateBiasData);</div><div class="line"><a name="l03826"></a><span class="lineno"> 3826</span> </div><div class="line"><a name="l03827"></a><span class="lineno"> 3827</span>  std::vector<float> cellBiasData(tensorInfo20.GetNumElements(), 0.0f);</div><div class="line"><a name="l03828"></a><span class="lineno"> 3828</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellBias(tensorInfo20, cellBiasData);</div><div class="line"><a name="l03829"></a><span class="lineno"> 3829</span> </div><div class="line"><a name="l03830"></a><span class="lineno"> 3830</span>  std::vector<float> outputGateBiasData(tensorInfo20.GetNumElements(), 0.0f);</div><div class="line"><a name="l03831"></a><span class="lineno"> 3831</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputGateBias(tensorInfo20, outputGateBiasData);</div><div class="line"><a name="l03832"></a><span class="lineno"> 3832</span> </div><div class="line"><a name="l03833"></a><span class="lineno"> 3833</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo20x16({numUnits, outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03834"></a><span class="lineno"> 3834</span>  std::vector<float> recurrentToInputWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);</div><div class="line"><a name="l03835"></a><span class="lineno"> 3835</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);</div><div class="line"><a name="l03836"></a><span class="lineno"> 3836</span> </div><div class="line"><a name="l03837"></a><span class="lineno"> 3837</span>  std::vector<float> recurrentToForgetWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);</div><div class="line"><a name="l03838"></a><span class="lineno"> 3838</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);</div><div class="line"><a name="l03839"></a><span class="lineno"> 3839</span> </div><div class="line"><a name="l03840"></a><span class="lineno"> 3840</span>  std::vector<float> recurrentToCellWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);</div><div class="line"><a name="l03841"></a><span class="lineno"> 3841</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);</div><div class="line"><a name="l03842"></a><span class="lineno"> 3842</span> </div><div class="line"><a name="l03843"></a><span class="lineno"> 3843</span>  std::vector<float> recurrentToOutputWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);</div><div class="line"><a name="l03844"></a><span class="lineno"> 3844</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);</div><div class="line"><a name="l03845"></a><span class="lineno"> 3845</span> </div><div class="line"><a name="l03846"></a><span class="lineno"> 3846</span>  std::vector<float> cellToInputWeightsData(tensorInfo20.GetNumElements(), 0.0f);</div><div class="line"><a name="l03847"></a><span class="lineno"> 3847</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToInputWeights(tensorInfo20, cellToInputWeightsData);</div><div class="line"><a name="l03848"></a><span class="lineno"> 3848</span> </div><div class="line"><a name="l03849"></a><span class="lineno"> 3849</span>  std::vector<float> cellToForgetWeightsData(tensorInfo20.GetNumElements(), 0.0f);</div><div class="line"><a name="l03850"></a><span class="lineno"> 3850</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);</div><div class="line"><a name="l03851"></a><span class="lineno"> 3851</span> </div><div class="line"><a name="l03852"></a><span class="lineno"> 3852</span>  std::vector<float> cellToOutputWeightsData(tensorInfo20.GetNumElements(), 0.0f);</div><div class="line"><a name="l03853"></a><span class="lineno"> 3853</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellToOutputWeights(tensorInfo20, cellToOutputWeightsData);</div><div class="line"><a name="l03854"></a><span class="lineno"> 3854</span> </div><div class="line"><a name="l03855"></a><span class="lineno"> 3855</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo16x20({outputSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03856"></a><span class="lineno"> 3856</span>  std::vector<float> projectionWeightsData(tensorInfo16x20.GetNumElements(), 0.0f);</div><div class="line"><a name="l03857"></a><span class="lineno"> 3857</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> projectionWeights(tensorInfo16x20, projectionWeightsData);</div><div class="line"><a name="l03858"></a><span class="lineno"> 3858</span> </div><div class="line"><a name="l03859"></a><span class="lineno"> 3859</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo16({outputSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03860"></a><span class="lineno"> 3860</span>  std::vector<float> projectionBiasData(outputSize, 0.0f);</div><div class="line"><a name="l03861"></a><span class="lineno"> 3861</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> projectionBias(tensorInfo16, projectionBiasData);</div><div class="line"><a name="l03862"></a><span class="lineno"> 3862</span> </div><div class="line"><a name="l03863"></a><span class="lineno"> 3863</span>  <a class="code" href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a> params;</div><div class="line"><a name="l03864"></a><span class="lineno"> 3864</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a> = &inputToForgetWeights;</div><div class="line"><a name="l03865"></a><span class="lineno"> 3865</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a> = &inputToCellWeights;</div><div class="line"><a name="l03866"></a><span class="lineno"> 3866</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &inputToOutputWeights;</div><div class="line"><a name="l03867"></a><span class="lineno"> 3867</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeights;</div><div class="line"><a name="l03868"></a><span class="lineno"> 3868</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a> = &recurrentToCellWeights;</div><div class="line"><a name="l03869"></a><span class="lineno"> 3869</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeights;</div><div class="line"><a name="l03870"></a><span class="lineno"> 3870</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &forgetGateBias;</div><div class="line"><a name="l03871"></a><span class="lineno"> 3871</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a> = &cellBias;</div><div class="line"><a name="l03872"></a><span class="lineno"> 3872</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &outputGateBias;</div><div class="line"><a name="l03873"></a><span class="lineno"> 3873</span> </div><div class="line"><a name="l03874"></a><span class="lineno"> 3874</span>  <span class="comment">// additional params because: descriptor.m_CifgEnabled = false</span></div><div class="line"><a name="l03875"></a><span class="lineno"> 3875</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a> = &inputToInputWeights;</div><div class="line"><a name="l03876"></a><span class="lineno"> 3876</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a> = &recurrentToInputWeights;</div><div class="line"><a name="l03877"></a><span class="lineno"> 3877</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">m_CellToInputWeights</a> = &cellToInputWeights;</div><div class="line"><a name="l03878"></a><span class="lineno"> 3878</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a> = &inputGateBias;</div><div class="line"><a name="l03879"></a><span class="lineno"> 3879</span> </div><div class="line"><a name="l03880"></a><span class="lineno"> 3880</span>  <span class="comment">// additional params because: descriptor.m_ProjectionEnabled = true</span></div><div class="line"><a name="l03881"></a><span class="lineno"> 3881</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">m_ProjectionWeights</a> = &projectionWeights;</div><div class="line"><a name="l03882"></a><span class="lineno"> 3882</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">m_ProjectionBias</a> = &projectionBias;</div><div class="line"><a name="l03883"></a><span class="lineno"> 3883</span> </div><div class="line"><a name="l03884"></a><span class="lineno"> 3884</span>  <span class="comment">// additional params because: descriptor.m_PeepholeEnabled = true</span></div><div class="line"><a name="l03885"></a><span class="lineno"> 3885</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">m_CellToForgetWeights</a> = &cellToForgetWeights;</div><div class="line"><a name="l03886"></a><span class="lineno"> 3886</span>  params.<a class="code" href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">m_CellToOutputWeights</a> = &cellToOutputWeights;</div><div class="line"><a name="l03887"></a><span class="lineno"> 3887</span> </div><div class="line"><a name="l03888"></a><span class="lineno"> 3888</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"lstm"</span>);</div><div class="line"><a name="l03889"></a><span class="lineno"> 3889</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ batchSize, inputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03890"></a><span class="lineno"> 3890</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateTensorInfo({ batchSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03891"></a><span class="lineno"> 3891</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateTensorInfo({ batchSize, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03892"></a><span class="lineno"> 3892</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03893"></a><span class="lineno"> 3893</span> </div><div class="line"><a name="l03894"></a><span class="lineno"> 3894</span>  VerifyLstmLayer checker(</div><div class="line"><a name="l03895"></a><span class="lineno"> 3895</span>  layerName,</div><div class="line"><a name="l03896"></a><span class="lineno"> 3896</span>  {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},</div><div class="line"><a name="l03897"></a><span class="lineno"> 3897</span>  {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l03898"></a><span class="lineno"> 3898</span>  descriptor,</div><div class="line"><a name="l03899"></a><span class="lineno"> 3899</span>  params);</div><div class="line"><a name="l03900"></a><span class="lineno"> 3900</span>  deserializedNetwork->Accept(checker);</div><div class="line"><a name="l03901"></a><span class="lineno"> 3901</span> }</div><div class="line"><a name="l03902"></a><span class="lineno"> 3902</span> <span class="keyword">class </span>VerifyQuantizedLstmLayer : <span class="keyword">public</span> LayerVerifierBase</div><div class="line"><a name="l03903"></a><span class="lineno"> 3903</span> {</div><div class="line"><a name="l03904"></a><span class="lineno"> 3904</span> </div><div class="line"><a name="l03905"></a><span class="lineno"> 3905</span> <span class="keyword">public</span>:</div><div class="line"><a name="l03906"></a><span class="lineno"> 3906</span>  VerifyQuantizedLstmLayer(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l03907"></a><span class="lineno"> 3907</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& inputInfos,</div><div class="line"><a name="l03908"></a><span class="lineno"> 3908</span>  <span class="keyword">const</span> std::vector<armnn::TensorInfo>& outputInfos,</div><div class="line"><a name="l03909"></a><span class="lineno"> 3909</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a>& inputParams)</div><div class="line"><a name="l03910"></a><span class="lineno"> 3910</span>  : LayerVerifierBase(layerName, inputInfos, outputInfos), m_InputParams(inputParams) {}</div><div class="line"><a name="l03911"></a><span class="lineno"> 3911</span> </div><div class="line"><a name="l03912"></a><span class="lineno"> 3912</span>  <span class="keywordtype">void</span> VisitQuantizedLstmLayer(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer,</div><div class="line"><a name="l03913"></a><span class="lineno"> 3913</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a>& params,</div><div class="line"><a name="l03914"></a><span class="lineno"> 3914</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* name)</div><div class="line"><a name="l03915"></a><span class="lineno"> 3915</span>  {</div><div class="line"><a name="l03916"></a><span class="lineno"> 3916</span>  VerifyNameAndConnections(layer, name);</div><div class="line"><a name="l03917"></a><span class="lineno"> 3917</span>  VerifyInputParameters(params);</div><div class="line"><a name="l03918"></a><span class="lineno"> 3918</span>  }</div><div class="line"><a name="l03919"></a><span class="lineno"> 3919</span> </div><div class="line"><a name="l03920"></a><span class="lineno"> 3920</span> <span class="keyword">protected</span>:</div><div class="line"><a name="l03921"></a><span class="lineno"> 3921</span>  <span class="keywordtype">void</span> VerifyInputParameters(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a>& params)</div><div class="line"><a name="l03922"></a><span class="lineno"> 3922</span>  {</div><div class="line"><a name="l03923"></a><span class="lineno"> 3923</span>  VerifyConstTensors(<span class="stringliteral">"m_InputToInputWeights"</span>,</div><div class="line"><a name="l03924"></a><span class="lineno"> 3924</span>  m_InputParams.m_InputToInputWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a>);</div><div class="line"><a name="l03925"></a><span class="lineno"> 3925</span>  VerifyConstTensors(<span class="stringliteral">"m_InputToForgetWeights"</span>,</div><div class="line"><a name="l03926"></a><span class="lineno"> 3926</span>  m_InputParams.m_InputToForgetWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a>);</div><div class="line"><a name="l03927"></a><span class="lineno"> 3927</span>  VerifyConstTensors(<span class="stringliteral">"m_InputToCellWeights"</span>,</div><div class="line"><a name="l03928"></a><span class="lineno"> 3928</span>  m_InputParams.m_InputToCellWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a>);</div><div class="line"><a name="l03929"></a><span class="lineno"> 3929</span>  VerifyConstTensors(<span class="stringliteral">"m_InputToOutputWeights"</span>,</div><div class="line"><a name="l03930"></a><span class="lineno"> 3930</span>  m_InputParams.m_InputToOutputWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a>);</div><div class="line"><a name="l03931"></a><span class="lineno"> 3931</span>  VerifyConstTensors(<span class="stringliteral">"m_RecurrentToInputWeights"</span>,</div><div class="line"><a name="l03932"></a><span class="lineno"> 3932</span>  m_InputParams.m_RecurrentToInputWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a>);</div><div class="line"><a name="l03933"></a><span class="lineno"> 3933</span>  VerifyConstTensors(<span class="stringliteral">"m_RecurrentToForgetWeights"</span>,</div><div class="line"><a name="l03934"></a><span class="lineno"> 3934</span>  m_InputParams.m_RecurrentToForgetWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a>);</div><div class="line"><a name="l03935"></a><span class="lineno"> 3935</span>  VerifyConstTensors(<span class="stringliteral">"m_RecurrentToCellWeights"</span>,</div><div class="line"><a name="l03936"></a><span class="lineno"> 3936</span>  m_InputParams.m_RecurrentToCellWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a>);</div><div class="line"><a name="l03937"></a><span class="lineno"> 3937</span>  VerifyConstTensors(<span class="stringliteral">"m_RecurrentToOutputWeights"</span>,</div><div class="line"><a name="l03938"></a><span class="lineno"> 3938</span>  m_InputParams.m_RecurrentToOutputWeights, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a>);</div><div class="line"><a name="l03939"></a><span class="lineno"> 3939</span>  VerifyConstTensors(<span class="stringliteral">"m_InputGateBias"</span>,</div><div class="line"><a name="l03940"></a><span class="lineno"> 3940</span>  m_InputParams.m_InputGateBias, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a>);</div><div class="line"><a name="l03941"></a><span class="lineno"> 3941</span>  VerifyConstTensors(<span class="stringliteral">"m_ForgetGateBias"</span>,</div><div class="line"><a name="l03942"></a><span class="lineno"> 3942</span>  m_InputParams.m_ForgetGateBias, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a>);</div><div class="line"><a name="l03943"></a><span class="lineno"> 3943</span>  VerifyConstTensors(<span class="stringliteral">"m_CellBias"</span>,</div><div class="line"><a name="l03944"></a><span class="lineno"> 3944</span>  m_InputParams.m_CellBias, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a>);</div><div class="line"><a name="l03945"></a><span class="lineno"> 3945</span>  VerifyConstTensors(<span class="stringliteral">"m_OutputGateBias"</span>,</div><div class="line"><a name="l03946"></a><span class="lineno"> 3946</span>  m_InputParams.m_OutputGateBias, params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a>);</div><div class="line"><a name="l03947"></a><span class="lineno"> 3947</span>  }</div><div class="line"><a name="l03948"></a><span class="lineno"> 3948</span> </div><div class="line"><a name="l03949"></a><span class="lineno"> 3949</span> <span class="keyword">private</span>:</div><div class="line"><a name="l03950"></a><span class="lineno"> 3950</span>  <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a> m_InputParams;</div><div class="line"><a name="l03951"></a><span class="lineno"> 3951</span> };</div><div class="line"><a name="l03952"></a><span class="lineno"> 3952</span> </div><div class="line"><a name="l03953"></a><span class="lineno"><a class="line" href="_serializer_tests_8cpp.xhtml#a1cb7aec5bf87ff679cfd0ee9aa7d41c2"> 3953</a></span> <a class="code" href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a>(SerializeDeserializeQuantizedLstm)</div><div class="line"><a name="l03954"></a><span class="lineno"> 3954</span> {</div><div class="line"><a name="l03955"></a><span class="lineno"> 3955</span>  <span class="keyword">const</span> uint32_t batchSize = 1;</div><div class="line"><a name="l03956"></a><span class="lineno"> 3956</span>  <span class="keyword">const</span> uint32_t inputSize = 2;</div><div class="line"><a name="l03957"></a><span class="lineno"> 3957</span>  <span class="keyword">const</span> uint32_t numUnits = 4;</div><div class="line"><a name="l03958"></a><span class="lineno"> 3958</span>  <span class="keyword">const</span> uint32_t outputSize = numUnits;</div><div class="line"><a name="l03959"></a><span class="lineno"> 3959</span> </div><div class="line"><a name="l03960"></a><span class="lineno"> 3960</span>  <span class="comment">// Scale/Offset for input/output, cellState In/Out, weights, bias</span></div><div class="line"><a name="l03961"></a><span class="lineno"> 3961</span>  <span class="keywordtype">float</span> inputOutputScale = 0.0078125f;</div><div class="line"><a name="l03962"></a><span class="lineno"> 3962</span>  int32_t inputOutputOffset = 128;</div><div class="line"><a name="l03963"></a><span class="lineno"> 3963</span> </div><div class="line"><a name="l03964"></a><span class="lineno"> 3964</span>  <span class="keywordtype">float</span> cellStateScale = 0.00048828125f;</div><div class="line"><a name="l03965"></a><span class="lineno"> 3965</span>  int32_t cellStateOffset = 0;</div><div class="line"><a name="l03966"></a><span class="lineno"> 3966</span> </div><div class="line"><a name="l03967"></a><span class="lineno"> 3967</span>  <span class="keywordtype">float</span> weightsScale = 0.00408021f;</div><div class="line"><a name="l03968"></a><span class="lineno"> 3968</span>  int32_t weightsOffset = 100;</div><div class="line"><a name="l03969"></a><span class="lineno"> 3969</span> </div><div class="line"><a name="l03970"></a><span class="lineno"> 3970</span>  <span class="keywordtype">float</span> biasScale = 3.1876640625e-05f;</div><div class="line"><a name="l03971"></a><span class="lineno"> 3971</span>  int32_t biasOffset = 0;</div><div class="line"><a name="l03972"></a><span class="lineno"> 3972</span> </div><div class="line"><a name="l03973"></a><span class="lineno"> 3973</span>  <span class="comment">// The shape of weight data is {outputSize, inputSize} = {4, 2}</span></div><div class="line"><a name="l03974"></a><span class="lineno"> 3974</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputToInputWeightsShape = {4, 2};</div><div class="line"><a name="l03975"></a><span class="lineno"> 3975</span>  std::vector<uint8_t> inputToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};</div><div class="line"><a name="l03976"></a><span class="lineno"> 3976</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputToInputWeightsInfo(inputToInputWeightsShape,</div><div class="line"><a name="l03977"></a><span class="lineno"> 3977</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l03978"></a><span class="lineno"> 3978</span>  weightsScale,</div><div class="line"><a name="l03979"></a><span class="lineno"> 3979</span>  weightsOffset);</div><div class="line"><a name="l03980"></a><span class="lineno"> 3980</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToInputWeights(inputToInputWeightsInfo, inputToInputWeightsData);</div><div class="line"><a name="l03981"></a><span class="lineno"> 3981</span> </div><div class="line"><a name="l03982"></a><span class="lineno"> 3982</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputToForgetWeightsShape = {4, 2};</div><div class="line"><a name="l03983"></a><span class="lineno"> 3983</span>  std::vector<uint8_t> inputToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};</div><div class="line"><a name="l03984"></a><span class="lineno"> 3984</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputToForgetWeightsInfo(inputToForgetWeightsShape,</div><div class="line"><a name="l03985"></a><span class="lineno"> 3985</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l03986"></a><span class="lineno"> 3986</span>  weightsScale,</div><div class="line"><a name="l03987"></a><span class="lineno"> 3987</span>  weightsOffset);</div><div class="line"><a name="l03988"></a><span class="lineno"> 3988</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToForgetWeights(inputToForgetWeightsInfo, inputToForgetWeightsData);</div><div class="line"><a name="l03989"></a><span class="lineno"> 3989</span> </div><div class="line"><a name="l03990"></a><span class="lineno"> 3990</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputToCellWeightsShape = {4, 2};</div><div class="line"><a name="l03991"></a><span class="lineno"> 3991</span>  std::vector<uint8_t> inputToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};</div><div class="line"><a name="l03992"></a><span class="lineno"> 3992</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputToCellWeightsInfo(inputToCellWeightsShape,</div><div class="line"><a name="l03993"></a><span class="lineno"> 3993</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l03994"></a><span class="lineno"> 3994</span>  weightsScale,</div><div class="line"><a name="l03995"></a><span class="lineno"> 3995</span>  weightsOffset);</div><div class="line"><a name="l03996"></a><span class="lineno"> 3996</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToCellWeights(inputToCellWeightsInfo, inputToCellWeightsData);</div><div class="line"><a name="l03997"></a><span class="lineno"> 3997</span> </div><div class="line"><a name="l03998"></a><span class="lineno"> 3998</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputToOutputWeightsShape = {4, 2};</div><div class="line"><a name="l03999"></a><span class="lineno"> 3999</span>  std::vector<uint8_t> inputToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};</div><div class="line"><a name="l04000"></a><span class="lineno"> 4000</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputToOutputWeightsInfo(inputToOutputWeightsShape,</div><div class="line"><a name="l04001"></a><span class="lineno"> 4001</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l04002"></a><span class="lineno"> 4002</span>  weightsScale,</div><div class="line"><a name="l04003"></a><span class="lineno"> 4003</span>  weightsOffset);</div><div class="line"><a name="l04004"></a><span class="lineno"> 4004</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputToOutputWeights(inputToOutputWeightsInfo, inputToOutputWeightsData);</div><div class="line"><a name="l04005"></a><span class="lineno"> 4005</span> </div><div class="line"><a name="l04006"></a><span class="lineno"> 4006</span>  <span class="comment">// The shape of recurrent weight data is {outputSize, outputSize} = {4, 4}</span></div><div class="line"><a name="l04007"></a><span class="lineno"> 4007</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> recurrentToInputWeightsShape = {4, 4};</div><div class="line"><a name="l04008"></a><span class="lineno"> 4008</span>  std::vector<uint8_t> recurrentToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};</div><div class="line"><a name="l04009"></a><span class="lineno"> 4009</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> recurrentToInputWeightsInfo(recurrentToInputWeightsShape,</div><div class="line"><a name="l04010"></a><span class="lineno"> 4010</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l04011"></a><span class="lineno"> 4011</span>  weightsScale,</div><div class="line"><a name="l04012"></a><span class="lineno"> 4012</span>  weightsOffset);</div><div class="line"><a name="l04013"></a><span class="lineno"> 4013</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToInputWeights(recurrentToInputWeightsInfo, recurrentToInputWeightsData);</div><div class="line"><a name="l04014"></a><span class="lineno"> 4014</span> </div><div class="line"><a name="l04015"></a><span class="lineno"> 4015</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> recurrentToForgetWeightsShape = {4, 4};</div><div class="line"><a name="l04016"></a><span class="lineno"> 4016</span>  std::vector<uint8_t> recurrentToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};</div><div class="line"><a name="l04017"></a><span class="lineno"> 4017</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> recurrentToForgetWeightsInfo(recurrentToForgetWeightsShape,</div><div class="line"><a name="l04018"></a><span class="lineno"> 4018</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l04019"></a><span class="lineno"> 4019</span>  weightsScale,</div><div class="line"><a name="l04020"></a><span class="lineno"> 4020</span>  weightsOffset);</div><div class="line"><a name="l04021"></a><span class="lineno"> 4021</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToForgetWeights(recurrentToForgetWeightsInfo, recurrentToForgetWeightsData);</div><div class="line"><a name="l04022"></a><span class="lineno"> 4022</span> </div><div class="line"><a name="l04023"></a><span class="lineno"> 4023</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> recurrentToCellWeightsShape = {4, 4};</div><div class="line"><a name="l04024"></a><span class="lineno"> 4024</span>  std::vector<uint8_t> recurrentToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};</div><div class="line"><a name="l04025"></a><span class="lineno"> 4025</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> recurrentToCellWeightsInfo(recurrentToCellWeightsShape,</div><div class="line"><a name="l04026"></a><span class="lineno"> 4026</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l04027"></a><span class="lineno"> 4027</span>  weightsScale,</div><div class="line"><a name="l04028"></a><span class="lineno"> 4028</span>  weightsOffset);</div><div class="line"><a name="l04029"></a><span class="lineno"> 4029</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToCellWeights(recurrentToCellWeightsInfo, recurrentToCellWeightsData);</div><div class="line"><a name="l04030"></a><span class="lineno"> 4030</span> </div><div class="line"><a name="l04031"></a><span class="lineno"> 4031</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> recurrentToOutputWeightsShape = {4, 4};</div><div class="line"><a name="l04032"></a><span class="lineno"> 4032</span>  std::vector<uint8_t> recurrentToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};</div><div class="line"><a name="l04033"></a><span class="lineno"> 4033</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> recurrentToOutputWeightsInfo(recurrentToOutputWeightsShape,</div><div class="line"><a name="l04034"></a><span class="lineno"> 4034</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l04035"></a><span class="lineno"> 4035</span>  weightsScale,</div><div class="line"><a name="l04036"></a><span class="lineno"> 4036</span>  weightsOffset);</div><div class="line"><a name="l04037"></a><span class="lineno"> 4037</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> recurrentToOutputWeights(recurrentToOutputWeightsInfo, recurrentToOutputWeightsData);</div><div class="line"><a name="l04038"></a><span class="lineno"> 4038</span> </div><div class="line"><a name="l04039"></a><span class="lineno"> 4039</span>  <span class="comment">// The shape of bias data is {outputSize} = {4}</span></div><div class="line"><a name="l04040"></a><span class="lineno"> 4040</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> inputGateBiasShape = {4};</div><div class="line"><a name="l04041"></a><span class="lineno"> 4041</span>  std::vector<int32_t> inputGateBiasData = {1, 2, 3, 4};</div><div class="line"><a name="l04042"></a><span class="lineno"> 4042</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputGateBiasInfo(inputGateBiasShape,</div><div class="line"><a name="l04043"></a><span class="lineno"> 4043</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l04044"></a><span class="lineno"> 4044</span>  biasScale,</div><div class="line"><a name="l04045"></a><span class="lineno"> 4045</span>  biasOffset);</div><div class="line"><a name="l04046"></a><span class="lineno"> 4046</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> inputGateBias(inputGateBiasInfo, inputGateBiasData);</div><div class="line"><a name="l04047"></a><span class="lineno"> 4047</span> </div><div class="line"><a name="l04048"></a><span class="lineno"> 4048</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> forgetGateBiasShape = {4};</div><div class="line"><a name="l04049"></a><span class="lineno"> 4049</span>  std::vector<int32_t> forgetGateBiasData = {1, 2, 3, 4};</div><div class="line"><a name="l04050"></a><span class="lineno"> 4050</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> forgetGateBiasInfo(forgetGateBiasShape,</div><div class="line"><a name="l04051"></a><span class="lineno"> 4051</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l04052"></a><span class="lineno"> 4052</span>  biasScale,</div><div class="line"><a name="l04053"></a><span class="lineno"> 4053</span>  biasOffset);</div><div class="line"><a name="l04054"></a><span class="lineno"> 4054</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> forgetGateBias(forgetGateBiasInfo, forgetGateBiasData);</div><div class="line"><a name="l04055"></a><span class="lineno"> 4055</span> </div><div class="line"><a name="l04056"></a><span class="lineno"> 4056</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> cellBiasShape = {4};</div><div class="line"><a name="l04057"></a><span class="lineno"> 4057</span>  std::vector<int32_t> cellBiasData = {1, 2, 3, 4};</div><div class="line"><a name="l04058"></a><span class="lineno"> 4058</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellBiasInfo(cellBiasShape,</div><div class="line"><a name="l04059"></a><span class="lineno"> 4059</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l04060"></a><span class="lineno"> 4060</span>  biasScale,</div><div class="line"><a name="l04061"></a><span class="lineno"> 4061</span>  biasOffset);</div><div class="line"><a name="l04062"></a><span class="lineno"> 4062</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> cellBias(cellBiasInfo, cellBiasData);</div><div class="line"><a name="l04063"></a><span class="lineno"> 4063</span> </div><div class="line"><a name="l04064"></a><span class="lineno"> 4064</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> outputGateBiasShape = {4};</div><div class="line"><a name="l04065"></a><span class="lineno"> 4065</span>  std::vector<int32_t> outputGateBiasData = {1, 2, 3, 4};</div><div class="line"><a name="l04066"></a><span class="lineno"> 4066</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputGateBiasInfo(outputGateBiasShape,</div><div class="line"><a name="l04067"></a><span class="lineno"> 4067</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l04068"></a><span class="lineno"> 4068</span>  biasScale,</div><div class="line"><a name="l04069"></a><span class="lineno"> 4069</span>  biasOffset);</div><div class="line"><a name="l04070"></a><span class="lineno"> 4070</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> outputGateBias(outputGateBiasInfo, outputGateBiasData);</div><div class="line"><a name="l04071"></a><span class="lineno"> 4071</span> </div><div class="line"><a name="l04072"></a><span class="lineno"> 4072</span>  <a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a> params;</div><div class="line"><a name="l04073"></a><span class="lineno"> 4073</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">m_InputToInputWeights</a> = &inputToInputWeights;</div><div class="line"><a name="l04074"></a><span class="lineno"> 4074</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">m_InputToForgetWeights</a> = &inputToForgetWeights;</div><div class="line"><a name="l04075"></a><span class="lineno"> 4075</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">m_InputToCellWeights</a> = &inputToCellWeights;</div><div class="line"><a name="l04076"></a><span class="lineno"> 4076</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">m_InputToOutputWeights</a> = &inputToOutputWeights;</div><div class="line"><a name="l04077"></a><span class="lineno"> 4077</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">m_RecurrentToInputWeights</a> = &recurrentToInputWeights;</div><div class="line"><a name="l04078"></a><span class="lineno"> 4078</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">m_RecurrentToForgetWeights</a> = &recurrentToForgetWeights;</div><div class="line"><a name="l04079"></a><span class="lineno"> 4079</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">m_RecurrentToCellWeights</a> = &recurrentToCellWeights;</div><div class="line"><a name="l04080"></a><span class="lineno"> 4080</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">m_RecurrentToOutputWeights</a> = &recurrentToOutputWeights;</div><div class="line"><a name="l04081"></a><span class="lineno"> 4081</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">m_InputGateBias</a> = &inputGateBias;</div><div class="line"><a name="l04082"></a><span class="lineno"> 4082</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">m_ForgetGateBias</a> = &forgetGateBias;</div><div class="line"><a name="l04083"></a><span class="lineno"> 4083</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">m_CellBias</a> = &cellBias;</div><div class="line"><a name="l04084"></a><span class="lineno"> 4084</span>  params.<a class="code" href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">m_OutputGateBias</a> = &outputGateBias;</div><div class="line"><a name="l04085"></a><span class="lineno"> 4085</span> </div><div class="line"><a name="l04086"></a><span class="lineno"> 4086</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> network = <a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a>();</div><div class="line"><a name="l04087"></a><span class="lineno"> 4087</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l04088"></a><span class="lineno"> 4088</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateIn = network->AddInputLayer(1);</div><div class="line"><a name="l04089"></a><span class="lineno"> 4089</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputStateIn = network->AddInputLayer(2);</div><div class="line"><a name="l04090"></a><span class="lineno"> 4090</span>  <span class="keyword">const</span> std::string layerName(<span class="stringliteral">"QuantizedLstm"</span>);</div><div class="line"><a name="l04091"></a><span class="lineno"> 4091</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> quantizedLstmLayer = network->AddQuantizedLstmLayer(params, layerName.c_str());</div><div class="line"><a name="l04092"></a><span class="lineno"> 4092</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> cellStateOut = network->AddOutputLayer(0);</div><div class="line"><a name="l04093"></a><span class="lineno"> 4093</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = network->AddOutputLayer(1);</div><div class="line"><a name="l04094"></a><span class="lineno"> 4094</span> </div><div class="line"><a name="l04095"></a><span class="lineno"> 4095</span>  <span class="comment">// Connect up</span></div><div class="line"><a name="l04096"></a><span class="lineno"> 4096</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ batchSize, inputSize },</div><div class="line"><a name="l04097"></a><span class="lineno"> 4097</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l04098"></a><span class="lineno"> 4098</span>  inputOutputScale,</div><div class="line"><a name="l04099"></a><span class="lineno"> 4099</span>  inputOutputOffset);</div><div class="line"><a name="l04100"></a><span class="lineno"> 4100</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateTensorInfo({ batchSize, numUnits },</div><div class="line"><a name="l04101"></a><span class="lineno"> 4101</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div><div class="line"><a name="l04102"></a><span class="lineno"> 4102</span>  cellStateScale,</div><div class="line"><a name="l04103"></a><span class="lineno"> 4103</span>  cellStateOffset);</div><div class="line"><a name="l04104"></a><span class="lineno"> 4104</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateTensorInfo({ batchSize, outputSize },</div><div class="line"><a name="l04105"></a><span class="lineno"> 4105</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l04106"></a><span class="lineno"> 4106</span>  inputOutputScale,</div><div class="line"><a name="l04107"></a><span class="lineno"> 4107</span>  inputOutputOffset);</div><div class="line"><a name="l04108"></a><span class="lineno"> 4108</span> </div><div class="line"><a name="l04109"></a><span class="lineno"> 4109</span>  inputLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(quantizedLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l04110"></a><span class="lineno"> 4110</span>  inputLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l04111"></a><span class="lineno"> 4111</span> </div><div class="line"><a name="l04112"></a><span class="lineno"> 4112</span>  cellStateIn-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(quantizedLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l04113"></a><span class="lineno"> 4113</span>  cellStateIn-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l04114"></a><span class="lineno"> 4114</span> </div><div class="line"><a name="l04115"></a><span class="lineno"> 4115</span>  outputStateIn-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(quantizedLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(2));</div><div class="line"><a name="l04116"></a><span class="lineno"> 4116</span>  outputStateIn-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l04117"></a><span class="lineno"> 4117</span> </div><div class="line"><a name="l04118"></a><span class="lineno"> 4118</span>  quantizedLstmLayer-><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#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(cellStateOut-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l04119"></a><span class="lineno"> 4119</span>  quantizedLstmLayer-><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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(cellStateTensorInfo);</div><div class="line"><a name="l04120"></a><span class="lineno"> 4120</span> </div><div class="line"><a name="l04121"></a><span class="lineno"> 4121</span>  quantizedLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l04122"></a><span class="lineno"> 4122</span>  quantizedLstmLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(1).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputStateTensorInfo);</div><div class="line"><a name="l04123"></a><span class="lineno"> 4123</span> </div><div class="line"><a name="l04124"></a><span class="lineno"> 4124</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a> deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));</div><div class="line"><a name="l04125"></a><span class="lineno"> 4125</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(deserializedNetwork);</div><div class="line"><a name="l04126"></a><span class="lineno"> 4126</span> </div><div class="line"><a name="l04127"></a><span class="lineno"> 4127</span>  VerifyQuantizedLstmLayer checker(layerName,</div><div class="line"><a name="l04128"></a><span class="lineno"> 4128</span>  {inputTensorInfo, cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l04129"></a><span class="lineno"> 4129</span>  {cellStateTensorInfo, outputStateTensorInfo},</div><div class="line"><a name="l04130"></a><span class="lineno"> 4130</span>  params);</div><div class="line"><a name="l04131"></a><span class="lineno"> 4131</span> </div><div class="line"><a name="l04132"></a><span class="lineno"> 4132</span>  deserializedNetwork->Accept(checker);</div><div class="line"><a name="l04133"></a><span class="lineno"> 4133</span> }</div><div class="line"><a name="l04134"></a><span class="lineno"> 4134</span> </div><div class="line"><a name="l04135"></a><span class="lineno"> 4135</span> <a class="code" href="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a>()</div><div class="ttc" id="_output_shape_of_squeeze_8cpp_xhtml_ae3a6cb217a792718f2bd0e8f45e3ca9e"><div class="ttname"><a href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a></div><div class="ttdeci">BOOST_AUTO_TEST_SUITE(TensorflowLiteParser)</div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Convolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00428">Descriptors.hpp:428</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::Convolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00438">Descriptors.hpp:438</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a2d299363c9fc33334c571fa29ca4f58ca8768a6821cd735aea4f5b0df88c1fc6a"><div class="ttname"><a href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58ca8768a6821cd735aea4f5b0df88c1fc6a">armnn::ComparisonOperation::Greater</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_ac2dac3b61c94de52093616be4ab17f8d"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">armnn::IConnectableLayer::GetNumOutputSlots</a></div><div class="ttdeci">virtual unsigned int GetNumOutputSlots() const =0</div><div class="ttdoc">Returns the number of connectable output slots. </div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a6c9de81fc65b3c4924cab11907075a17"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">armnn::LstmDescriptor::m_ProjectionEnabled</a></div><div class="ttdeci">bool m_ProjectionEnabled</div><div class="ttdoc">Enable/disable the projection layer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00871">Descriptors.hpp:871</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Convolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00440">Descriptors.hpp:440</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_ab03e6e1514f74427916c892f473fe04c"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#ab03e6e1514f74427916c892f473fe04c">armnn::LstmInputParams::m_ProjectionWeights</a></div><div class="ttdeci">const ConstTensor * m_ProjectionWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00055">LstmParams.hpp:55</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a855293b1be0581fb61ef6a1c5b027d0f"><div class="ttname"><a href="namespacearmnn.xhtml#a855293b1be0581fb61ef6a1c5b027d0f">armnn::Dequantize</a></div><div class="ttdeci">float Dequantize(QuantizedType value, float scale, int32_t offset)</div><div class="ttdoc">Dequantize an 8-bit data type into a floating point data type. </div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8cpp_source.xhtml#l00047">TypesUtils.cpp:47</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a49e11acda22742cbaf6f1b259ead0d84"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">armnn::QuantizedLstmInputParams::m_InputToCellWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00035">QuantizedLstmParams.hpp:35</a></div></div> +<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml">armnn::ViewsDescriptor</a></div><div class="ttdoc">A ViewsDescriptor for the SplitterLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00190">Descriptors.hpp:190</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00061">INetwork.hpp:61</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_ab509802c659de19929f18bad14a35c58"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ab509802c659de19929f18bad14a35c58">armnn::DetectionPostProcessDescriptor::m_ScaleW</a></div><div class="ttdeci">float m_ScaleW</div><div class="ttdoc">Center size encoding scale weight. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00545">Descriptors.hpp:545</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a4a9d678146f572808a361dbdc5489f38"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">armnn::LstmInputParams::m_CellBias</a></div><div class="ttdeci">const ConstTensor * m_CellBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00053">LstmParams.hpp:53</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::DepthwiseConvolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00490">Descriptors.hpp:490</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a9c2cba04b6d7ace4fc2a2436b82a5a63"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">armnn::IConnectableLayer::GetNumInputSlots</a></div><div class="ttdeci">virtual unsigned int GetNumInputSlots() const =0</div><div class="ttdoc">Returns the number of connectable input slots. </div></div> +<div class="ttc" id="namespacearmnn_xhtml_a044ea0cc993d4d1fbe4ec877b17b8d39"><div class="ttname"><a href="namespacearmnn.xhtml#a044ea0cc993d4d1fbe4ec877b17b8d39">armnn::Slice</a></div><div class="ttdeci">void Slice(const TensorInfo &inputInfo, const SliceDescriptor &descriptor, const void *inputData, void *outputData, unsigned int dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_slice_8cpp_source.xhtml#l00016">Slice.cpp:16</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">armnn::TransposeConvolution2dDescriptor</a></div><div class="ttdoc">A TransposeConvolution2dDescriptor for the TransposeConvolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01079">Descriptors.hpp:1079</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a4a9d678146f572808a361dbdc5489f38"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a4a9d678146f572808a361dbdc5489f38">armnn::QuantizedLstmInputParams::m_CellBias</a></div><div class="ttdeci">const ConstTensor * m_CellBias</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00045">QuantizedLstmParams.hpp:45</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00088">Tensor.hpp:88</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a56b81ca8ba4b4937e0787e4951f043fc"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">armnn::QuantizedLstmInputParams::m_RecurrentToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00041">QuantizedLstmParams.hpp:41</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::DepthwiseConvolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00480">Descriptors.hpp:480</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a86e88bef0df4df96df752b4b8955a3af"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">armnn::LstmDescriptor::m_ClippingThresProj</a></div><div class="ttdeci">float m_ClippingThresProj</div><div class="ttdoc">Clipping threshold value for the projection. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00865">Descriptors.hpp:865</a></div></div> +<div class="ttc" id="classarmnn_1_1_optional_xhtml"><div class="ttname"><a href="classarmnn_1_1_optional.xhtml">armnn::Optional< armnn::ConstTensor ></a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a2d299363c9fc33334c571fa29ca4f58caf5f286e73bda105e538310b3190f75c5"><div class="ttname"><a href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58caf5f286e73bda105e538310b3190f75c5">armnn::ComparisonOperation::Equal</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3"><div class="ttname"><a href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</a></div></div> +<div class="ttc" id="structarmnn_1_1_reshape_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_reshape_descriptor.xhtml">armnn::ReshapeDescriptor</a></div><div class="ttdoc">A ReshapeDescriptor for the ReshapeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00758">Descriptors.hpp:758</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a374120de442fe42c26536bb4f1e2a5a1"><div class="ttname"><a href="namespacearmnn.xhtml#a374120de442fe42c26536bb4f1e2a5a1">armnn::ArgMinMax</a></div><div class="ttdeci">void ArgMinMax(Decoder< float > &in, int32_t *out, const TensorInfo &inputTensorInfo, const TensorInfo &outputTensorInfo, ArgMinMaxFunction function, int axis)</div><div class="ttdef"><b>Definition:</b> <a href="_arg_min_max_8cpp_source.xhtml#l00015">ArgMinMax.cpp:15</a></div></div> +<div class="ttc" id="namespacearmnn_serializer_xhtml"><div class="ttname"><a href="namespacearmnn_serializer.xhtml">armnnSerializer</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_serializer_8hpp_source.xhtml#l00011">ISerializer.hpp:11</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_afe204ca375b74e9a72640c9227918d0a"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#afe204ca375b74e9a72640c9227918d0a">armnn::LstmInputParams::m_CellToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_CellToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00050">LstmParams.hpp:50</a></div></div> +<div class="ttc" id="_deprecated_8hpp_xhtml_ab66a241a0ed3ee89c866e777b035d0ed"><div class="ttname"><a href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_BEGIN</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.xhtml#l00033">Deprecated.hpp:33</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_ae83131e16df1cace69395a5f99bc5ecb"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">armnn::QuantizedLstmInputParams::m_RecurrentToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00039">QuantizedLstmParams.hpp:39</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::DepthwiseConvolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00492">Descriptors.hpp:492</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div> +<div class="ttc" id="structarmnn_1_1_comparison_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_comparison_descriptor.xhtml">armnn::ComparisonDescriptor</a></div><div class="ttdoc">A ComparisonDescriptor for the ComparisonLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00062">Descriptors.hpp:62</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_ae64523937ea910030ad66fee6fddd51f"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae64523937ea910030ad66fee6fddd51f">armnn::DetectionPostProcessDescriptor::m_ScaleX</a></div><div class="ttdeci">float m_ScaleX</div><div class="ttdoc">Center size encoding scale x. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00541">Descriptors.hpp:541</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_bilinear_descriptor_xhtml_adcf5037208faac36c0788239a073f75c"><div class="ttname"><a href="structarmnn_1_1_resize_bilinear_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">armnn::ResizeBilinearDescriptor::m_TargetWidth</a></div><div class="ttdeci">uint32_t m_TargetWidth</div><div class="ttdoc">Target width value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00716">Descriptors.hpp:716</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml_a281fcaec86e17c97f7b8402633f6b55a"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">armnn::FullyConnectedDescriptor::m_TransposeWeightMatrix</a></div><div class="ttdeci">bool m_TransposeWeightMatrix</div><div class="ttdoc">Enable/disable transpose weight matrix. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00388">Descriptors.hpp:388</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a></div><div class="ttdoc">A Convolution2dDescriptor for the Convolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00392">Descriptors.hpp:392</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_ace7a1f1f1041b412b7d8ef82b95ff352"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">armnn::QuantizedLstmInputParams::m_ForgetGateBias</a></div><div class="ttdeci">const ConstTensor * m_ForgetGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00044">QuantizedLstmParams.hpp:44</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::DepthwiseConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00474">Descriptors.hpp:474</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::TransposeConvolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01117">Descriptors.hpp:1117</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a484bafa2f8453a7c5a4a00b92a61b006"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a484bafa2f8453a7c5a4a00b92a61b006">armnn::LstmInputParams::m_CellToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_CellToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00048">LstmParams.hpp:48</a></div></div> +<div class="ttc" id="_quantized_lstm_params_8hpp_xhtml"><div class="ttname"><a href="_quantized_lstm_params_8hpp.xhtml">QuantizedLstmParams.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::BaseTensor::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00169">Tensor.hpp:169</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a869254cb56968986a78a79e1d6d4a86b"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a869254cb56968986a78a79e1d6d4a86b">armnn::ResizeDescriptor::m_Method</a></div><div class="ttdeci">ResizeMethod m_Method</div><div class="ttdoc">The Interpolation method to use (Bilinear, NearestNeighbor). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00749">Descriptors.hpp:749</a></div></div> +<div class="ttc" id="structarmnn_1_1_instance_normalization_descriptor_xhtml_a5e078fd505aef7bccaa05c8058e096cc"><div class="ttname"><a href="structarmnn_1_1_instance_normalization_descriptor.xhtml#a5e078fd505aef7bccaa05c8058e096cc">armnn::InstanceNormalizationDescriptor::m_Gamma</a></div><div class="ttdeci">float m_Gamma</div><div class="ttdoc">Gamma, the scale scalar value applied for the normalized tensor. Defaults to 1.0. ...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00647">Descriptors.hpp:647</a></div></div> +<div class="ttc" id="structarmnn_1_1_softmax_descriptor_xhtml_a8275d51ef9a584feb95726ea0522f6e5"><div class="ttname"><a href="structarmnn_1_1_softmax_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">armnn::SoftmaxDescriptor::m_Beta</a></div><div class="ttdeci">float m_Beta</div><div class="ttdoc">Exponentiation value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00136">Descriptors.hpp:136</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6"><div class="ttname"><a href="namespacearmnn.xhtml#a3888429b6ebc79f9a7df549e5e4d9a2fa843f2812f595e7ec7c5036e89fde02d6">armnn::PaddingMethod::Exclude</a></div><div class="ttdoc">The padding fields don&#39;t count and are ignored. </div></div> +<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml_a11c821c7524251004a72ed13c510853c"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">armnn::BatchNormalizationDescriptor::m_Eps</a></div><div class="ttdeci">float m_Eps</div><div class="ttdoc">Value to add to the variance. Used to avoid dividing by zero. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00623">Descriptors.hpp:623</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a9e081a9b94defb30d1558dc912507e0e"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">armnn::LstmInputParams::m_InputGateBias</a></div><div class="ttdeci">const ConstTensor * m_InputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00051">LstmParams.hpp:51</a></div></div> +<div class="ttc" id="structarmnn_1_1_arg_min_max_descriptor_xhtml_ab1ae6f520bb1a4da191a0ae907477f23"><div class="ttname"><a href="structarmnn_1_1_arg_min_max_descriptor.xhtml#ab1ae6f520bb1a4da191a0ae907477f23">armnn::ArgMinMaxDescriptor::m_Function</a></div><div class="ttdeci">ArgMinMaxFunction m_Function</div><div class="ttdoc">Specify if the function is to find Min or Max. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00056">Descriptors.hpp:56</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a7e2f87544b8bc7e497e1dec8d3ca4055"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7e2f87544b8bc7e497e1dec8d3ca4055">armnn::DetectionPostProcessDescriptor::m_DetectionsPerClass</a></div><div class="ttdeci">uint32_t m_DetectionsPerClass</div><div class="ttdoc">Detections per classes, used in Regular NMS. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00531">Descriptors.hpp:531</a></div></div> +<div class="ttc" id="_neon_end_to_end_tests_8cpp_xhtml_a358cb7cd3c0647b25be049fd734b8c22"><div class="ttname"><a href="_neon_end_to_end_tests_8cpp.xhtml#a358cb7cd3c0647b25be049fd734b8c22">anchorsInfo</a></div><div class="ttdeci">armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::Float32)</div></div> +<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::BatchToSpaceNdDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00684">Descriptors.hpp:684</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a9e081a9b94defb30d1558dc912507e0e"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a9e081a9b94defb30d1558dc912507e0e">armnn::QuantizedLstmInputParams::m_InputGateBias</a></div><div class="ttdeci">const ConstTensor * m_InputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00043">QuantizedLstmParams.hpp:43</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml">armnn::INetwork</a></div><div class="ttdoc">Main network class which provides the interface for building up a neural network. ...</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00105">INetwork.hpp:105</a></div></div> +<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">armnn::BaseTensor::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00175">Tensor.hpp:175</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a1759754ccb88ecc9af44f3aae6e244ee"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">armnn::LstmInputParams::m_RecurrentToCellWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00046">LstmParams.hpp:46</a></div></div> +<div class="ttc" id="classarmnn_serializer_1_1_serializer_xhtml_a62dbb19d4776b489161b699f608f0150"><div class="ttname"><a href="classarmnn_serializer_1_1_serializer.xhtml#a62dbb19d4776b489161b699f608f0150">armnnSerializer::Serializer::Serialize</a></div><div class="ttdeci">void Serialize(const armnn::INetwork &inNetwork) override</div><div class="ttdoc">Serializes the network to ArmNN SerializedGraph. </div><div class="ttdef"><b>Definition:</b> <a href="_serializer_8cpp_source.xhtml#l01564">Serializer.cpp:1564</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml_a405d5f966ec992d1717711e5a2d7909d"><div class="ttname"><a href="namespacearmnn_utils.xhtml#a405d5f966ec992d1717711e5a2d7909d">armnnUtils::Transpose</a></div><div class="ttdeci">void Transpose(const armnn::TensorShape &dstShape, const armnn::PermutationVector &mappings, const void *src, void *dst, size_t dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="armnn_utils_2_transpose_8cpp_source.xhtml#l00120">Transpose.cpp:120</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ab023d9a7687e35c0f108458a094c1f56"><div class="ttname"><a href="namespacearmnn.xhtml#ab023d9a7687e35c0f108458a094c1f56">armnn::DepthToSpace</a></div><div class="ttdeci">void DepthToSpace(const TensorInfo &inputInfo, const DepthToSpaceDescriptor &descriptor, const void *inputData, void *outputData, unsigned int dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="_depth_to_space_8cpp_source.xhtml#l00018">DepthToSpace.cpp:18</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Convolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00424">Descriptors.hpp:424</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::NormalizationDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00587">Descriptors.hpp:587</a></div></div> +<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_aa81f67ac64f0c249e26499600c45d996"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#aa81f67ac64f0c249e26499600c45d996">armnn::BaseTensor::GetMemoryArea</a></div><div class="ttdeci">MemoryType GetMemoryArea() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00177">Tensor.hpp:177</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_af0f796fba1a2be9c56b4c9ee534577ee"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#af0f796fba1a2be9c56b4c9ee534577ee">armnn::LstmInputParams::m_ForgetLayerNormWeights</a></div><div class="ttdeci">const ConstTensor * m_ForgetLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00058">LstmParams.hpp:58</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a35b112e30c3eb153806a2a8c16d178e3"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a35b112e30c3eb153806a2a8c16d178e3">armnn::LstmInputParams::m_CellToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_CellToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00049">LstmParams.hpp:49</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml">armnn::QuantizedLstmInputParams</a></div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00013">QuantizedLstmParams.hpp:13</a></div></div> +<div class="ttc" id="_lstm_params_8hpp_xhtml"><div class="ttname"><a href="_lstm_params_8hpp.xhtml">LstmParams.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::TransposeConvolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01111">Descriptors.hpp:1111</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a302b688d88dd73cde0fb1faef6679907"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">armnn::Convolution2dDescriptor::m_DilationY</a></div><div class="ttdeci">uint32_t m_DilationY</div><div class="ttdoc">Dilation along y axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00436">Descriptors.hpp:436</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_ac37e49c0d6e6e54f9d2015d0f11f8ee7"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#ac37e49c0d6e6e54f9d2015d0f11f8ee7">armnn::StridedSliceDescriptor::m_EndMask</a></div><div class="ttdeci">int32_t m_EndMask</div><div class="ttdoc">End mask value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01052">Descriptors.hpp:1052</a></div></div> +<div class="ttc" id="structarmnn_1_1_space_to_depth_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_space_to_depth_descriptor.xhtml">armnn::SpaceToDepthDescriptor</a></div><div class="ttdoc">A SpaceToDepthDescriptor for the SpaceToDepthLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00810">Descriptors.hpp:810</a></div></div> +<div class="ttc" id="classarmnn_1_1_optional_reference_switch_xhtml_a77c7d528ac063d870b8c8426ec81c1c3"><div class="ttname"><a href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">armnn::OptionalReferenceSwitch::value</a></div><div class="ttdeci">const T & value() const</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00146">Optional.hpp:146</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a302b688d88dd73cde0fb1faef6679907"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">armnn::DepthwiseConvolution2dDescriptor::m_DilationY</a></div><div class="ttdeci">uint32_t m_DilationY</div><div class="ttdoc">Dilation factor value for height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00488">Descriptors.hpp:488</a></div></div> +<div class="ttc" id="_serializer_tests_8cpp_xhtml_aa74276e373d72fd796aa827017af2196"><div class="ttname"><a href="_serializer_tests_8cpp.xhtml#aa74276e373d72fd796aa827017af2196">DECLARE_LAYER_VERIFIER_CLASS</a></div><div class="ttdeci">#define DECLARE_LAYER_VERIFIER_CLASS(name)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_tests_8cpp_source.xhtml#l00025">SerializerTests.cpp:25</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">armnn::BatchToSpaceNdDescriptor</a></div><div class="ttdoc">A BatchToSpaceNdDescriptor for the BatchToSpaceNdLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00657">Descriptors.hpp:657</a></div></div> +<div class="ttc" id="_file_only_profiling_decorator_tests_8cpp_xhtml_a0c262ba6f6c189a2d092d127c1b7627b"><div class="ttname"><a href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a></div><div class="ttdeci">BOOST_CHECK(profilingService.GetCurrentState()==ProfilingState::WaitingForAck)</div></div> +<div class="ttc" id="namespacearmnn_xhtml_ab8cf8f9fb6792e654c2d8d8382f6f01b"><div class="ttname"><a href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a></div><div class="ttdeci">int LayerBindingId</div><div class="ttdoc">Type of identifiers for bindable layers (inputs, outputs). </div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00171">Types.hpp:171</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a8c0f6d48705f40c5590dde09be262222"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">armnn::LstmInputParams::m_OutputGateBias</a></div><div class="ttdeci">const ConstTensor * m_OutputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00054">LstmParams.hpp:54</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a6ef2dcac2ec0683d52df1b051404e7d6"><div class="ttname"><a href="namespacearmnn.xhtml#a6ef2dcac2ec0683d52df1b051404e7d6">armnn::Stack</a></div><div class="ttdeci">void Stack(const StackQueueDescriptor &data, std::vector< std::unique_ptr< Decoder< float >>> &inputs, Encoder< float > &output)</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_stack_8cpp_source.xhtml#l00012">Stack.cpp:12</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a5ee4a6c9a2481245487b1b1a70d20fd0"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">armnn::IOutputSlot::SetTensorInfo</a></div><div class="ttdeci">virtual void SetTensorInfo(const TensorInfo &tensorInfo)=0</div></div> +<div class="ttc" id="namespacearmnn_xhtml_a81b5ff8545adad19a1c9d4ca076d552c"><div class="ttname"><a href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">armnn::GetDataTypeName</a></div><div class="ttdeci">constexpr const char * GetDataTypeName(DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8hpp_source.xhtml#l00168">TypesUtils.hpp:168</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml">armnn::ResizeDescriptor</a></div><div class="ttdoc">A ResizeDescriptor for the ResizeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00724">Descriptors.hpp:724</a></div></div> +<div class="ttc" id="_serializer_tests_8cpp_xhtml_aafd0924b96830cf275f533b32ade856e"><div class="ttname"><a href="_serializer_tests_8cpp.xhtml#aafd0924b96830cf275f533b32ade856e">BOOST_AUTO_TEST_CASE</a></div><div class="ttdeci">BOOST_AUTO_TEST_CASE(SerializeAddition)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_tests_8cpp_source.xhtml#l00271">SerializerTests.cpp:271</a></div></div> +<div class="ttc" id="structarmnn_1_1_l2_normalization_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_l2_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::L2NormalizationDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00606">Descriptors.hpp:606</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a9ae2c9796692ebeafe19a4d3f09c8ea8"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a9ae2c9796692ebeafe19a4d3f09c8ea8">armnn::DetectionPostProcessDescriptor::m_MaxClassesPerDetection</a></div><div class="ttdeci">uint32_t m_MaxClassesPerDetection</div><div class="ttdoc">Maximum numbers of classes per detection, used in Fast NMS. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00529">Descriptors.hpp:529</a></div></div> +<div class="ttc" id="structarmnn_1_1_mean_descriptor_xhtml_a1f0d67b087c491248bd1cde3ff995a95"><div class="ttname"><a href="structarmnn_1_1_mean_descriptor.xhtml#a1f0d67b087c491248bd1cde3ff995a95">armnn::MeanDescriptor::m_Axis</a></div><div class="ttdeci">std::vector< unsigned int > m_Axis</div><div class="ttdoc">Values for the dimensions to reduce. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00895">Descriptors.hpp:895</a></div></div> +<div class="ttc" id="structarmnn_1_1_stack_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_stack_descriptor.xhtml">armnn::StackDescriptor</a></div><div class="ttdoc">A StackDescriptor for the StackLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00950">Descriptors.hpp:950</a></div></div> +<div class="ttc" id="structarmnn_1_1_space_to_batch_nd_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::SpaceToBatchNdDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00806">Descriptors.hpp:806</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_affcee5f4ab5994a21bee3b78b4e43de3"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">armnn::QuantizedLstmInputParams::m_InputToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00033">QuantizedLstmParams.hpp:33</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a28e115f5d28500324b53fae9e6c00b77"><div class="ttname"><a href="namespacearmnn.xhtml#a28e115f5d28500324b53fae9e6c00b77">armnn::Pad</a></div><div class="ttdeci">void Pad(const TensorInfo &inputInfo, const TensorInfo &outputInfo, std::vector< std::pair< unsigned int, unsigned int >> m_padList, const T *inputData, T *outData, const float padValue)</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_pad_8cpp_source.xhtml#l00022">Pad.cpp:22</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Convolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00426">Descriptors.hpp:426</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_ae72089bcab60ac175557f4241b16a014"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae72089bcab60ac175557f4241b16a014">armnn::DetectionPostProcessDescriptor::m_MaxDetections</a></div><div class="ttdeci">uint32_t m_MaxDetections</div><div class="ttdoc">Maximum numbers of detections. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00527">Descriptors.hpp:527</a></div></div> +<div class="ttc" id="structarmnn_1_1_pad_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pad_descriptor.xhtml">armnn::PadDescriptor</a></div><div class="ttdoc">A PadDescriptor for the PadLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00901">Descriptors.hpp:901</a></div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml_af3c74017185773dd61d8ca6662d65d43"><div class="ttname"><a href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a></div><div class="ttdeci">void Permute(const armnn::TensorShape &dstShape, const armnn::PermutationVector &mappings, const void *src, void *dst, size_t dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.xhtml#l00121">Permute.cpp:121</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Convolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00430">Descriptors.hpp:430</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a0cd848f65ec31778d708852f0042fe37"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a0cd848f65ec31778d708852f0042fe37">armnn::LstmInputParams::m_InputLayerNormWeights</a></div><div class="ttdeci">const ConstTensor * m_InputLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00057">LstmParams.hpp:57</a></div></div> +<div class="ttc" id="_types_utils_8hpp_xhtml"><div class="ttname"><a href="_types_utils_8hpp.xhtml">TypesUtils.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::DepthwiseConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00482">Descriptors.hpp:482</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="structarmnn_1_1_detection_post_process_descriptor_xhtml_a53c8a7f33a40e1e240256bcfcf41b101"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a53c8a7f33a40e1e240256bcfcf41b101">armnn::DetectionPostProcessDescriptor::m_NmsIouThreshold</a></div><div class="ttdeci">float m_NmsIouThreshold</div><div class="ttdoc">Intersection over union threshold. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00535">Descriptors.hpp:535</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a56b81ca8ba4b4937e0787e4951f043fc"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a56b81ca8ba4b4937e0787e4951f043fc">armnn::LstmInputParams::m_RecurrentToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00047">LstmParams.hpp:47</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a></div><div class="ttdoc">An LstmDescriptor for the LstmLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00837">Descriptors.hpp:837</a></div></div> +<div class="ttc" id="_deprecated_8hpp_xhtml_ad762b11b48e5c1d1c1743f529485728a"><div class="ttname"><a href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_END</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.xhtml#l00034">Deprecated.hpp:34</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aa3c6a77a963a98ccb8ea7b8fd008a8c1"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">armnn::DepthwiseConvolution2dDescriptor::m_DilationX</a></div><div class="ttdeci">uint32_t m_DilationX</div><div class="ttdoc">Dilation factor value for width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00486">Descriptors.hpp:486</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::DepthwiseConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00478">Descriptors.hpp:478</a></div></div> +<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml_aae0893695f5803a3517985c7cb1ccb2e"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">armnn::ViewsDescriptor::SetViewSize</a></div><div class="ttdeci">Status SetViewSize(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">Set the size of the views. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00307">Descriptors.cpp:307</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233"><div class="ttname"><a href="namespacearmnn.xhtml#ae7e8cbf71db6a490789ca6dcaa8deeaea6a061313d22e51e0f25b7cd4dc065233">armnn::ArgMinMaxFunction::Max</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a></div><div class="ttdoc">An output connection slot for a layer. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00037">INetwork.hpp:37</a></div></div> +<div class="ttc" id="structarmnn_1_1_l2_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_l2_normalization_descriptor.xhtml">armnn::L2NormalizationDescriptor</a></div><div class="ttdoc">A L2NormalizationDescriptor for the L2NormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00591">Descriptors.hpp:591</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a44b0e6b16708df7f0d2bbab141688aaa"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a44b0e6b16708df7f0d2bbab141688aaa">armnn::LstmInputParams::m_ProjectionBias</a></div><div class="ttdeci">const ConstTensor * m_ProjectionBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00056">LstmParams.hpp:56</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a770b51078da02f44a819e9f95d8058b5"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">armnn::TensorInfo::GetQuantizationOffset</a></div><div class="ttdeci">int32_t GetQuantizationOffset() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00264">Tensor.cpp:264</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a31da1ead6794dd64571afdd0b6efc771"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">armnn::QuantizedLstmInputParams::m_InputToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00034">QuantizedLstmParams.hpp:34</a></div></div> +<div class="ttc" id="structarmnn_1_1_arg_min_max_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_arg_min_max_descriptor.xhtml">armnn::ArgMinMaxDescriptor</a></div><div class="ttdoc">An ArgMinMaxDescriptor for ArgMinMaxLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00043">Descriptors.hpp:43</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a047ca888c43bd7fb5702853bf72410d0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">armnn::TensorInfo::GetQuantizationScale</a></div><div class="ttdeci">float GetQuantizationScale() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00247">Tensor.cpp:247</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#l00095">Tensor.hpp:95</a></div></div> +<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a></div><div class="ttdoc">An OriginsDescriptor for the ConcatLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00147">Descriptors.hpp:147</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</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="structarmnn_1_1_fully_connected_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a></div><div class="ttdoc">A FullyConnectedDescriptor for the FullyConnectedLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00373">Descriptors.hpp:373</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::FullyConnectedDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00386">Descriptors.hpp:386</a></div></div> +<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00199">Tensor.hpp:199</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml">armnn::LstmInputParams</a></div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00013">LstmParams.hpp:13</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a8c0f6d48705f40c5590dde09be262222"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a8c0f6d48705f40c5590dde09be262222">armnn::QuantizedLstmInputParams::m_OutputGateBias</a></div><div class="ttdeci">const ConstTensor * m_OutputGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00046">QuantizedLstmParams.hpp:46</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_adcf5037208faac36c0788239a073f75c"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">armnn::ResizeDescriptor::m_TargetWidth</a></div><div class="ttdeci">uint32_t m_TargetWidth</div><div class="ttdoc">Target width value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00744">Descriptors.hpp:744</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a2837b4396f20c956952d1a7286cab5f8"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">armnn::LstmDescriptor::m_PeepholeEnabled</a></div><div class="ttdeci">bool m_PeepholeEnabled</div><div class="ttdoc">Enable/disable peephole. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00869">Descriptors.hpp:869</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a3a04b0ccee4bb2f21721ee5045e83df4"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a3a04b0ccee4bb2f21721ee5045e83df4">armnn::DetectionPostProcessDescriptor::m_NumClasses</a></div><div class="ttdeci">uint32_t m_NumClasses</div><div class="ttdoc">Number of classes. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00537">Descriptors.hpp:537</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021"><div class="ttname"><a href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a2d299363c9fc33334c571fa29ca4f58ca19bb0af2c3c530538cb41aff7f235b96"><div class="ttname"><a href="namespacearmnn.xhtml#a2d299363c9fc33334c571fa29ca4f58ca19bb0af2c3c530538cb41aff7f235b96">armnn::ComparisonOperation::NotEqual</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::TransposeConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01109">Descriptors.hpp:1109</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a685739c4eb65a580e075282cfe6787d6"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">armnn::TensorInfo::SetQuantizationScale</a></div><div class="ttdeci">void SetQuantizationScale(float scale)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00259">Tensor.cpp:259</a></div></div> +<div class="ttc" id="structarmnn_1_1_stand_in_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_stand_in_descriptor.xhtml">armnn::StandInDescriptor</a></div><div class="ttdoc">A StandInDescriptor for the StandIn layer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00980">Descriptors.hpp:980</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad773a034fb9983e15f3094b4c5c7c30c"><div class="ttname"><a href="namespacearmnn.xhtml#ad773a034fb9983e15f3094b4c5c7c30c">armnn::Quantize</a></div><div class="ttdeci">QuantizedType Quantize(float value, float scale, int32_t offset)</div><div class="ttdoc">Quantize a floating point data type into an 8-bit data type. </div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8cpp_source.xhtml#l00031">TypesUtils.cpp:31</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a7ed9bc7c26df67d274d5dd4cd83adf0f"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7ed9bc7c26df67d274d5dd4cd83adf0f">armnn::DetectionPostProcessDescriptor::m_UseRegularNms</a></div><div class="ttdeci">bool m_UseRegularNms</div><div class="ttdoc">Use Regular NMS. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00539">Descriptors.hpp:539</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::TransposeConvolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01119">Descriptors.hpp:1119</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a25dc224be48103343302b5a6fd588fe7"><div class="ttname"><a href="namespacearmnn.xhtml#a25dc224be48103343302b5a6fd588fe7">armnn::Resize</a></div><div class="ttdeci">void Resize(Decoder< float > &in, const TensorInfo &inputInfo, Encoder< float > &out, const TensorInfo &outputInfo, DataLayoutIndexed dataLayout, armnn::ResizeMethod resizeMethod, bool alignCorners)</div><div class="ttdef"><b>Definition:</b> <a href="_resize_8cpp_source.xhtml#l00035">Resize.cpp:35</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ac52e04c0e349e25bcdaa72c27395ef8f"><div class="ttname"><a href="namespacearmnn.xhtml#ac52e04c0e349e25bcdaa72c27395ef8f">armnn::LogSoftmax</a></div><div class="ttdeci">void LogSoftmax(Decoder< float > &input, Encoder< float > &output, const TensorInfo &inputInfo, const LogSoftmaxDescriptor &descriptor)</div><div class="ttdef"><b>Definition:</b> <a href="_log_softmax_8cpp_source.xhtml#l00030">LogSoftmax.cpp:30</a></div></div> +<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_a8aeddebdcf02e1832b22203c08a6b678"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#a8aeddebdcf02e1832b22203c08a6b678">armnn::BaseTensor::GetInfo</a></div><div class="ttdeci">const TensorInfo & GetInfo() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00167">Tensor.hpp:167</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a46c3fa15c46fb0d1dcdc24d0ea5cb5cd"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a46c3fa15c46fb0d1dcdc24d0ea5cb5cd">armnn::ResizeDescriptor::m_TargetHeight</a></div><div class="ttdeci">uint32_t m_TargetHeight</div><div class="ttdoc">Target height value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00746">Descriptors.hpp:746</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_ae1b07ed928036004bd257169e5aeeef4"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">armnn::LstmDescriptor::m_ActivationFunc</a></div><div class="ttdeci">uint32_t m_ActivationFunc</div><div class="ttdoc">The activation function to use. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00861">Descriptors.hpp:861</a></div></div> +<div class="ttc" id="structarmnn_1_1_slice_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_slice_descriptor.xhtml">armnn::SliceDescriptor</a></div><div class="ttdoc">A SliceDescriptor for the SliceLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00927">Descriptors.hpp:927</a></div></div> +<div class="ttc" id="classarmnn_1_1_layer_visitor_base_xhtml"><div class="ttname"><a href="classarmnn_1_1_layer_visitor_base.xhtml">armnn::LayerVisitorBase</a></div><div class="ttdoc">Visitor base class with empty implementations. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_visitor_base_8hpp_source.xhtml#l00025">LayerVisitorBase.hpp:25</a></div></div> +<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00173">Types.hpp:173</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Convolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00432">Descriptors.hpp:432</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a4a180e425d4c19b2cdea4ce5760180e1"><div class="ttname"><a href="namespacearmnn.xhtml#a4a180e425d4c19b2cdea4ce5760180e1">armnn::SpaceToBatchNd</a></div><div class="ttdeci">void SpaceToBatchNd(const TensorInfo &inputInfo, const TensorInfo &outputInfo, const SpaceToBatchNdDescriptor &params, Decoder< float > &inputData, Encoder< float > &outputData)</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_space_to_batch_n_d_8cpp_source.xhtml#l00034">SpaceToBatchNd.cpp:34</a></div></div> +<div class="ttc" id="classarmnn_serializer_1_1_serializer_xhtml_af21f36069c661c4afa7221a305de80e0"><div class="ttname"><a href="classarmnn_serializer_1_1_serializer.xhtml#af21f36069c661c4afa7221a305de80e0">armnnSerializer::Serializer::SaveSerializedToStream</a></div><div class="ttdeci">bool SaveSerializedToStream(std::ostream &stream) override</div><div class="ttdoc">Serializes the SerializedGraph to the stream. </div><div class="ttdef"><b>Definition:</b> <a href="_serializer_8cpp_source.xhtml#l01582">Serializer.cpp:1582</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a435d3651482bbfcc11263b4e4e0c900f"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">armnn::QuantizedLstmInputParams::m_RecurrentToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00038">QuantizedLstmParams.hpp:38</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a531a3907ec13d3772370da88030191a5"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">armnn::LstmDescriptor::m_ClippingThresCell</a></div><div class="ttdeci">float m_ClippingThresCell</div><div class="ttdoc">Clipping threshold value for the cell state. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00863">Descriptors.hpp:863</a></div></div> +<div class="ttc" id="structarmnn_1_1_space_to_depth_descriptor_xhtml_a6c6b8957f1e176867e5fb05b1a1a1486"><div class="ttname"><a href="structarmnn_1_1_space_to_depth_descriptor.xhtml#a6c6b8957f1e176867e5fb05b1a1a1486">armnn::SpaceToDepthDescriptor::m_BlockSize</a></div><div class="ttdeci">unsigned int m_BlockSize</div><div class="ttdoc">Scalar specifying the input block size. It must be >= 1. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00827">Descriptors.hpp:827</a></div></div> +<div class="ttc" id="_i_network_8hpp_xhtml"><div class="ttname"><a href="_i_network_8hpp.xhtml">INetwork.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Pooling2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00369">Descriptors.hpp:369</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_aa61510cbd529870182e918ac6e8b9d72"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#aa61510cbd529870182e918ac6e8b9d72">armnn::DetectionPostProcessDescriptor::m_ScaleH</a></div><div class="ttdeci">float m_ScaleH</div><div class="ttdoc">Center size encoding scale height. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00547">Descriptors.hpp:547</a></div></div> +<div class="ttc" id="structarmnn_1_1_comparison_descriptor_xhtml_a865dc4f43cb0ff01a1dcf78036912fd1"><div class="ttname"><a href="structarmnn_1_1_comparison_descriptor.xhtml#a865dc4f43cb0ff01a1dcf78036912fd1">armnn::ComparisonDescriptor::m_Operation</a></div><div class="ttdeci">ComparisonOperation m_Operation</div><div class="ttdoc">Specifies the comparison operation to execute. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00078">Descriptors.hpp:78</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_ad0b8c32bb5381f4cc999093ba3b98b43"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#ad0b8c32bb5381f4cc999093ba3b98b43">armnn::LstmInputParams::m_CellLayerNormWeights</a></div><div class="ttdeci">const ConstTensor * m_CellLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00059">LstmParams.hpp:59</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_ace7a1f1f1041b412b7d8ef82b95ff352"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#ace7a1f1f1041b412b7d8ef82b95ff352">armnn::LstmInputParams::m_ForgetGateBias</a></div><div class="ttdeci">const ConstTensor * m_ForgetGateBias</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00052">LstmParams.hpp:52</a></div></div> +<div class="ttc" id="structarmnn_1_1_space_to_batch_nd_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml">armnn::SpaceToBatchNdDescriptor</a></div><div class="ttdoc">A SpaceToBatchNdDescriptor for the SpaceToBatchNdLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00778">Descriptors.hpp:778</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a49e11acda22742cbaf6f1b259ead0d84"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a49e11acda22742cbaf6f1b259ead0d84">armnn::LstmInputParams::m_InputToCellWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00042">LstmParams.hpp:42</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a6e30c7b3451da3ea9cf4259fb602e6e6"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">armnn::LstmInputParams::m_InputToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00043">LstmParams.hpp:43</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aa3c6a77a963a98ccb8ea7b8fd008a8c1"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">armnn::Convolution2dDescriptor::m_DilationX</a></div><div class="ttdeci">uint32_t m_DilationX</div><div class="ttdoc">Dilation along x axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00434">Descriptors.hpp:434</a></div></div> +<div class="ttc" id="_profiler_tests_8cpp_xhtml_af7f71af5c6c124222dd1c42c5df892f4"><div class="ttname"><a href="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a></div><div class="ttdeci">BOOST_AUTO_TEST_SUITE_END()</div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_ad474e5c51a0b194ef32e812b86c0cbdb"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">armnn::LstmDescriptor::m_CifgEnabled</a></div><div class="ttdeci">bool m_CifgEnabled</div><div class="ttdoc">Enable/disable cifg (coupled input & forget gate). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00867">Descriptors.hpp:867</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::TransposeConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01105">Descriptors.hpp:1105</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a86d7a7168ac00b75b4971f9aad623698"><div class="ttname"><a href="namespacearmnn.xhtml#a86d7a7168ac00b75b4971f9aad623698">armnn::StridedSlice</a></div><div class="ttdeci">void StridedSlice(const TensorInfo &inputInfo, const StridedSliceDescriptor &params, const void *inputData, void *outputData, unsigned int dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_strided_slice_8cpp_source.xhtml#l00090">StridedSlice.cpp:90</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::TransposeConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01113">Descriptors.hpp:1113</a></div></div> +<div class="ttc" id="_i_deserializer_8hpp_xhtml"><div class="ttname"><a href="_i_deserializer_8hpp.xhtml">IDeserializer.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_ae83131e16df1cace69395a5f99bc5ecb"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#ae83131e16df1cace69395a5f99bc5ecb">armnn::LstmInputParams::m_RecurrentToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00045">LstmParams.hpp:45</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::DepthwiseConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00484">Descriptors.hpp:484</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::TransposeConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01115">Descriptors.hpp:1115</a></div></div> +<div class="ttc" id="classarmnn_1_1_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_exception.xhtml">armnn::Exception</a></div><div class="ttdoc">Base class for all ArmNN exceptions so that users can filter to just those. </div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00046">Exceptions.hpp:46</a></div></div> +<div class="ttc" id="_descriptors_8hpp_xhtml"><div class="ttname"><a href="_descriptors_8hpp.xhtml">Descriptors.hpp</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="structarmnn_1_1_quantized_lstm_input_params_xhtml_a1759754ccb88ecc9af44f3aae6e244ee"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a1759754ccb88ecc9af44f3aae6e244ee">armnn::QuantizedLstmInputParams::m_RecurrentToCellWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToCellWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00040">QuantizedLstmParams.hpp:40</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="structarmnn_1_1_lstm_input_params_xhtml_a435d3651482bbfcc11263b4e4e0c900f"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a435d3651482bbfcc11263b4e4e0c900f">armnn::LstmInputParams::m_RecurrentToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_RecurrentToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00044">LstmParams.hpp:44</a></div></div> +<div class="ttc" id="structarmnn_1_1_mean_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_mean_descriptor.xhtml">armnn::MeanDescriptor</a></div><div class="ttdoc">A MeanDescriptor for the MeanLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00877">Descriptors.hpp:877</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_input_params_xhtml_a6e30c7b3451da3ea9cf4259fb602e6e6"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_input_params.xhtml#a6e30c7b3451da3ea9cf4259fb602e6e6">armnn::QuantizedLstmInputParams::m_InputToOutputWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToOutputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_params_8hpp_source.xhtml#l00036">QuantizedLstmParams.hpp:36</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a165ae372a7f67cad64ef3395d30122ce"><div class="ttname"><a href="namespacearmnn.xhtml#a165ae372a7f67cad64ef3395d30122ce">armnn::Mean</a></div><div class="ttdeci">void Mean(const armnn::TensorInfo &inputInfo, const armnn::TensorInfo &outputInfo, const std::vector< unsigned int > &axis, Decoder< float > &input, Encoder< float > &output)</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_mean_8cpp_source.xhtml#l00071">Mean.cpp:71</a></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> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a4a8ec49f130084445d44297549254780"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a4a8ec49f130084445d44297549254780">armnn::LstmDescriptor::m_LayerNormEnabled</a></div><div class="ttdeci">bool m_LayerNormEnabled</div><div class="ttdoc">Enable/disable layer normalization. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00873">Descriptors.hpp:873</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::TransposeConvolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01107">Descriptors.hpp:1107</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_transpose_descriptor.xhtml">armnn::TransposeDescriptor</a></div><div class="ttdoc">A TransposeDescriptor for the TransposeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01123">Descriptors.hpp:1123</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml">armnn::StridedSliceDescriptor</a></div><div class="ttdoc">A StridedSliceDescriptor for the StridedSliceLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01002">Descriptors.hpp:1002</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a9943775a364fc4ab53b85ac88f311886"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">armnn::IOutputSlot::GetTensorInfo</a></div><div class="ttdeci">virtual const TensorInfo & GetTensorInfo() const =0</div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a80ac4eda2e7f2757ec9dd96fc96dbd16"><div class="ttname"><a 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_a9a2af2f8c4af4f9efa8e79417d505ac4aaa020331bb30d2fa2ecf7c3a0777823f"><div class="ttname"><a href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaa020331bb30d2fa2ecf7c3a0777823f">armnn::ResizeMethod::NearestNeighbor</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a5e1dc69443b64ad16b669388a6023f7a"><div class="ttname"><a href="namespacearmnn.xhtml#a5e1dc69443b64ad16b669388a6023f7a">armnn::SpaceToDepth</a></div><div class="ttdeci">void SpaceToDepth(const TensorInfo &inputInfo, const TensorInfo &outputInfo, const SpaceToDepthDescriptor &params, Decoder< float > &inputData, Encoder< float > &outputData)</div><div class="ttdef"><b>Definition:</b> <a href="_space_to_depth_8cpp_source.xhtml#l00036">SpaceToDepth.cpp:36</a></div></div> +<div class="ttc" id="classarmnn_serializer_1_1_serializer_xhtml"><div class="ttname"><a href="classarmnn_serializer_1_1_serializer.xhtml">armnnSerializer::Serializer</a></div><div class="ttdef"><b>Definition:</b> <a href="_serializer_8hpp_source.xhtml#l00324">Serializer.hpp:324</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a7a2156ec7d9c012ce00bbcc6afcb9028"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7a2156ec7d9c012ce00bbcc6afcb9028">armnn::DetectionPostProcessDescriptor::m_ScaleY</a></div><div class="ttdeci">float m_ScaleY</div><div class="ttdoc">Center size encoding scale y. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00543">Descriptors.hpp:543</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a733ae6b70d0bfa43433c3e7606992328"><div class="ttname"><a href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">armnn::CreateDescriptorForConcatenation</a></div><div class="ttdeci">OriginsDescriptor CreateDescriptorForConcatenation(TensorShapeIt first, TensorShapeIt last, unsigned int concatenationDimension)</div><div class="ttdoc">Convenience template to create an OriginsDescriptor to use when creating a ConcatLayer for performing...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00242">Descriptors.hpp:242</a></div></div> +<div class="ttc" id="_serializer_tests_8cpp_xhtml_a664bc60b972af17162b98d384ea031c1"><div class="ttname"><a href="_serializer_tests_8cpp.xhtml#a664bc60b972af17162b98d384ea031c1">DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR</a></div><div class="ttdeci">#define DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(name)</div><div class="ttdef"><b>Definition:</b> <a href="_serializer_tests_8cpp_source.xhtml#l00040">SerializerTests.cpp:40</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml_a4392dd6b4862cc9cf95ae8f1001ba592"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a4392dd6b4862cc9cf95ae8f1001ba592">armnn::DetectionPostProcessDescriptor::m_NmsScoreThreshold</a></div><div class="ttdeci">float m_NmsScoreThreshold</div><div class="ttdoc">NMS score threshold. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00533">Descriptors.hpp:533</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00101">INetwork.hpp:101</a></div></div> +<div class="ttc" id="classarmnn_deserializer_1_1_i_deserializer_xhtml"><div class="ttname"><a href="classarmnn_deserializer_1_1_i_deserializer.xhtml">armnnDeserializer::IDeserializer</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_deserializer_8hpp_source.xhtml#l00027">IDeserializer.hpp:27</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a8746512fab5ec10c2c57800c66311ba7"><div class="ttname"><a href="namespacearmnn.xhtml#a8746512fab5ec10c2c57800c66311ba7">armnn::BatchToSpaceNd</a></div><div class="ttdeci">void BatchToSpaceNd(const DataLayoutIndexed &dataLayout, const TensorInfo &inputTensorInfo, const TensorInfo &outputTensorInfo, const std::vector< unsigned int > &blockShape, const std::vector< std::pair< unsigned int, unsigned int >> &cropsData, Decoder< float > &inputDecoder, Encoder< float > &outputEncoder)</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_batch_to_space_n_d_8cpp_source.xhtml#l00035">BatchToSpaceNd.cpp:35</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_ac1835f8756a9f03c02fcf9664e3a0fce"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">armnn::IOutputSlot::Connect</a></div><div class="ttdeci">virtual int Connect(IInputSlot &destination)=0</div></div> +<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#aea909c7327109228ef618d459015def3">armnn::BaseTensor::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00172">Tensor.hpp:172</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml">armnn::Pooling2dDescriptor</a></div><div class="ttdoc">A Pooling2dDescriptor for the Pooling2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00313">Descriptors.hpp:313</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a9b18daea2e9f42386055326fd016519a"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a9b18daea2e9f42386055326fd016519a">armnn::LstmInputParams::m_OutputLayerNormWeights</a></div><div class="ttdeci">const ConstTensor * m_OutputLayerNormWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00060">LstmParams.hpp:60</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml">armnn::NormalizationDescriptor</a></div><div class="ttdoc">A NormalizationDescriptor for the NormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00551">Descriptors.hpp:551</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ae2e93e304cf516841c521e3eaee025cd"><div class="ttname"><a href="namespacearmnn.xhtml#ae2e93e304cf516841c521e3eaee025cd">armnn::Pooling2d</a></div><div class="ttdeci">void Pooling2d(Decoder< float > &rInputDecoder, Encoder< float > &rOutputEncoder, const TensorInfo &inputInfo, const TensorInfo &outputInfo, const Pooling2dDescriptor &params)</div><div class="ttdoc">Computes the Pooling2d operation. </div><div class="ttdef"><b>Definition:</b> <a href="_pooling2d_8cpp_source.xhtml#l00143">Pooling2d.cpp:143</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::ResizeDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00751">Descriptors.hpp:751</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_descriptor.xhtml">armnn::DetectionPostProcessDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00495">Descriptors.hpp:495</a></div></div> +<div class="ttc" id="structarmnn_1_1_instance_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_instance_normalization_descriptor.xhtml">armnn::InstanceNormalizationDescriptor</a></div><div class="ttdoc">An InstanceNormalizationDescriptor for InstanceNormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00629">Descriptors.hpp:629</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_bilinear_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_resize_bilinear_descriptor.xhtml">armnn::ResizeBilinearDescriptor</a></div><div class="ttdoc">A ResizeBilinearDescriptor for the ResizeBilinearLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00707">Descriptors.hpp:707</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a427c3d26d05b518b1ace407035f5920e"><div class="ttname"><a href="namespacearmnn.xhtml#a427c3d26d05b518b1ace407035f5920e">armnn::Splitter</a></div><div class="ttdeci">void Splitter(const SplitterQueueDescriptor &data)</div><div class="ttdef"><b>Definition:</b> <a href="_splitter_8hpp_source.xhtml#l00017">Splitter.hpp:17</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_aa999ff2585ad75b95954a9323f63c32b"><div class="ttname"><a href="namespacearmnn.xhtml#aa999ff2585ad75b95954a9323f63c32b">armnn::Softmax</a></div><div class="ttdeci">void Softmax(Decoder< float > &in, Encoder< float > &out, const TensorInfo &inputTensorInfo, float beta, int axis)</div><div class="ttdoc">Computes the softmax function on some inputs, into outputs, with a shape given by tensorInfo...</div><div class="ttdef"><b>Definition:</b> <a href="backends_2reference_2workloads_2_softmax_8cpp_source.xhtml#l00017">Softmax.cpp:17</a></div></div> +<div class="ttc" id="structarmnn_1_1_softmax_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_softmax_descriptor.xhtml">armnn::SoftmaxDescriptor</a></div><div class="ttdoc">A SoftmaxDescriptor for the SoftmaxLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00123">Descriptors.hpp:123</a></div></div> +<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml_a2b125117aa61f9baf3a9cb8658aa61a2"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">armnn::ViewsDescriptor::SetViewOriginCoord</a></div><div class="ttdeci">Status SetViewOriginCoord(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">Set the view origin coordinates. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00302">Descriptors.cpp:302</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a706f7345af3f18f4b16e226a672214c6"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create()</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00049">Network.cpp:49</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">armnn::DepthwiseConvolution2dDescriptor</a></div><div class="ttdoc">A DepthwiseConvolution2dDescriptor for the DepthwiseConvolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00444">Descriptors.hpp:444</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml">armnn::BatchNormalizationDescriptor</a></div><div class="ttdoc">A BatchNormalizationDescriptor for the BatchNormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00610">Descriptors.hpp:610</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Convolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00422">Descriptors.hpp:422</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div> +<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_abcbdfb544ece4c31d0b37715ad0f3be0"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">armnn::BaseTensor::GetNumBytes</a></div><div class="ttdeci">unsigned int GetNumBytes() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00174">Tensor.hpp:174</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_a31da1ead6794dd64571afdd0b6efc771"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#a31da1ead6794dd64571afdd0b6efc771">armnn::LstmInputParams::m_InputToForgetWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToForgetWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00041">LstmParams.hpp:41</a></div></div> +<div class="ttc" id="structarmnn_1_1_permute_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_permute_descriptor.xhtml">armnn::PermuteDescriptor</a></div><div class="ttdoc">A PermuteDescriptor for the PermuteLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00102">Descriptors.hpp:102</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::DepthwiseConvolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00476">Descriptors.hpp:476</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_input_params_xhtml_affcee5f4ab5994a21bee3b78b4e43de3"><div class="ttname"><a href="structarmnn_1_1_lstm_input_params.xhtml#affcee5f4ab5994a21bee3b78b4e43de3">armnn::LstmInputParams::m_InputToInputWeights</a></div><div class="ttdeci">const ConstTensor * m_InputToInputWeights</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_params_8hpp_source.xhtml#l00040">LstmParams.hpp:40</a></div></div> +<div class="ttc" id="_neon_end_to_end_tests_8cpp_xhtml_ac0981848e4ae57729f14f72bd4caa9f8"><div class="ttname"><a href="_neon_end_to_end_tests_8cpp.xhtml#ac0981848e4ae57729f14f72bd4caa9f8">anchors</a></div><div class="ttdeci">std::vector< float > anchors({ 0.5f, 0.5f, 1.0f, 1.0f, 0.5f, 0.5f, 1.0f, 1.0f, 0.5f, 0.5f, 1.0f, 1.0f, 0.5f, 10.5f, 1.0f, 1.0f, 0.5f, 10.5f, 1.0f, 1.0f, 0.5f, 100.5f, 1.0f, 1.0f })</div></div> +</div><!-- fragment --></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="dir_68267d1309a1af8e8297ef4c3efbcdba.xhtml">src</a></li><li class="navelem"><a class="el" href="dir_bff0d59bec81fb3d13742442d8f4421d.xhtml">armnnSerializer</a></li><li class="navelem"><a class="el" href="dir_fa9774f03679f86fc845ac51a8a81eba.xhtml">test</a></li><li class="navelem"><a class="el" href="_serializer_tests_8cpp.xhtml">SerializerTests.cpp</a></li> + <li class="footer">Generated on Fri Mar 13 2020 16:09:10 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> |