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diff --git a/20.02/_tf_lite_parser_8cpp_source.xhtml b/20.02/_tf_lite_parser_8cpp_source.xhtml new file mode 100644 index 0000000000..d8ee8088c0 --- /dev/null +++ b/20.02/_tf_lite_parser_8cpp_source.xhtml @@ -0,0 +1,341 @@ +<!-- 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/armnnTfLiteParser/TfLiteParser.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('_tf_lite_parser_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">TfLiteParser.cpp</div> </div> +</div><!--header--> +<div class="contents"> +<a href="_tf_lite_parser_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 "<a class="code" href="_tf_lite_parser_8hpp.xhtml">TfLiteParser.hpp</a>"</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="_exceptions_8hpp.xhtml">armnn/Exceptions.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_logging_8hpp.xhtml">armnn/Logging.hpp</a>></span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_types_utils_8hpp.xhtml">armnn/TypesUtils.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <<a class="code" href="_ignore_unused_8hpp.xhtml">armnn/utility/IgnoreUnused.hpp</a>></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> </div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment">// armnnUtils:</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <<a class="code" href="_permute_8hpp.xhtml">armnnUtils/Permute.hpp</a>></span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <<a class="code" href="_parser_helper_8hpp.xhtml">ParserHelper.hpp</a>></span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include <<a class="code" href="_verification_helpers_8hpp.xhtml">VerificationHelpers.hpp</a>></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="comment">// The generated code based on the Tf Lite schema:</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="preprocessor">#include <schema_generated.h></span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> </div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="preprocessor">#include <flatbuffers/flexbuffers.h></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"> 25</span> <span class="preprocessor">#include <boost/assert.hpp></span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#include <boost/format.hpp></span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include <boost/numeric/conversion/cast.hpp></span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include <boost/filesystem.hpp></span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include <fstream></span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include <algorithm></span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include <limits></span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include <numeric></span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#include <sstream></span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> </div><div class="line"><a name="l00036"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9"> 36</a></span> <span class="preprocessor">#define ARMNN_THROW_PARSE_EXCEPTION(msg) \</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"> throw armnn::ParseException( static_cast<const std::stringstream&>( std::stringstream() << msg \</span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="preprocessor"> << ": " \</span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="preprocessor"> << CHECK_LOCATION().AsString()).str()); \</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="preprocessor"> }</span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> </div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="keyword">using</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">armnn::CheckLocation</a>;</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn_tf_lite_parser.xhtml">armnnTfLiteParser</a></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> {</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> {</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> <span class="keyword">const</span> uint32_t VIRTUAL_OPERATOR_ID = std::numeric_limits<uint32_t>::max();</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <span class="keywordtype">void</span> CheckSubgraph(<span class="keyword">const</span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">TfLiteParser::ModelPtr</a> & model,</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a> & location)</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> {</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordflow">if</span> (model.get() == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  {</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  boost::str(</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  boost::format(<span class="stringliteral">"%1% was called with invalid (null) model. "</span></div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="stringliteral">"Possible reason is that the model is not yet loaded and Unpack(ed). "</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="stringliteral">"subgraph:%2% at %3%"</span>) %</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a> %</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  subgraphIndex %</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (subgraphIndex >= model->subgraphs.size())</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="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  boost::str(</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  boost::format(<span class="stringliteral">"%1% was called with an invalid subgraph index. "</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="stringliteral">"subgraph:%2% at %3%"</span>) %</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a> %</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  subgraphIndex %</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  }</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> }</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00079"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a"> 79</a></span> <span class="preprocessor">#define CHECK_SUBGRAPH(MODEL, SUBGRAPH_INDEX) \</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> <span class="preprocessor"> CheckSubgraph(MODEL, SUBGRAPH_INDEX, CHECK_LOCATION())</span></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> </div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> <span class="keywordtype">void</span> CheckModel(<span class="keyword">const</span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">TfLiteParser::ModelPtr</a> & model,</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keywordtype">size_t</span> operatorIndex,</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a> & location)</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> {</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keywordflow">if</span> (model.get() == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  {</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  boost::str(</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  boost::format(<span class="stringliteral">"%1% was called with invalid (null) model. "</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="stringliteral">"Possible reason is that the model is not yet loaded and Unpack(ed). "</span></div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="stringliteral">"subgraph:%2% operator:%3% at %4%"</span>) %</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a> %</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  subgraphIndex %</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  operatorIndex %</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  }</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (subgraphIndex >= model->subgraphs.size())</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  boost::str(</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  boost::format(<span class="stringliteral">"%1% was called with an invalid subgraph index. "</span></div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="stringliteral">"subgraph:%2% operator:%3% at %4%"</span>) %</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a> %</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  subgraphIndex %</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  operatorIndex %</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  }</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (operatorIndex >= model->subgraphs[subgraphIndex]->operators.size() &&</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  operatorIndex != VIRTUAL_OPERATOR_ID)</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  {</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  boost::str(</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  boost::format(<span class="stringliteral">"%1% was called with an invalid operator index. "</span></div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="stringliteral">"subgraph:%2% operator:%3% at %4%"</span>) %</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a> %</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  subgraphIndex %</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  operatorIndex %</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</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> </div><div class="line"><a name="l00124"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb"> 124</a></span> <span class="preprocessor">#define CHECK_MODEL(MODEL, SUBGRAPH_INDEX, OPERATOR_INDEX) \</span></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> <span class="preprocessor"> CheckModel(MODEL, SUBGRAPH_INDEX, OPERATOR_INDEX, CHECK_LOCATION())</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> <span class="keywordtype">void</span> <a class="code" href="_tensor_test_8cpp.xhtml#ad80e179ec400af9d2547f172f3ca05f3">CheckTensor</a>(<span class="keyword">const</span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">TfLiteParser::ModelPtr</a> & model,</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="keywordtype">size_t</span> tensorIndex,</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a> & location)</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> {</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="comment">// not checking model, because I assume CHECK_MODEL already run</span></div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="comment">// and checked that. An assert would do.</span></div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  BOOST_ASSERT_MSG(model.get() != <span class="keyword">nullptr</span>, <span class="stringliteral">"Expecting a valid model in this function"</span>);</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> </div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="comment">// also subgraph index should be checked by CHECK_MODEL so</span></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="comment">// I only add an assert here</span></div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  BOOST_ASSERT_MSG(subgraphIndex < model->subgraphs.size(), <span class="stringliteral">"Expecting a valid subgraph index"</span>);</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> </div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="comment">// the tensor index is the only one to check here</span></div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keywordflow">if</span> (tensorIndex >= model->subgraphs[subgraphIndex]->tensors.size())</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="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  boost::str(</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  boost::format(<span class="stringliteral">"%1% was called with an invalid tensor index. "</span></div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="stringliteral">"subgraph:%2% tensor:%3% at %4%"</span>) %</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a> %</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  subgraphIndex %</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  tensorIndex %</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</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> }</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> </div><div class="line"><a name="l00154"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#aa1664dc13adbc85ac12fb584b76bfdae"> 154</a></span> <span class="preprocessor">#define CHECK_TENSOR(MODEL, SUBGRAPH_INDEX, TENSOR_INDEX) \</span></div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> <span class="preprocessor"> CheckTensor(MODEL, SUBGRAPH_INDEX, TENSOR_INDEX, CHECK_LOCATION())</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> <span class="keywordtype">void</span> CheckTensorPtr(<a class="code" href="_parser_flatbuffers_serialize_fixture_8hpp.xhtml#a15c20a0693cd3fc4d85565e2f920d8ef">TfLiteParser::TensorRawPtr</a> rawPtr,</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a> & location)</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>  <span class="keywordflow">if</span> (rawPtr == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  {</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  boost::str(</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  boost::format(<span class="stringliteral">"%1% was called with a null tensor pointer. "</span></div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="stringliteral">"at %2%"</span>) %</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a> %</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> </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> }</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span> </div><div class="line"><a name="l00172"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#ae38d96fe05581ea025713b3e781c5a43"> 172</a></span> <span class="preprocessor">#define CHECK_TENSOR_PTR(TENSOR_PTR) \</span></div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> <span class="preprocessor"> CheckTensorPtr(TENSOR_PTR, CHECK_LOCATION())</span></div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> </div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> <span class="keywordtype">void</span> CheckBuffer(<span class="keyword">const</span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">TfLiteParser::ModelPtr</a> & model,</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keywordtype">size_t</span> bufferIndex,</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a> & location)</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> {</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keywordflow">if</span> (model.get() == <span class="keyword">nullptr</span>)</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  boost::str(</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  boost::format(<span class="stringliteral">"%1% was called with invalid (null) model. "</span></div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="stringliteral">"Possible reason is that the model is not yet loaded and Unpack(ed). "</span></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="stringliteral">"buffer:%2% at %3%"</span>) %</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a> %</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  bufferIndex %</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (bufferIndex >= model->buffers.size())</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  {</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  boost::str(</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  boost::format(<span class="stringliteral">"%1% was called with an invalid buffer index. "</span></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="stringliteral">"buffer index:%2% at %3%"</span>) %</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">m_Function</a> %</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  bufferIndex %</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">FileLine</a>()));</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  }</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (model->buffers[bufferIndex].get() == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  {</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  boost::str(</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  boost::format(<span class="stringliteral">"The buffer #%1% is null. %3%"</span>) %</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  bufferIndex %</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a5e3562cda960da001597e7dd5679b140">AsString</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> }</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span> </div><div class="line"><a name="l00210"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#a7c88d54e3f895030c70330a4c9d76a7a"> 210</a></span> <span class="preprocessor">#define CHECK_BUFFER(MODEL, BUFFER_INDEX) \</span></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> <span class="preprocessor"> CheckBuffer(MODEL, BUFFER_INDEX, CHECK_LOCATION())</span></div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span> </div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span> <span class="keywordtype">void</span> CheckBufferSize(TfLiteParser::BufferRawPtr bufferPtr,</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> & tensorInfo,</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  uint32_t bufferId,</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a> & location)</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> {</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keywordflow">if</span> (bufferPtr == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  {</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  boost::str(</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  boost::format(<span class="stringliteral">"BufferPtr is null for buffer:%1%. %2%"</span>) %</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  bufferId %</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a5e3562cda960da001597e7dd5679b140">AsString</a>()));</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  }</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() > bufferPtr->data.size() ||</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>() > bufferPtr->data.size())</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  {</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  std::stringstream ss;</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  ss << <span class="stringliteral">"Buffer #"</span> << bufferId << <span class="stringliteral">" has "</span> << bufferPtr->data.size() << <span class="stringliteral">" bytes. "</span></div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  << <span class="stringliteral">"For tensor: "</span> << tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  << <span class="stringliteral">" expecting: "</span> << tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>() << <span class="stringliteral">" bytes and "</span></div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  << tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() << <span class="stringliteral">" elements. "</span> << location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a5e3562cda960da001597e7dd5679b140">AsString</a>();</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(ss.str());</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> }</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> </div><div class="line"><a name="l00238"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#ae6440b8ea95cf981cd7bbffa52c22fe1"> 238</a></span> <span class="preprocessor">#define CHECK_BUFFER_SIZE(BUFFER_PTR, TENSOR_INFO, BUFFER_ID) \</span></div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> <span class="preprocessor"> CheckBufferSize(BUFFER_PTR, TENSOR_INFO, BUFFER_ID, CHECK_LOCATION())</span></div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span> </div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> <span class="keywordtype">bool</span> <a class="code" href="namespacearmnn.xhtml#a58bfb9626d373249745d78b95543116e">IsActivationSupported</a>(tflite::ActivationFunctionType activationType)</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span> {</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="keywordflow">switch</span>(activationType)</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  {</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="keywordflow">case</span> tflite::ActivationFunctionType_NONE:</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keywordflow">case</span> tflite::ActivationFunctionType_RELU:</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="keywordflow">case</span> tflite::ActivationFunctionType_RELU6:</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="keywordflow">case</span> tflite::ActivationFunctionType_TANH:</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  {</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  }</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  {</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  }</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  }</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span> }</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> </div><div class="line"><a name="l00259"></a><span class="lineno"><a class="line" href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf"> 259</a></span> <span class="preprocessor">#define CHECK_SUPPORTED_FUSED_ACTIVATION(OPTION, SUBGRAPH_INDEX, OPERATOR_INDEX) \</span></div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> <span class="preprocessor"> do { \</span></div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> <span class="preprocessor"> if (IsActivationSupported(OPTION->fused_activation_function) == false) \</span></div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> <span class="preprocessor"> { \</span></div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span> <span class="preprocessor"> throw ParseException( \</span></div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span> <span class="preprocessor"> boost::str( \</span></div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span> <span class="preprocessor"> boost::format("TfLite parser doesn't suppport fused activation: " \</span></div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span> <span class="preprocessor"> "%1%/%2% in %3% subgraph:%4% operator:%5% at %6%") % \</span></div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> <span class="preprocessor"> OPTION->fused_activation_function % \</span></div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span> <span class="preprocessor"> tflite::EnumNameActivationFunctionType(\</span></div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> <span class="preprocessor"> OPTION->fused_activation_function) % \</span></div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> <span class="preprocessor"> __func__ % \</span></div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span> <span class="preprocessor"> SUBGRAPH_INDEX % \</span></div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span> <span class="preprocessor"> OPERATOR_INDEX % \</span></div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span> <span class="preprocessor"> CHECK_LOCATION().FileLine())); \</span></div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span> <span class="preprocessor"> } \</span></div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span> <span class="preprocessor"> } while(false)</span></div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span> </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> std::vector<unsigned int> AsUnsignedVector(<span class="keyword">const</span> std::vector<int32_t> & in)</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span> {</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  std::vector<unsigned int> result;</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  result.reserve(in.size());</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> & i : in)</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>  result.push_back(<a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(i));</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  }</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="keywordflow">return</span> result;</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> </div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(uint32_t inputSize,</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  uint32_t filterSize,</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  uint32_t stride,</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  uint32_t dilation,</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  uint32_t& paddingFront,</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  uint32_t& paddingBack,</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  tflite::Padding padding)</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span> {</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  paddingFront = 0;</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  paddingBack = 0;</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <span class="keywordflow">if</span> (padding == tflite::Padding_SAME)</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>  uint32_t outputSize = (inputSize + stride - 1) / stride;</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  uint32_t dilatedSize = filterSize + (dilation - 1) * (filterSize - 1);</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  uint32_t temp = (outputSize - 1) * stride + dilatedSize;</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keywordflow">if</span> (temp > inputSize)</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  {</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  paddingFront = (temp - inputSize) / 2;</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  paddingBack = (temp - inputSize) - paddingFront;</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  }</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  }</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> </div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(<a class="code" href="_parser_flatbuffers_serialize_fixture_8hpp.xhtml#a15c20a0693cd3fc4d85565e2f920d8ef">TfLiteParser::TensorRawPtr</a> tensorPtr, <span class="keyword">const</span> std::vector<unsigned int>& shapes,</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a>& dimensionMappings = {0, 1, 2, 3})</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span> {</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> type;</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ae38d96fe05581ea025713b3e781c5a43">CHECK_TENSOR_PTR</a>(tensorPtr);</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span> </div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  <span class="keywordflow">switch</span> (tensorPtr->type)</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  {</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <span class="keywordflow">case</span> tflite::TensorType_UINT8:</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>;</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <span class="keywordflow">case</span> tflite::TensorType_FLOAT32:</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>;</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <span class="keywordflow">case</span> tflite::TensorType_INT8:</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <span class="keywordflow">if</span> (tensorPtr->quantization->zero_point.size() == 1)</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  {</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <span class="comment">// Per-tensor</span></div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>;</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  }</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  {</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <span class="comment">// Per-channel</span></div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>;</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  }</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="keywordflow">case</span> tflite::TensorType_INT16:</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>;</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keywordflow">case</span> tflite::TensorType_INT32:</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>;</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span> </div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  {</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <a class="code" href="structarmnn_1_1_check_location.xhtml">CheckLocation</a> location = <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>();</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  boost::str(</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  boost::format(<span class="stringliteral">"Unsupported data type %1% = %2% for tensor: %3%. %4%"</span>) %</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  tensorPtr->type %</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  tflite::EnumNameTensorType(tensorPtr->type) %</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  tensorPtr->name %</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a5e3562cda960da001597e7dd5679b140">AsString</a>()));</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  }</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  }</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  std::vector<unsigned int> safeShape = shapes;</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="keywordflow">if</span> (safeShape.size() == 0)</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>  safeShape.push_back(1);</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  }</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span> </div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="keywordtype">float</span> quantizationScale = 0.0f;</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  int32_t quantizationOffset = 0;</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="keywordflow">if</span> (tensorPtr->quantization.get())</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  {</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="keywordflow">if</span> (tensorPtr->quantization->scale.size() <= 1)</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  {</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(tensorPtr->quantization->zero_point.size(), 0, 1);</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(tensorPtr->quantization->zero_point.size(), 0, 1);</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span> </div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <span class="keywordflow">if</span> (tensorPtr->quantization->scale.size() == 1)</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  {</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  quantizationScale = tensorPtr->quantization->scale[0];</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="keywordflow">if</span> (tensorPtr->quantization->zero_point.size() == 1)</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  {</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <span class="comment">// NOTE: we lose precision here when converting from 64 bit to 32</span></div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <span class="comment">// but this is what we support at the moment in ArmNN</span></div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  quantizationOffset = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><int32_t>(tensorPtr->quantization->zero_point[0]);</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  }</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span> </div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> result(boost::numeric_cast<unsigned int>(safeShape.size()),</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  safeShape.data(),</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  type,</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  quantizationScale,</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  quantizationOffset);</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span> </div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  }</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  {</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  std::vector<float> quantizationScales;</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  std::vector<int32_t> quantizationOffsets;</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span> </div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="comment">// Scale</span></div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  std::copy(tensorPtr->quantization->scale.begin(),</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  tensorPtr->quantization->scale.end(),</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  std::back_inserter(quantizationScales));</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>  <span class="comment">// QSymmS8 Per-axis</span></div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> result(boost::numeric_cast<unsigned int>(safeShape.size()),</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  safeShape.data(),</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  type,</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  quantizationScales,</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  dimensionMappings[<a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  tensorPtr->quantization->quantized_dimension)]);</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <span class="keywordflow">return</span> result;</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>  }</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  {</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> result(boost::numeric_cast<unsigned int>(safeShape.size()),</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  safeShape.data(),</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  type,</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  quantizationScale,</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  quantizationOffset);</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  }</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span> }</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span> </div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(<a class="code" href="_parser_flatbuffers_serialize_fixture_8hpp.xhtml#a15c20a0693cd3fc4d85565e2f920d8ef">TfLiteParser::TensorRawPtr</a> tensorPtr, </div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a>& dimensionMappings = {0, 1, 2, 3})</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span> {</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="keyword">auto</span> <span class="keyword">const</span> & dimensions = AsUnsignedVector(tensorPtr->shape);</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(tensorPtr, dimensions, dimensionMappings);</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> </div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span> <span class="keyword">template</span><<span class="keyword">typename</span> T></div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span> std::pair<armnn::ConstTensor, std::unique_ptr<T[]>></div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span> CreateConstTensorImpl(TfLiteParser::BufferRawPtr bufferPtr,</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <a class="code" href="_parser_flatbuffers_serialize_fixture_8hpp.xhtml#a15c20a0693cd3fc4d85565e2f920d8ef">TfLiteParser::TensorRawPtr</a> tensorPtr,</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& tensorInfo,</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::PermutationVector&></a> permutationVector)</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>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(tensorPtr);</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  BOOST_ASSERT_MSG(tensorPtr != <span class="keyword">nullptr</span>, <span class="stringliteral">"tensorPtr is null"</span>);</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  BOOST_ASSERT_MSG(bufferPtr != <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  boost::str(</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  boost::format(<span class="stringliteral">"Buffer for buffer:%1% is null"</span>) % tensorPtr->buffer).c_str());</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>  std::unique_ptr<T[]> data(<span class="keyword">new</span> T[tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>()]);</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span> </div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  <span class="keywordflow">if</span> (permutationVector.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>() && permutationVector.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>().<a class="code" href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>() > 0)</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  {</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  tensorInfo = <a class="code" href="namespacearmnn_utils.xhtml#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a>(tensorInfo, permutationVector.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>());</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7">armnnUtils::Permute</a>(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), permutationVector.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>(),</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="keyword">reinterpret_cast<</span><span class="keyword">const </span>T*<span class="keyword">></span>(bufferPtr->data.data()), data.get(), <span class="keyword">sizeof</span>(T));</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="keywordflow">else</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>  ::memcpy(data.get(), bufferPtr->data.data(), tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</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> </div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <span class="keywordflow">return</span> std::make_pair(<a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(tensorInfo, data.get()), std::move(data));</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> </div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a> GenerateLayerBindingId(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> tensorIndex)</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span> {</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <span class="comment">// generate the binding id by shifting the tensor id by 8 bit</span></div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="comment">// and add the subgraph id, which allows 256 subgraphs</span></div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="keywordflow">return</span> <span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">armnn::LayerBindingId</a><span class="keyword">></span>((tensorIndex<<8)+subgraphIndex);</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span> }</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> <span class="keywordtype">bool</span> <a class="code" href="namespacearmnn_deserializer.xhtml#a97b50f22cd99f0e09e6e48d20a35f6b2">CheckShape</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& actual, <span class="keyword">const</span> std::vector<int32_t>& expected)</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span> {</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> actualSize = actual.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <span class="keywordflow">if</span> (actualSize != expected.size())</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  {</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  }</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span> </div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0u; i < actualSize; i++)</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  {</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <span class="keywordflow">if</span> (expected[i] < 0 ||</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  actual[i] != static_cast<unsigned int>(expected[i]))</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  {</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="keywordflow">return</span> <span class="keyword">false</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"> 481</span>  }</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="keywordflow">return</span> <span class="keyword">true</span>;</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> </div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span> } <span class="comment">// <anonymous></span></div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span> </div><div class="line"><a name="l00488"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a2ed4853234865d838da50085da99b2a6"> 488</a></span> TfLiteParser::TfLiteParser(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ITfLiteParser::TfLiteParserOptions></a>& <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>)</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span> : m_Options(options)</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span> , m_Network(nullptr, nullptr)</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span> , m_ParserFunctions(tflite::BuiltinOperator_MAX+1, &<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml">TfLiteParser</a>::ParseUnsupportedOperator)</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="comment">// register supported operators</span></div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  m_ParserFunctions[tflite::BuiltinOperator_ADD] = &TfLiteParser::ParseAdd;</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  m_ParserFunctions[tflite::BuiltinOperator_AVERAGE_POOL_2D] = &TfLiteParser::ParseAveragePool2D;</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  m_ParserFunctions[tflite::BuiltinOperator_BATCH_TO_SPACE_ND] = &TfLiteParser::ParseBatchToSpaceND;</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  m_ParserFunctions[tflite::BuiltinOperator_CONCATENATION] = &TfLiteParser::ParseConcatenation;</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  m_ParserFunctions[tflite::BuiltinOperator_CONV_2D] = &TfLiteParser::ParseConv2D;</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  m_ParserFunctions[tflite::BuiltinOperator_CUSTOM] = &TfLiteParser::ParseCustomOperator;</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  m_ParserFunctions[tflite::BuiltinOperator_DEPTHWISE_CONV_2D] = &TfLiteParser::ParseDepthwiseConv2D;</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  m_ParserFunctions[tflite::BuiltinOperator_DEQUANTIZE] = &TfLiteParser::ParseDequantize;</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  m_ParserFunctions[tflite::BuiltinOperator_FULLY_CONNECTED] = &TfLiteParser::ParseFullyConnected;</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  m_ParserFunctions[tflite::BuiltinOperator_LOGISTIC] = &TfLiteParser::ParseLogistic;</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  m_ParserFunctions[tflite::BuiltinOperator_L2_NORMALIZATION] = &TfLiteParser::ParseL2Normalization;</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  m_ParserFunctions[tflite::BuiltinOperator_MAX_POOL_2D] = &TfLiteParser::ParseMaxPool2D;</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  m_ParserFunctions[tflite::BuiltinOperator_MAXIMUM] = &TfLiteParser::ParseMaximum;</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  m_ParserFunctions[tflite::BuiltinOperator_MEAN] = &TfLiteParser::ParseMean;</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  m_ParserFunctions[tflite::BuiltinOperator_MINIMUM] = &TfLiteParser::ParseMinimum;</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  m_ParserFunctions[tflite::BuiltinOperator_MUL] = &TfLiteParser::ParseMul;</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  m_ParserFunctions[tflite::BuiltinOperator_PACK] = &TfLiteParser::ParsePack;</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  m_ParserFunctions[tflite::BuiltinOperator_PAD] = &TfLiteParser::ParsePad;</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  m_ParserFunctions[tflite::BuiltinOperator_QUANTIZE] = &TfLiteParser::ParseQuantize;</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  m_ParserFunctions[tflite::BuiltinOperator_RELU] = &TfLiteParser::ParseRelu;</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  m_ParserFunctions[tflite::BuiltinOperator_RELU6] = &TfLiteParser::ParseRelu6;</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  m_ParserFunctions[tflite::BuiltinOperator_RESHAPE] = &TfLiteParser::ParseReshape;</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  m_ParserFunctions[tflite::BuiltinOperator_RESIZE_BILINEAR] = &TfLiteParser::ParseResizeBilinear;</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  m_ParserFunctions[tflite::BuiltinOperator_RESIZE_NEAREST_NEIGHBOR] = &TfLiteParser::ParseResizeNearestNeighbor;</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  m_ParserFunctions[tflite::BuiltinOperator_SLICE] = &TfLiteParser::ParseSlice;</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  m_ParserFunctions[tflite::BuiltinOperator_SOFTMAX] = &TfLiteParser::ParseSoftmax;</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  m_ParserFunctions[tflite::BuiltinOperator_SPACE_TO_BATCH_ND] = &TfLiteParser::ParseSpaceToBatchND;</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  m_ParserFunctions[tflite::BuiltinOperator_SPLIT] = &TfLiteParser::ParseSplit;</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  m_ParserFunctions[tflite::BuiltinOperator_SQUEEZE] = &TfLiteParser::ParseSqueeze;</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  m_ParserFunctions[tflite::BuiltinOperator_STRIDED_SLICE] = &TfLiteParser::ParseStridedSlice;</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  m_ParserFunctions[tflite::BuiltinOperator_SUB] = &TfLiteParser::ParseSub;</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  m_ParserFunctions[tflite::BuiltinOperator_TANH] = &TfLiteParser::ParseTanH;</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  m_ParserFunctions[tflite::BuiltinOperator_TRANSPOSE] = &TfLiteParser::ParseTranspose;</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  m_ParserFunctions[tflite::BuiltinOperator_TRANSPOSE_CONV] = &TfLiteParser::ParseTransposeConv;</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  m_ParserFunctions[tflite::BuiltinOperator_UNPACK] = &TfLiteParser::ParseUnpack;</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>  <span class="comment">// register supported custom operators</span></div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  m_CustomParserFunctions[<span class="stringliteral">"TFLite_Detection_PostProcess"</span>] = &TfLiteParser::ParseDetectionPostProcess;</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"> 534</span> <span class="keywordtype">void</span> TfLiteParser::ResetParser()</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>  m_Network = <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a>(<span class="keyword">nullptr</span>, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  m_Model = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  m_SubgraphConnections.clear();</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span> }</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span> </div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span> <span class="keywordtype">void</span> TfLiteParser::AddBroadcastReshapeLayer(<span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="keywordtype">size_t</span> operatorIndex,</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a> *layer)</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span> {</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span> </div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & subgraphPtr = m_Model->subgraphs[subgraphIndex];</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = subgraphPtr->operators[operatorIndex];</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span> </div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  BOOST_ASSERT(operatorPtr->inputs.size() > 1);</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span> </div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  uint32_t reshapedInputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(operatorPtr->inputs[0]);</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">TensorRawPtr</a> tensorPtr = subgraphPtr->tensors[reshapedInputId].get();</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  uint32_t inputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(operatorPtr->inputs[1]);</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">TensorRawPtr</a> tensorPtr1 = subgraphPtr->tensors[inputId].get();</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span> </div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> reshapedTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(tensorPtr);</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(tensorPtr1);</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span> </div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() < reshapedTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  {</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  uint32_t <span class="keywordtype">id</span> = reshapedInputId;</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  reshapedInputId = inputId;</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  inputId = id;</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span> </div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  reshapedTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(tensorPtr1);</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(tensorPtr);</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>  uint32_t numDimensions = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span> </div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  std::vector<unsigned> reshapedDim;</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < reshapedTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); ++i)</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>  reshapedDim.push_back(reshapedTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i]);</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  }</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span> </div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  std::vector<unsigned int> reshapedDimensions(numDimensions, 1);</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  std::copy_backward (reshapedDim.begin(), reshapedDim.end(), reshapedDimensions.end());</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span> </div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  reshapedTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>{ numDimensions, reshapedDimensions.data() });</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::string layerName = boost::str(boost::format(<span class="stringliteral">"Reshape_for:%1%"</span>) % layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>());</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">armnn::ReshapeDescriptor</a> desc;</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  desc.<a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = reshapedTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* reshapeLayer = m_Network->AddReshapeLayer(desc, layerName.c_str());</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span> </div><div class="line"><a name="l00589"></a><span class="lineno"> 589</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>(reshapedTensorInfo);</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</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>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span> </div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  RegisterInputSlots(subgraphIndex, operatorIndex, reshapeLayer, {reshapedInputId});</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span> </div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  <a class="code" href="classarmnn_1_1_i_input_slot.xhtml">armnn::IInputSlot</a>* input1Slot = &(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(1));</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  RegisterConsumerOfTensor(subgraphIndex, inputId, input1Slot);</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span> }</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span> </div><div class="line"><a name="l00598"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a012b24cafd443425314d4f9e06cec6c1"> 598</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a012b24cafd443425314d4f9e06cec6c1">TfLiteParser::CreateNetworkFromBinaryFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile)</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span> {</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  ResetParser();</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  m_Model = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ac5411554ab8c02ca286af52c98f6bd87">LoadModelFromFile</a>(graphFile);</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  <span class="keywordflow">return</span> CreateNetworkFromModel();</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span> }</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span> </div><div class="line"><a name="l00605"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ae8ee09f5e3e78ecfdf00acfdc37588dc"> 605</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ae8ee09f5e3e78ecfdf00acfdc37588dc">TfLiteParser::CreateNetworkFromBinary</a>(<span class="keyword">const</span> std::vector<uint8_t> & binaryContent)</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span> {</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  ResetParser();</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  m_Model = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a858104f225c302988fba35c1cb299066">LoadModelFromBinary</a>(binaryContent.data(), binaryContent.size());</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  <span class="keywordflow">return</span> CreateNetworkFromModel();</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> </div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> TfLiteParser::CreateNetworkFromModel()</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>  m_Network = INetwork::Create();</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  BOOST_ASSERT(m_Model.get() != <span class="keyword">nullptr</span>);</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>  <span class="keywordtype">bool</span> failedToCreate = <span class="keyword">false</span>;</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  std::stringstream errors;</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>  <span class="keywordflow">if</span> (m_Model->subgraphs.size() != 1)</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  {</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  boost::str(</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  boost::format(<span class="stringliteral">"Current TfLite parser only supports 1 subgraph. Current one has: %1% %2%"</span>) %</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  m_Model->subgraphs.size() %</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  <span class="keywordtype">size_t</span> subgraphIndex = 0;</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  <span class="keywordflow">for</span> (<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a988cb5e216eb87d854414c6a0282eeb4">SubgraphPtr</a> <span class="keyword">const</span> & subgraph : m_Model->subgraphs)</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  {</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  m_SubgraphConnections.emplace_back(subgraph->tensors.size());</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span> </div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  <span class="keywordtype">size_t</span> operatorIndex = 0;</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  <span class="keywordflow">for</span> (<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aadad81a95152fe5aad839db352d4012c">OperatorPtr</a> <span class="keyword">const</span> & op : subgraph->operators)</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>  <span class="keywordflow">try</span></div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  {</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  <span class="keyword">auto</span> <span class="keyword">const</span> & opCodePtr = m_Model->operator_codes[op->opcode_index];</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <span class="keyword">auto</span> builtinCode = opCodePtr->builtin_code;</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>  <span class="keywordflow">if</span> (builtinCode > tflite::BuiltinOperator_MAX)</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  {</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  boost::str(</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  boost::format(<span class="stringliteral">"Operator code %1% is out of range 0-%2%. "</span></div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  <span class="stringliteral">"subgraph:%3% operator idx:%4%. %5%"</span>) %</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  builtinCode %</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  tflite::BuiltinOperator_MAX %</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  subgraphIndex %</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  operatorIndex %</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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"> 655</span>  <span class="comment">// lookup and call the parser function</span></div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  <span class="keyword">auto</span> & parserFunction = m_ParserFunctions[builtinCode];</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  (this->*parserFunction)(subgraphIndex, operatorIndex);</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  }</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <span class="keywordflow">catch</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>& e)</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  {</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  failedToCreate = <span class="keyword">true</span>;</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  std::stringstream errorString;</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span> </div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  errorString << <span class="stringliteral">"Failed to parse operator #"</span> << operatorIndex</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  << <span class="stringliteral">" within subgraph #"</span> << subgraphIndex</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  << <span class="stringliteral">" error: "</span> << e.<a class="code" href="classarmnn_1_1_exception.xhtml#abf843cbb29dec939d0731e491bab6f70">what</a>();</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  <a class="code" href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a>) << errorString.str();</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span> </div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  errors << errorString.str() << <span class="stringliteral">"\n"</span>;</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>  ++operatorIndex;</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  }</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span> </div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  SetupInputLayers(subgraphIndex);</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  SetupOutputLayers(subgraphIndex);</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  SetupConstantLayers(subgraphIndex);</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span> </div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  ++subgraphIndex;</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> </div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  <span class="keywordflow">if</span> (failedToCreate)</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">// we can skip everything and let the outer exception handler deal with the error</span></div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(errors.str());</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  }</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="comment">// establish the connections from the layer outputs to the inputs of the subsequent layers</span></div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> subgraphIndex = 0; subgraphIndex < m_SubgraphConnections.size(); ++subgraphIndex)</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  {</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> tensorIndex = 0; tensorIndex < m_SubgraphConnections[subgraphIndex].size(); ++tensorIndex)</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>  <span class="keywordflow">if</span> (m_SubgraphConnections[subgraphIndex][tensorIndex].outputSlot != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  {</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> inputSlotIdx = 0;</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  inputSlotIdx < m_SubgraphConnections[subgraphIndex][tensorIndex].inputSlots.size();</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  ++inputSlotIdx)</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>  m_SubgraphConnections[subgraphIndex][tensorIndex].outputSlot->Connect(</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  *(m_SubgraphConnections[subgraphIndex][tensorIndex].inputSlots[inputSlotIdx]));</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  }</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>  }</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  }</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>  <span class="keywordflow">return</span> std::move(m_Network);</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span> }</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> <span class="keywordtype">void</span> TfLiteParser::RegisterProducerOfTensor(<span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  <span class="keywordtype">size_t</span> tensorIndex,</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>* slot)</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span> {</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#aa1664dc13adbc85ac12fb584b76bfdae">CHECK_TENSOR</a>(m_Model, subgraphIndex, tensorIndex);</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  BOOST_ASSERT(m_SubgraphConnections.size() > subgraphIndex);</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  BOOST_ASSERT(m_SubgraphConnections[subgraphIndex].size() > tensorIndex);</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span> </div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  TensorSlots & tensorSlots = m_SubgraphConnections[subgraphIndex][tensorIndex];</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span> </div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  <span class="comment">// assuming there is only one producer for that tensor</span></div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  <span class="keywordflow">if</span> (tensorSlots.outputSlot != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  {</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  boost::format(<span class="stringliteral">"Another layer has already registered itself as the producer of "</span></div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  <span class="stringliteral">"subgraph:%1% tensor:%2% %3%"</span>) %</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  subgraphIndex %</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  tensorIndex %</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  }</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span> </div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  tensorSlots.outputSlot = slot;</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span> }</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> <span class="keywordtype">void</span> TfLiteParser::RegisterConsumerOfTensor(<span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  <span class="keywordtype">size_t</span> tensorIndex,</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  <a class="code" href="classarmnn_1_1_i_input_slot.xhtml">armnn::IInputSlot</a>* slot)</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span> {</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#aa1664dc13adbc85ac12fb584b76bfdae">CHECK_TENSOR</a>(m_Model, subgraphIndex, tensorIndex);</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  BOOST_ASSERT(m_SubgraphConnections.size() > subgraphIndex);</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  BOOST_ASSERT(m_SubgraphConnections[subgraphIndex].size() > tensorIndex);</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span> </div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  TensorSlots & tensorSlots = m_SubgraphConnections[subgraphIndex][tensorIndex];</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  tensorSlots.inputSlots.push_back(slot);</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span> }</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> <span class="keywordtype">void</span> TfLiteParser::ParseCustomOperator(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span> {</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span> </div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  <span class="comment">// NOTE: By default we presume the custom operator is not supported</span></div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  <span class="keyword">auto</span> customParserFunction = &TfLiteParser::ParseUnsupportedOperator;</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span> </div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  <span class="comment">// Identify custom code defined for custom operator</span></div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& customCode = m_Model->operator_codes[operatorPtr->opcode_index]->custom_code;</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span> </div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  <span class="comment">// Find parser function that correspondes to custom code (if any)</span></div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  <span class="keyword">auto</span> iterator = m_CustomParserFunctions.find(customCode);</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  <span class="keywordflow">if</span> (iterator != m_CustomParserFunctions.end())</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  {</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  customParserFunction = iterator->second;</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> </div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  <span class="comment">// Run parser function</span></div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  (this->*customParserFunction)(subgraphIndex, operatorIndex);</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span> }</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span> </div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span> <span class="keywordtype">void</span> TfLiteParser::ParseUnsupportedOperator(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span> {</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</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>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span> </div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  <span class="keyword">auto</span> opcodeIndex = operatorPtr->opcode_index;</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  <span class="keyword">auto</span> opcode = m_Model->operator_codes[opcodeIndex]->builtin_code;</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span> </div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  <span class="keywordflow">if</span> (!m_Options || !m_Options.value().m_StandInLayerForUnsupported)</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  {</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  <span class="comment">// Do not add StandInLayer, throw ParseException instead</span></div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  boost::str(</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  boost::format(<span class="stringliteral">"Operator not supported. "</span></div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  <span class="stringliteral">"subgraph:%1% operator:%2% "</span></div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  <span class="stringliteral">"opcode_index:%3% opcode:%4% / %5% %6%"</span>) %</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  subgraphIndex %</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  operatorIndex %</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  opcodeIndex %</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  opcode %</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  tflite::EnumNameBuiltinOperator(opcode) %</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  }</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span> </div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span> </div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numInputs = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(inputs.size());</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numOutputs = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputs.size());</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span> </div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  <a class="code" href="structarmnn_1_1_stand_in_descriptor.xhtml">StandInDescriptor</a> descriptor(numInputs, numOutputs);</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"StandIn:%1%:%2%:%3%"</span>) % subgraphIndex % operatorIndex % opcode);</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="comment">// Add a non-executable StandInLayer as a placeholder for any unsupported operator</span></div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddStandInLayer(descriptor, layerName.c_str());</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0u; i < numOutputs; ++i)</div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  {</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[i]));</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">auto</span> inputTensorIds = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  <span class="keyword">auto</span> outputTensorIds = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span> </div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, inputTensorIds);</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIds);</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> <span class="keywordtype">void</span> TfLiteParser::ParseConv2D(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span> {</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span> </div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr->builtin_options.AsConv2DOptions();</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span> </div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf">CHECK_SUPPORTED_FUSED_ACTIVATION</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span> </div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> desc;</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->stride_w);</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->stride_h);</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  desc.<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="l00828"></a><span class="lineno"> 828</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->dilation_w_factor);</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->dilation_h_factor);</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span> </div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2, 3);</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span> </div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span> </div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> filterTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span> </div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  <span class="comment">// assuming input is NHWC</span></div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = inputTensorInfo.GetShape()[1];</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = inputTensorInfo.GetShape()[2];</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>  <span class="comment">// assuming the filter is OHWI : Output, H, W, Input</span></div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  <span class="comment">// which is essentially the same as NHWC</span></div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> filterHeight = filterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> filterWidth = filterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2];</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>  <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputHeight, filterHeight, desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>,</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a>, desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>, desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->padding);</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputWidth, filterWidth, desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>,</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a>, desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>, desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->padding);</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span> </div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  <span class="keyword">auto</span> filterTensorAndData = CreateConstTensor(inputs[1],</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  filterTensorInfo,</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::PermutationVector&></a>());</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer = <span class="keyword">nullptr</span>;</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>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"Conv2D:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span> </div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  <span class="keywordflow">if</span> (inputs.size() == 3)</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  {</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  desc.<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="l00864"></a><span class="lineno"> 864</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[2]);</div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  <span class="keyword">auto</span> biasTensorAndData = CreateConstTensor(inputs[2],</div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  biasTensorInfo,</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::PermutationVector&></a>());</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  layer = m_Network->AddConvolution2dLayer(desc,</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  filterTensorAndData.first,</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(biasTensorAndData.first),</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  layerName.c_str());</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  }</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  {</div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  layer = m_Network->AddConvolution2dLayer(desc,</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  filterTensorAndData.first,</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  <a class="code" href="structarmnn_1_1_empty_optional.xhtml">EmptyOptional</a>(),</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  layerName.c_str());</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> </div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span> </div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span> </div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</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>  layer = AddFusedActivationLayer(layer, 0, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->fused_activation_function);</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span> }</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span> </div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span> <span class="keywordtype">void</span> TfLiteParser::ParseDepthwiseConv2D(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</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>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span> </div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr->builtin_options.AsDepthwiseConv2DOptions();</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span> </div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf">CHECK_SUPPORTED_FUSED_ACTIVATION</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>, subgraphIndex, operatorIndex);</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>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a> desc;</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->stride_w);</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->stride_h);</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span>  desc.<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="l00911"></a><span class="lineno"> 911</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->depth_multiplier);</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="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2, 3);</div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->dilation_w_factor);</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->dilation_h_factor);</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="comment">// Mappings from TensorflowLite filter tensors to the ArmNN filter tensors (ArmNN weights have to be [M, I, H, W])</span></div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>  <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> permutationVector{ 2, 3, 1, 0 }; <span class="comment">// [H, W, I, M] -> [M, I, H, W]</span></div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  </div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> filterTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1], permutationVector);</div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span> </div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  <span class="comment">// Assuming input is NHWC</span></div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2];</div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span> </div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span>  <span class="comment">// TensorflowLite weights come in the format [1, H, W, I * M]</span></div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> filterHeight = filterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> filterWidth = filterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2];</div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span> </div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  <span class="comment">// Reshape weights as [ H, W, I, M ]</span></div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  filterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>({ filterHeight,</div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  filterWidth,</div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3],</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  filterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3] / inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3] });</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span> </div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputHeight, filterHeight, desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>,</div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a>, desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>, desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->padding);</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputWidth, filterWidth, desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>,</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a>, desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>, desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->padding);</div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span> </div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  <span class="keyword">auto</span> filterTensorAndData = CreateConstTensor(inputs[1], filterTensorInfo, permutationVector);</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"DepthwiseConv2D:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span> </div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  <span class="keywordflow">if</span> (inputs.size() == 3)</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  {</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  desc.<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="l00952"></a><span class="lineno"> 952</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[2]);</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span>  <span class="keyword">auto</span> biasTensorAndData = CreateConstTensor(inputs[2],</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  biasTensorInfo,</div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::PermutationVector&></a>());</div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  layer = m_Network->AddDepthwiseConvolution2dLayer(desc,</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span>  filterTensorAndData.first,</div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(biasTensorAndData.first),</div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span>  layerName.c_str());</div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  }</div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  {</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  layer = m_Network->AddDepthwiseConvolution2dLayer(desc,</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  filterTensorAndData.first,</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span>  <a class="code" href="structarmnn_1_1_empty_optional.xhtml">EmptyOptional</a>(),</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span>  layerName.c_str());</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  }</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span> </div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span> </div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span> </div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span>  layer = AddFusedActivationLayer(layer, 0, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->fused_activation_function);</div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span> }</div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span> </div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span> <span class="keywordtype">void</span> TfLiteParser::ParseDequantize(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span> {</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span> </div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span> </div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span> </div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"Dequantize:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</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="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddDequantizeLayer(layerName.c_str());</div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span> </div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span> </div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes);</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span> }</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span> </div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span> <span class="keywordtype">void</span> TfLiteParser::ParseTranspose(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span> {</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span> </div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1, 2);</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span> </div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span> </div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"Transpose:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span> </div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>  <a class="code" href="structarmnn_1_1_transpose_descriptor.xhtml">TransposeDescriptor</a> desc;</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span> </div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>  <span class="keywordflow">if</span> (inputs.size() == 2)</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="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> permuteTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> permuteBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[1]->buffer);</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>  <span class="keyword">auto</span> numPermVecElements = permuteTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>();</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  std::vector<unsigned int> permuteShape(numPermVecElements);</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>  ::memcpy(permuteShape.data(), permuteBufferPtr->data.data(), permuteTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>  <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> permutationVector(permuteShape.data(), permuteTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span> </div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span>  desc = <a class="code" href="structarmnn_1_1_transpose_descriptor.xhtml">TransposeDescriptor</a>(permutationVector);</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> </div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>  layer = m_Network->AddTransposeLayer(desc, layerName.c_str());</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>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span> </div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>  layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span> </div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span> </div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</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> </div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span> <span class="keywordtype">void</span> TfLiteParser::ParseTransposeConv(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span> {</div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span> </div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>  <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr->builtin_options.AsTransposeConvOptions();</div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span> </div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>  <a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml">TransposeConvolution2dDescriptor</a> desc;</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->stride_w);</div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->stride_h);</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span>  desc.<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="l01062"></a><span class="lineno"> 1062</span> </div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 3);</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span> </div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[2]);</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> filterTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span> </div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>  <span class="comment">// TfLite uses NHWC tensors</span></div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = inputTensorInfo.GetShape()[1];</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = inputTensorInfo.GetShape()[2];</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span> </div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> filterHeight = filterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> filterWidth = filterTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2];</div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span> </div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>  <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputHeight,</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>  filterHeight,</div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>,</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span>  1, <span class="comment">// DilationY</span></div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>,</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>,</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->padding);</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span> </div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>  <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputWidth,</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>  filterWidth,</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>,</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>  1, <span class="comment">// DilationX</span></div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>,</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>  desc.<a class="code" href="structarmnn_1_1_transpose_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>,</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->padding);</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span> </div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>  <span class="keyword">auto</span> filterTensorAndData = CreateConstTensor(inputs[1],</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>  filterTensorInfo,</div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::PermutationVector&></a>());</div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span> </div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"TransposeConv:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span> </div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>  layer = m_Network->AddTransposeConvolution2dLayer(desc,</div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span>  filterTensorAndData.first,</div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>  <a class="code" href="structarmnn_1_1_empty_optional.xhtml">EmptyOptional</a>(),</div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>  layerName.c_str());</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>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span> </div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span> </div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const (filter) tensor</span></div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[2]});</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span> </div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span> }</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span> </div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span> <span class="keywordtype">void</span> TfLiteParser::ParseAveragePool2D(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span> {</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>  ParsePool(subgraphIndex, operatorIndex, PoolingAlgorithm::Average);</div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span> }</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> <span class="keywordtype">void</span> TfLiteParser::ParseBatchToSpaceND(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span> {</div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span> </div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 3);</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span> </div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span> </div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> blockShapeTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> blockShapeBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[1]->buffer);</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span> </div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cropsTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[2]);</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> cropsBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[2]->buffer);</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>  std::vector<unsigned int> blockShape(blockShapeTensorInfo.GetNumElements());</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>  ::memcpy(blockShape.data(), blockShapeBufferPtr->data.data(), blockShapeTensorInfo.GetNumBytes());</div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span> </div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>  std::vector<unsigned int> cropsVector(cropsTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>  ::memcpy(cropsVector.data(), cropsBufferPtr->data.data(), cropsTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span> </div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>  <span class="keywordtype">size_t</span> step = 2;</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>  std::vector<std::pair<unsigned int, unsigned int>> crops;</div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < cropsTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / step; ++i)</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>  {</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>  crops.emplace_back(cropsVector[i * step], cropsVector[i * step + 1]);</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>  }</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>  <a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">armnn::BatchToSpaceNdDescriptor</a> desc;</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  desc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a02e143524aefddd40b485fcf7dea6696">m_BlockShape</a> = blockShape;</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  desc.<a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a3941f674c071c9503e00d2b59e92e454">m_Crops</a> = crops;</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</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#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span> </div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</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>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"BatchToSpaceND:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddBatchToSpaceNdLayer(desc, layerName.c_str());</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>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span> </div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span> </div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span> }</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> TfLiteParser::ParseL2Normalization(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span> {</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span> </div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</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>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span> </div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  <a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml">L2NormalizationDescriptor</a> desc;</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  desc.<a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"L2Normalization:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddL2NormalizationLayer(desc, layerName.c_str());</div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span> </div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span> </div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span> </div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</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">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</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> </div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span> <span class="keywordtype">void</span> TfLiteParser::ParseMaxPool2D(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span> {</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>  ParsePool(subgraphIndex, operatorIndex, PoolingAlgorithm::Max);</div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span> }</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span> </div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span> <span class="keywordtype">void</span> TfLiteParser::ParseMaximum(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</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>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span> </div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span> </div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</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="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input1TensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</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>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"Maximum:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddMaximumLayer(layerName.c_str());</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</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="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>  <span class="keywordflow">if</span> (inputTensorInfo.GetNumDimensions() != input1TensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>  {</div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>  AddBroadcastReshapeLayer(subgraphIndex, operatorIndex, layer);</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>  }</div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span>  <span class="keywordflow">else</span></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>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>  }</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span> </div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span> }</div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span> </div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span> <span class="keywordtype">void</span> TfLiteParser::ParseMinimum(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span> {</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span> </div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span> </div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span> </div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input1TensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span> </div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"Minimum:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddMinimumLayer(layerName.c_str());</div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span> </div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span> </div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>  <span class="keywordflow">if</span> (inputTensorInfo.GetNumDimensions() != input1TensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>  {</div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>  AddBroadcastReshapeLayer(subgraphIndex, operatorIndex, layer);</div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span>  }</div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>  {</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>  }</div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span> </div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</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> </div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span> <span class="keywordtype">void</span> TfLiteParser::ParsePool(<span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>  <span class="keywordtype">size_t</span> operatorIndex,</div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>  <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718">PoolingAlgorithm</a> algorithm)</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>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span> </div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>  <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr->builtin_options.AsPool2DOptions();</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span> </div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf">CHECK_SUPPORTED_FUSED_ACTIVATION</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span> </div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span>  std::string layerName;</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>  <span class="keywordflow">switch</span> (algorithm)</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="keywordflow">case</span> PoolingAlgorithm::Average:</div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>  layerName =</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span>  boost::str(boost::format(<span class="stringliteral">"AveragePool2D:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>  <span class="keywordflow">case</span> PoolingAlgorithm::Max:</div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>  layerName =</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>  boost::str(boost::format(<span class="stringliteral">"MaxPool2D:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>  BOOST_ASSERT_MSG(<span class="keyword">false</span>, <span class="stringliteral">"Unsupported Pooling Algorithm"</span>);</div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>  }</div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span> </div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> desc;</div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span> </div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = algorithm;</div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->stride_w);</div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->stride_h);</div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->filter_width);</div><div class="line"><a name="l01304"></a><span class="lineno"> 1304</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->filter_height);</div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = PaddingMethod::Exclude;</div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#affb5b68b3eba3ed45a06c7cde7781962">m_OutputShapeRounding</a> = OutputShapeRounding::Floor;</div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</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="l01308"></a><span class="lineno"> 1308</span> </div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</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>  <span class="comment">// assuming input is NHWC</span></div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = inputTensorInfo.GetShape()[1];</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = inputTensorInfo.GetShape()[2];</div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span> </div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>  <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputHeight, desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a>, desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>, 1u,</div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>, desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->padding);</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>  <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputWidth, desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a>, desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>, 1u,</div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>, desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->padding);</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span> </div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</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="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddPooling2dLayer(desc, layerName.c_str());</div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span> </div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span> </div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>  layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);</div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span> </div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span> </div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>  layer = AddFusedActivationLayer(layer, 0, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->fused_activation_function);</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</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"> 1343</span> <span class="keywordtype">void</span> TfLiteParser::ParseSlice(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</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>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01346"></a><span class="lineno"> 1346</span> </div><div class="line"><a name="l01347"></a><span class="lineno"> 1347</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 3);</div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</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>  <a class="code" href="structarmnn_1_1_slice_descriptor.xhtml">SliceDescriptor</a> desc;</div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span> </div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span>  <span class="comment">// set begin tensor info for slice descriptor</span></div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> beginTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> beginBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[1]->buffer);</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span> </div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>  std::vector<unsigned int> begin(beginTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span>  ::memcpy(begin.data(), beginBufferPtr->data.data(), beginTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span> </div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>  <span class="comment">// set size tensor info for slice descriptor</span></div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> sizeTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[2]);</div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> sizeBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[2]->buffer);</div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span> </div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span>  std::vector<unsigned int> size(sizeTensorInfo.GetNumElements());</div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>  ::memcpy(size.data(), sizeBufferPtr->data.data(), sizeTensorInfo.GetNumBytes());</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>  desc = <a class="code" href="structarmnn_1_1_slice_descriptor.xhtml">SliceDescriptor</a>(begin, size);</div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span> </div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"Slice:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network->AddSliceLayer(desc, layerName.c_str());</div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span> </div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01373"></a><span class="lineno"> 1373</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</span> </div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span> </div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01382"></a><span class="lineno"> 1382</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span> }</div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</span> </div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span> <span class="keywordtype">void</span> TfLiteParser::ParseSoftmax(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</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>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l01389"></a><span class="lineno"> 1389</span>  <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr->builtin_options.AsSoftmaxOptions();</div><div class="line"><a name="l01390"></a><span class="lineno"> 1390</span> </div><div class="line"><a name="l01391"></a><span class="lineno"> 1391</span>  <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">SoftmaxDescriptor</a> desc;</div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span>  desc.<a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->beta;</div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span> </div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span> </div><div class="line"><a name="l01399"></a><span class="lineno"> 1399</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"Softmax:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network->AddSoftmaxLayer(desc, layerName.c_str());</div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span> </div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span> </div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</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>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01413"></a><span class="lineno"> 1413</span> }</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> <span class="keywordtype">void</span> TfLiteParser::ParseSpaceToBatchND(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span> {</div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span> </div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 3);</div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span> </div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span> </div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> blockShapeTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> blockShapeBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[1]->buffer);</div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span> </div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> padListTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[2]);</div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> padListBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[2]->buffer);</div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span> </div><div class="line"><a name="l01431"></a><span class="lineno"> 1431</span>  std::vector<unsigned int> blockShape(blockShapeTensorInfo.GetNumElements());</div><div class="line"><a name="l01432"></a><span class="lineno"> 1432</span>  ::memcpy(blockShape.data(), blockShapeBufferPtr->data.data(), blockShapeTensorInfo.GetNumBytes());</div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</span> </div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>  std::vector<unsigned int> padListVector(padListTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l01435"></a><span class="lineno"> 1435</span>  ::memcpy(padListVector.data(), padListBufferPtr->data.data(), padListTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l01436"></a><span class="lineno"> 1436</span> </div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</span>  <span class="keywordtype">size_t</span> step = 2;</div><div class="line"><a name="l01438"></a><span class="lineno"> 1438</span>  std::vector<std::pair<unsigned int, unsigned int>> padList;</div><div class="line"><a name="l01439"></a><span class="lineno"> 1439</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < padListTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / step; ++i)</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>  padList.emplace_back(padListVector[i * step], padListVector[i * step + 1]);</div><div class="line"><a name="l01442"></a><span class="lineno"> 1442</span>  }</div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span> </div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>  <a class="code" href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml">armnn::SpaceToBatchNdDescriptor</a> desc;</div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span>  desc.<a class="code" href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml#a02e143524aefddd40b485fcf7dea6696">m_BlockShape</a> = blockShape;</div><div class="line"><a name="l01446"></a><span class="lineno"> 1446</span>  desc.<a class="code" href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml#a85f98c94e11f65a6b73f831735c040f3">m_PadList</a> = padList;</div><div class="line"><a name="l01447"></a><span class="lineno"> 1447</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#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</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="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span> </div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"SpaceToBatchND:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddSpaceToBatchNdLayer(desc, layerName.c_str());</div><div class="line"><a name="l01453"></a><span class="lineno"> 1453</span> </div><div class="line"><a name="l01454"></a><span class="lineno"> 1454</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span> </div><div class="line"><a name="l01456"></a><span class="lineno"> 1456</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span> </div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01460"></a><span class="lineno"> 1460</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01461"></a><span class="lineno"> 1461</span> }</div><div class="line"><a name="l01462"></a><span class="lineno"> 1462</span> </div><div class="line"><a name="l01463"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aa0a90d432c9c41f9846f41f11c9e54c9"> 1463</a></span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aa0a90d432c9c41f9846f41f11c9e54c9">TfLiteParser::OutputShapeOfSqueeze</a>(<span class="keyword">const</span> std::vector<uint32_t> & squeezeDimsIn,</div><div class="line"><a name="l01464"></a><span class="lineno"> 1464</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> & inputTensorInfo)</div><div class="line"><a name="l01465"></a><span class="lineno"> 1465</span> {</div><div class="line"><a name="l01466"></a><span class="lineno"> 1466</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(squeezeDimsIn.size(), 0, 1, 2, 3, 4);</div><div class="line"><a name="l01467"></a><span class="lineno"> 1467</span>  std::vector<uint32_t> squeezeDims = squeezeDimsIn;</div><div class="line"><a name="l01468"></a><span class="lineno"> 1468</span>  <span class="keyword">static</span> <span class="keyword">const</span> uint32_t dimensionSequence[] = { 0, 1, 2, 3 };</div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span> </div><div class="line"><a name="l01470"></a><span class="lineno"> 1470</span>  <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() > 4)</div><div class="line"><a name="l01471"></a><span class="lineno"> 1471</span>  {</div><div class="line"><a name="l01472"></a><span class="lineno"> 1472</span>  std::stringstream ss;</div><div class="line"><a name="l01473"></a><span class="lineno"> 1473</span>  ss << <span class="stringliteral">"Input tensor has unexpected number of dimensions:"</span> << inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()</div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</span>  << <span class="stringliteral">" shape:"</span> << inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>() << <span class="stringliteral">" "</span></div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span>  << <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString();</div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(ss.str());</div><div class="line"><a name="l01477"></a><span class="lineno"> 1477</span>  }</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>  <span class="keywordflow">if</span> (squeezeDims.empty())</div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span>  {</div><div class="line"><a name="l01481"></a><span class="lineno"> 1481</span>  squeezeDims.assign(dimensionSequence,</div><div class="line"><a name="l01482"></a><span class="lineno"> 1482</span>  dimensionSequence+inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>());</div><div class="line"><a name="l01483"></a><span class="lineno"> 1483</span>  }</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>  std::vector<uint32_t> outputDims;</div><div class="line"><a name="l01486"></a><span class="lineno"> 1486</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); i++)</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>  <span class="keywordtype">bool</span> skipSqueeze = (std::find(squeezeDims.begin(), squeezeDims.end(), i) == squeezeDims.end());</div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span>  <span class="keyword">auto</span> currentDimension = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i];</div><div class="line"><a name="l01490"></a><span class="lineno"> 1490</span>  <span class="keywordflow">if</span> (skipSqueeze || currentDimension != 1)</div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span>  {</div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span>  outputDims.push_back(currentDimension);</div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span>  }</div><div class="line"><a name="l01494"></a><span class="lineno"> 1494</span>  }</div><div class="line"><a name="l01495"></a><span class="lineno"> 1495</span> </div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span>  <span class="keywordflow">if</span> (outputDims.size() > 4)</div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span>  {</div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span>  std::stringstream ss;</div><div class="line"><a name="l01499"></a><span class="lineno"> 1499</span>  ss << <span class="stringliteral">"Output tensor has unexpected number of dimensions:"</span> << inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()</div><div class="line"><a name="l01500"></a><span class="lineno"> 1500</span>  << <span class="stringliteral">" shape:"</span> << inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>() << <span class="stringliteral">" "</span></div><div class="line"><a name="l01501"></a><span class="lineno"> 1501</span>  << <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString();</div><div class="line"><a name="l01502"></a><span class="lineno"> 1502</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(ss.str());</div><div class="line"><a name="l01503"></a><span class="lineno"> 1503</span>  }</div><div class="line"><a name="l01504"></a><span class="lineno"> 1504</span> </div><div class="line"><a name="l01505"></a><span class="lineno"> 1505</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outShape = <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(static_cast<unsigned int>(outputDims.size()),</div><div class="line"><a name="l01506"></a><span class="lineno"> 1506</span>  outputDims.data());</div><div class="line"><a name="l01507"></a><span class="lineno"> 1507</span> </div><div class="line"><a name="l01508"></a><span class="lineno"> 1508</span>  <span class="comment">// we need to preserve the tensor type and the quantization data as well</span></div><div class="line"><a name="l01509"></a><span class="lineno"> 1509</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outTensorInfo = inputTensorInfo;</div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</span>  outTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(outShape);</div><div class="line"><a name="l01511"></a><span class="lineno"> 1511</span> </div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span>  <span class="keywordflow">return</span> outTensorInfo;</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> </div><div class="line"><a name="l01515"></a><span class="lineno"> 1515</span> <span class="keywordtype">void</span> TfLiteParser::ParseSqueeze(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01516"></a><span class="lineno"> 1516</span> {</div><div class="line"><a name="l01517"></a><span class="lineno"> 1517</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01518"></a><span class="lineno"> 1518</span> </div><div class="line"><a name="l01519"></a><span class="lineno"> 1519</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01520"></a><span class="lineno"> 1520</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l01521"></a><span class="lineno"> 1521</span> </div><div class="line"><a name="l01522"></a><span class="lineno"> 1522</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01524"></a><span class="lineno"> 1524</span> </div><div class="line"><a name="l01525"></a><span class="lineno"> 1525</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span>  <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr->builtin_options.AsSqueezeOptions();</div><div class="line"><a name="l01527"></a><span class="lineno"> 1527</span> </div><div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo =</div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aa0a90d432c9c41f9846f41f11c9e54c9">TfLiteParser::OutputShapeOfSqueeze</a>(AsUnsignedVector(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->squeeze_dims),</div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span>  inputTensorInfo);</div><div class="line"><a name="l01532"></a><span class="lineno"> 1532</span> </div><div class="line"><a name="l01533"></a><span class="lineno"> 1533</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">ReshapeDescriptor</a> reshapeDesc;</div><div class="line"><a name="l01534"></a><span class="lineno"> 1534</span>  reshapeDesc.<a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l01535"></a><span class="lineno"> 1535</span> </div><div class="line"><a name="l01536"></a><span class="lineno"> 1536</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"Squeeze:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01537"></a><span class="lineno"> 1537</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str());</div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span> </div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01542"></a><span class="lineno"> 1542</span> </div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span> }</div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span> </div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span> <span class="keywordtype">void</span> TfLiteParser::ParseStridedSlice(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span> {</div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01550"></a><span class="lineno"> 1550</span> </div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 4);</div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span> </div><div class="line"><a name="l01554"></a><span class="lineno"> 1554</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span> </div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l01558"></a><span class="lineno"> 1558</span>  <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr->builtin_options.AsStridedSliceOptions();</div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span> </div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>  <a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml">StridedSliceDescriptor</a> desc;</div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a61081be1483984e33db452c75d569f51">m_BeginMask</a> = <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->begin_mask;</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#af996d82c47e43a16f4c8faa6c6b3e030">m_EllipsisMask</a> = <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->ellipsis_mask;</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#ac37e49c0d6e6e54f9d2015d0f11f8ee7">m_EndMask</a> = <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->end_mask;</div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a7c91eda2b331d607bae92cd8ebf50bb9">m_NewAxisMask</a> = <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->new_axis_mask;</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a6d0384878432cfc9652b7ae8bc59506f">m_ShrinkAxisMask</a> = <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->shrink_axis_mask;</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span> </div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> beginTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> beginBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[1]->buffer);</div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span> </div><div class="line"><a name="l01571"></a><span class="lineno"> 1571</span>  std::vector<int> begin(beginTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span>  ::memcpy(begin.data(), beginBufferPtr->data.data(), beginTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span> </div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> endTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[2]);</div><div class="line"><a name="l01575"></a><span class="lineno"> 1575</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> endBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[2]->buffer);</div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span> </div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span>  std::vector<int> end(endTensorInfo.GetNumElements());</div><div class="line"><a name="l01578"></a><span class="lineno"> 1578</span>  ::memcpy(end.data(), endBufferPtr->data.data(), endTensorInfo.GetNumBytes());</div><div class="line"><a name="l01579"></a><span class="lineno"> 1579</span> </div><div class="line"><a name="l01580"></a><span class="lineno"> 1580</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> strideTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[3]);</div><div class="line"><a name="l01581"></a><span class="lineno"> 1581</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> strideBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[3]->buffer);</div><div class="line"><a name="l01582"></a><span class="lineno"> 1582</span> </div><div class="line"><a name="l01583"></a><span class="lineno"> 1583</span>  std::vector<int> stride(strideTensorInfo.GetNumElements());</div><div class="line"><a name="l01584"></a><span class="lineno"> 1584</span>  ::memcpy(stride.data(), strideBufferPtr->data.data(), strideTensorInfo.GetNumBytes());</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>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a118fe06b7c2599da60398ee311ede923">m_Begin</a> = begin;</div><div class="line"><a name="l01587"></a><span class="lineno"> 1587</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#aa68194dd6258ab5b04123005a066ea25">m_End</a> = end;</div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a0d53caff836b84204adbd1c28752a201">m_Stride</a> = stride;</div><div class="line"><a name="l01589"></a><span class="lineno"> 1589</span> </div><div class="line"><a name="l01590"></a><span class="lineno"> 1590</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"StridedSlice:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddStridedSliceLayer(desc, layerName.c_str());</div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span> </div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</span> </div><div class="line"><a name="l01596"></a><span class="lineno"> 1596</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01597"></a><span class="lineno"> 1597</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01598"></a><span class="lineno"> 1598</span> </div><div class="line"><a name="l01599"></a><span class="lineno"> 1599</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01600"></a><span class="lineno"> 1600</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01601"></a><span class="lineno"> 1601</span> }</div><div class="line"><a name="l01602"></a><span class="lineno"> 1602</span> </div><div class="line"><a name="l01603"></a><span class="lineno"> 1603</span> <span class="keywordtype">void</span> TfLiteParser::ParseSub(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01604"></a><span class="lineno"> 1604</span> {</div><div class="line"><a name="l01605"></a><span class="lineno"> 1605</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01606"></a><span class="lineno"> 1606</span> </div><div class="line"><a name="l01607"></a><span class="lineno"> 1607</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l01608"></a><span class="lineno"> 1608</span>  <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr->builtin_options.AsSubOptions();</div><div class="line"><a name="l01609"></a><span class="lineno"> 1609</span> </div><div class="line"><a name="l01610"></a><span class="lineno"> 1610</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01611"></a><span class="lineno"> 1611</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l01612"></a><span class="lineno"> 1612</span> </div><div class="line"><a name="l01613"></a><span class="lineno"> 1613</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01614"></a><span class="lineno"> 1614</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01615"></a><span class="lineno"> 1615</span> </div><div class="line"><a name="l01616"></a><span class="lineno"> 1616</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l01617"></a><span class="lineno"> 1617</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input1TensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l01618"></a><span class="lineno"> 1618</span> </div><div class="line"><a name="l01619"></a><span class="lineno"> 1619</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"Sub:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01620"></a><span class="lineno"> 1620</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddSubtractionLayer(layerName.c_str());</div><div class="line"><a name="l01621"></a><span class="lineno"> 1621</span> </div><div class="line"><a name="l01622"></a><span class="lineno"> 1622</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01623"></a><span class="lineno"> 1623</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01624"></a><span class="lineno"> 1624</span> </div><div class="line"><a name="l01625"></a><span class="lineno"> 1625</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01626"></a><span class="lineno"> 1626</span>  <span class="keywordflow">if</span> (inputTensorInfo.GetNumDimensions() != input1TensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div><div class="line"><a name="l01627"></a><span class="lineno"> 1627</span>  {</div><div class="line"><a name="l01628"></a><span class="lineno"> 1628</span>  AddBroadcastReshapeLayer(subgraphIndex, operatorIndex, layer);</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span>  {</div><div class="line"><a name="l01632"></a><span class="lineno"> 1632</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</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> </div><div class="line"><a name="l01635"></a><span class="lineno"> 1635</span>  layer = AddFusedActivationLayer(layer, 0, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->fused_activation_function);</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">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01638"></a><span class="lineno"> 1638</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01639"></a><span class="lineno"> 1639</span> }</div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span> </div><div class="line"><a name="l01641"></a><span class="lineno"> 1641</span> <span class="keywordtype">void</span> TfLiteParser::ParseAdd(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01642"></a><span class="lineno"> 1642</span> {</div><div class="line"><a name="l01643"></a><span class="lineno"> 1643</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</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>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l01646"></a><span class="lineno"> 1646</span>  <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr->builtin_options.AsAddOptions();</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">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span> </div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</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="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input1TensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</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>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"Add:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddAdditionLayer(layerName.c_str());</div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span> </div><div class="line"><a name="l01660"></a><span class="lineno"> 1660</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</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>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01664"></a><span class="lineno"> 1664</span>  <span class="keywordflow">if</span> (inputTensorInfo.GetNumDimensions() != input1TensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div><div class="line"><a name="l01665"></a><span class="lineno"> 1665</span>  {</div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span>  AddBroadcastReshapeLayer(subgraphIndex, operatorIndex, layer);</div><div class="line"><a name="l01667"></a><span class="lineno"> 1667</span>  }</div><div class="line"><a name="l01668"></a><span class="lineno"> 1668</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01669"></a><span class="lineno"> 1669</span>  {</div><div class="line"><a name="l01670"></a><span class="lineno"> 1670</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l01671"></a><span class="lineno"> 1671</span>  }</div><div class="line"><a name="l01672"></a><span class="lineno"> 1672</span> </div><div class="line"><a name="l01673"></a><span class="lineno"> 1673</span>  layer = AddFusedActivationLayer(layer, 0, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->fused_activation_function);</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="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01676"></a><span class="lineno"> 1676</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01677"></a><span class="lineno"> 1677</span> }</div><div class="line"><a name="l01678"></a><span class="lineno"> 1678</span> </div><div class="line"><a name="l01679"></a><span class="lineno"> 1679</span> <span class="keywordtype">void</span> TfLiteParser::ParseMul(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01680"></a><span class="lineno"> 1680</span> {</div><div class="line"><a name="l01681"></a><span class="lineno"> 1681</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</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>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span>  <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr->builtin_options.AsMulOptions();</div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span> </div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01687"></a><span class="lineno"> 1687</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l01688"></a><span class="lineno"> 1688</span> </div><div class="line"><a name="l01689"></a><span class="lineno"> 1689</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01690"></a><span class="lineno"> 1690</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01691"></a><span class="lineno"> 1691</span> </div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input1TensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l01694"></a><span class="lineno"> 1694</span> </div><div class="line"><a name="l01695"></a><span class="lineno"> 1695</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"Mul:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01696"></a><span class="lineno"> 1696</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddMultiplicationLayer(layerName.c_str());</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01699"></a><span class="lineno"> 1699</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01700"></a><span class="lineno"> 1700</span> </div><div class="line"><a name="l01701"></a><span class="lineno"> 1701</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01702"></a><span class="lineno"> 1702</span>  <span class="keywordflow">if</span> (inputTensorInfo.GetNumDimensions() != input1TensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div><div class="line"><a name="l01703"></a><span class="lineno"> 1703</span>  {</div><div class="line"><a name="l01704"></a><span class="lineno"> 1704</span>  AddBroadcastReshapeLayer(subgraphIndex, operatorIndex, layer);</div><div class="line"><a name="l01705"></a><span class="lineno"> 1705</span>  }</div><div class="line"><a name="l01706"></a><span class="lineno"> 1706</span>  <span class="keywordflow">else</span></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>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</div><div class="line"><a name="l01709"></a><span class="lineno"> 1709</span>  }</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>  layer = AddFusedActivationLayer(layer, 0, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->fused_activation_function);</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">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01714"></a><span class="lineno"> 1714</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</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> </div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span> <span class="keywordtype">void</span> TfLiteParser::ParseMean(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01718"></a><span class="lineno"> 1718</span> {</div><div class="line"><a name="l01719"></a><span class="lineno"> 1719</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span> </div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span> </div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> dimTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l01727"></a><span class="lineno"> 1727</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> bufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[1]->buffer);</div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span> </div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span>  <a class="code" href="structarmnn_1_1_mean_descriptor.xhtml">armnn::MeanDescriptor</a> desc;</div><div class="line"><a name="l01730"></a><span class="lineno"> 1730</span>  std::vector<unsigned int> axis(dimTensorInfo.GetNumElements());</div><div class="line"><a name="l01731"></a><span class="lineno"> 1731</span>  ::memcpy(axis.data(), bufferPtr->data.data(), dimTensorInfo.GetNumBytes());</div><div class="line"><a name="l01732"></a><span class="lineno"> 1732</span>  desc.<a class="code" href="structarmnn_1_1_mean_descriptor.xhtml#a1f0d67b087c491248bd1cde3ff995a95">m_Axis</a> = axis;</div><div class="line"><a name="l01733"></a><span class="lineno"> 1733</span> </div><div class="line"><a name="l01734"></a><span class="lineno"> 1734</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l01735"></a><span class="lineno"> 1735</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01736"></a><span class="lineno"> 1736</span> </div><div class="line"><a name="l01737"></a><span class="lineno"> 1737</span>  desc.<a class="code" href="structarmnn_1_1_mean_descriptor.xhtml#a28e0548abfc4e79c48f29a3d11a062e9">m_KeepDims</a> =</div><div class="line"><a name="l01738"></a><span class="lineno"> 1738</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() == outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() ?</div><div class="line"><a name="l01739"></a><span class="lineno"> 1739</span>  <a class="code" href="_cl_layer_tests_8cpp.xhtml#a37f1c3ccc9fc906be85185350dd83d48">true</a> : <span class="keyword">false</span>;</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">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"Mean:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01742"></a><span class="lineno"> 1742</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddMeanLayer(desc, layerName.c_str());</div><div class="line"><a name="l01743"></a><span class="lineno"> 1743</span> </div><div class="line"><a name="l01744"></a><span class="lineno"> 1744</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01745"></a><span class="lineno"> 1745</span> </div><div class="line"><a name="l01746"></a><span class="lineno"> 1746</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01747"></a><span class="lineno"> 1747</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01748"></a><span class="lineno"> 1748</span> </div><div class="line"><a name="l01749"></a><span class="lineno"> 1749</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01751"></a><span class="lineno"> 1751</span> }</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> <span class="keywordtype">void</span> TfLiteParser::ParsePad(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01754"></a><span class="lineno"> 1754</span> {</div><div class="line"><a name="l01755"></a><span class="lineno"> 1755</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</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>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TfLiteParser::TensorRawPtrVector</a> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01758"></a><span class="lineno"> 1758</span> </div><div class="line"><a name="l01759"></a><span class="lineno"> 1759</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TfLiteParser::TensorRawPtrVector</a> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01760"></a><span class="lineno"> 1760</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span> </div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> padTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> bufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[1]->buffer);</div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span> </div><div class="line"><a name="l01765"></a><span class="lineno"> 1765</span>  std::vector<unsigned int> padBuffer(padTensorInfo.GetNumElements());</div><div class="line"><a name="l01766"></a><span class="lineno"> 1766</span>  ::memcpy(padBuffer.data(), bufferPtr->data.data(), padTensorInfo.GetNumBytes());</div><div class="line"><a name="l01767"></a><span class="lineno"> 1767</span> </div><div class="line"><a name="l01768"></a><span class="lineno"> 1768</span>  <span class="keywordtype">size_t</span> step = 2;</div><div class="line"><a name="l01769"></a><span class="lineno"> 1769</span>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">armnn::PadDescriptor</a> desc;</div><div class="line"><a name="l01770"></a><span class="lineno"> 1770</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < padTensorInfo.GetNumElements() / step; ++i)</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>  desc.<a class="code" href="structarmnn_1_1_pad_descriptor.xhtml#a85f98c94e11f65a6b73f831735c040f3">m_PadList</a>.emplace_back(padBuffer[i * step], padBuffer[i * step + 1]);</div><div class="line"><a name="l01773"></a><span class="lineno"> 1773</span>  }</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>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"Pad:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddPadLayer(desc, layerName.c_str());</div><div class="line"><a name="l01777"></a><span class="lineno"> 1777</span> </div><div class="line"><a name="l01778"></a><span class="lineno"> 1778</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span> </div><div class="line"><a name="l01781"></a><span class="lineno"> 1781</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span> </div><div class="line"><a name="l01784"></a><span class="lineno"> 1784</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[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> </div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span> <span class="keywordtype">void</span> TfLiteParser::ParseQuantize(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</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>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01791"></a><span class="lineno"> 1791</span> </div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l01794"></a><span class="lineno"> 1794</span> </div><div class="line"><a name="l01795"></a><span class="lineno"> 1795</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01796"></a><span class="lineno"> 1796</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01797"></a><span class="lineno"> 1797</span> </div><div class="line"><a name="l01798"></a><span class="lineno"> 1798</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"Quantize:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</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="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddQuantizeLayer(layerName.c_str());</div><div class="line"><a name="l01801"></a><span class="lineno"> 1801</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01802"></a><span class="lineno"> 1802</span> </div><div class="line"><a name="l01803"></a><span class="lineno"> 1803</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01804"></a><span class="lineno"> 1804</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01805"></a><span class="lineno"> 1805</span> </div><div class="line"><a name="l01806"></a><span class="lineno"> 1806</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01807"></a><span class="lineno"> 1807</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01808"></a><span class="lineno"> 1808</span> </div><div class="line"><a name="l01809"></a><span class="lineno"> 1809</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01810"></a><span class="lineno"> 1810</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes);</div><div class="line"><a name="l01811"></a><span class="lineno"> 1811</span> }</div><div class="line"><a name="l01812"></a><span class="lineno"> 1812</span> </div><div class="line"><a name="l01813"></a><span class="lineno"> 1813</span> <span class="keywordtype">void</span> TfLiteParser::ParseRelu(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</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>  ParseActivation(subgraphIndex,operatorIndex, ActivationFunction::ReLu);</div><div class="line"><a name="l01816"></a><span class="lineno"> 1816</span> }</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> <span class="keywordtype">void</span> TfLiteParser::ParseRelu6(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01819"></a><span class="lineno"> 1819</span> {</div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span>  ParseActivation(subgraphIndex,operatorIndex, ActivationFunction::BoundedReLu);</div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span> }</div><div class="line"><a name="l01822"></a><span class="lineno"> 1822</span> </div><div class="line"><a name="l01823"></a><span class="lineno"> 1823</span> <span class="keywordtype">void</span> TfLiteParser::ParseLogistic(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01824"></a><span class="lineno"> 1824</span> {</div><div class="line"><a name="l01825"></a><span class="lineno"> 1825</span>  ParseActivation(subgraphIndex,operatorIndex,ActivationFunction::Sigmoid);</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"> 1828</span> <span class="keywordtype">void</span> TfLiteParser::ParseTanH(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</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>  ParseActivation(subgraphIndex,operatorIndex,ActivationFunction::TanH);</div><div class="line"><a name="l01831"></a><span class="lineno"> 1831</span> }</div><div class="line"><a name="l01832"></a><span class="lineno"> 1832</span> </div><div class="line"><a name="l01833"></a><span class="lineno"> 1833</span> </div><div class="line"><a name="l01834"></a><span class="lineno"> 1834</span> <span class="keywordtype">void</span> TfLiteParser::ParseActivation(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex, <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9ea">ActivationFunction</a> activationType)</div><div class="line"><a name="l01835"></a><span class="lineno"> 1835</span> {</div><div class="line"><a name="l01836"></a><span class="lineno"> 1836</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01837"></a><span class="lineno"> 1837</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l01838"></a><span class="lineno"> 1838</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(operatorPtr);</div><div class="line"><a name="l01839"></a><span class="lineno"> 1839</span> </div><div class="line"><a name="l01840"></a><span class="lineno"> 1840</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01841"></a><span class="lineno"> 1841</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</div><div class="line"><a name="l01842"></a><span class="lineno"> 1842</span> </div><div class="line"><a name="l01843"></a><span class="lineno"> 1843</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01844"></a><span class="lineno"> 1844</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01845"></a><span class="lineno"> 1845</span> </div><div class="line"><a name="l01846"></a><span class="lineno"> 1846</span>  <span class="keyword">auto</span> layerName = str(boost::format(<span class="stringliteral">"Activation:"</span>));</div><div class="line"><a name="l01847"></a><span class="lineno"> 1847</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDesc;</div><div class="line"><a name="l01848"></a><span class="lineno"> 1848</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = activationType;</div><div class="line"><a name="l01849"></a><span class="lineno"> 1849</span> </div><div class="line"><a name="l01850"></a><span class="lineno"> 1850</span>  <span class="keywordflow">switch</span> (activationType)</div><div class="line"><a name="l01851"></a><span class="lineno"> 1851</span>  {</div><div class="line"><a name="l01852"></a><span class="lineno"> 1852</span>  <span class="keywordflow">case</span> ActivationFunction::ReLu:</div><div class="line"><a name="l01853"></a><span class="lineno"> 1853</span>  {</div><div class="line"><a name="l01854"></a><span class="lineno"> 1854</span>  layerName += str(boost::format(<span class="stringliteral">"RELU:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01855"></a><span class="lineno"> 1855</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01856"></a><span class="lineno"> 1856</span>  }</div><div class="line"><a name="l01857"></a><span class="lineno"> 1857</span>  <span class="keywordflow">case</span> ActivationFunction::BoundedReLu:</div><div class="line"><a name="l01858"></a><span class="lineno"> 1858</span>  {</div><div class="line"><a name="l01859"></a><span class="lineno"> 1859</span>  layerName += str(boost::format(<span class="stringliteral">"RELU6:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01860"></a><span class="lineno"> 1860</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 6.0f;</div><div class="line"><a name="l01861"></a><span class="lineno"> 1861</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = 0.0f;</div><div class="line"><a name="l01862"></a><span class="lineno"> 1862</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01863"></a><span class="lineno"> 1863</span>  }</div><div class="line"><a name="l01864"></a><span class="lineno"> 1864</span>  <span class="keywordflow">case</span> ActivationFunction::Sigmoid:</div><div class="line"><a name="l01865"></a><span class="lineno"> 1865</span>  {</div><div class="line"><a name="l01866"></a><span class="lineno"> 1866</span>  layerName += str(boost::format(<span class="stringliteral">"SIGMOID:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01867"></a><span class="lineno"> 1867</span>  <span class="keywordflow">break</span>;</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>  <span class="keywordflow">case</span> ActivationFunction::TanH:</div><div class="line"><a name="l01870"></a><span class="lineno"> 1870</span>  {</div><div class="line"><a name="l01871"></a><span class="lineno"> 1871</span>  layerName += str(boost::format(<span class="stringliteral">"TANH:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l01872"></a><span class="lineno"> 1872</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l01873"></a><span class="lineno"> 1873</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = 1.0f;</div><div class="line"><a name="l01874"></a><span class="lineno"> 1874</span>  <span class="keywordflow">break</span>;</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>  <span class="keywordflow">default</span>:</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01879"></a><span class="lineno"> 1879</span>  boost::str(boost::format(<span class="stringliteral">"Unexpected ActivationFunction[%1%] when creating layerName "</span></div><div class="line"><a name="l01880"></a><span class="lineno"> 1880</span>  <span class="stringliteral">" %2% "</span>) %static_cast<int>(activationType)% <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01881"></a><span class="lineno"> 1881</span>  }</div><div class="line"><a name="l01882"></a><span class="lineno"> 1882</span>  }</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="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network->AddActivationLayer(activationDesc, layerName.c_str());</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01887"></a><span class="lineno"> 1887</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01888"></a><span class="lineno"> 1888</span> </div><div class="line"><a name="l01889"></a><span class="lineno"> 1889</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01890"></a><span class="lineno"> 1890</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l01891"></a><span class="lineno"> 1891</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01892"></a><span class="lineno"> 1892</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l01893"></a><span class="lineno"> 1893</span> </div><div class="line"><a name="l01894"></a><span class="lineno"> 1894</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01895"></a><span class="lineno"> 1895</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l01896"></a><span class="lineno"> 1896</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l01897"></a><span class="lineno"> 1897</span> }</div><div class="line"><a name="l01898"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaebfa9a01a0bb8a0935114ff0140cc45"> 1898</a></span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaebfa9a01a0bb8a0935114ff0140cc45">TfLiteParser::OutputShapeOfReshape</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> & inputTensorInfo,</div><div class="line"><a name="l01899"></a><span class="lineno"> 1899</span>  <span class="keyword">const</span> std::vector<int32_t> & targetDimsIn)</div><div class="line"><a name="l01900"></a><span class="lineno"> 1900</span> {</div><div class="line"><a name="l01901"></a><span class="lineno"> 1901</span>  std::vector<unsigned int> outputDims(targetDimsIn.begin(), targetDimsIn.end());</div><div class="line"><a name="l01902"></a><span class="lineno"> 1902</span>  <span class="keyword">const</span> <span class="keyword">auto</span> stretchDim = std::find(targetDimsIn.begin(), targetDimsIn.end(), -1);</div><div class="line"><a name="l01903"></a><span class="lineno"> 1903</span> </div><div class="line"><a name="l01904"></a><span class="lineno"> 1904</span>  <span class="keywordflow">if</span> (stretchDim != targetDimsIn.end())</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>  <span class="keywordflow">if</span> (std::find(std::next(stretchDim), targetDimsIn.end(), -1) != targetDimsIn.end())</div><div class="line"><a name="l01907"></a><span class="lineno"> 1907</span>  {</div><div class="line"><a name="l01908"></a><span class="lineno"> 1908</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01909"></a><span class="lineno"> 1909</span>  boost::str(</div><div class="line"><a name="l01910"></a><span class="lineno"> 1910</span>  boost::format(<span class="stringliteral">"At most one component of shape can be -1 %1%"</span>) % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l01913"></a><span class="lineno"> 1913</span>  <span class="keyword">auto</span> targetNumElements =</div><div class="line"><a name="l01914"></a><span class="lineno"> 1914</span>  <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(</div><div class="line"><a name="l01915"></a><span class="lineno"> 1915</span>  std::accumulate(targetDimsIn.begin(), targetDimsIn.end(), -1, std::multiplies<int32_t>()));</div><div class="line"><a name="l01916"></a><span class="lineno"> 1916</span> </div><div class="line"><a name="l01917"></a><span class="lineno"> 1917</span>  <span class="keyword">auto</span> stretchIndex = <span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(std::distance(targetDimsIn.begin(), stretchDim));</div><div class="line"><a name="l01918"></a><span class="lineno"> 1918</span>  outputDims[stretchIndex] = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / targetNumElements;</div><div class="line"><a name="l01919"></a><span class="lineno"> 1919</span>  }</div><div class="line"><a name="l01920"></a><span class="lineno"> 1920</span> </div><div class="line"><a name="l01921"></a><span class="lineno"> 1921</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape = <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(static_cast<unsigned int>(outputDims.size()), outputDims.data());</div><div class="line"><a name="l01922"></a><span class="lineno"> 1922</span> </div><div class="line"><a name="l01923"></a><span class="lineno"> 1923</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> reshapeInfo = inputTensorInfo;</div><div class="line"><a name="l01924"></a><span class="lineno"> 1924</span>  reshapeInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(outputShape);</div><div class="line"><a name="l01925"></a><span class="lineno"> 1925</span> </div><div class="line"><a name="l01926"></a><span class="lineno"> 1926</span>  <span class="keywordflow">return</span> reshapeInfo;</div><div class="line"><a name="l01927"></a><span class="lineno"> 1927</span> }</div><div class="line"><a name="l01928"></a><span class="lineno"> 1928</span> </div><div class="line"><a name="l01929"></a><span class="lineno"> 1929</span> <span class="keywordtype">void</span> TfLiteParser::ParseReshape(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l01930"></a><span class="lineno"> 1930</span> {</div><div class="line"><a name="l01931"></a><span class="lineno"> 1931</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01932"></a><span class="lineno"> 1932</span> </div><div class="line"><a name="l01933"></a><span class="lineno"> 1933</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01934"></a><span class="lineno"> 1934</span> </div><div class="line"><a name="l01935"></a><span class="lineno"> 1935</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l01936"></a><span class="lineno"> 1936</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l01937"></a><span class="lineno"> 1937</span> </div><div class="line"><a name="l01938"></a><span class="lineno"> 1938</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l01939"></a><span class="lineno"> 1939</span>  <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr->builtin_options.AsReshapeOptions();</div><div class="line"><a name="l01940"></a><span class="lineno"> 1940</span> </div><div class="line"><a name="l01941"></a><span class="lineno"> 1941</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l01942"></a><span class="lineno"> 1942</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> actualOutputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l01943"></a><span class="lineno"> 1943</span> </div><div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>  std::vector<int32_t> targetShape;</div><div class="line"><a name="l01945"></a><span class="lineno"> 1945</span>  <span class="keywordflow">if</span> (inputs.size() > 1 && inputs[1] != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l01946"></a><span class="lineno"> 1946</span>  {</div><div class="line"><a name="l01947"></a><span class="lineno"> 1947</span>  <span class="keywordflow">if</span> (<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> != <span class="keyword">nullptr</span>)</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>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a>(<span class="stringliteral">"Target shape defined in reshape parameters and input tensor. "</span></div><div class="line"><a name="l01950"></a><span class="lineno"> 1950</span>  <span class="stringliteral">"Only one method expected"</span>);</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> </div><div class="line"><a name="l01953"></a><span class="lineno"> 1953</span>  <span class="keywordflow">if</span> (inputs[1]->is_variable)</div><div class="line"><a name="l01954"></a><span class="lineno"> 1954</span>  {</div><div class="line"><a name="l01955"></a><span class="lineno"> 1955</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a>( <span class="stringliteral">"Target shapes defined in non-const input tensors is not supported"</span>);</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> </div><div class="line"><a name="l01958"></a><span class="lineno"> 1958</span>  <span class="keywordflow">if</span> (inputs[1]->shape.size() != 1)</div><div class="line"><a name="l01959"></a><span class="lineno"> 1959</span>  {</div><div class="line"><a name="l01960"></a><span class="lineno"> 1960</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a>(<span class="stringliteral">"Target 'shape' input is not a 1D tensor"</span>);</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> </div><div class="line"><a name="l01963"></a><span class="lineno"> 1963</span>  <span class="keywordflow">if</span> (inputs[1]->type != tflite::TensorType_INT32)</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>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a>(<span class="stringliteral">"Target 'shape' input is not an int32 type"</span>);</div><div class="line"><a name="l01966"></a><span class="lineno"> 1966</span>  }</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>  <span class="keyword">auto</span> bufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[1]->buffer);</div><div class="line"><a name="l01969"></a><span class="lineno"> 1969</span>  <span class="keyword">auto</span> vals = <span class="keyword">reinterpret_cast<</span><span class="keyword">const </span>int32_t*<span class="keyword">></span>(bufferPtr->data.data());</div><div class="line"><a name="l01970"></a><span class="lineno"> 1970</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i=0; i < inputs[1]->shape[0]; i++)</div><div class="line"><a name="l01971"></a><span class="lineno"> 1971</span>  {</div><div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>  targetShape.push_back(vals[i]);</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"> 1975</span>  <span class="keywordflow">else</span></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>  <span class="keywordflow">if</span> (<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> == <span class="keyword">nullptr</span>)</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>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a>(<span class="stringliteral">"Target shape not defined in reshape parameters or input tensor. "</span></div><div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>  <span class="stringliteral">"At least one method required"</span>);</div><div class="line"><a name="l01981"></a><span class="lineno"> 1981</span>  }</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>  targetShape = <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->new_shape;</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> </div><div class="line"><a name="l01986"></a><span class="lineno"> 1986</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> reshapeOutputTensorInfo =</div><div class="line"><a name="l01987"></a><span class="lineno"> 1987</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaebfa9a01a0bb8a0935114ff0140cc45">TfLiteParser::OutputShapeOfReshape</a>(inputTensorInfo, targetShape);</div><div class="line"><a name="l01988"></a><span class="lineno"> 1988</span> </div><div class="line"><a name="l01989"></a><span class="lineno"> 1989</span>  <span class="comment">// Check for valid input size and that reshape parameters equal output shape</span></div><div class="line"><a name="l01990"></a><span class="lineno"> 1990</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& reshapeOutputTensorShape = reshapeOutputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l01991"></a><span class="lineno"> 1991</span>  <span class="keywordflow">if</span> (inputs.size() > 1 && !<a class="code" href="namespacearmnn_deserializer.xhtml#a97b50f22cd99f0e09e6e48d20a35f6b2">CheckShape</a>(reshapeOutputTensorShape, outputs[0]->shape))</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>  std::stringstream ss;</div><div class="line"><a name="l01994"></a><span class="lineno"> 1994</span>  ss << <span class="stringliteral">"New shape defined in reshape parameters "</span></div><div class="line"><a name="l01995"></a><span class="lineno"> 1995</span>  << reshapeOutputTensorShape</div><div class="line"><a name="l01996"></a><span class="lineno"> 1996</span>  << <span class="stringliteral">" does not equal output shape "</span></div><div class="line"><a name="l01997"></a><span class="lineno"> 1997</span>  << actualOutputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()</div><div class="line"><a name="l01998"></a><span class="lineno"> 1998</span>  << <span class="stringliteral">": "</span></div><div class="line"><a name="l01999"></a><span class="lineno"> 1999</span>  << <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString();</div><div class="line"><a name="l02000"></a><span class="lineno"> 2000</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(ss.str());</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"> 2003</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">ReshapeDescriptor</a> reshapeDesc;</div><div class="line"><a name="l02004"></a><span class="lineno"> 2004</span>  reshapeDesc.<a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = reshapeOutputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l02005"></a><span class="lineno"> 2005</span> </div><div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"Reshape:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l02007"></a><span class="lineno"> 2007</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str());</div><div class="line"><a name="l02008"></a><span class="lineno"> 2008</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(reshapeOutputTensorInfo);</div><div class="line"><a name="l02009"></a><span class="lineno"> 2009</span> </div><div class="line"><a name="l02010"></a><span class="lineno"> 2010</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02011"></a><span class="lineno"> 2011</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l02012"></a><span class="lineno"> 2012</span> </div><div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02014"></a><span class="lineno"> 2014</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</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> </div><div class="line"><a name="l02017"></a><span class="lineno"> 2017</span> <span class="keywordtype">void</span> TfLiteParser::ParseResizeBilinear(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02018"></a><span class="lineno"> 2018</span> {</div><div class="line"><a name="l02019"></a><span class="lineno"> 2019</span>  ParseResize(subgraphIndex, operatorIndex, ResizeMethod::Bilinear);</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> </div><div class="line"><a name="l02022"></a><span class="lineno"> 2022</span> <span class="keywordtype">void</span> TfLiteParser::ParseResizeNearestNeighbor(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</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>  ParseResize(subgraphIndex, operatorIndex, ResizeMethod::NearestNeighbor);</div><div class="line"><a name="l02025"></a><span class="lineno"> 2025</span> }</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> <span class="keywordtype">void</span> TfLiteParser::ParseResize(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex, <a class="code" href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4">ResizeMethod</a> resizeMethod)</div><div class="line"><a name="l02028"></a><span class="lineno"> 2028</span> {</div><div class="line"><a name="l02029"></a><span class="lineno"> 2029</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02030"></a><span class="lineno"> 2030</span> </div><div class="line"><a name="l02031"></a><span class="lineno"> 2031</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02032"></a><span class="lineno"> 2032</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</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">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02035"></a><span class="lineno"> 2035</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02036"></a><span class="lineno"> 2036</span> </div><div class="line"><a name="l02037"></a><span class="lineno"> 2037</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> sizeTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l02038"></a><span class="lineno"> 2038</span> </div><div class="line"><a name="l02039"></a><span class="lineno"> 2039</span>  <span class="comment">// Data for the parsed tensor args (size) must be stored locally.</span></div><div class="line"><a name="l02040"></a><span class="lineno"> 2040</span>  std::vector<int32_t> sizeTensorData(sizeTensorInfo.GetNumElements());</div><div class="line"><a name="l02041"></a><span class="lineno"> 2041</span> </div><div class="line"><a name="l02042"></a><span class="lineno"> 2042</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> sizeBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[1]->buffer);</div><div class="line"><a name="l02043"></a><span class="lineno"> 2043</span>  ::memcpy(sizeTensorData.data(), sizeBufferPtr->data.data(), sizeTensorInfo.GetNumBytes());</div><div class="line"><a name="l02044"></a><span class="lineno"> 2044</span> </div><div class="line"><a name="l02045"></a><span class="lineno"> 2045</span>  <a class="code" href="structarmnn_1_1_resize_descriptor.xhtml">ResizeDescriptor</a> desc;</div><div class="line"><a name="l02046"></a><span class="lineno"> 2046</span>  desc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a869254cb56968986a78a79e1d6d4a86b">m_Method</a> = resizeMethod;</div><div class="line"><a name="l02047"></a><span class="lineno"> 2047</span>  desc.m_TargetHeight = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span> (sizeTensorData[0]);</div><div class="line"><a name="l02048"></a><span class="lineno"> 2048</span>  desc.m_TargetWidth = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span> (sizeTensorData[1]);</div><div class="line"><a name="l02049"></a><span class="lineno"> 2049</span>  desc.m_DataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l02050"></a><span class="lineno"> 2050</span> </div><div class="line"><a name="l02051"></a><span class="lineno"> 2051</span>  <span class="keyword">auto</span> layerName = str(boost::format(<span class="stringliteral">"Resize:"</span>));</div><div class="line"><a name="l02052"></a><span class="lineno"> 2052</span> </div><div class="line"><a name="l02053"></a><span class="lineno"> 2053</span>  <span class="keywordflow">switch</span> (resizeMethod)</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>  <span class="keywordflow">case</span> ResizeMethod::Bilinear:</div><div class="line"><a name="l02056"></a><span class="lineno"> 2056</span>  {</div><div class="line"><a name="l02057"></a><span class="lineno"> 2057</span>  layerName += str(boost::format(<span class="stringliteral">"BILINEAR:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l02058"></a><span class="lineno"> 2058</span> </div><div class="line"><a name="l02059"></a><span class="lineno"> 2059</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l02060"></a><span class="lineno"> 2060</span>  <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr->builtin_options.AsResizeBilinearOptions();</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>  desc.m_BilinearAlignCorners = <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->align_corners;</div><div class="line"><a name="l02063"></a><span class="lineno"> 2063</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02064"></a><span class="lineno"> 2064</span>  }</div><div class="line"><a name="l02065"></a><span class="lineno"> 2065</span>  <span class="keywordflow">case</span> ResizeMethod::NearestNeighbor:</div><div class="line"><a name="l02066"></a><span class="lineno"> 2066</span>  {</div><div class="line"><a name="l02067"></a><span class="lineno"> 2067</span>  layerName += str(boost::format(<span class="stringliteral">"NEARESTNEIGHBOR:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l02068"></a><span class="lineno"> 2068</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02069"></a><span class="lineno"> 2069</span>  }</div><div class="line"><a name="l02070"></a><span class="lineno"> 2070</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l02071"></a><span class="lineno"> 2071</span>  {</div><div class="line"><a name="l02072"></a><span class="lineno"> 2072</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02073"></a><span class="lineno"> 2073</span>  boost::str(boost::format(<span class="stringliteral">"Unexpected ResizeMethod[%1%] when creating layerName "</span></div><div class="line"><a name="l02074"></a><span class="lineno"> 2074</span>  <span class="stringliteral">" %2% "</span>) %static_cast<int>(resizeMethod)% <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02075"></a><span class="lineno"> 2075</span>  }</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> </div><div class="line"><a name="l02078"></a><span class="lineno"> 2078</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddResizeLayer(desc, layerName.c_str());</div><div class="line"><a name="l02079"></a><span class="lineno"> 2079</span> </div><div class="line"><a name="l02080"></a><span class="lineno"> 2080</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l02081"></a><span class="lineno"> 2081</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02082"></a><span class="lineno"> 2082</span> </div><div class="line"><a name="l02083"></a><span class="lineno"> 2083</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02084"></a><span class="lineno"> 2084</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l02085"></a><span class="lineno"> 2085</span> </div><div class="line"><a name="l02086"></a><span class="lineno"> 2086</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02087"></a><span class="lineno"> 2087</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes);</div><div class="line"><a name="l02088"></a><span class="lineno"> 2088</span> }</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> <span class="keywordtype">void</span> TfLiteParser::ParseConcatenation(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02091"></a><span class="lineno"> 2091</span> {</div><div class="line"><a name="l02092"></a><span class="lineno"> 2092</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02093"></a><span class="lineno"> 2093</span> </div><div class="line"><a name="l02094"></a><span class="lineno"> 2094</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l02095"></a><span class="lineno"> 2095</span>  <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr->builtin_options.AsConcatenationOptions();</div><div class="line"><a name="l02096"></a><span class="lineno"> 2096</span> </div><div class="line"><a name="l02097"></a><span class="lineno"> 2097</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf">CHECK_SUPPORTED_FUSED_ACTIVATION</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>, subgraphIndex, operatorIndex);</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="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02100"></a><span class="lineno"> 2100</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02101"></a><span class="lineno"> 2101</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02102"></a><span class="lineno"> 2102</span> </div><div class="line"><a name="l02103"></a><span class="lineno"> 2103</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numConcatView = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(inputs.size());</div><div class="line"><a name="l02104"></a><span class="lineno"> 2104</span>  uint32_t inputRank = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]).<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</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>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> concatDimInput = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(</div><div class="line"><a name="l02107"></a><span class="lineno"> 2107</span>  (<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(inputRank) + <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->axis) % <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(inputRank));</div><div class="line"><a name="l02108"></a><span class="lineno"> 2108</span> </div><div class="line"><a name="l02109"></a><span class="lineno"> 2109</span>  <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a> concatDescriptor(static_cast<uint32_t>(numConcatView), inputRank);</div><div class="line"><a name="l02110"></a><span class="lineno"> 2110</span>  concatDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a5b192c5fcd96a0f75542524cf646b355">SetConcatAxis</a>(concatDimInput);</div><div class="line"><a name="l02111"></a><span class="lineno"> 2111</span> </div><div class="line"><a name="l02112"></a><span class="lineno"> 2112</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> mergeDimOrigin = 0;</div><div class="line"><a name="l02113"></a><span class="lineno"> 2113</span> </div><div class="line"><a name="l02114"></a><span class="lineno"> 2114</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> viewIndex = 0; viewIndex < numConcatView; ++viewIndex)</div><div class="line"><a name="l02115"></a><span class="lineno"> 2115</span>  {</div><div class="line"><a name="l02116"></a><span class="lineno"> 2116</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[viewIndex]);</div><div class="line"><a name="l02117"></a><span class="lineno"> 2117</span> </div><div class="line"><a name="l02118"></a><span class="lineno"> 2118</span>  <span class="comment">// This set up concatDescriptor view origin</span></div><div class="line"><a name="l02119"></a><span class="lineno"> 2119</span>  <a class="code" href="namespacearmnn_utils.xhtml#a523deabeb7d0a884028b35eebfd1cb6c">armnnUtils::ProcessConcatInputTensorInfo</a>(</div><div class="line"><a name="l02120"></a><span class="lineno"> 2120</span>  inputTensorInfo, concatDescriptor, concatDimInput, viewIndex, mergeDimOrigin);</div><div class="line"><a name="l02121"></a><span class="lineno"> 2121</span>  }</div><div class="line"><a name="l02122"></a><span class="lineno"> 2122</span> </div><div class="line"><a name="l02123"></a><span class="lineno"> 2123</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"Concatenation:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l02124"></a><span class="lineno"> 2124</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddConcatLayer(concatDescriptor, layerName.c_str());</div><div class="line"><a name="l02125"></a><span class="lineno"> 2125</span> </div><div class="line"><a name="l02126"></a><span class="lineno"> 2126</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02127"></a><span class="lineno"> 2127</span> </div><div class="line"><a name="l02128"></a><span class="lineno"> 2128</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l02129"></a><span class="lineno"> 2129</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02130"></a><span class="lineno"> 2130</span> </div><div class="line"><a name="l02131"></a><span class="lineno"> 2131</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02132"></a><span class="lineno"> 2132</span> </div><div class="line"><a name="l02133"></a><span class="lineno"> 2133</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes});</div><div class="line"><a name="l02134"></a><span class="lineno"> 2134</span> </div><div class="line"><a name="l02135"></a><span class="lineno"> 2135</span>  <span class="comment">// add fused activation layer</span></div><div class="line"><a name="l02136"></a><span class="lineno"> 2136</span>  layer = AddFusedActivationLayer(layer, 0, <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->fused_activation_function);</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>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02139"></a><span class="lineno"> 2139</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02140"></a><span class="lineno"> 2140</span> }</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="keywordtype">void</span> TfLiteParser::ParseFullyConnected(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02143"></a><span class="lineno"> 2143</span> {</div><div class="line"><a name="l02144"></a><span class="lineno"> 2144</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02145"></a><span class="lineno"> 2145</span> </div><div class="line"><a name="l02146"></a><span class="lineno"> 2146</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorRfr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l02147"></a><span class="lineno"> 2147</span>  <span class="keyword">const</span> <span class="keyword">auto</span> <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorRfr->builtin_options.AsFullyConnectedOptions();</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>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf">CHECK_SUPPORTED_FUSED_ACTIVATION</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02150"></a><span class="lineno"> 2150</span> </div><div class="line"><a name="l02151"></a><span class="lineno"> 2151</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a> desc;</div><div class="line"><a name="l02152"></a><span class="lineno"> 2152</span>  desc.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l02153"></a><span class="lineno"> 2153</span>  desc.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">m_TransposeWeightMatrix</a> = <span class="keyword">true</span>;</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>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02156"></a><span class="lineno"> 2156</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02157"></a><span class="lineno"> 2157</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> filterTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l02160"></a><span class="lineno"> 2160</span> </div><div class="line"><a name="l02161"></a><span class="lineno"> 2161</span>  <span class="comment">// Fully Connected Layer accepts two dimensional weights input</span></div><div class="line"><a name="l02162"></a><span class="lineno"> 2162</span>  int32_t weightsDimension = <span class="keyword">static_cast<</span>int32_t<span class="keyword">></span>(filterTensorInfo.GetNumDimensions());</div><div class="line"><a name="l02163"></a><span class="lineno"> 2163</span>  <span class="keywordflow">if</span> (weightsDimension != 2)</div><div class="line"><a name="l02164"></a><span class="lineno"> 2164</span>  {</div><div class="line"><a name="l02165"></a><span class="lineno"> 2165</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02166"></a><span class="lineno"> 2166</span>  boost::str(</div><div class="line"><a name="l02167"></a><span class="lineno"> 2167</span>  boost::format(</div><div class="line"><a name="l02168"></a><span class="lineno"> 2168</span>  <span class="stringliteral">"Dimension %1% for Fully Connected weights is not supported by Armnn. "</span></div><div class="line"><a name="l02169"></a><span class="lineno"> 2169</span>  <span class="stringliteral">"Node %2%"</span>)</div><div class="line"><a name="l02170"></a><span class="lineno"> 2170</span>  % weightsDimension</div><div class="line"><a name="l02171"></a><span class="lineno"> 2171</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l02174"></a><span class="lineno"> 2174</span>  <span class="keyword">auto</span> filterTensorAndData = CreateConstTensor(inputs[1],</div><div class="line"><a name="l02175"></a><span class="lineno"> 2175</span>  filterTensorInfo,</div><div class="line"><a name="l02176"></a><span class="lineno"> 2176</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::PermutationVector&></a>());</div><div class="line"><a name="l02177"></a><span class="lineno"> 2177</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l02178"></a><span class="lineno"> 2178</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"FullyConnected:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l02179"></a><span class="lineno"> 2179</span> </div><div class="line"><a name="l02180"></a><span class="lineno"> 2180</span>  <span class="keywordflow">if</span> (inputs.size() == 3)</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>  desc.<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="l02183"></a><span class="lineno"> 2183</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[2]);</div><div class="line"><a name="l02184"></a><span class="lineno"> 2184</span>  <span class="keyword">auto</span> biasTensorAndData = CreateConstTensor(inputs[2],</div><div class="line"><a name="l02185"></a><span class="lineno"> 2185</span>  biasTensorInfo,</div><div class="line"><a name="l02186"></a><span class="lineno"> 2186</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::PermutationVector&></a>());</div><div class="line"><a name="l02187"></a><span class="lineno"> 2187</span>  layer = m_Network->AddFullyConnectedLayer(desc,</div><div class="line"><a name="l02188"></a><span class="lineno"> 2188</span>  filterTensorAndData.first,</div><div class="line"><a name="l02189"></a><span class="lineno"> 2189</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(biasTensorAndData.first),</div><div class="line"><a name="l02190"></a><span class="lineno"> 2190</span>  layerName.c_str());</div><div class="line"><a name="l02191"></a><span class="lineno"> 2191</span>  }</div><div class="line"><a name="l02192"></a><span class="lineno"> 2192</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l02193"></a><span class="lineno"> 2193</span>  {</div><div class="line"><a name="l02194"></a><span class="lineno"> 2194</span>  layer = m_Network->AddFullyConnectedLayer(desc,</div><div class="line"><a name="l02195"></a><span class="lineno"> 2195</span>  filterTensorAndData.first,</div><div class="line"><a name="l02196"></a><span class="lineno"> 2196</span>  <a class="code" href="structarmnn_1_1_empty_optional.xhtml">EmptyOptional</a>(),</div><div class="line"><a name="l02197"></a><span class="lineno"> 2197</span>  layerName.c_str());</div><div class="line"><a name="l02198"></a><span class="lineno"> 2198</span>  }</div><div class="line"><a name="l02199"></a><span class="lineno"> 2199</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l02202"></a><span class="lineno"> 2202</span> </div><div class="line"><a name="l02203"></a><span class="lineno"> 2203</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02204"></a><span class="lineno"> 2204</span> </div><div class="line"><a name="l02205"></a><span class="lineno"> 2205</span>  <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() > 2)</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>  <span class="comment">// Add reshape to flatten to 2D [batch_size, input_size],</span></div><div class="line"><a name="l02208"></a><span class="lineno"> 2208</span>  <span class="comment">// where "input_size" corresponds to the number of inputs to the layer,</span></div><div class="line"><a name="l02209"></a><span class="lineno"> 2209</span>  <span class="comment">// matching the second dimension of weights,</span></div><div class="line"><a name="l02210"></a><span class="lineno"> 2210</span>  <span class="comment">// and "batch_size" is calculated by dividing the number of elements by "input_size".</span></div><div class="line"><a name="l02211"></a><span class="lineno"> 2211</span>  std::vector<unsigned int> reshapedDimensions(2);</div><div class="line"><a name="l02212"></a><span class="lineno"> 2212</span>  reshapedDimensions[1] = filterTensorInfo.GetShape()[1];</div><div class="line"><a name="l02213"></a><span class="lineno"> 2213</span>  reshapedDimensions[0] = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / reshapedDimensions[1];</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="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() % reshapedDimensions[1] != 0)</div><div class="line"><a name="l02216"></a><span class="lineno"> 2216</span>  {</div><div class="line"><a name="l02217"></a><span class="lineno"> 2217</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02218"></a><span class="lineno"> 2218</span>  boost::str(</div><div class="line"><a name="l02219"></a><span class="lineno"> 2219</span>  boost::format(</div><div class="line"><a name="l02220"></a><span class="lineno"> 2220</span>  <span class="stringliteral">"Failed to deduce input tensor shape from filter size %1%"</span>)</div><div class="line"><a name="l02221"></a><span class="lineno"> 2221</span>  % reshapedDimensions[1]</div><div class="line"><a name="l02222"></a><span class="lineno"> 2222</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l02225"></a><span class="lineno"> 2225</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> reshapedTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l02226"></a><span class="lineno"> 2226</span>  reshapedTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>{ 2, reshapedDimensions.data() });</div><div class="line"><a name="l02227"></a><span class="lineno"> 2227</span> </div><div class="line"><a name="l02228"></a><span class="lineno"> 2228</span>  std::string reshapeLayerName = boost::str(boost::format(<span class="stringliteral">"Reshape_for:%1%"</span>) % layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>());</div><div class="line"><a name="l02229"></a><span class="lineno"> 2229</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">armnn::ReshapeDescriptor</a> desc;</div><div class="line"><a name="l02230"></a><span class="lineno"> 2230</span>  desc.m_TargetShape = reshapedTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l02231"></a><span class="lineno"> 2231</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* reshapeLayer = m_Network->AddReshapeLayer(desc, layerName.c_str());</div><div class="line"><a name="l02232"></a><span class="lineno"> 2232</span> </div><div class="line"><a name="l02233"></a><span class="lineno"> 2233</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>(reshapedTensorInfo);</div><div class="line"><a name="l02234"></a><span class="lineno"> 2234</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>(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02235"></a><span class="lineno"> 2235</span> </div><div class="line"><a name="l02236"></a><span class="lineno"> 2236</span>  RegisterInputSlots(subgraphIndex, operatorIndex, reshapeLayer, {inputTensorIndexes[0]});</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l02239"></a><span class="lineno"> 2239</span>  {</div><div class="line"><a name="l02240"></a><span class="lineno"> 2240</span>  <span class="comment">// register the input connection slot for the layer</span></div><div class="line"><a name="l02241"></a><span class="lineno"> 2241</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l02242"></a><span class="lineno"> 2242</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</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> </div><div class="line"><a name="l02245"></a><span class="lineno"> 2245</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l02246"></a><span class="lineno"> 2246</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</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="comment">// we need to add the activation layer and fortunately we don't need to care about the data layout</span></div><div class="line"><a name="l02249"></a><span class="lineno"> 2249</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* fusedActivationLayer = AddFusedActivationLayer(layer, 0,</div><div class="line"><a name="l02250"></a><span class="lineno"> 2250</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->fused_activation_function);</div><div class="line"><a name="l02251"></a><span class="lineno"> 2251</span> </div><div class="line"><a name="l02252"></a><span class="lineno"> 2252</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l02253"></a><span class="lineno"> 2253</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02254"></a><span class="lineno"> 2254</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, fusedActivationLayer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02255"></a><span class="lineno"> 2255</span> }</div><div class="line"><a name="l02256"></a><span class="lineno"> 2256</span> </div><div class="line"><a name="l02257"></a><span class="lineno"> 2257</span> <span class="keywordtype">void</span> TfLiteParser::ParseDetectionPostProcess(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02258"></a><span class="lineno"> 2258</span> {</div><div class="line"><a name="l02259"></a><span class="lineno"> 2259</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02260"></a><span class="lineno"> 2260</span> </div><div class="line"><a name="l02261"></a><span class="lineno"> 2261</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</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>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02264"></a><span class="lineno"> 2264</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02265"></a><span class="lineno"> 2265</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 4);</div><div class="line"><a name="l02266"></a><span class="lineno"> 2266</span> </div><div class="line"><a name="l02267"></a><span class="lineno"> 2267</span>  <span class="comment">// Obtain custom options from flexbuffers</span></div><div class="line"><a name="l02268"></a><span class="lineno"> 2268</span>  <span class="keyword">auto</span> custom_options = operatorPtr->custom_options;</div><div class="line"><a name="l02269"></a><span class="lineno"> 2269</span>  <span class="keyword">const</span> flexbuffers::Map& m = flexbuffers::GetRoot(custom_options.data(), custom_options.size()).AsMap();</div><div class="line"><a name="l02270"></a><span class="lineno"> 2270</span> </div><div class="line"><a name="l02271"></a><span class="lineno"> 2271</span>  <span class="comment">// Obtain descriptor information from tf lite</span></div><div class="line"><a name="l02272"></a><span class="lineno"> 2272</span>  <a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml">DetectionPostProcessDescriptor</a> desc;</div><div class="line"><a name="l02273"></a><span class="lineno"> 2273</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae72089bcab60ac175557f4241b16a014">m_MaxDetections</a> = m[<span class="stringliteral">"max_detections"</span>].AsUInt32();</div><div class="line"><a name="l02274"></a><span class="lineno"> 2274</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a9ae2c9796692ebeafe19a4d3f09c8ea8">m_MaxClassesPerDetection</a> = m[<span class="stringliteral">"max_classes_per_detection"</span>].AsUInt32();</div><div class="line"><a name="l02275"></a><span class="lineno"> 2275</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a4392dd6b4862cc9cf95ae8f1001ba592">m_NmsScoreThreshold</a> = m[<span class="stringliteral">"nms_score_threshold"</span>].AsFloat();</div><div class="line"><a name="l02276"></a><span class="lineno"> 2276</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a53c8a7f33a40e1e240256bcfcf41b101">m_NmsIouThreshold</a> = m[<span class="stringliteral">"nms_iou_threshold"</span>].AsFloat();</div><div class="line"><a name="l02277"></a><span class="lineno"> 2277</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a3a04b0ccee4bb2f21721ee5045e83df4">m_NumClasses</a> = m[<span class="stringliteral">"num_classes"</span>].AsUInt32();</div><div class="line"><a name="l02278"></a><span class="lineno"> 2278</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#aa61510cbd529870182e918ac6e8b9d72">m_ScaleH</a> = m[<span class="stringliteral">"h_scale"</span>].AsFloat();</div><div class="line"><a name="l02279"></a><span class="lineno"> 2279</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ab509802c659de19929f18bad14a35c58">m_ScaleW</a> = m[<span class="stringliteral">"w_scale"</span>].AsFloat();</div><div class="line"><a name="l02280"></a><span class="lineno"> 2280</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae64523937ea910030ad66fee6fddd51f">m_ScaleX</a> = m[<span class="stringliteral">"x_scale"</span>].AsFloat();</div><div class="line"><a name="l02281"></a><span class="lineno"> 2281</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7a2156ec7d9c012ce00bbcc6afcb9028">m_ScaleY</a> = m[<span class="stringliteral">"y_scale"</span>].AsFloat();</div><div class="line"><a name="l02282"></a><span class="lineno"> 2282</span> </div><div class="line"><a name="l02283"></a><span class="lineno"> 2283</span>  <span class="keywordflow">if</span> (!(m[<span class="stringliteral">"use_regular_nms"</span>].IsNull()))</div><div class="line"><a name="l02284"></a><span class="lineno"> 2284</span>  {</div><div class="line"><a name="l02285"></a><span class="lineno"> 2285</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7ed9bc7c26df67d274d5dd4cd83adf0f">m_UseRegularNms</a> = m[<span class="stringliteral">"use_regular_nms"</span>].AsBool();</div><div class="line"><a name="l02286"></a><span class="lineno"> 2286</span>  }</div><div class="line"><a name="l02287"></a><span class="lineno"> 2287</span>  <span class="keywordflow">if</span> (!(m[<span class="stringliteral">"detections_per_class"</span>].IsNull()))</div><div class="line"><a name="l02288"></a><span class="lineno"> 2288</span>  {</div><div class="line"><a name="l02289"></a><span class="lineno"> 2289</span>  desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a7e2f87544b8bc7e497e1dec8d3ca4055">m_DetectionsPerClass</a> = m[<span class="stringliteral">"detections_per_class"</span>].AsUInt32();</div><div class="line"><a name="l02290"></a><span class="lineno"> 2290</span>  }</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>  <span class="keywordflow">if</span> (desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a53c8a7f33a40e1e240256bcfcf41b101">m_NmsIouThreshold</a> <= 0.0f || desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a53c8a7f33a40e1e240256bcfcf41b101">m_NmsIouThreshold</a> > 1.0f)</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">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"DetectionPostProcessTFLiteParser: Intersection over union threshold "</span></div><div class="line"><a name="l02295"></a><span class="lineno"> 2295</span>  <span class="stringliteral">"must be positive and less than or equal to 1."</span>);</div><div class="line"><a name="l02296"></a><span class="lineno"> 2296</span>  }</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> anchorTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[2]);</div><div class="line"><a name="l02299"></a><span class="lineno"> 2299</span>  <span class="keyword">auto</span> anchorTensorAndData = CreateConstTensor(inputs[2], anchorTensorInfo,</div><div class="line"><a name="l02300"></a><span class="lineno"> 2300</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::PermutationVector&></a>());</div><div class="line"><a name="l02301"></a><span class="lineno"> 2301</span> </div><div class="line"><a name="l02302"></a><span class="lineno"> 2302</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"DetectionPostProcess:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l02303"></a><span class="lineno"> 2303</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddDetectionPostProcessLayer(desc, anchorTensorAndData.first,</div><div class="line"><a name="l02304"></a><span class="lineno"> 2304</span>  layerName.c_str());</div><div class="line"><a name="l02305"></a><span class="lineno"> 2305</span> </div><div class="line"><a name="l02306"></a><span class="lineno"> 2306</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02307"></a><span class="lineno"> 2307</span> </div><div class="line"><a name="l02308"></a><span class="lineno"> 2308</span>  <span class="comment">// The model does not specify the output shapes.</span></div><div class="line"><a name="l02309"></a><span class="lineno"> 2309</span>  <span class="comment">// The output shapes are calculated from the max_detection and max_classes_per_detection.</span></div><div class="line"><a name="l02310"></a><span class="lineno"> 2310</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDetectedBox = desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#ae72089bcab60ac175557f4241b16a014">m_MaxDetections</a> * desc.<a class="code" href="structarmnn_1_1_detection_post_process_descriptor.xhtml#a9ae2c9796692ebeafe19a4d3f09c8ea8">m_MaxClassesPerDetection</a>;</div><div class="line"><a name="l02311"></a><span class="lineno"> 2311</span>  m_OverridenOutputShapes.push_back({ 1, numDetectedBox, 4 });</div><div class="line"><a name="l02312"></a><span class="lineno"> 2312</span>  m_OverridenOutputShapes.push_back({ 1, numDetectedBox });</div><div class="line"><a name="l02313"></a><span class="lineno"> 2313</span>  m_OverridenOutputShapes.push_back({ 1, numDetectedBox });</div><div class="line"><a name="l02314"></a><span class="lineno"> 2314</span>  m_OverridenOutputShapes.push_back({ 1 });</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>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0 ; i < outputs.size() ; ++i)</div><div class="line"><a name="l02317"></a><span class="lineno"> 2317</span>  {</div><div class="line"><a name="l02318"></a><span class="lineno"> 2318</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> detectionBoxOutputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[i], m_OverridenOutputShapes[i]);</div><div class="line"><a name="l02319"></a><span class="lineno"> 2319</span>  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#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(detectionBoxOutputTensorInfo);</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> </div><div class="line"><a name="l02322"></a><span class="lineno"> 2322</span>  <span class="comment">// Register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l02323"></a><span class="lineno"> 2323</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l02324"></a><span class="lineno"> 2324</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02325"></a><span class="lineno"> 2325</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0], inputTensorIndexes[1]});</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>  <span class="comment">// Register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l02328"></a><span class="lineno"> 2328</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02329"></a><span class="lineno"> 2329</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0],</div><div class="line"><a name="l02330"></a><span class="lineno"> 2330</span>  outputTensorIndexes[1],</div><div class="line"><a name="l02331"></a><span class="lineno"> 2331</span>  outputTensorIndexes[2],</div><div class="line"><a name="l02332"></a><span class="lineno"> 2332</span>  outputTensorIndexes[3]});</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> <span class="comment"></span></div><div class="line"><a name="l02335"></a><span class="lineno"> 2335</span> <span class="comment">/// The TfLite Pack operator is equivalent to the ArmNN Stack operator</span></div><div class="line"><a name="l02336"></a><span class="lineno"> 2336</span> <span class="comment"></span><span class="keywordtype">void</span> TfLiteParser::ParsePack(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02337"></a><span class="lineno"> 2337</span> {</div><div class="line"><a name="l02338"></a><span class="lineno"> 2338</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02339"></a><span class="lineno"> 2339</span> </div><div class="line"><a name="l02340"></a><span class="lineno"> 2340</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02341"></a><span class="lineno"> 2341</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02342"></a><span class="lineno"> 2342</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), 1);</div><div class="line"><a name="l02343"></a><span class="lineno"> 2343</span> </div><div class="line"><a name="l02344"></a><span class="lineno"> 2344</span>  <span class="keywordflow">if</span> (inputs.size() < 1)</div><div class="line"><a name="l02345"></a><span class="lineno"> 2345</span>  {</div><div class="line"><a name="l02346"></a><span class="lineno"> 2346</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">"Pack must have at least one input."</span>);</div><div class="line"><a name="l02347"></a><span class="lineno"> 2347</span>  }</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>  <span class="keyword">const</span> <span class="keyword">auto</span>& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l02350"></a><span class="lineno"> 2350</span>  <span class="keyword">const</span> <span class="keyword">auto</span>* <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr->builtin_options.AsPackOptions();</div><div class="line"><a name="l02351"></a><span class="lineno"> 2351</span> </div><div class="line"><a name="l02352"></a><span class="lineno"> 2352</span>  <a class="code" href="structarmnn_1_1_stack_descriptor.xhtml">StackDescriptor</a> desc;</div><div class="line"><a name="l02353"></a><span class="lineno"> 2353</span>  desc.<a class="code" href="structarmnn_1_1_stack_descriptor.xhtml#ab218de7805899c8412d75d1fd1d846d2">m_Axis</a> = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->axis);</div><div class="line"><a name="l02354"></a><span class="lineno"> 2354</span>  desc.<a class="code" href="structarmnn_1_1_stack_descriptor.xhtml#aed6086070440ceb94129bef06f70173f">m_NumInputs</a> = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(inputs.size());</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>  <span class="comment">// Use the tensor shape of the first input as the "correct" input shape in the descriptor</span></div><div class="line"><a name="l02357"></a><span class="lineno"> 2357</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l02358"></a><span class="lineno"> 2358</span>  desc.<a class="code" href="structarmnn_1_1_stack_descriptor.xhtml#a2bea87b470268bb0b73457c3733dbc04">m_InputShape</a> = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l02359"></a><span class="lineno"> 2359</span> </div><div class="line"><a name="l02360"></a><span class="lineno"> 2360</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"Pack:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l02361"></a><span class="lineno"> 2361</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddStackLayer(desc, layerName.c_str());</div><div class="line"><a name="l02362"></a><span class="lineno"> 2362</span> </div><div class="line"><a name="l02363"></a><span class="lineno"> 2363</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02364"></a><span class="lineno"> 2364</span> </div><div class="line"><a name="l02365"></a><span class="lineno"> 2365</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[0]);</div><div class="line"><a name="l02366"></a><span class="lineno"> 2366</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l02367"></a><span class="lineno"> 2367</span> </div><div class="line"><a name="l02368"></a><span class="lineno"> 2368</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02369"></a><span class="lineno"> 2369</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes});</div><div class="line"><a name="l02370"></a><span class="lineno"> 2370</span> </div><div class="line"><a name="l02371"></a><span class="lineno"> 2371</span>  <span class="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02372"></a><span class="lineno"> 2372</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});</div><div class="line"><a name="l02373"></a><span class="lineno"> 2373</span> }</div><div class="line"><a name="l02374"></a><span class="lineno"> 2374</span> </div><div class="line"><a name="l02375"></a><span class="lineno"> 2375</span> <span class="keywordtype">void</span> TfLiteParser::ParseUnpack(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02376"></a><span class="lineno"> 2376</span> {</div><div class="line"><a name="l02377"></a><span class="lineno"> 2377</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</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>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l02380"></a><span class="lineno"> 2380</span>  <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr->builtin_options.AsUnpackOptions();</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>  <span class="comment">// This unpackAxis indicates the axis to unpack</span></div><div class="line"><a name="l02383"></a><span class="lineno"> 2383</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> unpackAxis = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->axis);</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>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02386"></a><span class="lineno"> 2386</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 1);</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="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l02389"></a><span class="lineno"> 2389</span> </div><div class="line"><a name="l02390"></a><span class="lineno"> 2390</span>  <span class="keywordflow">if</span> (unpackAxis >= inputTensorInfo.GetNumDimensions())</div><div class="line"><a name="l02391"></a><span class="lineno"> 2391</span>  {</div><div class="line"><a name="l02392"></a><span class="lineno"> 2392</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02393"></a><span class="lineno"> 2393</span>  boost::str(</div><div class="line"><a name="l02394"></a><span class="lineno"> 2394</span>  boost::format(</div><div class="line"><a name="l02395"></a><span class="lineno"> 2395</span>  <span class="stringliteral">"The unpack axis: %1% cannot be greater than or equal to "</span></div><div class="line"><a name="l02396"></a><span class="lineno"> 2396</span>  <span class="stringliteral">"the number of input dimension %2% %3%"</span>)</div><div class="line"><a name="l02397"></a><span class="lineno"> 2397</span>  % unpackAxis</div><div class="line"><a name="l02398"></a><span class="lineno"> 2398</span>  % inputTensorInfo.GetNumDimensions()</div><div class="line"><a name="l02399"></a><span class="lineno"> 2399</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02400"></a><span class="lineno"> 2400</span>  }</div><div class="line"><a name="l02401"></a><span class="lineno"> 2401</span> </div><div class="line"><a name="l02402"></a><span class="lineno"> 2402</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> unpackNum = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->num);</div><div class="line"><a name="l02403"></a><span class="lineno"> 2403</span>  <span class="comment">// If num is not defined, automatically infer from the length of the dimension axis.</span></div><div class="line"><a name="l02404"></a><span class="lineno"> 2404</span>  <span class="keywordflow">if</span>(unpackNum == 0)</div><div class="line"><a name="l02405"></a><span class="lineno"> 2405</span>  {</div><div class="line"><a name="l02406"></a><span class="lineno"> 2406</span>  unpackNum = inputTensorInfo.GetShape()[unpackAxis];</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> </div><div class="line"><a name="l02409"></a><span class="lineno"> 2409</span>  <span class="comment">// If unpack number cannot be inferred and is still zero, throw ParseException.</span></div><div class="line"><a name="l02410"></a><span class="lineno"> 2410</span>  <span class="keywordflow">if</span>(unpackNum == 0)</div><div class="line"><a name="l02411"></a><span class="lineno"> 2411</span>  {</div><div class="line"><a name="l02412"></a><span class="lineno"> 2412</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">"Number to unpack must greater than zero."</span>);</div><div class="line"><a name="l02413"></a><span class="lineno"> 2413</span>  }</div><div class="line"><a name="l02414"></a><span class="lineno"> 2414</span> </div><div class="line"><a name="l02415"></a><span class="lineno"> 2415</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02416"></a><span class="lineno"> 2416</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), unpackNum);</div><div class="line"><a name="l02417"></a><span class="lineno"> 2417</span> </div><div class="line"><a name="l02418"></a><span class="lineno"> 2418</span>  <span class="keyword">auto</span> inputDimSize = inputTensorInfo.GetNumDimensions();</div><div class="line"><a name="l02419"></a><span class="lineno"> 2419</span>  std::vector<unsigned int> unpackDimSizes(inputDimSize);</div><div class="line"><a name="l02420"></a><span class="lineno"> 2420</span> </div><div class="line"><a name="l02421"></a><span class="lineno"> 2421</span>  <span class="comment">// Add current input shape to unpackDimSizes</span></div><div class="line"><a name="l02422"></a><span class="lineno"> 2422</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < inputDimSize; ++i)</div><div class="line"><a name="l02423"></a><span class="lineno"> 2423</span>  {</div><div class="line"><a name="l02424"></a><span class="lineno"> 2424</span>  unpackDimSizes[i] = inputTensorInfo.GetShape()[i];</div><div class="line"><a name="l02425"></a><span class="lineno"> 2425</span>  }</div><div class="line"><a name="l02426"></a><span class="lineno"> 2426</span> </div><div class="line"><a name="l02427"></a><span class="lineno"> 2427</span>  <span class="keywordflow">if</span> (unpackDimSizes[unpackAxis] != unpackNum)</div><div class="line"><a name="l02428"></a><span class="lineno"> 2428</span>  {</div><div class="line"><a name="l02429"></a><span class="lineno"> 2429</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">"Number to unpack must be the same as length of the dimension to "</span></div><div class="line"><a name="l02430"></a><span class="lineno"> 2430</span>  <span class="stringliteral">"unpack along."</span>);</div><div class="line"><a name="l02431"></a><span class="lineno"> 2431</span>  }</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>  unpackDimSizes[unpackAxis] /= unpackNum;</div><div class="line"><a name="l02434"></a><span class="lineno"> 2434</span> </div><div class="line"><a name="l02435"></a><span class="lineno"> 2435</span>  <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">SplitterDescriptor</a> splitDesc(unpackNum, static_cast<unsigned int>(unpackDimSizes.size()));</div><div class="line"><a name="l02436"></a><span class="lineno"> 2436</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> j = 0; j < unpackNum; ++j)</div><div class="line"><a name="l02437"></a><span class="lineno"> 2437</span>  {</div><div class="line"><a name="l02438"></a><span class="lineno"> 2438</span>  <span class="comment">// Set the size of the views.</span></div><div class="line"><a name="l02439"></a><span class="lineno"> 2439</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIdx = 0; dimIdx < unpackDimSizes.size(); ++dimIdx)</div><div class="line"><a name="l02440"></a><span class="lineno"> 2440</span>  {</div><div class="line"><a name="l02441"></a><span class="lineno"> 2441</span>  splitDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">SetViewSize</a>(j, dimIdx, unpackDimSizes[dimIdx]);</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>  splitDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(j, unpackAxis, unpackDimSizes[unpackAxis] * j);</div><div class="line"><a name="l02444"></a><span class="lineno"> 2444</span>  }</div><div class="line"><a name="l02445"></a><span class="lineno"> 2445</span> </div><div class="line"><a name="l02446"></a><span class="lineno"> 2446</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"Unpack:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l02447"></a><span class="lineno"> 2447</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddSplitterLayer(splitDesc, layerName.c_str());</div><div class="line"><a name="l02448"></a><span class="lineno"> 2448</span> </div><div class="line"><a name="l02449"></a><span class="lineno"> 2449</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> splitOutShape = <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(static_cast<unsigned int>(unpackDimSizes.size()),</div><div class="line"><a name="l02450"></a><span class="lineno"> 2450</span>  unpackDimSizes.data());</div><div class="line"><a name="l02451"></a><span class="lineno"> 2451</span> </div><div class="line"><a name="l02452"></a><span class="lineno"> 2452</span>  <span class="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02453"></a><span class="lineno"> 2453</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});</div><div class="line"><a name="l02454"></a><span class="lineno"> 2454</span> </div><div class="line"><a name="l02455"></a><span class="lineno"> 2455</span>  <span class="comment">// Create reshape to remove the unpacked dimension for unpack operator of each output from Splitter.</span></div><div class="line"><a name="l02456"></a><span class="lineno"> 2456</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> k = 0; k < layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>(); ++k)</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[k]);</div><div class="line"><a name="l02459"></a><span class="lineno"> 2459</span>  std::string reshapeLayerName = boost::str(boost::format(<span class="stringliteral">"Reshape_for:%1%"</span>) % layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>());</div><div class="line"><a name="l02460"></a><span class="lineno"> 2460</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">armnn::ReshapeDescriptor</a> desc;</div><div class="line"><a name="l02461"></a><span class="lineno"> 2461</span>  desc.<a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l02462"></a><span class="lineno"> 2462</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* reshapeLayer = m_Network->AddReshapeLayer(desc, layerName.c_str());</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>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(k).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(splitOutShape,</div><div class="line"><a name="l02465"></a><span class="lineno"> 2465</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>(),</div><div class="line"><a name="l02466"></a><span class="lineno"> 2466</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l02467"></a><span class="lineno"> 2467</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>()));</div><div class="line"><a name="l02468"></a><span class="lineno"> 2468</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(k).<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="l02469"></a><span class="lineno"> 2469</span> </div><div class="line"><a name="l02470"></a><span class="lineno"> 2470</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>(outputTensorInfo);</div><div class="line"><a name="l02471"></a><span class="lineno"> 2471</span> </div><div class="line"><a name="l02472"></a><span class="lineno"> 2472</span>  uint32_t reshapedOutputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(operatorPtr->outputs[k]);</div><div class="line"><a name="l02473"></a><span class="lineno"> 2473</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>* slot = &(reshapeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0));</div><div class="line"><a name="l02474"></a><span class="lineno"> 2474</span>  RegisterProducerOfTensor(subgraphIndex, reshapedOutputId, slot);</div><div class="line"><a name="l02475"></a><span class="lineno"> 2475</span>  }</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> </div><div class="line"><a name="l02478"></a><span class="lineno"> 2478</span> <span class="keywordtype">void</span> TfLiteParser::ParseSplit(<span class="keywordtype">size_t</span> subgraphIndex, <span class="keywordtype">size_t</span> operatorIndex)</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="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02481"></a><span class="lineno"> 2481</span> </div><div class="line"><a name="l02482"></a><span class="lineno"> 2482</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];</div><div class="line"><a name="l02483"></a><span class="lineno"> 2483</span>  <span class="keyword">const</span> <span class="keyword">auto</span> * <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a> = operatorPtr->builtin_options.AsSplitOptions();</div><div class="line"><a name="l02484"></a><span class="lineno"> 2484</span> </div><div class="line"><a name="l02485"></a><span class="lineno"> 2485</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numSplits = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>->num_splits);</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>  <span class="comment">// If number of splits cannot be inferred and is zero, throw ParseException.</span></div><div class="line"><a name="l02488"></a><span class="lineno"> 2488</span>  <span class="keywordflow">if</span>(numSplits == 0)</div><div class="line"><a name="l02489"></a><span class="lineno"> 2489</span>  {</div><div class="line"><a name="l02490"></a><span class="lineno"> 2490</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">"Number to splits must greater than zero."</span>);</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> </div><div class="line"><a name="l02493"></a><span class="lineno"> 2493</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">GetInputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02494"></a><span class="lineno"> 2494</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(inputs.size(), 2);</div><div class="line"><a name="l02495"></a><span class="lineno"> 2495</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">GetOutputs</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02496"></a><span class="lineno"> 2496</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(outputs.size(), numSplits);</div><div class="line"><a name="l02497"></a><span class="lineno"> 2497</span> </div><div class="line"><a name="l02498"></a><span class="lineno"> 2498</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[1]);</div><div class="line"><a name="l02499"></a><span class="lineno"> 2499</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> axisTensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(inputs[0]);</div><div class="line"><a name="l02500"></a><span class="lineno"> 2500</span> </div><div class="line"><a name="l02501"></a><span class="lineno"> 2501</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">BufferRawPtr</a> axisBufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, inputs[0]->buffer);</div><div class="line"><a name="l02502"></a><span class="lineno"> 2502</span>  std::vector<unsigned int> axisData(axisTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l02503"></a><span class="lineno"> 2503</span>  ::memcpy(axisData.data(), axisBufferPtr->data.data(), axisTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l02504"></a><span class="lineno"> 2504</span> </div><div class="line"><a name="l02505"></a><span class="lineno"> 2505</span>  BOOST_ASSERT(axisTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() == 1);</div><div class="line"><a name="l02506"></a><span class="lineno"> 2506</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitDim = axisData[0];</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">auto</span> inputDimSize = inputTensorInfo.GetNumDimensions();</div><div class="line"><a name="l02509"></a><span class="lineno"> 2509</span>  <span class="keywordflow">if</span> (inputDimSize > <a class="code" href="namespacearmnn.xhtml#abdcd184ed3bd648bb31d385040cafd5d">MaxNumOfTensorDimensions</a>)</div><div class="line"><a name="l02510"></a><span class="lineno"> 2510</span>  {</div><div class="line"><a name="l02511"></a><span class="lineno"> 2511</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02512"></a><span class="lineno"> 2512</span>  boost::str(</div><div class="line"><a name="l02513"></a><span class="lineno"> 2513</span>  boost::format(</div><div class="line"><a name="l02514"></a><span class="lineno"> 2514</span>  <span class="stringliteral">"The number of dimensions: %1% for input tensors of the "</span></div><div class="line"><a name="l02515"></a><span class="lineno"> 2515</span>  <span class="stringliteral">"split op cannot be greater than %2% %3%"</span>)</div><div class="line"><a name="l02516"></a><span class="lineno"> 2516</span>  % inputTensorInfo.GetNumDimensions()</div><div class="line"><a name="l02517"></a><span class="lineno"> 2517</span>  % <a class="code" href="namespacearmnn.xhtml#abdcd184ed3bd648bb31d385040cafd5d">MaxNumOfTensorDimensions</a></div><div class="line"><a name="l02518"></a><span class="lineno"> 2518</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02519"></a><span class="lineno"> 2519</span>  }</div><div class="line"><a name="l02520"></a><span class="lineno"> 2520</span> </div><div class="line"><a name="l02521"></a><span class="lineno"> 2521</span>  std::vector<unsigned int> splitterDimSizes(inputDimSize);</div><div class="line"><a name="l02522"></a><span class="lineno"> 2522</span> </div><div class="line"><a name="l02523"></a><span class="lineno"> 2523</span>  <span class="comment">// Add current input shape to splitterDimSizes</span></div><div class="line"><a name="l02524"></a><span class="lineno"> 2524</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < inputDimSize; ++i)</div><div class="line"><a name="l02525"></a><span class="lineno"> 2525</span>  {</div><div class="line"><a name="l02526"></a><span class="lineno"> 2526</span>  splitterDimSizes[i] = inputTensorInfo.GetShape()[i];</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> </div><div class="line"><a name="l02529"></a><span class="lineno"> 2529</span>  <span class="keywordflow">if</span> (splitterDimSizes[splitDim] % numSplits != 0)</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">"Number of splits must evenly divide the dimension"</span>);</div><div class="line"><a name="l02532"></a><span class="lineno"> 2532</span>  }</div><div class="line"><a name="l02533"></a><span class="lineno"> 2533</span>  splitterDimSizes[splitDim] /= numSplits;</div><div class="line"><a name="l02534"></a><span class="lineno"> 2534</span> </div><div class="line"><a name="l02535"></a><span class="lineno"> 2535</span>  <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">SplitterDescriptor</a> splitDesc(numSplits, inputDimSize);</div><div class="line"><a name="l02536"></a><span class="lineno"> 2536</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> j = 0; j < numSplits; ++j)</div><div class="line"><a name="l02537"></a><span class="lineno"> 2537</span>  {</div><div class="line"><a name="l02538"></a><span class="lineno"> 2538</span>  <span class="comment">// Set the size of the views.</span></div><div class="line"><a name="l02539"></a><span class="lineno"> 2539</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIdx = 0; dimIdx < splitterDimSizes.size(); ++dimIdx)</div><div class="line"><a name="l02540"></a><span class="lineno"> 2540</span>  {</div><div class="line"><a name="l02541"></a><span class="lineno"> 2541</span>  splitDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">SetViewSize</a>(j, dimIdx, splitterDimSizes[dimIdx]);</div><div class="line"><a name="l02542"></a><span class="lineno"> 2542</span>  }</div><div class="line"><a name="l02543"></a><span class="lineno"> 2543</span>  splitDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(j, splitDim, splitterDimSizes[splitDim] * j);</div><div class="line"><a name="l02544"></a><span class="lineno"> 2544</span>  }</div><div class="line"><a name="l02545"></a><span class="lineno"> 2545</span> </div><div class="line"><a name="l02546"></a><span class="lineno"> 2546</span>  <span class="keyword">auto</span> layerName = boost::str(boost::format(<span class="stringliteral">"Split:%1%:%2%"</span>) % subgraphIndex % operatorIndex);</div><div class="line"><a name="l02547"></a><span class="lineno"> 2547</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddSplitterLayer(splitDesc, layerName.c_str());</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="keyword">auto</span> inputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">GetInputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02550"></a><span class="lineno"> 2550</span>  RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[1]});</div><div class="line"><a name="l02551"></a><span class="lineno"> 2551</span> </div><div class="line"><a name="l02552"></a><span class="lineno"> 2552</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> k = 0; k < layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>(); ++k)</div><div class="line"><a name="l02553"></a><span class="lineno"> 2553</span>  {</div><div class="line"><a name="l02554"></a><span class="lineno"> 2554</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(outputs[k]);</div><div class="line"><a name="l02555"></a><span class="lineno"> 2555</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(k).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l02556"></a><span class="lineno"> 2556</span>  }</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="keyword">auto</span> outputTensorIndexes = AsUnsignedVector(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">GetOutputTensorIds</a>(m_Model, subgraphIndex, operatorIndex));</div><div class="line"><a name="l02559"></a><span class="lineno"> 2559</span>  RegisterOutputSlots(subgraphIndex, operatorIndex, layer, outputTensorIndexes);</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> </div><div class="line"><a name="l02562"></a><span class="lineno"> 2562</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* TfLiteParser::AddFusedActivationLayer(<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* prevLayer,</div><div class="line"><a name="l02563"></a><span class="lineno"> 2563</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSlot,</div><div class="line"><a name="l02564"></a><span class="lineno"> 2564</span>  tflite::ActivationFunctionType activationType)</div><div class="line"><a name="l02565"></a><span class="lineno"> 2565</span> {</div><div class="line"><a name="l02566"></a><span class="lineno"> 2566</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDesc;</div><div class="line"><a name="l02567"></a><span class="lineno"> 2567</span>  std::string layerName = prevLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>();</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>  <span class="keywordflow">switch</span>(activationType)</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="keywordflow">case</span> tflite::ActivationFunctionType_NONE:</div><div class="line"><a name="l02572"></a><span class="lineno"> 2572</span>  {</div><div class="line"><a name="l02573"></a><span class="lineno"> 2573</span>  <span class="comment">// this is a no-op: return previous layer</span></div><div class="line"><a name="l02574"></a><span class="lineno"> 2574</span>  <span class="keywordflow">return</span> prevLayer;</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="keywordflow">case</span> tflite::ActivationFunctionType_RELU:</div><div class="line"><a name="l02577"></a><span class="lineno"> 2577</span>  {</div><div class="line"><a name="l02578"></a><span class="lineno"> 2578</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::ReLu;</div><div class="line"><a name="l02579"></a><span class="lineno"> 2579</span>  layerName += <span class="stringliteral">":RELU"</span>;</div><div class="line"><a name="l02580"></a><span class="lineno"> 2580</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02581"></a><span class="lineno"> 2581</span>  }</div><div class="line"><a name="l02582"></a><span class="lineno"> 2582</span>  <span class="keywordflow">case</span> tflite::ActivationFunctionType_RELU6:</div><div class="line"><a name="l02583"></a><span class="lineno"> 2583</span>  {</div><div class="line"><a name="l02584"></a><span class="lineno"> 2584</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::BoundedReLu;</div><div class="line"><a name="l02585"></a><span class="lineno"> 2585</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 6.0f;</div><div class="line"><a name="l02586"></a><span class="lineno"> 2586</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = 0.0f;</div><div class="line"><a name="l02587"></a><span class="lineno"> 2587</span>  layerName += <span class="stringliteral">":RELU6"</span>;</div><div class="line"><a name="l02588"></a><span class="lineno"> 2588</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02589"></a><span class="lineno"> 2589</span>  }</div><div class="line"><a name="l02590"></a><span class="lineno"> 2590</span>  <span class="keywordflow">case</span> tflite::ActivationFunctionType_TANH:</div><div class="line"><a name="l02591"></a><span class="lineno"> 2591</span>  {</div><div class="line"><a name="l02592"></a><span class="lineno"> 2592</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::TanH;</div><div class="line"><a name="l02593"></a><span class="lineno"> 2593</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l02594"></a><span class="lineno"> 2594</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = 1.0f;</div><div class="line"><a name="l02595"></a><span class="lineno"> 2595</span>  layerName += <span class="stringliteral">":TANH"</span>;</div><div class="line"><a name="l02596"></a><span class="lineno"> 2596</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02597"></a><span class="lineno"> 2597</span>  }</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>  <span class="comment">// I only put these here as a reminder what others we could support</span></div><div class="line"><a name="l02600"></a><span class="lineno"> 2600</span>  <span class="keywordflow">case</span> tflite::ActivationFunctionType_RELU_N1_TO_1:</div><div class="line"><a name="l02601"></a><span class="lineno"> 2601</span>  <span class="keywordflow">case</span> tflite::ActivationFunctionType_SIGN_BIT:</div><div class="line"><a name="l02602"></a><span class="lineno"> 2602</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l02603"></a><span class="lineno"> 2603</span>  {</div><div class="line"><a name="l02604"></a><span class="lineno"> 2604</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02605"></a><span class="lineno"> 2605</span>  boost::str(</div><div class="line"><a name="l02606"></a><span class="lineno"> 2606</span>  boost::format(<span class="stringliteral">"TfLite parser doesn't suppport fused activation: "</span></div><div class="line"><a name="l02607"></a><span class="lineno"> 2607</span>  <span class="stringliteral">"%1%/%2% %3% "</span>) %</div><div class="line"><a name="l02608"></a><span class="lineno"> 2608</span>  activationType %</div><div class="line"><a name="l02609"></a><span class="lineno"> 2609</span>  tflite::EnumNameActivationFunctionType(activationType) %</div><div class="line"><a name="l02610"></a><span class="lineno"> 2610</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02611"></a><span class="lineno"> 2611</span> </div><div class="line"><a name="l02612"></a><span class="lineno"> 2612</span>  }</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> </div><div class="line"><a name="l02615"></a><span class="lineno"> 2615</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* activationLayer =</div><div class="line"><a name="l02616"></a><span class="lineno"> 2616</span>  m_Network->AddActivationLayer(activationDesc, layerName.c_str());</div><div class="line"><a name="l02617"></a><span class="lineno"> 2617</span> </div><div class="line"><a name="l02618"></a><span class="lineno"> 2618</span>  <span class="keyword">auto</span> & prevOutputSlot = prevLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(outputSlot);</div><div class="line"><a name="l02619"></a><span class="lineno"> 2619</span>  prevOutputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(activationLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02620"></a><span class="lineno"> 2620</span>  activationLayer-><a class="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>(prevOutputSlot.GetTensorInfo());</div><div class="line"><a name="l02621"></a><span class="lineno"> 2621</span>  <span class="keywordflow">return</span> activationLayer;</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> </div><div class="line"><a name="l02624"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ac5411554ab8c02ca286af52c98f6bd87"> 2624</a></span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abfc86471394295357a23b3addd0b5b1c">TfLiteParser::ModelPtr</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ac5411554ab8c02ca286af52c98f6bd87">TfLiteParser::LoadModelFromFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span> * fileName)</div><div class="line"><a name="l02625"></a><span class="lineno"> 2625</span> {</div><div class="line"><a name="l02626"></a><span class="lineno"> 2626</span>  <span class="keywordflow">if</span> (fileName == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l02627"></a><span class="lineno"> 2627</span>  {</div><div class="line"><a name="l02628"></a><span class="lineno"> 2628</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(boost::str(boost::format(<span class="stringliteral">"Invalid (null) file name %1%"</span>) %</div><div class="line"><a name="l02629"></a><span class="lineno"> 2629</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02630"></a><span class="lineno"> 2630</span>  }</div><div class="line"><a name="l02631"></a><span class="lineno"> 2631</span>  boost::system::error_code errorCode;</div><div class="line"><a name="l02632"></a><span class="lineno"> 2632</span>  boost::filesystem::path pathToFile(fileName);</div><div class="line"><a name="l02633"></a><span class="lineno"> 2633</span>  <span class="keywordflow">if</span> (!boost::filesystem::exists(pathToFile, errorCode))</div><div class="line"><a name="l02634"></a><span class="lineno"> 2634</span>  {</div><div class="line"><a name="l02635"></a><span class="lineno"> 2635</span>  std::string locationString = <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString();</div><div class="line"><a name="l02636"></a><span class="lineno"> 2636</span>  std::string msg = boost::str(boost::format(<span class="stringliteral">"Cannot find the file (%1%) errorCode: %2% %3%"</span>) %</div><div class="line"><a name="l02637"></a><span class="lineno"> 2637</span>  fileName %</div><div class="line"><a name="l02638"></a><span class="lineno"> 2638</span>  errorCode %</div><div class="line"><a name="l02639"></a><span class="lineno"> 2639</span>  locationString);</div><div class="line"><a name="l02640"></a><span class="lineno"> 2640</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_file_not_found_exception.xhtml">FileNotFoundException</a>(msg);</div><div class="line"><a name="l02641"></a><span class="lineno"> 2641</span>  }</div><div class="line"><a name="l02642"></a><span class="lineno"> 2642</span>  std::ifstream file(fileName, std::ios::binary);</div><div class="line"><a name="l02643"></a><span class="lineno"> 2643</span>  std::string fileContent((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());</div><div class="line"><a name="l02644"></a><span class="lineno"> 2644</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a858104f225c302988fba35c1cb299066">LoadModelFromBinary</a>(reinterpret_cast<const uint8_t *>(fileContent.c_str()),</div><div class="line"><a name="l02645"></a><span class="lineno"> 2645</span>  fileContent.size());</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> </div><div class="line"><a name="l02648"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a858104f225c302988fba35c1cb299066"> 2648</a></span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abfc86471394295357a23b3addd0b5b1c">TfLiteParser::ModelPtr</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a858104f225c302988fba35c1cb299066">TfLiteParser::LoadModelFromBinary</a>(<span class="keyword">const</span> uint8_t * binaryContent, <span class="keywordtype">size_t</span> len)</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>  <span class="keywordflow">if</span> (binaryContent == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l02651"></a><span class="lineno"> 2651</span>  {</div><div class="line"><a name="l02652"></a><span class="lineno"> 2652</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(boost::str(boost::format(<span class="stringliteral">"Invalid (null) binary content %1%"</span>) %</div><div class="line"><a name="l02653"></a><span class="lineno"> 2653</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02654"></a><span class="lineno"> 2654</span>  }</div><div class="line"><a name="l02655"></a><span class="lineno"> 2655</span>  flatbuffers::Verifier verifier(binaryContent, len);</div><div class="line"><a name="l02656"></a><span class="lineno"> 2656</span>  <span class="keywordflow">if</span> (verifier.VerifyBuffer<tflite::Model>() == <span class="keyword">false</span>)</div><div class="line"><a name="l02657"></a><span class="lineno"> 2657</span>  {</div><div class="line"><a name="l02658"></a><span class="lineno"> 2658</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02659"></a><span class="lineno"> 2659</span>  boost::str(boost::format(<span class="stringliteral">"Buffer doesn't conform to the expected Tensorflow Lite "</span></div><div class="line"><a name="l02660"></a><span class="lineno"> 2660</span>  <span class="stringliteral">"flatbuffers format. size:%1% %2%"</span>) %</div><div class="line"><a name="l02661"></a><span class="lineno"> 2661</span>  len %</div><div class="line"><a name="l02662"></a><span class="lineno"> 2662</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02663"></a><span class="lineno"> 2663</span>  }</div><div class="line"><a name="l02664"></a><span class="lineno"> 2664</span>  <span class="keywordflow">return</span> tflite::UnPackModel(binaryContent);</div><div class="line"><a name="l02665"></a><span class="lineno"> 2665</span> }</div><div class="line"><a name="l02666"></a><span class="lineno"> 2666</span> </div><div class="line"><a name="l02667"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876"> 2667</a></span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TfLiteParser::TensorRawPtrVector</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">TfLiteParser::GetInputs</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a> & model,</div><div class="line"><a name="l02668"></a><span class="lineno"> 2668</span>  <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l02669"></a><span class="lineno"> 2669</span>  <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02670"></a><span class="lineno"> 2670</span> {</div><div class="line"><a name="l02671"></a><span class="lineno"> 2671</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(model, subgraphIndex, operatorIndex);</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>  <span class="keyword">const</span> <span class="keyword">auto</span> & subgraphPtr = model->subgraphs[subgraphIndex];</div><div class="line"><a name="l02674"></a><span class="lineno"> 2674</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = subgraphPtr->operators[operatorIndex];</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="keywordtype">size_t</span> inputCount = operatorPtr->inputs.size();</div><div class="line"><a name="l02677"></a><span class="lineno"> 2677</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TensorRawPtrVector</a> result(inputCount);</div><div class="line"><a name="l02678"></a><span class="lineno"> 2678</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i=0; i<inputCount; ++i)</div><div class="line"><a name="l02679"></a><span class="lineno"> 2679</span>  {</div><div class="line"><a name="l02680"></a><span class="lineno"> 2680</span>  uint32_t inputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(operatorPtr->inputs[i]);</div><div class="line"><a name="l02681"></a><span class="lineno"> 2681</span>  result[i] = subgraphPtr->tensors[inputId].get();</div><div class="line"><a name="l02682"></a><span class="lineno"> 2682</span>  }</div><div class="line"><a name="l02683"></a><span class="lineno"> 2683</span>  <span class="keywordflow">return</span> result;</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> </div><div class="line"><a name="l02686"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8"> 2686</a></span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TfLiteParser::TensorRawPtrVector</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">TfLiteParser::GetOutputs</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a> & model,</div><div class="line"><a name="l02687"></a><span class="lineno"> 2687</span>  <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l02688"></a><span class="lineno"> 2688</span>  <span class="keywordtype">size_t</span> operatorIndex)</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>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02691"></a><span class="lineno"> 2691</span> </div><div class="line"><a name="l02692"></a><span class="lineno"> 2692</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & subgraphPtr = model->subgraphs[subgraphIndex];</div><div class="line"><a name="l02693"></a><span class="lineno"> 2693</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = subgraphPtr->operators[operatorIndex];</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="keywordtype">size_t</span> outputCount = operatorPtr->outputs.size();</div><div class="line"><a name="l02696"></a><span class="lineno"> 2696</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abd8bee7fb9b86485a60bc7ee05114270">TensorRawPtrVector</a> result(outputCount);</div><div class="line"><a name="l02697"></a><span class="lineno"> 2697</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i=0; i<outputCount; ++i)</div><div class="line"><a name="l02698"></a><span class="lineno"> 2698</span>  {</div><div class="line"><a name="l02699"></a><span class="lineno"> 2699</span>  uint32_t outputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(operatorPtr->outputs[i]);</div><div class="line"><a name="l02700"></a><span class="lineno"> 2700</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#aa1664dc13adbc85ac12fb584b76bfdae">CHECK_TENSOR</a>(model, subgraphIndex, outputId);</div><div class="line"><a name="l02701"></a><span class="lineno"> 2701</span>  result[i] = subgraphPtr->tensors[outputId].get();</div><div class="line"><a name="l02702"></a><span class="lineno"> 2702</span>  }</div><div class="line"><a name="l02703"></a><span class="lineno"> 2703</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l02704"></a><span class="lineno"> 2704</span> }</div><div class="line"><a name="l02705"></a><span class="lineno"> 2705</span> </div><div class="line"><a name="l02706"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abda91b07a94d0f498b76655e03647d9a"> 2706</a></span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a86428e0c674542488c7292dfbe2ce381">TfLiteParser::TensorIdRawPtrVector</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abda91b07a94d0f498b76655e03647d9a">TfLiteParser::GetSubgraphInputs</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a> & model,</div><div class="line"><a name="l02707"></a><span class="lineno"> 2707</span>  <span class="keywordtype">size_t</span> subgraphIndex)</div><div class="line"><a name="l02708"></a><span class="lineno"> 2708</span> {</div><div class="line"><a name="l02709"></a><span class="lineno"> 2709</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(model, subgraphIndex);</div><div class="line"><a name="l02710"></a><span class="lineno"> 2710</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & subgraphPtr = model->subgraphs[subgraphIndex];</div><div class="line"><a name="l02711"></a><span class="lineno"> 2711</span> </div><div class="line"><a name="l02712"></a><span class="lineno"> 2712</span>  <span class="keywordtype">size_t</span> inputCount = subgraphPtr->inputs.size();</div><div class="line"><a name="l02713"></a><span class="lineno"> 2713</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a86428e0c674542488c7292dfbe2ce381">TensorIdRawPtrVector</a> result(inputCount);</div><div class="line"><a name="l02714"></a><span class="lineno"> 2714</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i=0; i<inputCount; ++i)</div><div class="line"><a name="l02715"></a><span class="lineno"> 2715</span>  {</div><div class="line"><a name="l02716"></a><span class="lineno"> 2716</span>  uint32_t inputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(subgraphPtr->inputs[i]);</div><div class="line"><a name="l02717"></a><span class="lineno"> 2717</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#aa1664dc13adbc85ac12fb584b76bfdae">CHECK_TENSOR</a>(model, subgraphIndex, inputId);</div><div class="line"><a name="l02718"></a><span class="lineno"> 2718</span>  result[i] = std::make_pair(inputId, subgraphPtr->tensors[inputId].get());</div><div class="line"><a name="l02719"></a><span class="lineno"> 2719</span>  }</div><div class="line"><a name="l02720"></a><span class="lineno"> 2720</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l02721"></a><span class="lineno"> 2721</span> }</div><div class="line"><a name="l02722"></a><span class="lineno"> 2722</span> </div><div class="line"><a name="l02723"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afba7f99227765786c4ed9cb2acc09739"> 2723</a></span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a86428e0c674542488c7292dfbe2ce381">TfLiteParser::TensorIdRawPtrVector</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afba7f99227765786c4ed9cb2acc09739">TfLiteParser::GetSubgraphOutputs</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a> & model,</div><div class="line"><a name="l02724"></a><span class="lineno"> 2724</span>  <span class="keywordtype">size_t</span> subgraphIndex)</div><div class="line"><a name="l02725"></a><span class="lineno"> 2725</span> {</div><div class="line"><a name="l02726"></a><span class="lineno"> 2726</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(model, subgraphIndex);</div><div class="line"><a name="l02727"></a><span class="lineno"> 2727</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & subgraphPtr = model->subgraphs[subgraphIndex];</div><div class="line"><a name="l02728"></a><span class="lineno"> 2728</span> </div><div class="line"><a name="l02729"></a><span class="lineno"> 2729</span>  <span class="keywordtype">size_t</span> outputCount = subgraphPtr->outputs.size();</div><div class="line"><a name="l02730"></a><span class="lineno"> 2730</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a86428e0c674542488c7292dfbe2ce381">TensorIdRawPtrVector</a> result(outputCount);</div><div class="line"><a name="l02731"></a><span class="lineno"> 2731</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i=0; i<outputCount; ++i)</div><div class="line"><a name="l02732"></a><span class="lineno"> 2732</span>  {</div><div class="line"><a name="l02733"></a><span class="lineno"> 2733</span>  uint32_t outputId = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(subgraphPtr->outputs[i]);</div><div class="line"><a name="l02734"></a><span class="lineno"> 2734</span>  result[i] = std::make_pair(outputId, subgraphPtr->tensors[outputId].get());</div><div class="line"><a name="l02735"></a><span class="lineno"> 2735</span>  }</div><div class="line"><a name="l02736"></a><span class="lineno"> 2736</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l02737"></a><span class="lineno"> 2737</span> }</div><div class="line"><a name="l02738"></a><span class="lineno"> 2738</span> </div><div class="line"><a name="l02739"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575"> 2739</a></span> std::vector<int32_t>& <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">TfLiteParser::GetInputTensorIds</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a>& model,</div><div class="line"><a name="l02740"></a><span class="lineno"> 2740</span>  <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l02741"></a><span class="lineno"> 2741</span>  <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02742"></a><span class="lineno"> 2742</span> {</div><div class="line"><a name="l02743"></a><span class="lineno"> 2743</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02744"></a><span class="lineno"> 2744</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & subgraphPtr = model->subgraphs[subgraphIndex];</div><div class="line"><a name="l02745"></a><span class="lineno"> 2745</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = subgraphPtr->operators[operatorIndex];</div><div class="line"><a name="l02746"></a><span class="lineno"> 2746</span>  <span class="keywordflow">return</span> operatorPtr->inputs;</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> </div><div class="line"><a name="l02749"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f"> 2749</a></span> std::vector<int32_t>& <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">TfLiteParser::GetOutputTensorIds</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a>& model,</div><div class="line"><a name="l02750"></a><span class="lineno"> 2750</span>  <span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l02751"></a><span class="lineno"> 2751</span>  <span class="keywordtype">size_t</span> operatorIndex)</div><div class="line"><a name="l02752"></a><span class="lineno"> 2752</span> {</div><div class="line"><a name="l02753"></a><span class="lineno"> 2753</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02754"></a><span class="lineno"> 2754</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & subgraphPtr = model->subgraphs[subgraphIndex];</div><div class="line"><a name="l02755"></a><span class="lineno"> 2755</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & operatorPtr = subgraphPtr->operators[operatorIndex];</div><div class="line"><a name="l02756"></a><span class="lineno"> 2756</span>  <span class="keywordflow">return</span> operatorPtr->outputs;</div><div class="line"><a name="l02757"></a><span class="lineno"> 2757</span> }</div><div class="line"><a name="l02758"></a><span class="lineno"> 2758</span> </div><div class="line"><a name="l02759"></a><span class="lineno"> 2759</span> <span class="keywordtype">void</span> TfLiteParser::RegisterInputSlots(<span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l02760"></a><span class="lineno"> 2760</span>  <span class="keywordtype">size_t</span> operatorIndex,</div><div class="line"><a name="l02761"></a><span class="lineno"> 2761</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer,</div><div class="line"><a name="l02762"></a><span class="lineno"> 2762</span>  <span class="keyword">const</span> std::vector<unsigned int>& tensorIndexes)</div><div class="line"><a name="l02763"></a><span class="lineno"> 2763</span> {</div><div class="line"><a name="l02764"></a><span class="lineno"> 2764</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02765"></a><span class="lineno"> 2765</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02766"></a><span class="lineno"> 2766</span>  <span class="keywordflow">if</span> (tensorIndexes.size() != layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">GetNumInputSlots</a>())</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02769"></a><span class="lineno"> 2769</span>  boost::str(boost::format(<span class="stringliteral">"The number of tensor inputs (%1%) does not match the number expected (%2%)"</span></div><div class="line"><a name="l02770"></a><span class="lineno"> 2770</span>  <span class="stringliteral">" for subgraph:%3% operator index:%4% %5%"</span>) %</div><div class="line"><a name="l02771"></a><span class="lineno"> 2771</span>  tensorIndexes.size() %</div><div class="line"><a name="l02772"></a><span class="lineno"> 2772</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">GetNumInputSlots</a>() %</div><div class="line"><a name="l02773"></a><span class="lineno"> 2773</span>  subgraphIndex %</div><div class="line"><a name="l02774"></a><span class="lineno"> 2774</span>  operatorIndex %</div><div class="line"><a name="l02775"></a><span class="lineno"> 2775</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02776"></a><span class="lineno"> 2776</span>  }</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>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> slotIndex = 0; slotIndex < layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">GetNumInputSlots</a>(); ++slotIndex)</div><div class="line"><a name="l02779"></a><span class="lineno"> 2779</span>  {</div><div class="line"><a name="l02780"></a><span class="lineno"> 2780</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tensorIndex = tensorIndexes[slotIndex];</div><div class="line"><a name="l02781"></a><span class="lineno"> 2781</span>  <a class="code" href="classarmnn_1_1_i_input_slot.xhtml">armnn::IInputSlot</a>* slot = &(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(slotIndex));</div><div class="line"><a name="l02782"></a><span class="lineno"> 2782</span>  RegisterConsumerOfTensor(subgraphIndex, tensorIndex, slot);</div><div class="line"><a name="l02783"></a><span class="lineno"> 2783</span>  }</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> </div><div class="line"><a name="l02786"></a><span class="lineno"> 2786</span> <span class="keywordtype">void</span> TfLiteParser::RegisterOutputSlots(<span class="keywordtype">size_t</span> subgraphIndex,</div><div class="line"><a name="l02787"></a><span class="lineno"> 2787</span>  <span class="keywordtype">size_t</span> operatorIndex,</div><div class="line"><a name="l02788"></a><span class="lineno"> 2788</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer,</div><div class="line"><a name="l02789"></a><span class="lineno"> 2789</span>  <span class="keyword">const</span> std::vector<unsigned int>& tensorIndexes)</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>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a>(m_Model, subgraphIndex, operatorIndex);</div><div class="line"><a name="l02792"></a><span class="lineno"> 2792</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02793"></a><span class="lineno"> 2793</span>  <span class="keywordflow">if</span> (tensorIndexes.size() != layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>())</div><div class="line"><a name="l02794"></a><span class="lineno"> 2794</span>  {</div><div class="line"><a name="l02795"></a><span class="lineno"> 2795</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02796"></a><span class="lineno"> 2796</span>  boost::str(boost::format(<span class="stringliteral">"The number of tensor outputs (%1%) does not match the number expected (%2%)"</span></div><div class="line"><a name="l02797"></a><span class="lineno"> 2797</span>  <span class="stringliteral">" for subgraph:%3% operator index:%4% %5%"</span>) %</div><div class="line"><a name="l02798"></a><span class="lineno"> 2798</span>  tensorIndexes.size() %</div><div class="line"><a name="l02799"></a><span class="lineno"> 2799</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>() %</div><div class="line"><a name="l02800"></a><span class="lineno"> 2800</span>  subgraphIndex %</div><div class="line"><a name="l02801"></a><span class="lineno"> 2801</span>  operatorIndex %</div><div class="line"><a name="l02802"></a><span class="lineno"> 2802</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02803"></a><span class="lineno"> 2803</span>  }</div><div class="line"><a name="l02804"></a><span class="lineno"> 2804</span> </div><div class="line"><a name="l02805"></a><span class="lineno"> 2805</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> slotIndex = 0; slotIndex < layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>(); ++slotIndex)</div><div class="line"><a name="l02806"></a><span class="lineno"> 2806</span>  {</div><div class="line"><a name="l02807"></a><span class="lineno"> 2807</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tensorIndex = tensorIndexes[slotIndex];</div><div class="line"><a name="l02808"></a><span class="lineno"> 2808</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>* slot = &(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(slotIndex));</div><div class="line"><a name="l02809"></a><span class="lineno"> 2809</span>  RegisterProducerOfTensor(subgraphIndex, tensorIndex, slot);</div><div class="line"><a name="l02810"></a><span class="lineno"> 2810</span>  }</div><div class="line"><a name="l02811"></a><span class="lineno"> 2811</span> }</div><div class="line"><a name="l02812"></a><span class="lineno"> 2812</span> </div><div class="line"><a name="l02813"></a><span class="lineno"> 2813</span> <span class="keywordtype">void</span> TfLiteParser::SetupInputLayers(<span class="keywordtype">size_t</span> subgraphIndex)</div><div class="line"><a name="l02814"></a><span class="lineno"> 2814</span> {</div><div class="line"><a name="l02815"></a><span class="lineno"> 2815</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphIndex);</div><div class="line"><a name="l02816"></a><span class="lineno"> 2816</span> </div><div class="line"><a name="l02817"></a><span class="lineno"> 2817</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abda91b07a94d0f498b76655e03647d9a">GetSubgraphInputs</a>(m_Model, subgraphIndex);</div><div class="line"><a name="l02818"></a><span class="lineno"> 2818</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span> & tensorIdAndPtr : inputs)</div><div class="line"><a name="l02819"></a><span class="lineno"> 2819</span>  {</div><div class="line"><a name="l02820"></a><span class="lineno"> 2820</span>  <span class="keyword">auto</span> bindingId = GenerateLayerBindingId(subgraphIndex, tensorIdAndPtr.first);</div><div class="line"><a name="l02821"></a><span class="lineno"> 2821</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer =</div><div class="line"><a name="l02822"></a><span class="lineno"> 2822</span>  m_Network->AddInputLayer(bindingId, tensorIdAndPtr.second->name.c_str());</div><div class="line"><a name="l02823"></a><span class="lineno"> 2823</span> </div><div class="line"><a name="l02824"></a><span class="lineno"> 2824</span>  <span class="keyword">auto</span> tensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(tensorIdAndPtr.second);</div><div class="line"><a name="l02825"></a><span class="lineno"> 2825</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</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>  RegisterOutputSlots(subgraphIndex,</div><div class="line"><a name="l02828"></a><span class="lineno"> 2828</span>  VIRTUAL_OPERATOR_ID,</div><div class="line"><a name="l02829"></a><span class="lineno"> 2829</span>  layer,</div><div class="line"><a name="l02830"></a><span class="lineno"> 2830</span>  { <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(tensorIdAndPtr.first) });</div><div class="line"><a name="l02831"></a><span class="lineno"> 2831</span>  }</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> </div><div class="line"><a name="l02834"></a><span class="lineno"> 2834</span> <span class="keywordtype">void</span> TfLiteParser::SetupOutputLayers(<span class="keywordtype">size_t</span> subgraphIndex)</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>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphIndex);</div><div class="line"><a name="l02837"></a><span class="lineno"> 2837</span> </div><div class="line"><a name="l02838"></a><span class="lineno"> 2838</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afba7f99227765786c4ed9cb2acc09739">GetSubgraphOutputs</a>(m_Model, subgraphIndex);</div><div class="line"><a name="l02839"></a><span class="lineno"> 2839</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span> & tensorIdAndPtr : outputs)</div><div class="line"><a name="l02840"></a><span class="lineno"> 2840</span>  {</div><div class="line"><a name="l02841"></a><span class="lineno"> 2841</span>  <span class="keyword">auto</span> bindingId = GenerateLayerBindingId(subgraphIndex, tensorIdAndPtr.first);</div><div class="line"><a name="l02842"></a><span class="lineno"> 2842</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer =</div><div class="line"><a name="l02843"></a><span class="lineno"> 2843</span>  m_Network->AddOutputLayer(bindingId, tensorIdAndPtr.second->name.c_str());</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>  RegisterInputSlots(subgraphIndex,</div><div class="line"><a name="l02846"></a><span class="lineno"> 2846</span>  VIRTUAL_OPERATOR_ID,</div><div class="line"><a name="l02847"></a><span class="lineno"> 2847</span>  layer,</div><div class="line"><a name="l02848"></a><span class="lineno"> 2848</span>  { <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(tensorIdAndPtr.first) });</div><div class="line"><a name="l02849"></a><span class="lineno"> 2849</span>  }</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> </div><div class="line"><a name="l02852"></a><span class="lineno"> 2852</span> <span class="keywordtype">void</span> TfLiteParser::SetupConstantLayers(<span class="keywordtype">size_t</span> subgraphIndex)</div><div class="line"><a name="l02853"></a><span class="lineno"> 2853</span> {</div><div class="line"><a name="l02854"></a><span class="lineno"> 2854</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphIndex);</div><div class="line"><a name="l02855"></a><span class="lineno"> 2855</span> </div><div class="line"><a name="l02856"></a><span class="lineno"> 2856</span>  <span class="keyword">const</span> <span class="keyword">auto</span> & subgraphPtr = m_Model->subgraphs[subgraphIndex];</div><div class="line"><a name="l02857"></a><span class="lineno"> 2857</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> subgraphIndex = 0; subgraphIndex < m_SubgraphConnections.size(); ++subgraphIndex)</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>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tensorIndex = 0; tensorIndex < m_SubgraphConnections[subgraphIndex].size(); ++tensorIndex)</div><div class="line"><a name="l02860"></a><span class="lineno"> 2860</span>  {</div><div class="line"><a name="l02861"></a><span class="lineno"> 2861</span>  <span class="keywordflow">if</span> (m_SubgraphConnections[subgraphIndex][tensorIndex].outputSlot == <span class="keyword">nullptr</span> &&</div><div class="line"><a name="l02862"></a><span class="lineno"> 2862</span>  m_SubgraphConnections[subgraphIndex][tensorIndex].inputSlots.size() > 0)</div><div class="line"><a name="l02863"></a><span class="lineno"> 2863</span>  {</div><div class="line"><a name="l02864"></a><span class="lineno"> 2864</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">TensorRawPtr</a> tensorPtr = subgraphPtr->tensors[tensorIndex].get();</div><div class="line"><a name="l02865"></a><span class="lineno"> 2865</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(tensorPtr);</div><div class="line"><a name="l02866"></a><span class="lineno"> 2866</span>  <span class="keyword">auto</span> tensorAndData = CreateConstTensor(tensorPtr,</div><div class="line"><a name="l02867"></a><span class="lineno"> 2867</span>  tensorInfo,</div><div class="line"><a name="l02868"></a><span class="lineno"> 2868</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::PermutationVector&></a>());</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>  std::string layerName = boost::str(boost::format(<span class="stringliteral">"Constant:%1%"</span>) % tensorPtr->name);</div><div class="line"><a name="l02871"></a><span class="lineno"> 2871</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a> *layer =</div><div class="line"><a name="l02872"></a><span class="lineno"> 2872</span>  m_Network->AddConstantLayer(tensorAndData.first, layerName.c_str());</div><div class="line"><a name="l02873"></a><span class="lineno"> 2873</span> </div><div class="line"><a name="l02874"></a><span class="lineno"> 2874</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(tensorInfo);</div><div class="line"><a name="l02875"></a><span class="lineno"> 2875</span>  RegisterOutputSlots(subgraphIndex,</div><div class="line"><a name="l02876"></a><span class="lineno"> 2876</span>  VIRTUAL_OPERATOR_ID,</div><div class="line"><a name="l02877"></a><span class="lineno"> 2877</span>  layer,</div><div class="line"><a name="l02878"></a><span class="lineno"> 2878</span>  { tensorIndex });</div><div class="line"><a name="l02879"></a><span class="lineno"> 2879</span> </div><div class="line"><a name="l02880"></a><span class="lineno"> 2880</span>  }</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>  }</div><div class="line"><a name="l02883"></a><span class="lineno"> 2883</span> }</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> <span class="comment">// example usage: BufferRawPtr bufferPtr = GetBuffer(m_Model, inputs[0]->buffer);</span></div><div class="line"><a name="l02886"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222"> 2886</a></span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">TfLiteParser::BufferRawPtr</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">TfLiteParser::GetBuffer</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abfc86471394295357a23b3addd0b5b1c">ModelPtr</a>& model, <span class="keywordtype">size_t</span> bufferIndex)</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="_tf_lite_parser_8cpp.xhtml#a7c88d54e3f895030c70330a4c9d76a7a">CHECK_BUFFER</a>(model, bufferIndex);</div><div class="line"><a name="l02889"></a><span class="lineno"> 2889</span>  <span class="keywordflow">return</span> model->buffers[bufferIndex].get();</div><div class="line"><a name="l02890"></a><span class="lineno"> 2890</span> }</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> <span class="keyword">template</span><<span class="keyword">typename</span> T></div><div class="line"><a name="l02893"></a><span class="lineno"> 2893</span> std::pair<armnn::ConstTensor, TfLiteParser::SupportedDataStorage></div><div class="line"><a name="l02894"></a><span class="lineno"> 2894</span> TfLiteParser::CreateConstTensorAndStoreData(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">TfLiteParser::BufferRawPtr</a> bufferPtr,</div><div class="line"><a name="l02895"></a><span class="lineno"> 2895</span>  <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">TfLiteParser::TensorRawPtr</a> tensorPtr,</div><div class="line"><a name="l02896"></a><span class="lineno"> 2896</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& tensorInfo,</div><div class="line"><a name="l02897"></a><span class="lineno"> 2897</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::PermutationVector&></a> permutationVector)</div><div class="line"><a name="l02898"></a><span class="lineno"> 2898</span> {</div><div class="line"><a name="l02899"></a><span class="lineno"> 2899</span>  <span class="keyword">auto</span> constData = CreateConstTensorImpl<T>(bufferPtr,</div><div class="line"><a name="l02900"></a><span class="lineno"> 2900</span>  tensorPtr,</div><div class="line"><a name="l02901"></a><span class="lineno"> 2901</span>  tensorInfo,</div><div class="line"><a name="l02902"></a><span class="lineno"> 2902</span>  permutationVector);</div><div class="line"><a name="l02903"></a><span class="lineno"> 2903</span>  TfLiteParser::SupportedDataStorage storage(std::move(constData.second));</div><div class="line"><a name="l02904"></a><span class="lineno"> 2904</span>  <span class="keywordflow">return</span> std::make_pair(constData.first, std::move(storage));</div><div class="line"><a name="l02905"></a><span class="lineno"> 2905</span> }</div><div class="line"><a name="l02906"></a><span class="lineno"> 2906</span> </div><div class="line"><a name="l02907"></a><span class="lineno"> 2907</span> std::pair<armnn::ConstTensor, TfLiteParser::SupportedDataStorage></div><div class="line"><a name="l02908"></a><span class="lineno"> 2908</span> TfLiteParser::CreateConstTensor(<a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">TensorRawPtr</a> tensorPtr,</div><div class="line"><a name="l02909"></a><span class="lineno"> 2909</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& tensorInfo,</div><div class="line"><a name="l02910"></a><span class="lineno"> 2910</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">armnn::Optional<armnn::PermutationVector&></a> permutationVector)</div><div class="line"><a name="l02911"></a><span class="lineno"> 2911</span> {</div><div class="line"><a name="l02912"></a><span class="lineno"> 2912</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ae38d96fe05581ea025713b3e781c5a43">CHECK_TENSOR_PTR</a>(tensorPtr);</div><div class="line"><a name="l02913"></a><span class="lineno"> 2913</span>  <span class="keyword">auto</span> bufferPtr = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">GetBuffer</a>(m_Model, tensorPtr->buffer);</div><div class="line"><a name="l02914"></a><span class="lineno"> 2914</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#ae6440b8ea95cf981cd7bbffa52c22fe1">CHECK_BUFFER_SIZE</a>(bufferPtr, tensorInfo, tensorPtr->buffer);</div><div class="line"><a name="l02915"></a><span class="lineno"> 2915</span> </div><div class="line"><a name="l02916"></a><span class="lineno"> 2916</span>  <span class="keywordflow">switch</span> (tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>())</div><div class="line"><a name="l02917"></a><span class="lineno"> 2917</span>  {</div><div class="line"><a name="l02918"></a><span class="lineno"> 2918</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>:</div><div class="line"><a name="l02919"></a><span class="lineno"> 2919</span>  <span class="keywordflow">return</span> CreateConstTensorAndStoreData<float>(bufferPtr,</div><div class="line"><a name="l02920"></a><span class="lineno"> 2920</span>  tensorPtr,</div><div class="line"><a name="l02921"></a><span class="lineno"> 2921</span>  tensorInfo,</div><div class="line"><a name="l02922"></a><span class="lineno"> 2922</span>  permutationVector);</div><div class="line"><a name="l02923"></a><span class="lineno"> 2923</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>:</div><div class="line"><a name="l02924"></a><span class="lineno"> 2924</span>  <span class="keywordflow">return</span> CreateConstTensorAndStoreData<uint8_t>(bufferPtr,</div><div class="line"><a name="l02925"></a><span class="lineno"> 2925</span>  tensorPtr,</div><div class="line"><a name="l02926"></a><span class="lineno"> 2926</span>  tensorInfo,</div><div class="line"><a name="l02927"></a><span class="lineno"> 2927</span>  permutationVector);</div><div class="line"><a name="l02928"></a><span class="lineno"> 2928</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a>:</div><div class="line"><a name="l02929"></a><span class="lineno"> 2929</span>  <span class="keywordflow">return</span> CreateConstTensorAndStoreData<int8_t>(bufferPtr,</div><div class="line"><a name="l02930"></a><span class="lineno"> 2930</span>  tensorPtr,</div><div class="line"><a name="l02931"></a><span class="lineno"> 2931</span>  tensorInfo,</div><div class="line"><a name="l02932"></a><span class="lineno"> 2932</span>  permutationVector);</div><div class="line"><a name="l02933"></a><span class="lineno"> 2933</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a>:</div><div class="line"><a name="l02934"></a><span class="lineno"> 2934</span>  <span class="keywordflow">return</span> CreateConstTensorAndStoreData<int8_t>(bufferPtr,</div><div class="line"><a name="l02935"></a><span class="lineno"> 2935</span>  tensorPtr,</div><div class="line"><a name="l02936"></a><span class="lineno"> 2936</span>  tensorInfo,</div><div class="line"><a name="l02937"></a><span class="lineno"> 2937</span>  permutationVector);</div><div class="line"><a name="l02938"></a><span class="lineno"> 2938</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>:</div><div class="line"><a name="l02939"></a><span class="lineno"> 2939</span>  <span class="keywordflow">return</span> CreateConstTensorAndStoreData<int32_t>(bufferPtr,</div><div class="line"><a name="l02940"></a><span class="lineno"> 2940</span>  tensorPtr,</div><div class="line"><a name="l02941"></a><span class="lineno"> 2941</span>  tensorInfo,</div><div class="line"><a name="l02942"></a><span class="lineno"> 2942</span>  permutationVector);</div><div class="line"><a name="l02943"></a><span class="lineno"> 2943</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l02944"></a><span class="lineno"> 2944</span>  {</div><div class="line"><a name="l02945"></a><span class="lineno"> 2945</span>  std::stringstream errString;</div><div class="line"><a name="l02946"></a><span class="lineno"> 2946</span>  errString << <span class="stringliteral">"Unexpected datatype when creating const tensor: "</span></div><div class="line"><a name="l02947"></a><span class="lineno"> 2947</span>  << <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">armnn::GetDataTypeName</a>(tensorInfo.GetDataType())</div><div class="line"><a name="l02948"></a><span class="lineno"> 2948</span>  << <span class="stringliteral">" shape:"</span> << tensorInfo.GetShape()</div><div class="line"><a name="l02949"></a><span class="lineno"> 2949</span>  << <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString();</div><div class="line"><a name="l02950"></a><span class="lineno"> 2950</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(errString.str());</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>  }</div><div class="line"><a name="l02953"></a><span class="lineno"> 2953</span> }</div><div class="line"><a name="l02954"></a><span class="lineno"> 2954</span> </div><div class="line"><a name="l02955"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a91ba75587a31033088cb4f156e847efb"> 2955</a></span> <a class="code" href="namespacearmnn_tf_lite_parser.xhtml#a9084adbf804022c874039ad40d1939e9">BindingPointInfo</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a91ba75587a31033088cb4f156e847efb">TfLiteParser::GetNetworkInputBindingInfo</a>(<span class="keywordtype">size_t</span> subgraphId,</div><div class="line"><a name="l02956"></a><span class="lineno"> 2956</span>  <span class="keyword">const</span> std::string& name)<span class="keyword"> const</span></div><div class="line"><a name="l02957"></a><span class="lineno"> 2957</span> <span class="keyword"></span>{</div><div class="line"><a name="l02958"></a><span class="lineno"> 2958</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphId);</div><div class="line"><a name="l02959"></a><span class="lineno"> 2959</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abda91b07a94d0f498b76655e03647d9a">GetSubgraphInputs</a>(m_Model, subgraphId);</div><div class="line"><a name="l02960"></a><span class="lineno"> 2960</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span> & input : inputs)</div><div class="line"><a name="l02961"></a><span class="lineno"> 2961</span>  {</div><div class="line"><a name="l02962"></a><span class="lineno"> 2962</span>  <span class="keywordflow">if</span> (input.second->name == name)</div><div class="line"><a name="l02963"></a><span class="lineno"> 2963</span>  {</div><div class="line"><a name="l02964"></a><span class="lineno"> 2964</span>  <span class="keyword">auto</span> bindingId = GenerateLayerBindingId(subgraphId, input.first);</div><div class="line"><a name="l02965"></a><span class="lineno"> 2965</span>  <span class="keywordflow">return</span> std::make_pair(bindingId, <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(input.second));</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>  }</div><div class="line"><a name="l02968"></a><span class="lineno"> 2968</span> </div><div class="line"><a name="l02969"></a><span class="lineno"> 2969</span>  std::stringstream bindings;</div><div class="line"><a name="l02970"></a><span class="lineno"> 2970</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span> & input : inputs)</div><div class="line"><a name="l02971"></a><span class="lineno"> 2971</span>  {</div><div class="line"><a name="l02972"></a><span class="lineno"> 2972</span>  bindings << <span class="stringliteral">"'"</span> << input.second->name << <span class="stringliteral">"' "</span>;</div><div class="line"><a name="l02973"></a><span class="lineno"> 2973</span>  }</div><div class="line"><a name="l02974"></a><span class="lineno"> 2974</span> </div><div class="line"><a name="l02975"></a><span class="lineno"> 2975</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02976"></a><span class="lineno"> 2976</span>  boost::str(</div><div class="line"><a name="l02977"></a><span class="lineno"> 2977</span>  boost::format(<span class="stringliteral">"No input binding found for subgraph:%1% and name:%2%. "</span></div><div class="line"><a name="l02978"></a><span class="lineno"> 2978</span>  <span class="stringliteral">"Possible inputs are: [%3%] %4%"</span>) %</div><div class="line"><a name="l02979"></a><span class="lineno"> 2979</span>  subgraphId %</div><div class="line"><a name="l02980"></a><span class="lineno"> 2980</span>  name %</div><div class="line"><a name="l02981"></a><span class="lineno"> 2981</span>  bindings.str() %</div><div class="line"><a name="l02982"></a><span class="lineno"> 2982</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02983"></a><span class="lineno"> 2983</span> }</div><div class="line"><a name="l02984"></a><span class="lineno"> 2984</span> </div><div class="line"><a name="l02985"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a475b7cb5db683bb6fbb1c3fae40cb2b3"> 2985</a></span> <a class="code" href="namespacearmnn_tf_lite_parser.xhtml#a9084adbf804022c874039ad40d1939e9">BindingPointInfo</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a475b7cb5db683bb6fbb1c3fae40cb2b3">TfLiteParser::GetNetworkOutputBindingInfo</a>(<span class="keywordtype">size_t</span> subgraphId,</div><div class="line"><a name="l02986"></a><span class="lineno"> 2986</span>  <span class="keyword">const</span> std::string& name)<span class="keyword"> const</span></div><div class="line"><a name="l02987"></a><span class="lineno"> 2987</span> <span class="keyword"></span>{</div><div class="line"><a name="l02988"></a><span class="lineno"> 2988</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphId);</div><div class="line"><a name="l02989"></a><span class="lineno"> 2989</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afba7f99227765786c4ed9cb2acc09739">GetSubgraphOutputs</a>(m_Model, subgraphId);</div><div class="line"><a name="l02990"></a><span class="lineno"> 2990</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < outputs.size(); ++i)</div><div class="line"><a name="l02991"></a><span class="lineno"> 2991</span>  {</div><div class="line"><a name="l02992"></a><span class="lineno"> 2992</span>  <span class="keyword">auto</span> <span class="keyword">const</span> output = outputs[i];</div><div class="line"><a name="l02993"></a><span class="lineno"> 2993</span>  <span class="keywordflow">if</span> (output.second->name == name)</div><div class="line"><a name="l02994"></a><span class="lineno"> 2994</span>  {</div><div class="line"><a name="l02995"></a><span class="lineno"> 2995</span>  <span class="keyword">auto</span> bindingId = GenerateLayerBindingId(subgraphId, output.first);</div><div class="line"><a name="l02996"></a><span class="lineno"> 2996</span>  std::vector<unsigned int> shape = m_OverridenOutputShapes.size() > 0 ?</div><div class="line"><a name="l02997"></a><span class="lineno"> 2997</span>  m_OverridenOutputShapes[i] : AsUnsignedVector(output.second->shape);</div><div class="line"><a name="l02998"></a><span class="lineno"> 2998</span>  <span class="keywordflow">return</span> std::make_pair(bindingId, <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(output.second, shape));</div><div class="line"><a name="l02999"></a><span class="lineno"> 2999</span>  }</div><div class="line"><a name="l03000"></a><span class="lineno"> 3000</span>  }</div><div class="line"><a name="l03001"></a><span class="lineno"> 3001</span> </div><div class="line"><a name="l03002"></a><span class="lineno"> 3002</span>  std::stringstream bindings;</div><div class="line"><a name="l03003"></a><span class="lineno"> 3003</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span> & output : outputs)</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>  bindings << <span class="stringliteral">"'"</span> << output.second->name << <span class="stringliteral">"' "</span>;</div><div class="line"><a name="l03006"></a><span class="lineno"> 3006</span>  }</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03009"></a><span class="lineno"> 3009</span>  boost::str(</div><div class="line"><a name="l03010"></a><span class="lineno"> 3010</span>  boost::format(<span class="stringliteral">"No output binding found for subgraph:%1% and name:%2%. "</span></div><div class="line"><a name="l03011"></a><span class="lineno"> 3011</span>  <span class="stringliteral">"Possible outputs are: [%3%] %4%"</span>) %</div><div class="line"><a name="l03012"></a><span class="lineno"> 3012</span>  subgraphId %</div><div class="line"><a name="l03013"></a><span class="lineno"> 3013</span>  name %</div><div class="line"><a name="l03014"></a><span class="lineno"> 3014</span>  bindings.str() %</div><div class="line"><a name="l03015"></a><span class="lineno"> 3015</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l03018"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ae18739116b52dbab31bbf490450beb90"> 3018</a></span> <span class="keywordtype">size_t</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ae18739116b52dbab31bbf490450beb90">TfLiteParser::GetSubgraphCount</a>()<span class="keyword"> const</span></div><div class="line"><a name="l03019"></a><span class="lineno"> 3019</span> <span class="keyword"></span>{</div><div class="line"><a name="l03020"></a><span class="lineno"> 3020</span>  <span class="keywordflow">return</span> m_Model->subgraphs.size();</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> </div><div class="line"><a name="l03023"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a58501692772880e7ef55485a4c95aab9"> 3023</a></span> std::vector<std::string> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a58501692772880e7ef55485a4c95aab9">TfLiteParser::GetSubgraphInputTensorNames</a>(<span class="keywordtype">size_t</span> subgraphId)<span class="keyword"> const</span></div><div class="line"><a name="l03024"></a><span class="lineno"> 3024</span> <span class="keyword"></span>{</div><div class="line"><a name="l03025"></a><span class="lineno"> 3025</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphId);</div><div class="line"><a name="l03026"></a><span class="lineno"> 3026</span>  <span class="keyword">auto</span> inputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abda91b07a94d0f498b76655e03647d9a">GetSubgraphInputs</a>(m_Model, subgraphId);</div><div class="line"><a name="l03027"></a><span class="lineno"> 3027</span>  std::vector<std::string> result;</div><div class="line"><a name="l03028"></a><span class="lineno"> 3028</span>  result.reserve(inputs.size());</div><div class="line"><a name="l03029"></a><span class="lineno"> 3029</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span> & input : inputs)</div><div class="line"><a name="l03030"></a><span class="lineno"> 3030</span>  {</div><div class="line"><a name="l03031"></a><span class="lineno"> 3031</span>  result.push_back(input.second->name);</div><div class="line"><a name="l03032"></a><span class="lineno"> 3032</span>  }</div><div class="line"><a name="l03033"></a><span class="lineno"> 3033</span>  <span class="keywordflow">return</span> result;</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> </div><div class="line"><a name="l03036"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ab97e69d07f06e392bd0cc2e5bcbf1be6"> 3036</a></span> std::vector<std::string> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ab97e69d07f06e392bd0cc2e5bcbf1be6">TfLiteParser::GetSubgraphOutputTensorNames</a>(<span class="keywordtype">size_t</span> subgraphId)<span class="keyword"> const</span></div><div class="line"><a name="l03037"></a><span class="lineno"> 3037</span> <span class="keyword"></span>{</div><div class="line"><a name="l03038"></a><span class="lineno"> 3038</span>  <a class="code" href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a>(m_Model, subgraphId);</div><div class="line"><a name="l03039"></a><span class="lineno"> 3039</span>  <span class="keyword">auto</span> outputs = <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afba7f99227765786c4ed9cb2acc09739">GetSubgraphOutputs</a>(m_Model, subgraphId);</div><div class="line"><a name="l03040"></a><span class="lineno"> 3040</span>  std::vector<std::string> result;</div><div class="line"><a name="l03041"></a><span class="lineno"> 3041</span>  result.reserve(outputs.size());</div><div class="line"><a name="l03042"></a><span class="lineno"> 3042</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span> & output : outputs)</div><div class="line"><a name="l03043"></a><span class="lineno"> 3043</span>  {</div><div class="line"><a name="l03044"></a><span class="lineno"> 3044</span>  result.push_back(output.second->name);</div><div class="line"><a name="l03045"></a><span class="lineno"> 3045</span>  }</div><div class="line"><a name="l03046"></a><span class="lineno"> 3046</span>  <span class="keywordflow">return</span> result;</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> </div><div class="line"><a name="l03049"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#acf8cc929eadbabf197b36f7364d3d2cb"> 3049</a></span> <a class="code" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml">ITfLiteParser</a>* <a class="code" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#acf8cc929eadbabf197b36f7364d3d2cb">ITfLiteParser::CreateRaw</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ITfLiteParser::TfLiteParserOptions></a>& <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>)</div><div class="line"><a name="l03050"></a><span class="lineno"> 3050</span> {</div><div class="line"><a name="l03051"></a><span class="lineno"> 3051</span>  <span class="keywordflow">return</span> <span class="keyword">new</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a2ed4853234865d838da50085da99b2a6">TfLiteParser</a>(options);</div><div class="line"><a name="l03052"></a><span class="lineno"> 3052</span> }</div><div class="line"><a name="l03053"></a><span class="lineno"> 3053</span> </div><div class="line"><a name="l03054"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a9932449a89a62cfcfd72a4fedbee1ab7"> 3054</a></span> <a class="code" href="namespacearmnn_tf_lite_parser.xhtml#af69bedce3c37be895f75146016ba8a17">ITfLiteParserPtr</a> <a class="code" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a9932449a89a62cfcfd72a4fedbee1ab7">ITfLiteParser::Create</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ITfLiteParser::TfLiteParserOptions></a>& <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>)</div><div class="line"><a name="l03055"></a><span class="lineno"> 3055</span> {</div><div class="line"><a name="l03056"></a><span class="lineno"> 3056</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn_tf_lite_parser.xhtml#af69bedce3c37be895f75146016ba8a17">ITfLiteParserPtr</a>(<a class="code" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#acf8cc929eadbabf197b36f7364d3d2cb">CreateRaw</a>(options), &<a class="code" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a29a2a153962a701843d5c8ae953cb032">ITfLiteParser::Destroy</a>);</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> </div><div class="line"><a name="l03059"></a><span class="lineno"><a class="line" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a29a2a153962a701843d5c8ae953cb032"> 3059</a></span> <span class="keywordtype">void</span> <a class="code" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a29a2a153962a701843d5c8ae953cb032">ITfLiteParser::Destroy</a>(<a class="code" href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml">ITfLiteParser</a>* parser)</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>  <span class="keyword">delete</span> parser;</div><div class="line"><a name="l03062"></a><span class="lineno"> 3062</span> }</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> TfLiteParser::SupportedDataStorage::SupportedDataStorage(std::unique_ptr<<span class="keywordtype">float</span>[]> && data)</div><div class="line"><a name="l03065"></a><span class="lineno"> 3065</span> : m_FloatData(std::move(data))</div><div class="line"><a name="l03066"></a><span class="lineno"> 3066</span> , m_Uint8Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03067"></a><span class="lineno"> 3067</span> , m_Int8Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03068"></a><span class="lineno"> 3068</span> , m_Int32Data(<span class="keyword">nullptr</span>)</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> }</div><div class="line"><a name="l03071"></a><span class="lineno"> 3071</span> </div><div class="line"><a name="l03072"></a><span class="lineno"> 3072</span> TfLiteParser::SupportedDataStorage::SupportedDataStorage(std::unique_ptr<uint8_t[]> && data)</div><div class="line"><a name="l03073"></a><span class="lineno"> 3073</span> : m_FloatData(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03074"></a><span class="lineno"> 3074</span> , m_Uint8Data(std::move(data))</div><div class="line"><a name="l03075"></a><span class="lineno"> 3075</span> , m_Int8Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03076"></a><span class="lineno"> 3076</span> , m_Int32Data(<span class="keyword">nullptr</span>)</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> }</div><div class="line"><a name="l03079"></a><span class="lineno"> 3079</span> </div><div class="line"><a name="l03080"></a><span class="lineno"> 3080</span> TfLiteParser::SupportedDataStorage::SupportedDataStorage(std::unique_ptr<int8_t[]> && data)</div><div class="line"><a name="l03081"></a><span class="lineno"> 3081</span> : m_FloatData(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03082"></a><span class="lineno"> 3082</span> , m_Uint8Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03083"></a><span class="lineno"> 3083</span> , m_Int8Data(std::move(data))</div><div class="line"><a name="l03084"></a><span class="lineno"> 3084</span> , m_Int32Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03085"></a><span class="lineno"> 3085</span> {</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> </div><div class="line"><a name="l03088"></a><span class="lineno"> 3088</span> TfLiteParser::SupportedDataStorage::SupportedDataStorage(std::unique_ptr<int32_t[]> && data)</div><div class="line"><a name="l03089"></a><span class="lineno"> 3089</span> : m_FloatData(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03090"></a><span class="lineno"> 3090</span> , m_Uint8Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03091"></a><span class="lineno"> 3091</span> , m_Int8Data(<span class="keyword">nullptr</span>)</div><div class="line"><a name="l03092"></a><span class="lineno"> 3092</span> , m_Int32Data(std::move(data))</div><div class="line"><a name="l03093"></a><span class="lineno"> 3093</span> {</div><div class="line"><a name="l03094"></a><span class="lineno"> 3094</span> }</div><div class="line"><a name="l03095"></a><span class="lineno"> 3095</span> </div><div class="line"><a name="l03096"></a><span class="lineno"> 3096</span> } <span class="comment">// armnnTfLiteParser</span></div><div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a012b24cafd443425314d4f9e06cec6c1"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a012b24cafd443425314d4f9e06cec6c1">armnnTfLiteParser::TfLiteParser::CreateNetworkFromBinaryFile</a></div><div class="ttdeci">virtual armnn::INetworkPtr CreateNetworkFromBinaryFile(const char *graphFile) override</div><div class="ttdoc">Create the network from a flatbuffers binary file on disk. </div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00598">TfLiteParser.cpp:598</a></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="_tf_lite_parser_8cpp_xhtml_afbe702264a4e175da37c4941c0894bdb"><div class="ttname"><a href="_tf_lite_parser_8cpp.xhtml#afbe702264a4e175da37c4941c0894bdb">CHECK_MODEL</a></div><div class="ttdeci">#define CHECK_MODEL(MODEL, SUBGRAPH_INDEX, OPERATOR_INDEX)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00124">TfLiteParser.cpp:124</a></div></div> +<div class="ttc" id="_ignore_unused_8hpp_xhtml"><div class="ttname"><a href="_ignore_unused_8hpp.xhtml">IgnoreUnused.hpp</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_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="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a475b7cb5db683bb6fbb1c3fae40cb2b3"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a475b7cb5db683bb6fbb1c3fae40cb2b3">armnnTfLiteParser::TfLiteParser::GetNetworkOutputBindingInfo</a></div><div class="ttdeci">virtual BindingPointInfo GetNetworkOutputBindingInfo(size_t subgraphId, const std::string &name) const override</div><div class="ttdoc">Retrieve binding info (layer id and tensor info) for the network output identified by the given layer...</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l02985">TfLiteParser.cpp:2985</a></div></div> +<div class="ttc" id="structarmnn_1_1_stack_descriptor_xhtml_ab218de7805899c8412d75d1fd1d846d2"><div class="ttname"><a href="structarmnn_1_1_stack_descriptor.xhtml#ab218de7805899c8412d75d1fd1d846d2">armnn::StackDescriptor::m_Axis</a></div><div class="ttdeci">uint32_t m_Axis</div><div class="ttdoc">0-based axis along which to stack the input tensors. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00972">Descriptors.hpp:972</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a988cb5e216eb87d854414c6a0282eeb4"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a988cb5e216eb87d854414c6a0282eeb4">armnnTfLiteParser::TfLiteParser::SubgraphPtr</a></div><div class="ttdeci">std::unique_ptr< tflite::SubGraphT > SubgraphPtr</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00024">TfLiteParser.hpp:24</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a5336d7700f4a5bcc272fbc9216541222"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a5336d7700f4a5bcc272fbc9216541222">armnnTfLiteParser::TfLiteParser::GetBuffer</a></div><div class="ttdeci">static BufferRawPtr GetBuffer(const ModelPtr &model, size_t bufferIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l02886">TfLiteParser.cpp:2886</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_pooling2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Pooling2dDescriptor::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#l00355">Descriptors.hpp:355</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_tf_lite_parser_1_1_tf_lite_parser_xhtml_a858104f225c302988fba35c1cb299066"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a858104f225c302988fba35c1cb299066">armnnTfLiteParser::TfLiteParser::LoadModelFromBinary</a></div><div class="ttdeci">static ModelPtr LoadModelFromBinary(const uint8_t *binaryContent, size_t len)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l02648">TfLiteParser.cpp:2648</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="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="_tf_lite_parser_8cpp_xhtml_a5fc762bb40634f39ff6ff4e23005a3a9"><div class="ttname"><a href="_tf_lite_parser_8cpp.xhtml#a5fc762bb40634f39ff6ff4e23005a3a9">ARMNN_THROW_PARSE_EXCEPTION</a></div><div class="ttdeci">#define ARMNN_THROW_PARSE_EXCEPTION(msg)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00036">TfLiteParser.cpp:36</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_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_pooling2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Pooling2dDescriptor::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#l00349">Descriptors.hpp:349</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_ae8ee09f5e3e78ecfdf00acfdc37588dc"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ae8ee09f5e3e78ecfdf00acfdc37588dc">armnnTfLiteParser::TfLiteParser::CreateNetworkFromBinary</a></div><div class="ttdeci">virtual armnn::INetworkPtr CreateNetworkFromBinary(const std::vector< uint8_t > &binaryContent) override</div><div class="ttdoc">Create the network from a flatbuffers binary. </div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00605">TfLiteParser.cpp:605</a></div></div> +<div class="ttc" id="classarmnn_1_1_optional_xhtml"><div class="ttname"><a href="classarmnn_1_1_optional.xhtml">armnn::Optional</a></div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00270">Optional.hpp:270</a></div></div> +<div class="ttc" id="structarmnn_1_1_check_location_xhtml_a5e3562cda960da001597e7dd5679b140"><div class="ttname"><a href="structarmnn_1_1_check_location.xhtml#a5e3562cda960da001597e7dd5679b140">armnn::CheckLocation::AsString</a></div><div class="ttdeci">std::string AsString() const</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00029">Exceptions.hpp:29</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_a6d0384878432cfc9652b7ae8bc59506f"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a6d0384878432cfc9652b7ae8bc59506f">armnn::StridedSliceDescriptor::m_ShrinkAxisMask</a></div><div class="ttdeci">int32_t m_ShrinkAxisMask</div><div class="ttdoc">Shrink axis mask value. If set, the nth specification shrinks the dimensionality by 1...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01054">Descriptors.hpp:1054</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="structarmnn_1_1_strided_slice_descriptor_xhtml_a118fe06b7c2599da60398ee311ede923"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a118fe06b7c2599da60398ee311ede923">armnn::StridedSliceDescriptor::m_Begin</a></div><div class="ttdeci">std::vector< int > m_Begin</div><div class="ttdoc">Begin values for the input that will be sliced. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01041">Descriptors.hpp:1041</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_abd8bee7fb9b86485a60bc7ee05114270"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abd8bee7fb9b86485a60bc7ee05114270">armnnTfLiteParser::TfLiteParser::TensorRawPtrVector</a></div><div class="ttdeci">std::vector< TensorRawPtr > TensorRawPtrVector</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00029">TfLiteParser.hpp:29</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_strided_slice_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::StridedSliceDescriptor::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#l01062">Descriptors.hpp:1062</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_stack_descriptor_xhtml_a2bea87b470268bb0b73457c3733dbc04"><div class="ttname"><a href="structarmnn_1_1_stack_descriptor.xhtml#a2bea87b470268bb0b73457c3733dbc04">armnn::StackDescriptor::m_InputShape</a></div><div class="ttdeci">TensorShape m_InputShape</div><div class="ttdoc">Required shape of all input tensors. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00976">Descriptors.hpp:976</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_pooling2d_descriptor_xhtml_a6d8fb685cc1ff224f25aa127fcf62c86"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">armnn::Pooling2dDescriptor::m_PoolWidth</a></div><div class="ttdeci">uint32_t m_PoolWidth</div><div class="ttdoc">Pooling width value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00357">Descriptors.hpp:357</a></div></div> +<div class="ttc" id="_tf_lite_parser_8hpp_xhtml"><div class="ttname"><a href="_tf_lite_parser_8hpp.xhtml">TfLiteParser.hpp</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_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="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_ac5411554ab8c02ca286af52c98f6bd87"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ac5411554ab8c02ca286af52c98f6bd87">armnnTfLiteParser::TfLiteParser::LoadModelFromFile</a></div><div class="ttdeci">static ModelPtr LoadModelFromFile(const char *fileName)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l02624">TfLiteParser.cpp:2624</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="classarmnn_1_1_tensor_info_xhtml_abcbdfb544ece4c31d0b37715ad0f3be0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">armnn::TensorInfo::GetNumBytes</a></div><div class="ttdeci">unsigned int GetNumBytes() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00213">Tensor.cpp:213</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_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="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml">armnnTfLiteParser::TfLiteParser</a></div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00019">TfLiteParser.hpp:19</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a8c29d6ea9b4186d69aad5961c910939c"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">armnn::Pooling2dDescriptor::m_PaddingMethod</a></div><div class="ttdeci">PaddingMethod m_PaddingMethod</div><div class="ttdoc">The padding method to be used. (Exclude, IgnoreValue). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00367">Descriptors.hpp:367</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="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="_tf_lite_parser_8cpp_xhtml_a7c88d54e3f895030c70330a4c9d76a7a"><div class="ttname"><a href="_tf_lite_parser_8cpp.xhtml#a7c88d54e3f895030c70330a4c9d76a7a">CHECK_BUFFER</a></div><div class="ttdeci">#define CHECK_BUFFER(MODEL, BUFFER_INDEX)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00210">TfLiteParser.cpp:210</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a4b49afca01112a4f4d023726ccd38876"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a4b49afca01112a4f4d023726ccd38876">armnnTfLiteParser::TfLiteParser::GetInputs</a></div><div class="ttdeci">static TensorRawPtrVector GetInputs(const ModelPtr &model, size_t subgraphIndex, size_t operatorIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l02667">TfLiteParser.cpp:2667</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser_xhtml"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml">armnnTfLiteParser::ITfLiteParser</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_tf_lite_parser_8hpp_source.xhtml#l00025">ITfLiteParser.hpp:25</a></div></div> +<div class="ttc" id="classarmnn_1_1_exception_xhtml_abf843cbb29dec939d0731e491bab6f70"><div class="ttname"><a href="classarmnn_1_1_exception.xhtml#abf843cbb29dec939d0731e491bab6f70">armnn::Exception::what</a></div><div class="ttdeci">virtual const char * what() const noexcept override</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8cpp_source.xhtml#l00032">Exceptions.cpp:32</a></div></div> +<div class="ttc" id="_logging_8hpp_xhtml_a7b6ad073975f437ec38ca7d20154727f"><div class="ttname"><a href="_logging_8hpp.xhtml#a7b6ad073975f437ec38ca7d20154727f">ARMNN_LOG</a></div><div class="ttdeci">#define ARMNN_LOG(severity)</div><div class="ttdef"><b>Definition:</b> <a href="_logging_8hpp_source.xhtml#l00163">Logging.hpp:163</a></div></div> +<div class="ttc" id="classarmnn_1_1_file_not_found_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_file_not_found_exception.xhtml">armnn::FileNotFoundException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00086">Exceptions.hpp:86</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Pooling2dDescriptor::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#l00353">Descriptors.hpp:353</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a></div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml_a523deabeb7d0a884028b35eebfd1cb6c"><div class="ttname"><a href="namespacearmnn_utils.xhtml#a523deabeb7d0a884028b35eebfd1cb6c">armnnUtils::ProcessConcatInputTensorInfo</a></div><div class="ttdeci">void ProcessConcatInputTensorInfo(armnn::TensorInfo &inputTensorInfo, armnn::OriginsDescriptor &concatDescriptor, const unsigned int &concatAxis, unsigned int inputIndex, unsigned int &mergeDimOrigin)</div><div class="ttdef"><b>Definition:</b> <a href="_parser_helper_8cpp_source.xhtml#l00019">ParserHelper.cpp:19</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_tf_parser_xhtml_aa78bf8d20e213dcd13d48072dfa9cd1f"><div class="ttname"><a href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">armnnTfParser::CalcPadding</a></div><div class="ttdeci">void CalcPadding(uint32_t input, uint32_t kernel, uint32_t stride, uint32_t &outPadHead, uint32_t &outPadTail, bool samePadding)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8cpp_source.xhtml#l00421">TfParser.cpp:421</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_pad_descriptor_xhtml_a85f98c94e11f65a6b73f831735c040f3"><div class="ttname"><a href="structarmnn_1_1_pad_descriptor.xhtml#a85f98c94e11f65a6b73f831735c040f3">armnn::PadDescriptor::m_PadList</a></div><div class="ttdeci">std::vector< std::pair< unsigned int, unsigned int > > m_PadList</div><div class="ttdoc">Specifies the padding for input dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00920">Descriptors.hpp:920</a></div></div> +<div class="ttc" id="namespacearmnn_tf_lite_parser_xhtml_af69bedce3c37be895f75146016ba8a17"><div class="ttname"><a href="namespacearmnn_tf_lite_parser.xhtml#af69bedce3c37be895f75146016ba8a17">armnnTfLiteParser::ITfLiteParserPtr</a></div><div class="ttdeci">std::unique_ptr< ITfLiteParser, void(*)(ITfLiteParser *parser)> ITfLiteParserPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_tf_lite_parser_8hpp_source.xhtml#l00023">ITfLiteParser.hpp:23</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a91ba75587a31033088cb4f156e847efb"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a91ba75587a31033088cb4f156e847efb">armnnTfLiteParser::TfLiteParser::GetNetworkInputBindingInfo</a></div><div class="ttdeci">virtual BindingPointInfo GetNetworkInputBindingInfo(size_t subgraphId, const std::string &name) const override</div><div class="ttdoc">Retrieve binding info (layer id and tensor info) for the network input identified by the given layer ...</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l02955">TfLiteParser.cpp:2955</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2020 ARM Limited. </div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00025">00_introduction.dox:25</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &&...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div> +<div class="ttc" id="_cl_layer_tests_8cpp_xhtml_a37f1c3ccc9fc906be85185350dd83d48"><div class="ttname"><a href="_cl_layer_tests_8cpp.xhtml#a37f1c3ccc9fc906be85185350dd83d48">true</a></div><div class="ttdeci">DataLayout::NCHW DataLayout::NCHW DataLayout::NHWC DataLayout::NHWC true</div><div class="ttdef"><b>Definition:</b> <a href="_cl_layer_tests_8cpp_source.xhtml#l00202">ClLayerTests.cpp:202</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="structarmnn_1_1_strided_slice_descriptor_xhtml_a61081be1483984e33db452c75d569f51"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a61081be1483984e33db452c75d569f51">armnn::StridedSliceDescriptor::m_BeginMask</a></div><div class="ttdeci">int32_t m_BeginMask</div><div class="ttdoc">Begin mask value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01049">Descriptors.hpp:1049</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="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_ae18739116b52dbab31bbf490450beb90"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ae18739116b52dbab31bbf490450beb90">armnnTfLiteParser::TfLiteParser::GetSubgraphCount</a></div><div class="ttdeci">virtual size_t GetSubgraphCount() const override</div><div class="ttdoc">Return the number of subgraphs in the parsed model. </div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l03018">TfLiteParser.cpp:3018</a></div></div> +<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml_a490ec6b59006d1fe1ec2ea30e69fb97c"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">armnn::PermutationVector::GetSize</a></div><div class="ttdeci">SizeType GetSize() const</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00202">Types.hpp:202</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="namespacearmnn_xhtml_a961bbfe1db71a848eff5a1f0ab775718"><div class="ttname"><a href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718">armnn::PoolingAlgorithm</a></div><div class="ttdeci">PoolingAlgorithm</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00096">Types.hpp:96</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< std::is_reference< T >::value, T >::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_space_to_batch_nd_descriptor_xhtml_a85f98c94e11f65a6b73f831735c040f3"><div class="ttname"><a href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml#a85f98c94e11f65a6b73f831735c040f3">armnn::SpaceToBatchNdDescriptor::m_PadList</a></div><div class="ttdeci">std::vector< std::pair< unsigned int, unsigned int > > m_PadList</div><div class="ttdoc">Specifies the padding values for the input dimension: heightPad{top, bottom} widthPad{left, right}. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00804">Descriptors.hpp:804</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="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="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">armnn::BoostLogSeverityMapping::error</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_aaebfa9a01a0bb8a0935114ff0140cc45"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaebfa9a01a0bb8a0935114ff0140cc45">armnnTfLiteParser::TfLiteParser::OutputShapeOfReshape</a></div><div class="ttdeci">static armnn::TensorInfo OutputShapeOfReshape(const armnn::TensorInfo &inputTensorInfo, const std::vector< int32_t > &targetDimsIn)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l01898">TfLiteParser.cpp:1898</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser_xhtml_a9932449a89a62cfcfd72a4fedbee1ab7"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a9932449a89a62cfcfd72a4fedbee1ab7">armnnTfLiteParser::ITfLiteParser::Create</a></div><div class="ttdeci">static ITfLiteParserPtr Create(const armnn::Optional< TfLiteParserOptions > &options=armnn::EmptyOptional())</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l03054">TfLiteParser.cpp:3054</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Pooling2dDescriptor::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#l00361">Descriptors.hpp:361</a></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="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="_tf_lite_parser_8cpp_xhtml_aa1664dc13adbc85ac12fb584b76bfdae"><div class="ttname"><a href="_tf_lite_parser_8cpp.xhtml#aa1664dc13adbc85ac12fb584b76bfdae">CHECK_TENSOR</a></div><div class="ttdeci">#define CHECK_TENSOR(MODEL, SUBGRAPH_INDEX, TENSOR_INDEX)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00154">TfLiteParser.cpp:154</a></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="classarmnn_1_1_tensor_info_xhtml_abe8889e8150beef5fd204b2d87b49298"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">armnn::TensorInfo::SetShape</a></div><div class="ttdeci">void SetShape(const TensorShape &newShape)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00090">Tensor.hpp:90</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="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_reshape_descriptor_xhtml_a1178f4dafdda81f59c15145ec327f7d9"><div class="ttname"><a href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">armnn::ReshapeDescriptor::m_TargetShape</a></div><div class="ttdeci">TensorShape m_TargetShape</div><div class="ttdoc">Target shape value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00774">Descriptors.hpp:774</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_abda91b07a94d0f498b76655e03647d9a"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abda91b07a94d0f498b76655e03647d9a">armnnTfLiteParser::TfLiteParser::GetSubgraphInputs</a></div><div class="ttdeci">static TensorIdRawPtrVector GetSubgraphInputs(const ModelPtr &model, size_t subgraphIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l02706">TfLiteParser.cpp:2706</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a84ad865bb9b5fa0e4841aa35a14a14d8"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a84ad865bb9b5fa0e4841aa35a14a14d8">armnnTfLiteParser::TfLiteParser::GetOutputs</a></div><div class="ttdeci">static TensorRawPtrVector GetOutputs(const ModelPtr &model, size_t subgraphIndex, size_t operatorIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l02686">TfLiteParser.cpp:2686</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a5699e8606c37d18c03910b242cd1b010"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">armnn::Pooling2dDescriptor::m_PoolHeight</a></div><div class="ttdeci">uint32_t m_PoolHeight</div><div class="ttdoc">Pooling height value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00359">Descriptors.hpp:359</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_deserializer_xhtml_aa28868b7dc87dc4d957db6c775a591c1"><div class="ttname"><a href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">armnnDeserializer::ToTensorInfo</a></div><div class="ttdeci">armnn::TensorInfo ToTensorInfo(Deserializer::TensorRawPtr tensorPtr)</div><div class="ttdef"><b>Definition:</b> <a href="_deserializer_8cpp_source.xhtml#l00501">Deserializer.cpp:501</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="namespacearmnn_onnx_parser_xhtml_a503ae4f55dae1486e53978657083b35d"><div class="ttname"><a href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">armnnOnnxParser::ModelPtr</a></div><div class="ttdeci">std::unique_ptr< onnx::ModelProto > ModelPtr</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8hpp_source.xhtml#l00023">OnnxParser.hpp:23</a></div></div> +<div class="ttc" id="_tf_lite_parser_8cpp_xhtml_a315ccf3e3cb207b1fbd10a2ad3e6333a"><div class="ttname"><a href="_tf_lite_parser_8cpp.xhtml#a315ccf3e3cb207b1fbd10a2ad3e6333a">CHECK_SUBGRAPH</a></div><div class="ttdeci">#define CHECK_SUBGRAPH(MODEL, SUBGRAPH_INDEX)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00079">TfLiteParser.cpp:79</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="_verification_helpers_8hpp_xhtml"><div class="ttname"><a href="_verification_helpers_8hpp.xhtml">VerificationHelpers.hpp</a></div></div> +<div class="ttc" id="_tensor_test_8cpp_xhtml_ad80e179ec400af9d2547f172f3ca05f3"><div class="ttname"><a href="_tensor_test_8cpp.xhtml#ad80e179ec400af9d2547f172f3ca05f3">CheckTensor</a></div><div class="ttdeci">void CheckTensor(const ConstTensor &t)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_test_8cpp_source.xhtml#l00100">TensorTest.cpp:100</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_afba7f99227765786c4ed9cb2acc09739"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afba7f99227765786c4ed9cb2acc09739">armnnTfLiteParser::TfLiteParser::GetSubgraphOutputs</a></div><div class="ttdeci">static TensorIdRawPtrVector GetSubgraphOutputs(const ModelPtr &model, size_t subgraphIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l02723">TfLiteParser.cpp:2723</a></div></div> +<div class="ttc" id="_permute_8hpp_xhtml"><div class="ttname"><a href="_permute_8hpp.xhtml">Permute.hpp</a></div></div> +<div class="ttc" id="namespacearmnn_deserializer_xhtml_a97b50f22cd99f0e09e6e48d20a35f6b2"><div class="ttname"><a href="namespacearmnn_deserializer.xhtml#a97b50f22cd99f0e09e6e48d20a35f6b2">armnnDeserializer::CheckShape</a></div><div class="ttdeci">bool CheckShape(const armnn::TensorShape &actual, const std::vector< uint32_t > &expected)</div><div class="ttdef"><b>Definition:</b> <a href="_deserializer_8cpp_source.xhtml#l00167">Deserializer.cpp:167</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_pooling2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Pooling2dDescriptor::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#l00351">Descriptors.hpp:351</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_check_location_xhtml_ac1184445a1323e07e0da084a54aec535"><div class="ttname"><a href="structarmnn_1_1_check_location.xhtml#ac1184445a1323e07e0da084a54aec535">armnn::CheckLocation::FileLine</a></div><div class="ttdeci">std::string FileLine() const</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00037">Exceptions.hpp:37</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="structarmnn_1_1_strided_slice_descriptor_xhtml_a7c91eda2b331d607bae92cd8ebf50bb9"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a7c91eda2b331d607bae92cd8ebf50bb9">armnn::StridedSliceDescriptor::m_NewAxisMask</a></div><div class="ttdeci">int32_t m_NewAxisMask</div><div class="ttdoc">New axis mask value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01059">Descriptors.hpp:1059</a></div></div> +<div class="ttc" id="structarmnn_1_1_mean_descriptor_xhtml_a28e0548abfc4e79c48f29a3d11a062e9"><div class="ttname"><a href="structarmnn_1_1_mean_descriptor.xhtml#a28e0548abfc4e79c48f29a3d11a062e9">armnn::MeanDescriptor::m_KeepDims</a></div><div class="ttdeci">bool m_KeepDims</div><div class="ttdoc">Enable/disable keep dimensions. If true, then the reduced dimensions that are of length 1 are kept...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00897">Descriptors.hpp:897</a></div></div> +<div class="ttc" id="_parser_flatbuffers_serialize_fixture_8hpp_xhtml_a15c20a0693cd3fc4d85565e2f920d8ef"><div class="ttname"><a href="_parser_flatbuffers_serialize_fixture_8hpp.xhtml#a15c20a0693cd3fc4d85565e2f920d8ef">TensorRawPtr</a></div><div class="ttdeci">armnnSerializer::TensorInfo * TensorRawPtr</div><div class="ttdef"><b>Definition:</b> <a href="_parser_flatbuffers_serialize_fixture_8hpp_source.xhtml#l00025">ParserFlatbuffersSerializeFixture.hpp:25</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_descriptor_xhtml_a02e143524aefddd40b485fcf7dea6696"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a02e143524aefddd40b485fcf7dea6696">armnn::BatchToSpaceNdDescriptor::m_BlockShape</a></div><div class="ttdeci">std::vector< unsigned int > m_BlockShape</div><div class="ttdoc">Block shape values. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00680">Descriptors.hpp:680</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser_xhtml_a29a2a153962a701843d5c8ae953cb032"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#a29a2a153962a701843d5c8ae953cb032">armnnTfLiteParser::ITfLiteParser::Destroy</a></div><div class="ttdeci">static void Destroy(ITfLiteParser *parser)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l03059">TfLiteParser.cpp:3059</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="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="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_tf_lite_parser_1_1_tf_lite_parser_xhtml_afe6c475f92d02dd1eb12acd746e4736f"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#afe6c475f92d02dd1eb12acd746e4736f">armnnTfLiteParser::TfLiteParser::GetOutputTensorIds</a></div><div class="ttdeci">static std::vector< int32_t > & GetOutputTensorIds(const ModelPtr &model, size_t subgraphIndex, size_t operatorIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l02749">TfLiteParser.cpp:2749</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_strided_slice_descriptor_xhtml_af996d82c47e43a16f4c8faa6c6b3e030"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#af996d82c47e43a16f4c8faa6c6b3e030">armnn::StridedSliceDescriptor::m_EllipsisMask</a></div><div class="ttdeci">int32_t m_EllipsisMask</div><div class="ttdoc">Ellipsis mask value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01056">Descriptors.hpp:1056</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="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4afa662c6eb71caef475b2b981ce8eccd7">armnn::LayerType::Permute</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="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser_xhtml_acf8cc929eadbabf197b36f7364d3d2cb"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_i_tf_lite_parser.xhtml#acf8cc929eadbabf197b36f7364d3d2cb">armnnTfLiteParser::ITfLiteParser::CreateRaw</a></div><div class="ttdeci">static ITfLiteParser * CreateRaw(const armnn::Optional< TfLiteParserOptions > &options=armnn::EmptyOptional())</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l03049">TfLiteParser.cpp:3049</a></div></div> +<div class="ttc" id="structarmnn_1_1_check_location_xhtml"><div class="ttname"><a href="structarmnn_1_1_check_location.xhtml">armnn::CheckLocation</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00014">Exceptions.hpp:14</a></div></div> +<div class="ttc" id="_verification_helpers_8hpp_xhtml_a479b2821a7a2cbb8fa8eb7f60a47065d"><div class="ttname"><a href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a></div><div class="ttdeci">#define CHECK_VALID_SIZE(ACTUAL,...)</div><div class="ttdef"><b>Definition:</b> <a href="_verification_helpers_8hpp_source.xhtml#l00032">VerificationHelpers.hpp:32</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="_verification_helpers_8hpp_xhtml_aaef93dc9a69f51b59f3cdd0ff0165927"><div class="ttname"><a href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a></div><div class="ttdeci">#define CHECKED_NON_NEGATIVE(VALUE)</div><div class="ttdef"><b>Definition:</b> <a href="_verification_helpers_8hpp_source.xhtml#l00035">VerificationHelpers.hpp:35</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="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_a37fa39012e90d568df7f774cd6d1e956"><div class="ttname"><a href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">armnn::numeric_cast</a></div><div class="ttdeci">std::enable_if_t< std::is_unsigned< Source >::value &&std::is_unsigned< Dest >::value, Dest > numeric_cast(Source source)</div><div class="ttdef"><b>Definition:</b> <a href="_numeric_cast_8hpp_source.xhtml#l00033">NumericCast.hpp:33</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="structarmnn_1_1_space_to_batch_nd_descriptor_xhtml_a02e143524aefddd40b485fcf7dea6696"><div class="ttname"><a href="structarmnn_1_1_space_to_batch_nd_descriptor.xhtml#a02e143524aefddd40b485fcf7dea6696">armnn::SpaceToBatchNdDescriptor::m_BlockShape</a></div><div class="ttdeci">std::vector< unsigned int > m_BlockShape</div><div class="ttdoc">Block shape value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00801">Descriptors.hpp:801</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml_a0d53caff836b84204adbd1c28752a201"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#a0d53caff836b84204adbd1c28752a201">armnn::StridedSliceDescriptor::m_Stride</a></div><div class="ttdeci">std::vector< int > m_Stride</div><div class="ttdoc">Stride values for the input that will be sliced. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01045">Descriptors.hpp:1045</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a58bfb9626d373249745d78b95543116e"><div class="ttname"><a href="namespacearmnn.xhtml#a58bfb9626d373249745d78b95543116e">armnn::IsActivationSupported</a></div><div class="ttdeci">bool IsActivationSupported(const BackendId &backend, const TensorInfo &input, const TensorInfo &output, const ActivationDescriptor &descriptor, char *reasonIfUnsupported=nullptr, size_t reasonIfUnsupportedMaxLength=1024)</div><div class="ttdoc">Deprecated in favor of IBackend and ILayerSupport interfaces. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_support_8cpp_source.xhtml#l00069">LayerSupport.cpp:69</a></div></div> +<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a></div><div class="ttdoc">An ActivationDescriptor for the ActivationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00020">Descriptors.hpp:20</a></div></div> +<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div> +<div class="ttc" id="_exceptions_8hpp_xhtml_aa3be76aec4ce713822a5ea1ecbb7bc61"><div class="ttname"><a href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a></div><div class="ttdeci">#define CHECK_LOCATION()</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00192">Exceptions.hpp:192</a></div></div> +<div class="ttc" id="structarmnn_1_1_stack_descriptor_xhtml_aed6086070440ceb94129bef06f70173f"><div class="ttname"><a href="structarmnn_1_1_stack_descriptor.xhtml#aed6086070440ceb94129bef06f70173f">armnn::StackDescriptor::m_NumInputs</a></div><div class="ttdeci">uint32_t m_NumInputs</div><div class="ttdoc">Number of input tensors. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00974">Descriptors.hpp:974</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_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="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a8b4b0b88a5e79a88b8b60db76398f575"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a8b4b0b88a5e79a88b8b60db76398f575">armnnTfLiteParser::TfLiteParser::GetInputTensorIds</a></div><div class="ttdeci">static std::vector< int32_t > & GetInputTensorIds(const ModelPtr &model, size_t subgraphIndex, size_t operatorIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l02739">TfLiteParser.cpp:2739</a></div></div> +<div class="ttc" id="_logging_8hpp_xhtml"><div class="ttname"><a href="_logging_8hpp.xhtml">Logging.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="_tf_lite_parser_8cpp_xhtml_ae38d96fe05581ea025713b3e781c5a43"><div class="ttname"><a href="_tf_lite_parser_8cpp.xhtml#ae38d96fe05581ea025713b3e781c5a43">CHECK_TENSOR_PTR</a></div><div class="ttdeci">#define CHECK_TENSOR_PTR(TENSOR_PTR)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00172">TfLiteParser.cpp:172</a></div></div> +<div class="ttc" id="_parser_helper_8hpp_xhtml"><div class="ttname"><a href="_parser_helper_8hpp.xhtml">ParserHelper.hpp</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_strided_slice_descriptor_xhtml_aa68194dd6258ab5b04123005a066ea25"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml#aa68194dd6258ab5b04123005a066ea25">armnn::StridedSliceDescriptor::m_End</a></div><div class="ttdeci">std::vector< int > m_End</div><div class="ttdoc">End values for the input that will be sliced. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01043">Descriptors.hpp:1043</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_activation_descriptor_xhtml_a017b2990003a014234f13e999dc7c689"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">armnn::ActivationDescriptor::m_A</a></div><div class="ttdeci">float m_A</div><div class="ttdoc">Alpha upper bound value used by the activation functions. (BoundedReLu, Linear, TanH). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00037">Descriptors.hpp:37</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="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="structarmnn_1_1_empty_optional_xhtml"><div class="ttname"><a href="structarmnn_1_1_empty_optional.xhtml">armnn::EmptyOptional</a></div><div class="ttdoc">EmptyOptional is used to initialize the Optional class in case we want to have default value for an O...</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00032">Optional.hpp:32</a></div></div> +<div class="ttc" id="classarmnn_1_1_parse_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_parse_exception.xhtml">armnn::ParseException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00092">Exceptions.hpp:92</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_aa0a90d432c9c41f9846f41f11c9e54c9"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aa0a90d432c9c41f9846f41f11c9e54c9">armnnTfLiteParser::TfLiteParser::OutputShapeOfSqueeze</a></div><div class="ttdeci">static armnn::TensorInfo OutputShapeOfSqueeze(const std::vector< uint32_t > &squeezeDims, const armnn::TensorInfo &inputTensorInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l01463">TfLiteParser.cpp:1463</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="_tf_lite_parser_8cpp_xhtml_ac578671d13bca92fee7f492110247cbf"><div class="ttname"><a href="_tf_lite_parser_8cpp.xhtml#ac578671d13bca92fee7f492110247cbf">CHECK_SUPPORTED_FUSED_ACTIVATION</a></div><div class="ttdeci">#define CHECK_SUPPORTED_FUSED_ACTIVATION(OPTION, SUBGRAPH_INDEX, OPERATOR_INDEX)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00259">TfLiteParser.cpp:259</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a0031997bf43bd2747656c31e4977793a"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">armnn::Pooling2dDescriptor::m_PoolType</a></div><div class="ttdeci">PoolingAlgorithm m_PoolType</div><div class="ttdoc">The pooling algorithm to use (Max. Average, L2). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00347">Descriptors.hpp:347</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="structarmnn_1_1_batch_to_space_nd_descriptor_xhtml_a3941f674c071c9503e00d2b59e92e454"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml#a3941f674c071c9503e00d2b59e92e454">armnn::BatchToSpaceNdDescriptor::m_Crops</a></div><div class="ttdeci">std::vector< std::pair< unsigned int, unsigned int > > m_Crops</div><div class="ttdoc">The values to crop from the input dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00682">Descriptors.hpp:682</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="_exceptions_8hpp_xhtml"><div class="ttname"><a href="_exceptions_8hpp.xhtml">Exceptions.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorShape::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00043">Tensor.hpp:43</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_affb5b68b3eba3ed45a06c7cde7781962"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#affb5b68b3eba3ed45a06c7cde7781962">armnn::Pooling2dDescriptor::m_OutputShapeRounding</a></div><div class="ttdeci">OutputShapeRounding m_OutputShapeRounding</div><div class="ttdoc">The rounding method for the output shape. (Floor, Ceiling). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00365">Descriptors.hpp:365</a></div></div> +<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml_a5b192c5fcd96a0f75542524cf646b355"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml#a5b192c5fcd96a0f75542524cf646b355">armnn::OriginsDescriptor::SetConcatAxis</a></div><div class="ttdeci">void SetConcatAxis(unsigned int concatAxis)</div><div class="ttdoc">Set the concatenation axis value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00150">Descriptors.cpp:150</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_aaafbda6e6816876b3d7963cfe64dd2f8"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aaafbda6e6816876b3d7963cfe64dd2f8">armnnTfLiteParser::TfLiteParser::BufferRawPtr</a></div><div class="ttdeci">const tflite::BufferT * BufferRawPtr</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00033">TfLiteParser.hpp:33</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="namespacearmnn_xhtml_a9a2af2f8c4af4f9efa8e79417d505ac4"><div class="ttname"><a href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4">armnn::ResizeMethod</a></div><div class="ttdeci">ResizeMethod</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00103">Types.hpp:103</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="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a58501692772880e7ef55485a4c95aab9"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a58501692772880e7ef55485a4c95aab9">armnnTfLiteParser::TfLiteParser::GetSubgraphInputTensorNames</a></div><div class="ttdeci">virtual std::vector< std::string > GetSubgraphInputTensorNames(size_t subgraphId) const override</div><div class="ttdoc">Return the input tensor names for a given subgraph. </div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l03023">TfLiteParser.cpp:3023</a></div></div> +<div class="ttc" id="namespacearmnn_tf_lite_parser_xhtml_a9084adbf804022c874039ad40d1939e9"><div class="ttname"><a href="namespacearmnn_tf_lite_parser.xhtml#a9084adbf804022c874039ad40d1939e9">armnnTfLiteParser::BindingPointInfo</a></div><div class="ttdeci">armnn::BindingPointInfo BindingPointInfo</div><div class="ttdef"><b>Definition:</b> <a href="_i_tf_lite_parser_8hpp_source.xhtml#l00020">ITfLiteParser.hpp:20</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="namespacearmnn_tf_lite_parser_xhtml"><div class="ttname"><a href="namespacearmnn_tf_lite_parser.xhtml">armnnTfLiteParser</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_tf_lite_parser_8hpp_source.xhtml#l00017">ITfLiteParser.hpp:17</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_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="classarmnn_1_1_i_connectable_layer_xhtml_afcc1c3a20bd2860e0ddd21674389246f"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">armnn::IConnectableLayer::GetName</a></div><div class="ttdeci">virtual const char * GetName() const =0</div><div class="ttdoc">Returns the name of the layer. </div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_ab97e69d07f06e392bd0cc2e5bcbf1be6"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ab97e69d07f06e392bd0cc2e5bcbf1be6">armnnTfLiteParser::TfLiteParser::GetSubgraphOutputTensorNames</a></div><div class="ttdeci">virtual std::vector< std::string > GetSubgraphOutputTensorNames(size_t subgraphId) const override</div><div class="ttdoc">Return the output tensor names for a given subgraph. </div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l03036">TfLiteParser.cpp:3036</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</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="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_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="structarmnn_1_1_check_location_xhtml_a46e3b4b140e2c550342337b5fcceb9c6"><div class="ttname"><a href="structarmnn_1_1_check_location.xhtml#a46e3b4b140e2c550342337b5fcceb9c6">armnn::CheckLocation::m_Function</a></div><div class="ttdeci">const char * m_Function</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00016">Exceptions.hpp:16</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="_file_only_profiling_decorator_tests_8cpp_xhtml_a6560146509197f3e197d8d36f76c1347"><div class="ttname"><a href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a></div><div class="ttdeci">armnn::Runtime::CreationOptions::ExternalProfilingOptions options</div><div class="ttdef"><b>Definition:</b> <a href="_file_only_profiling_decorator_tests_8cpp_source.xhtml#l00045">FileOnlyProfilingDecoratorTests.cpp:45</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="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_abfc86471394295357a23b3addd0b5b1c"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#abfc86471394295357a23b3addd0b5b1c">armnnTfLiteParser::TfLiteParser::ModelPtr</a></div><div class="ttdeci">std::unique_ptr< tflite::ModelT > ModelPtr</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00023">TfLiteParser.hpp:23</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorInfo::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00092">Tensor.hpp:92</a></div></div> +<div class="ttc" id="_tf_lite_parser_8cpp_xhtml_ae6440b8ea95cf981cd7bbffa52c22fe1"><div class="ttname"><a href="_tf_lite_parser_8cpp.xhtml#ae6440b8ea95cf981cd7bbffa52c22fe1">CHECK_BUFFER_SIZE</a></div><div class="ttdeci">#define CHECK_BUFFER_SIZE(BUFFER_PTR, TENSOR_INFO, BUFFER_ID)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00238">TfLiteParser.cpp:238</a></div></div> +<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml_a28c4c9cb15f6be3499abbc46b356060b"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">armnn::ActivationDescriptor::m_B</a></div><div class="ttdeci">float m_B</div><div class="ttdoc">Beta lower bound value used by the activation functions. (BoundedReLu, Linear, TanH). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00039">Descriptors.hpp:39</a></div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml_abeaf4f6785039866fd075f4569ba8e84"><div class="ttname"><a href="namespacearmnn_utils.xhtml#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a></div><div class="ttdeci">armnn::TensorShape Permuted(const armnn::TensorShape &srcShape, const armnn::PermutationVector &mappings)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.xhtml#l00098">Permute.cpp:98</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="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_ac3486e6c1a291aa67efd8b280ffb83cc"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#ac3486e6c1a291aa67efd8b280ffb83cc">armnnTfLiteParser::TfLiteParser::TensorRawPtr</a></div><div class="ttdeci">const tflite::TensorT * TensorRawPtr</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00028">TfLiteParser.hpp:28</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="structarmnn_1_1_activation_descriptor_xhtml_af10fa7883e3579950f477bee92a64844"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">armnn::ActivationDescriptor::m_Function</a></div><div class="ttdeci">ActivationFunction m_Function</div><div class="ttdoc">The activation function to use (Sigmoid, TanH, Linear, ReLu, BoundedReLu, SoftReLu, LeakyReLu, Abs, Sqrt, Square). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00035">Descriptors.hpp:35</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_aadad81a95152fe5aad839db352d4012c"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#aadad81a95152fe5aad839db352d4012c">armnnTfLiteParser::TfLiteParser::OperatorPtr</a></div><div class="ttdeci">std::unique_ptr< tflite::OperatorT > OperatorPtr</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00025">TfLiteParser.hpp:25</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_input_slot_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_input_slot.xhtml">armnn::IInputSlot</a></div><div class="ttdoc">An input connection slot for a layer. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00024">INetwork.hpp:24</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a2ed4853234865d838da50085da99b2a6"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a2ed4853234865d838da50085da99b2a6">armnnTfLiteParser::TfLiteParser::TfLiteParser</a></div><div class="ttdeci">TfLiteParser(const armnn::Optional< ITfLiteParser::TfLiteParserOptions > &options=armnn::EmptyOptional())</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8cpp_source.xhtml#l00488">TfLiteParser.cpp:488</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Pooling2dDescriptor::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#l00363">Descriptors.hpp:363</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="namespacearmnn_xhtml_abdcd184ed3bd648bb31d385040cafd5d"><div class="ttname"><a href="namespacearmnn.xhtml#abdcd184ed3bd648bb31d385040cafd5d">armnn::MaxNumOfTensorDimensions</a></div><div class="ttdeci">constexpr unsigned int MaxNumOfTensorDimensions</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00018">Types.hpp:18</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="classarmnn_1_1_tensor_info_xhtml_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorInfo::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00093">Tensor.hpp:93</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="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9ea"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9ea">armnn::ActivationFunction</a></div><div class="ttdeci">ActivationFunction</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00055">Types.hpp:55</a></div></div> +<div class="ttc" id="classarmnn_tf_lite_parser_1_1_tf_lite_parser_xhtml_a86428e0c674542488c7292dfbe2ce381"><div class="ttname"><a href="classarmnn_tf_lite_parser_1_1_tf_lite_parser.xhtml#a86428e0c674542488c7292dfbe2ce381">armnnTfLiteParser::TfLiteParser::TensorIdRawPtrVector</a></div><div class="ttdeci">std::vector< TensorIdRawPtr > TensorIdRawPtrVector</div><div class="ttdef"><b>Definition:</b> <a href="_tf_lite_parser_8hpp_source.xhtml#l00031">TfLiteParser.hpp:31</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><!-- 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_510324e450b9df55f9aac2d01fae83d8.xhtml">armnnTfLiteParser</a></li><li class="navelem"><a class="el" href="_tf_lite_parser_8cpp.xhtml">TfLiteParser.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> |