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diff --git a/20.02/_tf_parser_8cpp_source.xhtml b/20.02/_tf_parser_8cpp_source.xhtml new file mode 100644 index 0000000000..d466daaa5b --- /dev/null +++ b/20.02/_tf_parser_8cpp_source.xhtml @@ -0,0 +1,321 @@ +<!-- 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/armnnTfParser/TfParser.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_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">TfParser.cpp</div> </div> +</div><!--header--> +<div class="contents"> +<a href="_tf_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_parser_8hpp.xhtml">TfParser.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="_types_utils_8hpp.xhtml">armnn/TypesUtils.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_descriptors_8hpp.xhtml">armnn/Descriptors.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_permute_8hpp.xhtml">armnnUtils/Permute.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <<a class="code" href="_data_layout_indexed_8hpp.xhtml">armnnUtils/DataLayoutIndexed.hpp</a>></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <<a class="code" href="_transpose_8hpp.xhtml">armnnUtils/Transpose.hpp</a>></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</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="l00015"></a><span class="lineno"> 15</span> </div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <<a class="code" href="_graph_topological_sort_8hpp.xhtml">GraphTopologicalSort.hpp</a>></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> </div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include <google/protobuf/io/zero_copy_stream_impl.h></span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#include <google/protobuf/text_format.h></span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="preprocessor">#include <tensorflow/core/framework/graph.pb.h></span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> </div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include <boost/format.hpp></span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#include <boost/format.hpp></span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#include <boost/numeric/conversion/cast.hpp></span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include <boost/polymorphic_cast.hpp></span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> </div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include <numeric></span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn_utils.xhtml">armnnUtils</a>;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn_tf_parser.xhtml">armnnTfParser</a></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> {</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> {</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> </div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> <a class="code" href="namespacearmnn_utils.xhtml#a12124184ac6aec018beb98b9715330c7">NHWCToArmNN</a> = { 0, 2, 3, 1 };</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> <a class="code" href="namespacearmnn_utils.xhtml#a59cbccbfbae7633020d200f8c23fe69e">ArmNNToNHWC</a> = { 0, 3, 1, 2 };</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</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">template</span> <<span class="keyword">typename</span> Callable></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="keywordtype">void</span> ReadMandatoryNodeAttributeImpl(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keyword">const</span> std::string& attribName,</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  tensorflow::AttrValue::ValueCase expectedValueCase,</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  Callable callable)</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>  <span class="keyword">auto</span> iter = nodeDef.attr().find(attribName);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keywordflow">if</span> (iter != nodeDef.attr().end())</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="keyword">const</span> <span class="keyword">auto</span>& attrValue = iter->second;</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordflow">if</span> (attrValue.value_case() == expectedValueCase)</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  {</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  callable(attrValue);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  }</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  {</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  boost::str(</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  boost::format(</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="stringliteral">"Attribute %1% of node %2% expected to have %3% as tensorflow::AttrValue::ValueCase, "</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="stringliteral">"but found %4% instead %5%"</span>)</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  % attribName</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  % nodeDef.name()</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  % <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(expectedValueCase)</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  % static_cast<int>(attrValue.value_case())</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  }</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  }</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  {</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  boost::str(</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  boost::format(</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="stringliteral">"Could not find required attribute %1% in node %2% %3%"</span>)</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  % attribName</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  % nodeDef.name()</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  }</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> }</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> </div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> <span class="keyword">template</span> <<span class="keyword">typename</span> Callable></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> <span class="keywordtype">void</span> ReadOptionalNodeAttributeImpl(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keyword">const</span> std::string& attribName,</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  tensorflow::AttrValue::ValueCase expectedValueCase,</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  Callable callable)</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="keyword">auto</span> iter = nodeDef.attr().find(attribName);</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keywordflow">if</span> (iter != nodeDef.attr().end())</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  {</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& attrValue = iter->second;</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keywordflow">if</span> (attrValue.value_case() == expectedValueCase)</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  {</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  callable(attrValue);</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  }</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordflow">else</span></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">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  boost::str(</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  boost::format(</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="stringliteral">"Attribute %1% of node %2% expected to have %3% as tensorflow::AttrValue::ValueCase, "</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="stringliteral">"but found %4% instead %5%"</span>)</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  % attribName</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  % nodeDef.name()</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  % <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(expectedValueCase)</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  % static_cast<int>(attrValue.value_case())</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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>  }</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> }</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> </div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> <span class="keywordtype">float</span> ReadMandatoryNodeFloatAttribute(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> std::string& name)</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> {</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="keywordtype">float</span> attribValue = 0.0f;</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  ReadMandatoryNodeAttributeImpl(nodeDef, name, tensorflow::AttrValue::kF,</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  [&attribValue](<span class="keyword">const</span> tensorflow::AttrValue& attrValue)</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  {</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  attribValue = attrValue.f();</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  });</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keywordflow">return</span> attribValue;</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"> 124</span> int32_t ReadMandatoryNodeInt32Attribute(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> std::string& name)</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> {</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  int32_t attribValue = 0u;</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  ReadMandatoryNodeAttributeImpl(nodeDef, name, tensorflow::AttrValue::kI,</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  [&attribValue](<span class="keyword">const</span> tensorflow::AttrValue& attrValue)</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  {</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  attribValue = <span class="keyword">static_cast<</span>int32_t<span class="keyword">></span>(attrValue.i());</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="keywordflow">return</span> attribValue;</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> }</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> </div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> <span class="keywordtype">bool</span> ReadMandatoryNodeBoolAttribute(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> std::string& name)</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> {</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keywordtype">bool</span> attribValue = <span class="keyword">false</span>;</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  ReadMandatoryNodeAttributeImpl(nodeDef, name, tensorflow::AttrValue::kB,</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  [&attribValue](<span class="keyword">const</span> tensorflow::AttrValue& attrValue)</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  {</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  attribValue = <span class="keyword">static_cast<</span><span class="keywordtype">bool</span><span class="keyword">></span>(attrValue.b());</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">return</span> attribValue;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> }</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> </div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> uint32_t ReadMandatoryNodeUint32Attribute(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> std::string& name)</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> {</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  uint32_t attribValue = 0u;</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  ReadMandatoryNodeAttributeImpl(nodeDef, name, tensorflow::AttrValue::kI,</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  [&attribValue](<span class="keyword">const</span> tensorflow::AttrValue& attrValue)</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>  attribValue = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(attrValue.i());</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  });</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="keywordflow">return</span> attribValue;</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</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> std::string ReadMandatoryNodeStringAttribute(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> std::string& name)</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> {</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  std::string attribValue = <span class="stringliteral">""</span>;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  ReadMandatoryNodeAttributeImpl(nodeDef, name, tensorflow::AttrValue::kS,</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  [&attribValue](<span class="keyword">const</span> tensorflow::AttrValue& attrValue)</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  {</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  attribValue = attrValue.s();</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  });</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="keywordflow">return</span> attribValue;</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> }</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> </div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> std::vector<uint32_t> ReadMandatoryNodeUint32ListAttribute(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keyword">const</span> std::string& name)</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>  std::vector<uint32_t> attriList;</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  ReadMandatoryNodeAttributeImpl(nodeDef, name, tensorflow::AttrValue::kList,</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  [&attriList](<span class="keyword">const</span> tensorflow::AttrValue& attrValue)</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="keywordflow">for</span> (<span class="keywordtype">int</span> attriNum = 0; attriNum < attrValue.list().i_size(); ++attriNum)</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  {</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  attriList.push_back(static_cast<uint32_t>(attrValue.list().i(attriNum)));</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>  });</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">return</span> attriList;</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> }</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> </div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span> std::vector<uint32_t> ReadOptionalNodeUint32ListAttribute(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="keyword">const</span> std::string& name)</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> {</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  std::vector<uint32_t> attriList;</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  ReadOptionalNodeAttributeImpl(nodeDef, name, tensorflow::AttrValue::kList,</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  [&attriList](<span class="keyword">const</span> tensorflow::AttrValue& attrValue)</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  {</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> attriNum = 0; attriNum < attrValue.list().i_size(); ++attriNum)</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  {</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  attriList.push_back(static_cast<uint32_t>(attrValue.list().i(attriNum)));</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  }</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  });</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> </div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="keywordflow">return</span> attriList;</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> }</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> </div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span> std::string ReadOptionalNodeStringAttribute(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <span class="keyword">const</span> std::string& name,</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="keyword">const</span> std::string& defaultValue = <span class="stringliteral">""</span>)</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span> {</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  std::string attribValue = defaultValue;</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  ReadOptionalNodeAttributeImpl(nodeDef, name, tensorflow::AttrValue::kS,</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  [&attribValue](<span class="keyword">const</span> tensorflow::AttrValue& attrValue)</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>  attribValue = attrValue.s();</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  });</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keywordflow">return</span> attribValue;</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</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">bool</span> ReadOptionalNodeBoolAttribute(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keyword">const</span> std::string& name,</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="keywordtype">bool</span> defaultValue = <span class="keyword">false</span>)</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span> {</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="keywordtype">bool</span> attribValue = defaultValue;</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  ReadOptionalNodeAttributeImpl(nodeDef, name, tensorflow::AttrValue::kB,</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  [&attribValue](<span class="keyword">const</span> tensorflow::AttrValue& attrValue)</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  {</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  attribValue = attrValue.b();</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  });</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="keywordflow">return</span> attribValue;</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> }</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span> </div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">tensorflow::DataType</a> ReadMandatoryNodeTypeAttribute(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> std::string& name)</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> {</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">tensorflow::DataType</a> attribValue = tensorflow::DT_INVALID;</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  ReadMandatoryNodeAttributeImpl(nodeDef, name, tensorflow::AttrValue::kType,</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  [&attribValue](<span class="keyword">const</span> tensorflow::AttrValue& attrValue)</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  {</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  attribValue = attrValue.type();</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  });</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keywordflow">return</span> attribValue;</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> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> PrepareReshape(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& input, <span class="keyword">const</span> std::vector<int32_t>& targetDims)</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span> {</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  std::vector<unsigned int> outDims(targetDims.begin(), targetDims.end());</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keyword">const</span> <span class="keyword">auto</span> stretchDim = std::find(targetDims.begin(), targetDims.end(), -1);</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> </div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="keywordflow">if</span> (stretchDim != targetDims.end())</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  {</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keywordflow">if</span> (std::find(std::next(stretchDim), targetDims.end(), -1) != targetDims.end())</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  {</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  boost::str(</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  boost::format(</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="stringliteral">"At most one component of shape can be -1 %1%"</span>)</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keyword">auto</span> targetNumElements =</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</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="l00255"></a><span class="lineno"> 255</span>  std::accumulate(targetDims.begin(), targetDims.end(), -1, std::multiplies<int32_t>()));</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</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(targetDims.begin(), stretchDim));</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  outDims[stretchIndex] = input.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / targetNumElements;</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  }</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span> </div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> reshapeInfo = input;</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  reshapeInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{ <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(outDims.size()), outDims.data() });</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> </div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="keywordflow">return</span> reshapeInfo;</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span> }</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span> </div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span> <span class="comment">// We need the input0Slot to guide the reshape for input1Slot.</span></div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* AddBroadcastReshapeLayer(<a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input0Slot, <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input1Slot, <span class="keywordtype">bool</span> isNHWC,</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <a class="code" href="classarmnn_1_1_i_network.xhtml">INetwork</a>& m_Network, <span class="keyword">const</span> tensorflow::NodeDef& nodeDef)</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> {</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& input1Info = input1Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> matchDim = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - (isNHWC ? 1 : 3);</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  std::array<unsigned int, MaxNumOfTensorDimensions> reshapedDimensions;</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  std::fill_n(reshapedDimensions.begin(), inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(), 1);</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  reshapedDimensions[matchDim] = input1Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0];</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> reshapedInfo = input1Info;</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  reshapedInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{ inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(), reshapedDimensions.data() });</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>  <span class="keyword">const</span> std::string reshapeLayerName = <span class="stringliteral">"reshape_for-"</span> + nodeDef.name();</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">ReshapeDescriptor</a> reshapeDesc;</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  reshapeDesc.<a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = reshapedInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> reshapeLayer = m_Network.<a class="code" href="classarmnn_1_1_i_network.xhtml#ac77b89eb982f9d745730c90fcbdddba4">AddReshapeLayer</a>(reshapeDesc, reshapeLayerName.c_str());</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span> </div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  input1Slot-><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="l00286"></a><span class="lineno"> 286</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>(reshapedInfo);</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>  input1Slot = &reshapeLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0);</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span> </div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="keywordflow">return</span> input1Slot;</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span> }</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span> </div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#abcf8e5fd95ba7e7bd8cd36fc24974223">OutputId</a> ParseOutputId(<span class="keyword">const</span> std::string & name)</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span> {</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = 0;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <span class="keywordtype">size_t</span> colonPos = name.find_last_of(<span class="stringliteral">":"</span>);</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <span class="keywordflow">if</span> (colonPos != std::string::npos)</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  {</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <span class="keywordtype">int</span> n = std::stoi(name.substr(colonPos+1));</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="keywordflow">if</span> (n<0 || n>100)</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  {</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  boost::str(</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  boost::format(</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="stringliteral">"Output tensor id is out of range for %1% %2%"</span>)</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  % name</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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>  outputNum = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(n);</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>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#abcf8e5fd95ba7e7bd8cd36fc24974223">OutputId</a>(name.substr(0,colonPos),outputNum);</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span> }</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span> </div><div class="line"><a name="l00314"></a><span class="lineno"><a class="line" href="_tf_parser_8cpp.xhtml#a3fb047570644cae325aa88d3cd7bb96e"> 314</a></span> <span class="preprocessor">#define CHECK_DATA_FORMAT(NODE_DEF, FORMAT, NODE_TYPE) \</span></div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span> <span class="preprocessor"> if( FORMAT != "NHWC" && FORMAT != "NCHW" ) \</span></div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span> <span class="preprocessor"> { \</span></div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span> <span class="preprocessor"> throw ParseException( \</span></div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span> <span class="preprocessor"> boost::str( \</span></div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span> <span class="preprocessor"> boost::format( \</span></div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span> <span class="preprocessor"> "Unsupported data format %1% passed for %2% node %3%. " \</span></div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span> <span class="preprocessor"> "Only NHWC and NCHW supported %4%") \</span></div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span> <span class="preprocessor"> % FORMAT \</span></div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span> <span class="preprocessor"> % NODE_TYPE \</span></div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span> <span class="preprocessor"> % NODE_DEF.name() \</span></div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span> <span class="preprocessor"> % CHECK_LOCATION().AsString())); \</span></div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span> <span class="preprocessor"> }</span></div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span> </div><div class="line"><a name="l00328"></a><span class="lineno"><a class="line" href="_tf_parser_8cpp.xhtml#aab838eb7734e531bb5be6f6dece673bf"> 328</a></span> <span class="preprocessor">#define CHECK_PADDING_TYPE(NODE_DEF, PADDING) \</span></div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span> <span class="preprocessor"> if(PADDING != "SAME" && PADDING != "VALID" ) \</span></div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span> <span class="preprocessor"> { \</span></div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span> <span class="preprocessor"> throw ParseException( \</span></div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span> <span class="preprocessor"> boost::str( \</span></div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span> <span class="preprocessor"> boost::format( \</span></div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span> <span class="preprocessor"> "Only 'SAME' and 'VALID' padding supported. Got %1% for %2% %3%") \</span></div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span> <span class="preprocessor"> % PADDING \</span></div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span> <span class="preprocessor"> % NODE_DEF.name() \</span></div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span> <span class="preprocessor"> % CHECK_LOCATION().AsString())); \</span></div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span> <span class="preprocessor"> } \</span></div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span> <span class="preprocessor"></span></div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span> } <span class="comment">// namespace</span></div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span> </div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span> <span class="keyword">const</span> std::map<std::string, TfParser::OperationParsingFunction> TfParser::ms_OperationNameToParsingFunctions = {</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  { <span class="stringliteral">"Const"</span>, &TfParser::ParseConst },</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  { <span class="stringliteral">"Add"</span>, &TfParser::ParseAdd },</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  { <span class="stringliteral">"AddN"</span>, &TfParser::ParseAddN },</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  { <span class="stringliteral">"BiasAdd"</span>, &TfParser::ParseBiasAdd },</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  { <span class="stringliteral">"Identity"</span>, &TfParser::ParseIdentity },</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  { <span class="stringliteral">"Conv2D"</span>, &TfParser::ParseConv2D },</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  { <span class="stringliteral">"DepthwiseConv2dNative"</span>, &TfParser::ParseDepthwiseConv2D },</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  { <span class="stringliteral">"ExpandDims"</span>, &TfParser::ParseExpandDims },</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  { <span class="stringliteral">"FusedBatchNorm"</span>, &TfParser::ParseFusedBatchNorm },</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  { <span class="stringliteral">"Gather"</span>, &TfParser::ParseGather},</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  { <span class="stringliteral">"Greater"</span>, &TfParser::ParseGreater},</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  { <span class="stringliteral">"ConcatV2"</span>, &TfParser::ParseConcat },</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  { <span class="stringliteral">"LRN"</span>, &TfParser::ParseLrn },</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  { <span class="stringliteral">"MatMul"</span>, &TfParser::ParseMatMul },</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  { <span class="stringliteral">"Mean"</span>, &TfParser::ParseMean },</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  { <span class="stringliteral">"Mul"</span>, &TfParser::ParseMul },</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  { <span class="stringliteral">"Placeholder"</span>, &TfParser::ParsePlaceholder },</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  { <span class="stringliteral">"RealDiv"</span>, &TfParser::ParseRealDiv },</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  { <span class="stringliteral">"Relu"</span>, &TfParser::ParseRelu },</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  { <span class="stringliteral">"Relu6"</span>, &TfParser::ParseRelu6 },</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  { <span class="stringliteral">"Reshape"</span>, &TfParser::ParseReshape },</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  { <span class="stringliteral">"ResizeBilinear"</span>, &TfParser::ParseResizeBilinear },</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  { <span class="stringliteral">"Rsqrt"</span>, &TfParser::ParseRsqrt },</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  { <span class="stringliteral">"Shape"</span>, &TfParser::ParseShape },</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  { <span class="stringliteral">"Squeeze"</span>, &TfParser::ParseSqueeze },</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  { <span class="stringliteral">"Sigmoid"</span>, &TfParser::ParseSigmoid },</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  { <span class="stringliteral">"Softmax"</span>, &TfParser::ParseSoftmax },</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  { <span class="stringliteral">"Softplus"</span>, &TfParser::ParseSoftplus },</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  { <span class="stringliteral">"Split"</span>, &TfParser::ParseSplit },</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  { <span class="stringliteral">"StridedSlice"</span>, &TfParser::ParseStridedSlice },</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  { <span class="stringliteral">"Tanh"</span>, &TfParser::ParseTanh },</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  { <span class="stringliteral">"MaxPool"</span>, &TfParser::ParseMaxPool },</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  { <span class="stringliteral">"AvgPool"</span>, &TfParser::ParseAvgPool },</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  { <span class="stringliteral">"Maximum"</span>, &TfParser::ParseMaximum },</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  { <span class="stringliteral">"Minimum"</span>, &TfParser::ParseMinimum },</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  { <span class="stringliteral">"Equal"</span>, &TfParser::ParseEqual },</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  { <span class="stringliteral">"Pad"</span>, &TfParser::ParsePad },</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  { <span class="stringliteral">"Sub"</span>, &TfParser::ParseSub },</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  { <span class="stringliteral">"Pack"</span> , &TfParser::ParseStack },</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  { <span class="stringliteral">"Stack"</span>, &TfParser::ParseStack },</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  { <span class="stringliteral">"Transpose"</span>, &TfParser::ParseTranspose },</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span> };</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span> </div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span> <span class="keyword">const</span> std::list<std::string> TfParser::m_ControlInputs = {</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <span class="stringliteral">"Assert"</span></div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span> };</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span> </div><div class="line"><a name="l00390"></a><span class="lineno"><a class="line" href="classarmnn_tf_parser_1_1_i_tf_parser.xhtml#acef5ea7cde55834460c7d8c79d646242"> 390</a></span> <a class="code" href="classarmnn_tf_parser_1_1_i_tf_parser.xhtml">ITfParser</a>* ITfParser::CreateRaw()</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">return</span> <span class="keyword">new</span> <a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml">TfParser</a>();</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> </div><div class="line"><a name="l00395"></a><span class="lineno"><a class="line" href="classarmnn_tf_parser_1_1_i_tf_parser.xhtml#a6abaf60481f0791c686ed3b97818bd0c"> 395</a></span> <a class="code" href="namespacearmnn_tf_parser.xhtml#af7cec8b9a69e02f18a5de38502675d94">ITfParserPtr</a> ITfParser::Create()</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="keywordflow">return</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#af7cec8b9a69e02f18a5de38502675d94">ITfParserPtr</a>(CreateRaw(), &ITfParser::Destroy);</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span> }</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span> </div><div class="line"><a name="l00400"></a><span class="lineno"><a class="line" href="classarmnn_tf_parser_1_1_i_tf_parser.xhtml#af462572a18d8158847b326970d42c246"> 400</a></span> <span class="keywordtype">void</span> ITfParser::Destroy(<a class="code" href="classarmnn_tf_parser_1_1_i_tf_parser.xhtml">ITfParser</a>* parser)</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="keyword">delete</span> parser;</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span> }</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span> </div><div class="line"><a name="l00405"></a><span class="lineno"><a class="line" href="namespacearmnn_tf_parser.xhtml#a0540bb475d62bab024eebe8685181845"> 405</a></span> <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#a0540bb475d62bab024eebe8685181845">CalculateSamePadding</a>(uint32_t inputSize, uint32_t stride,</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  uint32_t filterSize, <span class="keywordtype">bool</span> samePadding,</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  uint32_t* paddingFront, uint32_t* paddingBack) {</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  *paddingFront = 0;</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  *paddingBack = 0;</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>  <span class="keywordflow">if</span> (samePadding) {</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  uint32_t outputSize = (inputSize + stride - 1) / stride;</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  uint32_t temp = (outputSize - 1) * stride + filterSize;</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <span class="keywordflow">if</span> (temp > inputSize) {</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  *paddingFront = (temp - inputSize) / 2;</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  *paddingBack = (temp - inputSize) - *paddingFront;</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  }</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  }</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> }</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span> </div><div class="line"><a name="l00421"></a><span class="lineno"><a class="line" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f"> 421</a></span> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(uint32_t input, uint32_t kernel, uint32_t stride, uint32_t& outPadHead, uint32_t& outPadTail,</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="keywordtype">bool</span> samePadding)</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span> {</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <a class="code" href="namespacearmnn_tf_parser.xhtml#a0540bb475d62bab024eebe8685181845">CalculateSamePadding</a>(input, stride, kernel, samePadding, &outPadHead, &outPadTail);</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="comment"></span></div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span> <span class="comment">/// An Abstract base class which represents a single tensorflow operation (node)</span></div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span> <span class="comment">/// that has been (potentially partially) converted to Armnn.</span></div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span> <span class="comment">/// It may not yet have been fully converted into actual Armnn layers.</span></div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span> <span class="comment"></span><span class="keyword">class </span>ParsedTfOperation</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span> {</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  ParsedTfOperation(<a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml">TfParser</a>* parser, <span class="keyword">const</span> tensorflow::NodeDef& node)</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  : m_Parser(parser)</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  , m_Node(node)</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>  }</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span> </div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  <span class="keyword">virtual</span> ~ParsedTfOperation() {};</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span> </div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <span class="keyword">const</span> tensorflow::NodeDef& GetNode()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_Node; }</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span> <span class="comment"></span></div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span> <span class="comment"> /// Gets the ArmNN IOutputSlot corresponding to the given output index of the Tensorflow operation.</span></div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span> <span class="comment"> /// This may result in the creation of Armnn layers if this was deferred (e.g. see ParsedConstTfOperation).</span></div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span> <span class="comment"></span> <span class="keyword">virtual</span> <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& ResolveArmnnOutputSlot(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tfOutputIndex) = 0;</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span> <span class="comment"></span></div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span> <span class="comment"> /// If this operation is an Identity then this will follow return the 'parent' operation (recursively).</span></div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span> <span class="comment"></span> <span class="keyword">virtual</span> ParsedTfOperation* ResolveIdentityOperations()</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  {</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  <span class="keywordflow">return</span> <span class="keyword">this</span>;</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  }</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span> </div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span> <span class="keyword">protected</span>:</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  <a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml">TfParser</a>* m_Parser;</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="keyword">const</span> tensorflow::NodeDef& m_Node;</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span> };</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span> <span class="comment"></span></div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span> <span class="comment">/// An ParsedTfOperation where the Armnn equivalent is a single layer,</span></div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span> <span class="comment">/// with output slots that correspond directly to the Tf node outputs.</span></div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span> <span class="comment"></span><span class="keyword">class </span>SingleLayerParsedTfOperation : <span class="keyword">public</span> ParsedTfOperation</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span> {</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  SingleLayerParsedTfOperation(<a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml">TfParser</a>* parser, <span class="keyword">const</span> tensorflow::NodeDef& node, <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer)</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  : ParsedTfOperation(parser, node)</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  , <a class="code" href="_subgraph_view_selector_8cpp.xhtml#ae7fc37b88ff10e9294ed1c72f2a25ac7">m_Layer</a>(layer)</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  {</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> </div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& ResolveArmnnOutputSlot(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tfOutputIndex)<span class="keyword"> override</span></div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  BOOST_ASSERT(<a class="code" href="_subgraph_view_selector_8cpp.xhtml#ae7fc37b88ff10e9294ed1c72f2a25ac7">m_Layer</a>);</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <span class="comment">// Assumes one-to-one mapping between Tf and armnn output slots.</span></div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> armnnOutputSlotIdx = tfOutputIndex;</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <span class="keywordflow">if</span> (armnnOutputSlotIdx >= <a class="code" href="_subgraph_view_selector_8cpp.xhtml#ae7fc37b88ff10e9294ed1c72f2a25ac7">m_Layer</a>-><a class="code" href="classarmnn_1_1_layer.xhtml#a1594bddc87d6477df300317658f566bb">GetNumOutputSlots</a>())</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  {</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  boost::str(</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  boost::format(</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="stringliteral">"The requested output slot #%1% "</span></div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <span class="stringliteral">"for %2% does not exist %3%"</span>)</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  % armnnOutputSlotIdx</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  % <a class="code" href="_subgraph_view_selector_8cpp.xhtml#ae7fc37b88ff10e9294ed1c72f2a25ac7">m_Layer</a>-><a class="code" href="classarmnn_1_1_layer.xhtml#a7ddf0cf6f620d59c10e63495ace795d0">GetName</a>()</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  }</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <span class="keywordflow">return</span> <a class="code" href="_subgraph_view_selector_8cpp.xhtml#ae7fc37b88ff10e9294ed1c72f2a25ac7">m_Layer</a>-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(armnnOutputSlotIdx);</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</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"> 488</span> <span class="keyword">protected</span>:</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <a class="code" href="_subgraph_view_selector_8cpp.xhtml#ae7fc37b88ff10e9294ed1c72f2a25ac7">m_Layer</a>;</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span> };</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span> <span class="comment"></span></div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span> <span class="comment">/// A SingleLayerParsedTfOperation for deferred layer creation.</span></div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span> <span class="comment"></span><span class="keyword">class </span>DeferredSingleLayerParsedTfOperation : <span class="keyword">public</span> SingleLayerParsedTfOperation</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span> {</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  DeferredSingleLayerParsedTfOperation(<a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml">TfParser</a>* parser, <span class="keyword">const</span> tensorflow::NodeDef& node)</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  : SingleLayerParsedTfOperation(parser, node, <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  {</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  }</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span> </div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& ResolveArmnnOutputSlot(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tfOutputIndex)<span class="keyword"> override</span></div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  <span class="keywordflow">if</span> (!<a class="code" href="_subgraph_view_selector_8cpp.xhtml#ae7fc37b88ff10e9294ed1c72f2a25ac7">m_Layer</a>)</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  {</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  CreateLayerDeferred();</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  }</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <span class="keywordflow">return</span> SingleLayerParsedTfOperation::ResolveArmnnOutputSlot(tfOutputIndex);</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  }</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span> </div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <span class="keyword">virtual</span> <span class="keywordtype">void</span> CreateLayerDeferred() = 0;</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span> };</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span> </div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span> </div><div class="line"><a name="l00515"></a><span class="lineno"><a class="line" href="classarmnn_tf_parser_1_1_tf_parser.xhtml#a52c2ae3fa88f24fcf37a08672d217100"> 515</a></span> TfParser::TfParser()</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  : m_Network(nullptr, nullptr)</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span> {</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span> }</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span> </div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span> </div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span> <span class="keyword">const</span> tensorflow::NodeDef* TfParser::ResolveIdentityNode(<span class="keyword">const</span> tensorflow::NodeDef* nodeDef)</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span> {</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  <span class="keywordflow">if</span> (nodeDef->op() != <span class="stringliteral">"Identity"</span>)</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  {</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  <span class="keywordflow">return</span> nodeDef;</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  }</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span> </div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  <span class="keywordflow">if</span> (nodeDef->input_size() != 1)</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="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  boost::str(</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  boost::format(</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="stringliteral">"Identity node should have a single input! %1% has %2% inputs %3%"</span>)</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  % nodeDef->name()</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  % nodeDef->input_size()</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  }</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span> </div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  <span class="keyword">auto</span> it = m_NodesByName.find(nodeDef->input(0));</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  <span class="keywordflow">if</span> (it != m_NodesByName.end())</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  {</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="keyword">const</span> tensorflow::NodeDef* inputNode = it->second;</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <span class="keywordflow">return</span> ResolveIdentityNode(inputNode);</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  {</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  boost::str(</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  boost::format(</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  <span class="stringliteral">"Cannot find what the Identity node %1% is linked to! %2%"</span>)</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  % nodeDef->name()</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  }</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span> }</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span> </div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span> std::vector<OutputOfConstNodeDef></div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span> TfParser::GetTfInputNodes(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef)<span class="keyword"> const</span></div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span> <span class="keyword"></span>{</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  std::vector<OutputOfConstNodeDef> ret;</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> (nodeDef.op() == <span class="stringliteral">"Const"</span>)</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>  <span class="comment">// For some reason const node can have "Control Inputs". We ignore them for now.</span></div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  }</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span> </div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  ret.reserve(boost::numeric_cast<size_t>(nodeDef.input_size()));</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j < nodeDef.input_size(); ++j)</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>  <a class="code" href="structarmnn_tf_parser_1_1_with_output_tensor_index.xhtml">OutputId</a> outputId = ParseOutputId(nodeDef.input(j));</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span> </div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <span class="keywordflow">if</span> (nodeDef.input(j)[0] == <span class="charliteral">'^'</span>) <span class="comment">// I couldn't find a better test for control inputs.</span></div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  {</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  <span class="comment">// We currently allow Control Input from TensorFlow graph but we ignore them from ArmNN graph.</span></div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <span class="keywordflow">continue</span>;</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  }</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span> </div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  <span class="keyword">auto</span> inputIt = m_NodesByName.find(outputId.<a class="code" href="structarmnn_tf_parser_1_1_with_output_tensor_index.xhtml#a08f2876bc5d60ed9c711ac7c26747305">m_IndexedValue</a>);</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  <span class="keywordflow">if</span> (inputIt == m_NodesByName.end())</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  {</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  boost::str(</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  boost::format(</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  <span class="stringliteral">"Can't find node '%1%', which is listed as an input of '%2%' %3%"</span>)</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  % nodeDef.input(j)</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  % nodeDef.name()</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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>  ret.push_back(<a class="code" href="namespacearmnn_tf_parser.xhtml#a4c8735480b01dbd0f75c63377fe054e9">OutputOfConstNodeDef</a>(inputIt->second,outputId.<a class="code" href="structarmnn_tf_parser_1_1_with_output_tensor_index.xhtml#a271b1a398c11fb4bf8603119041562c9">m_Index</a>));</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  }</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>  <span class="keywordflow">return</span> ret;</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> </div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span> std::vector<OutputOfParsedTfOperation></div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span> TfParser::GetInputParsedTfOperationsChecked(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  std::size_t expectedNumInputs)</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span> {</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  <span class="comment">// Fetches the tensorflow nodes connected as inputs and validate the size.</span></div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  std::vector<OutputOfConstNodeDef> nodes = GetTfInputNodes(nodeDef);</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  <span class="keyword">const</span> std::size_t numInputs = nodes.size();</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  <span class="keywordflow">if</span> (numInputs != expectedNumInputs)</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  boost::str(</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  boost::format(</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <span class="stringliteral">"Unexpected number of inputs for node %1%. Expected %2%, found %3% %4%"</span>)</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  % nodeDef.name()</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  % expectedNumInputs</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  % numInputs</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  }</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  <span class="comment">// Fetches the corresponding ParsedTfOperation operations</span></div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  std::vector<OutputOfParsedTfOperation> result;</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& node : nodes)</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="keyword">auto</span> it = m_ParsedTfOperations.find(node.m_IndexedValue->name());</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  <span class="keywordflow">if</span> (it == m_ParsedTfOperations.end())</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">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  boost::str(</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  boost::format(</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  <span class="stringliteral">"Node with name '%1%' has not been parsed %2%"</span>)</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  % node.m_IndexedValue->name()</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  }</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  ParsedTfOperation* parsedOp = it->second.get();</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  <span class="comment">// Transparently 'skip' any Identity operations. This simplifies the logic inside the ParseXXX() functions.</span></div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  parsedOp = parsedOp->ResolveIdentityOperations();</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  result.push_back(<a class="code" href="namespacearmnn_tf_parser.xhtml#ad85fe4a9bf2aff90c53bc2f50c8931e6">OutputOfParsedTfOperation</a>(parsedOp,node.m_Index));</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>  <span class="keywordflow">return</span> result;</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> </div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* TfParser::CreateAdditionLayer(</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  <span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input0Slot,</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input1Slot,</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  <span class="keyword">const</span> std::string& layerName)</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span> {</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& input0Info = input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& input1Info = input1Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</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="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input0Dim = input0Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input1Dim = input1Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  <span class="keywordflow">if</span> (input0Dim != input1Dim)</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  {</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  <span class="comment">// broadcasting where input0 and input1 have different number of dimensions</span></div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  <span class="comment">// is only supported for 1D and 4D tensors pair</span></div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  <span class="keywordflow">if</span> (input0Dim == 1 && input1Dim == 4)</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  {</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  input0Slot = AddBroadcastReshapeLayer(input1Slot, input0Slot, <span class="keyword">true</span>, *m_Network, nodeDef);</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (input0Dim == 4 && input1Dim == 1)</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  {</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  input1Slot = AddBroadcastReshapeLayer(input0Slot, input1Slot, <span class="keyword">true</span>, *m_Network, nodeDef);</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  }</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  {</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  boost::str(</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  boost::format(<span class="stringliteral">"Unsupported broadcast configuration for %1% operation %2% %3%"</span>)</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  % layerName</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  % nodeDef.name()</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  }</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  }</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a4812e0137ee610310d23059efed2cb84">AddAdditionLayer</a>(layerName.c_str());</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span> </div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  input0Slot-><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="l00671"></a><span class="lineno"> 671</span>  input1Slot-><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>(1));</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>  <span class="comment">// Ensure the output tensor has the correct dimensions even if a broadcast has been done</span></div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo = input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  std::vector<unsigned int> outputShape;</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span> </div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& input0Shape = input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& input1Shape = input1Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span> </div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < input0Shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); i++)</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  {</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  outputShape.push_back(std::max(input0Shape[i], input1Shape[i]));</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  }</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span> </div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(input0Shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(), outputShape.data()));</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</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>(outputInfo);</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span> </div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  <span class="keywordflow">return</span> layer;</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> </div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* TfParser::CreateAdditionLayer(</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  <span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layerOne,</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layerTwo,</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberOfAddition,</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">long</span> numberOfLayersToConnect,</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <span class="keywordtype">bool</span> isOdd)</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span> {</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input0Slot = &layerOne-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0);</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input1Slot = &layerTwo-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0);</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  std::string layerName(nodeDef.name());</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  <span class="keywordflow">if</span> (isOdd || numberOfLayersToConnect != 2)</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>  <span class="comment">// we are not connecting the final layer</span></div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  layerName.append(<span class="stringliteral">"_addN_"</span>).append(std::to_string(numberOfAddition));</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>  <span class="keywordflow">return</span> CreateAdditionLayer(nodeDef, input0Slot, input1Slot, layerName);</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span> }</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span> </div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* TfParser::CreateAdditionLayer(</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  <span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_tf_parser_1_1_with_output_tensor_index.xhtml">OutputOfParsedTfOperation</a>& opOne,</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_tf_parser_1_1_with_output_tensor_index.xhtml">OutputOfParsedTfOperation</a>& opTwo,</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberOfAddition)</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>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input0Slot = &opOne.<a class="code" href="structarmnn_tf_parser_1_1_with_output_tensor_index.xhtml#a08f2876bc5d60ed9c711ac7c26747305">m_IndexedValue</a>->ResolveArmnnOutputSlot(opOne.<a class="code" href="structarmnn_tf_parser_1_1_with_output_tensor_index.xhtml#a271b1a398c11fb4bf8603119041562c9">m_Index</a>);</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input1Slot = &opTwo.<a class="code" href="structarmnn_tf_parser_1_1_with_output_tensor_index.xhtml#a08f2876bc5d60ed9c711ac7c26747305">m_IndexedValue</a>->ResolveArmnnOutputSlot(opTwo.<a class="code" href="structarmnn_tf_parser_1_1_with_output_tensor_index.xhtml#a271b1a398c11fb4bf8603119041562c9">m_Index</a>);</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  std::string layerName(nodeDef.name());</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  layerName.append(<span class="stringliteral">"_addN_"</span>).append(std::to_string(numberOfAddition));</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  <span class="keywordflow">return</span> CreateAdditionLayer(nodeDef, input0Slot, input1Slot, layerName);</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span> }</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span> </div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* TfParser::CreateAdditionLayer(</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  <span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_tf_parser_1_1_with_output_tensor_index.xhtml">OutputOfParsedTfOperation</a>& op,</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer)</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>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input0Slot = &op.<a class="code" href="structarmnn_tf_parser_1_1_with_output_tensor_index.xhtml#a08f2876bc5d60ed9c711ac7c26747305">m_IndexedValue</a>->ResolveArmnnOutputSlot(op.<a class="code" href="structarmnn_tf_parser_1_1_with_output_tensor_index.xhtml#a271b1a398c11fb4bf8603119041562c9">m_Index</a>);</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input1Slot = &layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0);</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  <span class="keywordflow">return</span> CreateAdditionLayer(nodeDef, input0Slot, input1Slot, nodeDef.name());</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> </div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseAddN(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span> {</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  uint32_t numberOfInputs = ReadMandatoryNodeUint32Attribute(nodeDef, <span class="stringliteral">"N"</span>);</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  <span class="keywordflow">if</span> (numberOfInputs < 2)</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  {</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  <span class="comment">// should never happen</span></div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  boost::str(</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  boost::format(</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  <span class="stringliteral">"AddN Node with name '%1%' has less than two (%2) inputs %3%"</span>)</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  % nodeDef.name()</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  % std::to_string(numberOfInputs)</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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="keywordflow">else</span> <span class="keywordflow">if</span> (numberOfInputs == 2)</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  {</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  <span class="comment">//this is the same as a simple Add operation</span></div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  <span class="keywordflow">return</span> AddAdditionLayer(nodeDef, <span class="keyword">false</span>);</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  }</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  <span class="keywordflow">else</span></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">// build a binary tree of Add layers and return the final Add as the return from the function</span></div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  <span class="comment">// if we have an odd number of inputs then the final Add will consist of a layer connecting to an</span></div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  <span class="comment">// OutputOfParsedTfOperation, otherwise it will be two layers being added together</span></div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, numberOfInputs);</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numberOfAdditions = 0;</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  std::vector<IConnectableLayer*> layers;</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  <span class="comment">// NOTE: at this point we will have a minimum of three inputs</span></div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < numberOfInputs; ++i)</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  {</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  <span class="comment">// every time i is odd we have two inputs to process.</span></div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  <span class="keywordtype">bool</span> onSecondItem = i % 2;</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  <span class="keywordflow">if</span> (onSecondItem)</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>  ++numberOfAdditions;</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* newLayer = CreateAdditionLayer(</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  nodeDef, inputs[ i - 1], inputs[i], numberOfAdditions);</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  layers.push_back(newLayer);</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  }</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  }</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>  std::vector<IConnectableLayer*> layersToConnect(layers);</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">long</span> numberOfLayersToConnect = layersToConnect.size();</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  <span class="keywordtype">bool</span> isOdd = numberOfInputs % 2;</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span> </div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  <span class="keywordflow">while</span> (numberOfLayersToConnect > 1)</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  {</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  layers.clear();</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">long</span> i = 0; i < numberOfLayersToConnect; ++i) {</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  <span class="keywordtype">bool</span> onSecondItem = i % 2;</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  <span class="keywordflow">if</span> (onSecondItem) {</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  ++numberOfAdditions;</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* newLayer = CreateAdditionLayer(</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  nodeDef,</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  layersToConnect[i - 1],</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  layersToConnect[i],</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  numberOfAdditions,</div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  numberOfLayersToConnect,</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  isOdd);</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  layers.push_back(newLayer);</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  }</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  }</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  <span class="comment">//OK... need to go again... maybe</span></div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  layersToConnect = layers;</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  numberOfLayersToConnect = layersToConnect.size();</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* finalLayer = layersToConnect[0];</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  <span class="comment">// if we had an odd number of inputs we need to connect the final layer to the</span></div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  <span class="comment">// last OutputOfParsedTfOperation in order to create the last Add layer we will</span></div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  <span class="comment">// be handing back.</span></div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  <span class="keywordflow">if</span> (isOdd)</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>  <span class="comment">// connect the final layer to the last op</span></div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  finalLayer = CreateAdditionLayer(nodeDef, inputs[numberOfInputs - 1], finalLayer);</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  }</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, finalLayer);</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  }</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span> }</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> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseAdd(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span> {</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2);</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="comment">// If one of the inputs is a MatMul and the other is a const, then we handle both nodes</span></div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  <span class="comment">// together as FullyConnected.</span></div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  <span class="keywordflow">if</span> (inputs[0].m_IndexedValue->GetNode().op() == <span class="stringliteral">"MatMul"</span> &&</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  HasParsedConstTensor<float>(inputs[1].m_IndexedValue->GetNode().name()))</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="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer =</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  AddFullyConnectedLayer(inputs[0].m_IndexedValue->GetNode(),</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  &nodeDef,nodeDef.name().c_str());</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  }</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (HasParsedConstTensor<float>(inputs[0].m_IndexedValue->GetNode().name()) &&</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  inputs[1].m_IndexedValue->GetNode().op() == <span class="stringliteral">"MatMul"</span>)</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer =</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  AddFullyConnectedLayer(inputs[1].m_IndexedValue->GetNode(),</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  &nodeDef,nodeDef.name().c_str());</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  }</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  {</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  <span class="comment">// Otherwise it's just a regular addition.</span></div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  <span class="keywordflow">return</span> AddAdditionLayer(nodeDef);</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  }</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span> }</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span> </div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseBiasAdd(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span> {</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  <span class="keywordflow">return</span> AddAdditionLayer(nodeDef, <span class="keyword">true</span>);</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span> }</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span> <span class="comment"></span></div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span> <span class="comment">/// An ParsedTfOperation which forwards to another (used for Identity nodes).</span></div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span> <span class="comment"></span><span class="keyword">class </span>ParsedIdentityTfOperation : <span class="keyword">public</span> ParsedTfOperation</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span> {</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  ParsedIdentityTfOperation(<a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml">TfParser</a>* parser, <span class="keyword">const</span> tensorflow::NodeDef& node, ParsedTfOperation* representative)</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  : ParsedTfOperation(parser, node)</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  , m_Representative(representative)</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  {</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</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">virtual</span> <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& ResolveArmnnOutputSlot(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tfOutputIndex)<span class="keyword"> override</span></div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  BOOST_ASSERT(m_Representative);</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  <span class="keywordflow">return</span> m_Representative->ResolveArmnnOutputSlot(tfOutputIndex);</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  }</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span> </div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  <span class="keyword">virtual</span> ParsedTfOperation* ResolveIdentityOperations()<span class="keyword"> override</span></div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  <span class="keywordflow">return</span> m_Representative->ResolveIdentityOperations();</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  }</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span> </div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  ParsedTfOperation* m_Representative;</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> </div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseIdentity(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span> {</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 1);</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  <span class="comment">// Any requests for the output slots of this node should be forwarded to the node connected as input.</span></div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  <span class="keywordflow">return</span> std::make_unique<ParsedIdentityTfOperation>(<span class="keyword">this</span>, nodeDef, inputs[0].m_IndexedValue);</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> <span class="comment"></span></div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span> <span class="comment">/// An ParsedTfOperation for a Const node.</span></div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span> <span class="comment">/// Creation of the armnn ConstLayer is deferred until it is actually needed, because Const nodes are mostly used</span></div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span> <span class="comment">/// for weight inputs to MatMul/Conv2D nodes and in these cases armnn doesn't need a ConstLayer.</span></div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span> <span class="comment"></span><span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span> <span class="keyword">class </span><a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml#ad3c8cd69190956793af7af503dc495cd">ParsedConstTfOperation</a> : <span class="keyword">public</span> DeferredSingleLayerParsedTfOperation</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span> {</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  <a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml#ad3c8cd69190956793af7af503dc495cd">ParsedConstTfOperation</a>(<a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml">TfParser</a>* parser, <span class="keyword">const</span> tensorflow::NodeDef& node,</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span>  <span class="keyword">const</span> T* tensorData, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensorInfo)</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span>  : DeferredSingleLayerParsedTfOperation(parser, node),</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  m_Storage(tensorData, tensorData + tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>()),</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  m_TensorInfo(tensorInfo)</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span>  {</div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  BOOST_ASSERT(<a class="code" href="namespacearmnn.xhtml#aa02b9e06fb20fa3c13da0427e6ee5ab2">GetDataTypeSize</a>(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()) == <span class="keyword">sizeof</span>(T));</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> </div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span>  <span class="keywordtype">void</span> CreateLayerDeferred()<span class="keyword"> override</span></div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  BOOST_ASSERT(<a class="code" href="_subgraph_view_selector_8cpp.xhtml#ae7fc37b88ff10e9294ed1c72f2a25ac7">m_Layer</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  <a class="code" href="_subgraph_view_selector_8cpp.xhtml#ae7fc37b88ff10e9294ed1c72f2a25ac7">m_Layer</a> = m_Parser->m_Network->AddConstantLayer(<a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(m_TensorInfo, m_Storage), m_Node.name().c_str());</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  <a class="code" href="_subgraph_view_selector_8cpp.xhtml#ae7fc37b88ff10e9294ed1c72f2a25ac7">m_Layer</a>-><a class="code" href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">SetTensorInfo</a>(m_TensorInfo);</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> </div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> GetConstTensor(std::vector<T>& outputTensorData)<span class="keyword"> const</span></div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  outputTensorData.resize(m_TensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span> </div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  memcpy(outputTensorData.data(), m_Storage.data(), m_TensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>());</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span> </div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  <span class="comment">// Updates the result to point to the user provided storage.</span></div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> constTensor(m_TensorInfo, outputTensorData);</div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span>  <span class="keywordflow">return</span> constTensor;</div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span>  }</div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span> </div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span>  <span class="keyword">const</span> T* GetStorage()<span class="keyword"> const</span></div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  <span class="keywordflow">return</span> m_Storage.data();</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> </div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">GetTensorInfo</a>()<span class="keyword"> const</span></div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  <span class="keywordflow">return</span> m_TensorInfo;</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>  }</div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span> </div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span> <span class="keyword">private</span>:<span class="comment"></span></div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span> <span class="comment"> ///< Manages the lifetime of the tensor data.</span></div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span> <span class="comment"></span> std::vector<T> m_Storage;<span class="comment"></span></div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span> <span class="comment"> ///< Describes the layout of the tensor and points to the data in m_Storage.</span></div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span> <span class="comment"></span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> m_TensorInfo;</div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span> };</div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span> </div><div class="line"><a name="l00933"></a><span class="lineno"><a class="line" href="namespacearmnn_tf_parser.xhtml#a3d934e14ca544ba7af4fe562def8a986"> 933</a></span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="namespacearmnn_tf_parser.xhtml#a3d934e14ca544ba7af4fe562def8a986">ConvertTfTensorDataType</a>(<span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">tensorflow::DataType</a> tfDataType,</div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  <span class="keyword">const</span> tensorflow::NodeDef& nodeDef)</div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span> {</div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  <span class="keywordflow">switch</span> (tfDataType)</div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  {</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  <span class="keywordflow">case</span> tensorflow::DT_FLOAT:</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span>  <span class="keywordflow">return</span> DataType::Float32;</div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  <span class="keywordflow">case</span> tensorflow::DT_INT32:</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  <span class="keywordflow">return</span> DataType::Signed32;</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  boost::str(</div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  boost::format(</div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span>  <span class="stringliteral">"Unknown DataType %1% for node %2% %3%"</span>)</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  % tensorflow::DataType_Name(tfDataType)</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  % nodeDef.name()</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  }</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span> }</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span> </div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span> <span class="keyword">struct </span>ParseTfTensorValueList</div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span> {</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span>  <span class="keyword">template</span><<span class="keyword">typename</span> DataType></div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  <span class="keyword">static</span> <span class="keywordtype">void</span> Parse(</div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span>  <span class="keyword">const</span> tensorflow::TensorProto& tfTensor,</div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dstElements,</div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  std::vector<int8_t>& outputData);</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>  <span class="keyword">template</span> <<span class="keyword">typename</span> DataType></div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  <span class="keyword">static</span> <span class="keywordtype">void</span> ReadData(<span class="keyword">const</span> <span class="keywordtype">void</span>* srcData, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numSrcElements,</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span>  std::vector<int8_t>& dstData, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDstElements)</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span>  {</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  <span class="comment">// If there are no entries in the list, perform no action.</span></div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  <span class="keywordflow">if</span> (numSrcElements == 0)</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>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  }</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">// If no size was provided, use the length of the value list.</span></div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span>  <span class="keywordflow">if</span> (numDstElements == 0)</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  {</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  numDstElements = numSrcElements;</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> </div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  <span class="comment">// Allocates memory.</span></div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  dstData.resize(std::max(numSrcElements, numDstElements) * <span class="keyword">sizeof</span>(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>));</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span> </div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>* srcTensor = <span class="keyword">reinterpret_cast<</span><span class="keyword">const </span><a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>*<span class="keyword">></span>(srcData);</div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>* dstTensor = <span class="keyword">reinterpret_cast<</span><a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>*<span class="keyword">></span>(dstData.data());</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span> </div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  <span class="comment">// Copies the value list entries into the destination.</span></div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  std::copy(srcTensor, srcTensor + numSrcElements, dstTensor);</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="keywordflow">if</span> (numDstElements > numSrcElements)</div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  {</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span>  <span class="comment">// Uses the last element in the list to fill the remaining entries.</span></div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  std::fill(dstTensor + numSrcElements, dstTensor + numDstElements, srcTensor[numSrcElements - 1]);</div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  }</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> </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> </div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span> <span class="keyword">template</span> <></div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span> <span class="keywordtype">void</span> ParseTfTensorValueList::Parse<float>(<span class="keyword">const</span> tensorflow::TensorProto& tfTensor,</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dstElements, std::vector<int8_t>& outputData)</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span> {</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>  ReadData<float>(tfTensor.float_val().data(), <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(tfTensor.float_val_size()),</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  outputData, dstElements);</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span> }</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span> </div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span> <span class="keyword">template</span> <></div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span> <span class="keywordtype">void</span> ParseTfTensorValueList::Parse<int32_t>(<span class="keyword">const</span> tensorflow::TensorProto& tfTensor,</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dstElements, std::vector<int8_t>& outputData)</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>  ReadData<int32_t>(tfTensor.int_val().data(), <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(tfTensor.int_val_size()),</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>  outputData, dstElements);</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span> }</div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span> </div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span> <span class="keyword">template</span> <<span class="keyword">template</span><<span class="keyword">typename</span>> <span class="keyword">class </span>OperatorType, <span class="keyword">typename</span> T = int8_t></div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span> <span class="keyword">struct </span>MakeTfOperation</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">template</span><<span class="keyword">typename</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>, <span class="keyword">class</span>... Args></div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>  <span class="keyword">inline</span> <span class="keyword">static</span> std::unique_ptr<OperatorType<DataType>> Parse(<a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml">TfParser</a>* parser, <span class="keyword">const</span> tensorflow::NodeDef& node,</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>  Args&&... args)</div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>  {</div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>  <span class="keywordflow">return</span> std::make_unique<OperatorType<DataType>>(parser, node, std::forward<Args>(args)...);</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> };</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="keyword">template</span> <></div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span> <span class="keyword">struct </span>MakeTfOperation<ParsedConstTfOperation></div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span> {</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  <span class="keyword">template</span><<span class="keyword">typename</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>, <span class="keyword">class</span>... Args></div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>  <span class="keyword">inline</span> <span class="keyword">static</span> std::unique_ptr<ParsedConstTfOperation<DataType>> Parse(<a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml">TfParser</a>* parser,</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  <span class="keyword">const</span> tensorflow::NodeDef& node, <span class="keyword">const</span> std::vector<int8_t>& tensorData, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensorInfo)</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>  {</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>  <span class="keywordflow">return</span> std::make_unique<ParsedConstTfOperation<DataType>>(parser, node,</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span>  <span class="keyword">reinterpret_cast<</span><span class="keyword">const </span>DataType*<span class="keyword">></span>(tensorData.data()), tensorInfo);</div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span>  }</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> <span class="keyword">template</span> <<span class="keyword">class</span> FuncType></div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span> <span class="keyword">struct </span>InvokeParseFunction</div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span> {</div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span>  <span class="keyword">template</span><<span class="keyword">class </span>ResType, <span class="keyword">class</span>... Args></div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>  <span class="keyword">inline</span> <span class="keyword">static</span> ResType Result(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dataType, Args&&... args)</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>  {</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>  <span class="keywordflow">if</span> (dataType == DataType::Float32)</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  {</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>  <span class="keywordflow">return</span> FuncType::template Parse<float>(std::forward<Args>(args)...);</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="keywordflow">else</span> <span class="keywordflow">if</span> (dataType == DataType::Signed32)</div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>  {</div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span>  <span class="keywordflow">return</span> FuncType::template Parse<int32_t>(std::forward<Args>(args)...);</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> </div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>  <span class="keywordflow">return</span> ResType();</div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>  }</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">template</span><<span class="keyword">class</span>... Args></div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>  <span class="keyword">inline</span> <span class="keyword">static</span> <span class="keywordtype">void</span> Result(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dataType, Args&&... args)</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>  <span class="keywordflow">if</span> (dataType == DataType::Float32)</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>  {</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>  FuncType::template Parse<float>(std::forward<Args>(args)...);</div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>  }</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (dataType == DataType::Signed32)</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>  FuncType::template Parse<int32_t>(std::forward<Args>(args)...);</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  }</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  }</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span> };</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span> </div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseConst(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span> {</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>  BOOST_ASSERT(nodeDef.op() == <span class="stringliteral">"Const"</span>);</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span> </div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>  <span class="keywordflow">if</span> (nodeDef.attr().count(<span class="stringliteral">"value"</span>) == 0)</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>  {</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>  boost::str(</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>  boost::format(</div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>  <span class="stringliteral">"Value not found for Const node - %1% %2%"</span>)</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>  % nodeDef.name()</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>  }</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span> </div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  <span class="keyword">const</span> tensorflow::TensorProto& tfTensor = nodeDef.attr().at(<span class="stringliteral">"value"</span>).tensor();</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>  <span class="keyword">const</span> tensorflow::TensorShapeProto& tfTensorShape = tfTensor.tensor_shape();</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">tensorflow::DataType</a> tfDataType = ReadMandatoryNodeTypeAttribute(nodeDef, <span class="stringliteral">"dtype"</span>);</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>  <span class="keyword">const</span> <span class="keyword">auto</span> GetDimensionSize = [](<span class="keyword">auto</span>& d) { <span class="keywordflow">return</span> d.size(); };</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span> </div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  std::vector<unsigned int> dimensionSizes;</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>  std::transform(tfTensorShape.dim().begin(), tfTensorShape.dim().end(),</div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>  std::back_inserter(dimensionSizes), GetDimensionSize);</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span> </div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>  <span class="comment">// Calculates number of elements.</span></div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dataType = <a class="code" href="namespacearmnn_tf_parser.xhtml#a3d934e14ca544ba7af4fe562def8a986">ConvertTfTensorDataType</a>(tfDataType, nodeDef);</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numElements = 0U;</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span> </div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>  <span class="keywordflow">if</span> (!dimensionSizes.empty())</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>  numElements = std::accumulate(dimensionSizes.begin(), dimensionSizes.end(),</div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>  1U, std::multiplies<unsigned int>());</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> </div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span>  std::vector<int8_t> tensorData;</div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span> </div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>  <span class="comment">// Get tensor data from the list of values attribute.</span></div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>  <span class="keywordflow">if</span> (tfTensor.tensor_content().empty())</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>  {</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>  InvokeParseFunction<ParseTfTensorValueList>::Result<<span class="keywordtype">void</span>>(dataType, tfTensor, numElements, tensorData);</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span> </div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>  <span class="comment">// If the tensor shape is not defined, but there is a value list, then interpret the data as a 1D</span></div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span>  <span class="comment">// tensor of the provided number of elements.</span></div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>  <span class="keywordflow">if</span> (numElements == 0)</div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>  {</div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tfNumElements =</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>  <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(tensorData.size()) / <a class="code" href="namespacearmnn.xhtml#aa02b9e06fb20fa3c13da0427e6ee5ab2">GetDataTypeSize</a>(dataType);</div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>  dimensionSizes.push_back(tfNumElements);</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>  }</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>  <span class="comment">// Gets tensor data from tensor content attribute.</span></div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span>  <span class="keywordflow">else</span></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>  tensorData.assign(tfTensor.tensor_content().begin(), tfTensor.tensor_content().end());</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>  <span class="comment">// Checks if a tensor shape is defined for the tensor content.</span></div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span>  <span class="keywordflow">if</span> (numElements == 0)</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>  boost::str(</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span>  boost::format(</div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>  <span class="stringliteral">"No tensor shape found for Const node - %1% %2%"</span>)</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  % nodeDef.name()</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>  }</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> </div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  <span class="comment">// Const node requires at least a list of values or a content attribute.</span></div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>  <span class="keywordflow">if</span> (tensorData.empty())</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>  {</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>  boost::str(</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>  boost::format(</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>  <span class="stringliteral">"No tensor data found for Const node - %1% %2%"</span>)</div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>  % nodeDef.name()</div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>  }</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="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> tensorInfo(static_cast<unsigned int>(dimensionSizes.size()),</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>  dimensionSizes.data(),</div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>  dataType);</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>  <span class="comment">// If we have a list of values, then the length of the list must be</span></div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>  <span class="comment">// less than or equal to the number of elements implied by the shape argument.</span></div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>  <span class="keywordflow">if</span> (tensorData.size() > tensorInfo.GetNumBytes())</div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  {</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  boost::str(</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>  boost::format(</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>  <span class="stringliteral">"Number of elements (%1%) should be less than or equal "</span></div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>  <span class="stringliteral">"to the number of elements implied by the shape argument (%2%) for Const node - %3% %4%"</span>)</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>  % (tensorData.size() / <a class="code" href="namespacearmnn.xhtml#aa02b9e06fb20fa3c13da0427e6ee5ab2">GetDataTypeSize</a>(dataType))</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>  % tensorInfo.GetNumElements()</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>  % nodeDef.name()</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>  }</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span> </div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>  <span class="keywordflow">return</span> InvokeParseFunction<MakeTfOperation<ParsedConstTfOperation>>::Result<ParsedTfOperationPtr>(</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  dataType, <span class="keyword">this</span>, nodeDef, tensorData, tensorInfo);</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> </div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span> <span class="keyword">template</span><<span class="keyword">typename</span> Type></div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span> <span class="keywordtype">bool</span> TfParser::HasParsedConstTensor(<span class="keyword">const</span> std::string & nodeName)<span class="keyword"> const</span></div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span> <span class="keyword"></span>{</div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>  <span class="keyword">auto</span> it = m_ParsedTfOperations.find(nodeName);</div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  <span class="keywordflow">if</span> (it == m_ParsedTfOperations.end())</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  {</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>  }</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  <span class="keywordflow">return</span> <span class="keyword">dynamic_cast<</span>ParsedConstTfOperation<Type>*<span class="keyword">></span>(it->second.get()) != <span class="keyword">nullptr</span>;</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span> }</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span> </div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span> <span class="keyword">template</span><<span class="keyword">typename</span> Type></div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span> <span class="keywordtype">bool</span> TfParser::HasParsedConstTensor(ParsedTfOperation* parsedTfOpPtr)<span class="keyword"> const</span></div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span> <span class="keyword"></span>{</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  <span class="keywordflow">return</span> <span class="keyword">dynamic_cast<</span>ParsedConstTfOperation<Type>*<span class="keyword">></span>(parsedTfOpPtr) != <span class="keyword">nullptr</span>;</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span> }</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span> </div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> TfParser::GetConstInputIndex(<span class="keyword">const</span> std::vector<OutputOfParsedTfOperation>& inputs)</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span> {</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < inputs.size(); i++)</div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  {</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>  <span class="keywordflow">if</span> (HasParsedConstTensor<int32_t>(inputs[i].m_IndexedValue->GetNode().name()))</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="keywordflow">return</span> i;</div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>  }</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="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  boost::str(</div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  boost::format(</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>  <span class="stringliteral">"ArmNN only supports operators with constant axis. %1%"</span>)</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> }</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> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseConv2D(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</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="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2);</div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& inputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = inputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</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="keywordflow">if</span> (!HasParsedConstTensor<float>(inputs[1].m_IndexedValue->GetNode().name()))</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>  {</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span>  boost::str(</div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>  boost::format(</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span>  <span class="stringliteral">"ArmNN only supports Convolution layers with constant weights for %1%, input %2% %3%"</span>)</div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span>  % nodeDef.name()</div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span>  % inputs[1].m_IndexedValue->GetNode().name()</div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span>  }</div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span>  ParsedConstTfOperation<float>* weightNode =</div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>  boost::polymorphic_downcast<ParsedConstTfOperation<float> *>(inputs[1].m_IndexedValue);</div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span> </div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>  std::string paddingString = ReadMandatoryNodeStringAttribute(nodeDef, <span class="stringliteral">"padding"</span>);</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>  std::string dataFormat = ReadMandatoryNodeStringAttribute(nodeDef, <span class="stringliteral">"data_format"</span>);</div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>  std::vector<uint32_t> strides = ReadMandatoryNodeUint32ListAttribute(nodeDef, <span class="stringliteral">"strides"</span>);</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="comment">// Read the dilations, if present - only [1,1,1,1] (the default) is supported.</span></div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span>  std::vector<uint32_t> dilations = ReadOptionalNodeUint32ListAttribute(nodeDef, <span class="stringliteral">"dilations"</span>);</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>  <span class="keywordflow">if</span> (!dilations.empty())</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>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> dilation : dilations)</div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>  {</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>  <span class="keywordflow">if</span> (dilation != 1u)</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span>  boost::str(</div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>  boost::format(</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  <span class="stringliteral">"ArmNN only supports Convolution layers with dilations [1,1,1,1] for %1% %2%"</span>)</div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>  % nodeDef.name()</div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  }</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>  }</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span> </div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> desc;</div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</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="l01249"></a><span class="lineno"> 1249</span> </div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>  <a class="code" href="_tf_parser_8cpp.xhtml#a3fb047570644cae325aa88d3cd7bb96e">CHECK_DATA_FORMAT</a>(nodeDef, dataFormat, <span class="stringliteral">"Conv2D"</span>);</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span> </div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout = dataFormat == <span class="stringliteral">"NHWC"</span> ? DataLayout::NHWC : DataLayout::NCHW;</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>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span> </div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>  <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">DataLayoutIndexed</a> dataLayoutIndexed(dataLayout);</div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span> </div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strides[dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>()];</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strides[dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>()];</div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span> </div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span>  uint32_t inputHeight = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>()];</div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>  uint32_t inputWidth = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>()];</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>  <span class="comment">// Mappings from TensorFlow filter tensors to the ArmNN filter tensors.</span></div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>  <span class="comment">// Tensorflow weights are [H, W, In, Out].</span></div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span>  <span class="comment">// ArmNN weights have to be [Out, H, W, In] when the data layout is NHWC,</span></div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>  <span class="comment">// and [Out, In, H, W] when the data layout is NCHW.</span></div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span>  <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> permutationVector =</div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span>  dataLayout == DataLayout::NHWC ?</div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>  std::initializer_list<unsigned int>{ 1, 2, 3, 0 } : <span class="comment">// NHWC: [H, W, In, Out] -> [Out, H, W, In]</span></div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span>  std::initializer_list<unsigned int>{ 2, 3, 1, 0 }; <span class="comment">// NCHW: [H, W, In, Out] -> [Out, In, H, W]</span></div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span> </div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>  <span class="comment">// Swizzle the tensor using the given permutation vector.</span></div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& weightTensorInfo = weightNode->GetTensorInfo();</div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> weightTensorSwizzledInfo = <a class="code" href="namespacearmnn_utils.xhtml#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a>(weightTensorInfo, permutationVector);</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="comment">// Swizzles the content of the tensor's permanent storage into a local storage.</span></div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>  std::vector<float> weightTensorSwizzledData(weightTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span>  <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(weightTensorSwizzledInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), permutationVector,</div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span>  weightNode->GetStorage(), weightTensorSwizzledData.data(), <span class="keyword">sizeof</span>(float));</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>  <span class="comment">// Create a weight tensor with the newly swizzled data.</span></div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weightTensor(weightTensorSwizzledInfo, weightTensorSwizzledData);</div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span> </div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>  uint32_t weightHeight = weightTensor.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>()];</div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>  uint32_t weightWidth = weightTensor.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>()];</div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span> </div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span>  <span class="keywordtype">bool</span> padding = <span class="keyword">false</span>;</div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo;</div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = 0;</div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = 0;</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span> </div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>  <a class="code" href="_tf_parser_8cpp.xhtml#aab838eb7734e531bb5be6f6dece673bf">CHECK_PADDING_TYPE</a>(nodeDef, paddingString);</div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span> </div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>  <span class="keywordflow">if</span> (paddingString == <span class="stringliteral">"SAME"</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>  padding = <span class="keyword">true</span>;</div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span> </div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>  outputHeight = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(ceil(static_cast<float>(inputHeight) /</div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span>  static_cast<float>(desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>)));</div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>  outputWidth = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(ceil(static_cast<float>(inputWidth) /</div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span>  static_cast<float>(desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>)));</div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span>  }</div><div class="line"><a name="l01304"></a><span class="lineno"> 1304</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (paddingString == <span class="stringliteral">"VALID"</span>)</div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span>  {</div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>  padding = <span class="keyword">false</span>;</div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span> </div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</span>  outputHeight = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(ceil(static_cast<float>(inputHeight - weightHeight + 1) /</div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span>  static_cast<float>(desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>)));</div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>  outputWidth = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(ceil(static_cast<float>(inputWidth - weightWidth + 1) /</div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span>  static_cast<float>(desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>)));</div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>  }</div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span> </div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>  <span class="keywordflow">switch</span> (dataLayout)</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span>  {</div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>  <span class="keywordflow">case</span> DataLayout::NHWC:</div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>  outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0],</div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>  outputHeight,</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>  outputWidth,</div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span>  weightTensor.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0] },</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>  DataType::Float32);</div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>  <span class="keywordflow">case</span> DataLayout::NCHW:</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>  outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0],</div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>  weightTensor.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0],</div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>  outputHeight,</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span>  outputWidth },</div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span>  DataType::Float32);</div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>  <span class="keywordflow">break</span>;</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> </div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>  <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputHeight, weightHeight, desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</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>, padding);</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>  <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputWidth, weightWidth, desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</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>, padding);</div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span> </div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a073e2f61f527d7d3801c26bdbd37dd7e">AddConvolution2dLayer</a>(desc,</div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>  weightTensor,</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span>  <a class="code" href="structarmnn_1_1_empty_optional.xhtml">EmptyOptional</a>(),</div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>  nodeDef.name().c_str());</div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</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>(outputInfo);</div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span>  inputSlot.<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="l01342"></a><span class="lineno"> 1342</span> </div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</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> </div><div class="line"><a name="l01346"></a><span class="lineno"> 1346</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseDepthwiseConv2D(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l01347"></a><span class="lineno"> 1347</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span> {</div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2);</div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& inputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = inputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</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="keywordflow">if</span> (!HasParsedConstTensor<float>(inputs[1].m_IndexedValue->GetNode().name()))</div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>  {</div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>  boost::str(</div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>  boost::format(</div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span>  <span class="stringliteral">"ArmNN only supports Depthwise Convolution layer with constant weights. "</span></div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span>  <span class="stringliteral">"Non const input found %1% for node %2% %3%"</span>)</div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>  % inputs[1].m_IndexedValue->GetNode().name()</div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>  % nodeDef.name()</div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>  ParsedConstTfOperation<float>* weightNode =</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>  boost::polymorphic_downcast<ParsedConstTfOperation<float> *>(inputs[1].m_IndexedValue);</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>  std::string paddingString = ReadMandatoryNodeStringAttribute(nodeDef, <span class="stringliteral">"padding"</span>);</div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>  std::string dataFormat = ReadMandatoryNodeStringAttribute(nodeDef, <span class="stringliteral">"data_format"</span>);</div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span>  std::vector<uint32_t> strides = ReadMandatoryNodeUint32ListAttribute(nodeDef, <span class="stringliteral">"strides"</span>);</div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span> </div><div class="line"><a name="l01373"></a><span class="lineno"> 1373</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a> desc;</div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</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="l01375"></a><span class="lineno"> 1375</span> </div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span>  <a class="code" href="_tf_parser_8cpp.xhtml#a3fb047570644cae325aa88d3cd7bb96e">CHECK_DATA_FORMAT</a>(nodeDef, dataFormat, <span class="stringliteral">"DepthwiseConv2dNative"</span>);</div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span> </div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout = dataFormat == <span class="stringliteral">"NHWC"</span> ? DataLayout::NHWC : DataLayout::NCHW;</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>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span> </div><div class="line"><a name="l01382"></a><span class="lineno"> 1382</span>  <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">DataLayoutIndexed</a> dataLayoutIndexed(dataLayout);</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>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strides[dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>()];</div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span>  desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strides[dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>()];</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>  uint32_t inputHeight = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>()];</div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</span>  uint32_t inputWidth = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>()];</div><div class="line"><a name="l01389"></a><span class="lineno"> 1389</span> </div><div class="line"><a name="l01390"></a><span class="lineno"> 1390</span>  <span class="comment">// Mappings from TensorFlow filter tensors to the ArmNN filter tensors.</span></div><div class="line"><a name="l01391"></a><span class="lineno"> 1391</span>  <span class="comment">// Tensorflow weights come in the format [H, W, I, M].</span></div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span>  <span class="comment">// ArmNN weights have to be [M, I, H, W].</span></div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</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="l01394"></a><span class="lineno"> 1394</span> </div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>  <span class="comment">// Swizzle the tensor using the given permutation vector.</span></div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& weightTensorInfo = weightNode->GetTensorInfo();</div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> weightTensorSwizzledInfo = <a class="code" href="namespacearmnn_utils.xhtml#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a>(weightTensorInfo, permutationVector);</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="comment">// Swizzles the content of the tensor's permanent storage into a local storage.</span></div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</span>  std::vector<float> weightTensorSwizzledData(weightTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span>  <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(weightTensorSwizzledInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), permutationVector,</div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>  weightNode->GetStorage(), weightTensorSwizzledData.data(), <span class="keyword">sizeof</span>(float));</div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span> </div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span>  <span class="comment">// Create a weight tensor with the newly swizzled data.</span></div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weightTensor(weightTensorSwizzledInfo, weightTensorSwizzledData);</div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span> </div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>  uint32_t weightHeight = weightTensor.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2];</div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>  uint32_t weightWidth = weightTensor.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3];</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="keywordtype">bool</span> padding = <span class="keyword">false</span>;</div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo;</div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = 0;</div><div class="line"><a name="l01413"></a><span class="lineno"> 1413</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = 0;</div><div class="line"><a name="l01414"></a><span class="lineno"> 1414</span> </div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span>  <a class="code" href="_tf_parser_8cpp.xhtml#aab838eb7734e531bb5be6f6dece673bf">CHECK_PADDING_TYPE</a>(nodeDef, paddingString);</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>  <span class="keywordflow">if</span> (paddingString == <span class="stringliteral">"SAME"</span>)</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>  padding = <span class="keyword">true</span>;</div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span> </div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>  outputHeight = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(ceil(static_cast<float>(inputHeight) /</div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>  static_cast<float>(desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>)));</div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span>  outputWidth = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(ceil(static_cast<float>(inputWidth) /</div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>  static_cast<float>(desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>)));</div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>  }</div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (paddingString == <span class="stringliteral">"VALID"</span>)</div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span>  {</div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</span>  padding = <span class="keyword">false</span>;</div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span> </div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span>  outputHeight = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(ceil(static_cast<float>(inputHeight - weightHeight + 1) /</div><div class="line"><a name="l01431"></a><span class="lineno"> 1431</span>  static_cast<float>(desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>)));</div><div class="line"><a name="l01432"></a><span class="lineno"> 1432</span>  outputWidth = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(ceil(static_cast<float>(inputWidth - weightWidth + 1) /</div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</span>  static_cast<float>(desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>)));</div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>  }</div><div class="line"><a name="l01435"></a><span class="lineno"> 1435</span> </div><div class="line"><a name="l01436"></a><span class="lineno"> 1436</span>  <span class="keywordflow">switch</span> (dataLayout)</div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</span>  {</div><div class="line"><a name="l01438"></a><span class="lineno"> 1438</span>  <span class="keywordflow">case</span> DataLayout::NHWC:</div><div class="line"><a name="l01439"></a><span class="lineno"> 1439</span>  outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0],</div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span>  outputHeight,</div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</span>  outputWidth,</div><div class="line"><a name="l01442"></a><span class="lineno"> 1442</span>  weightTensor.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0] * weightTensor.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1]},</div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span>  DataType::Float32);</div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span>  <span class="keywordflow">case</span> DataLayout::NCHW:</div><div class="line"><a name="l01446"></a><span class="lineno"> 1446</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l01447"></a><span class="lineno"> 1447</span>  outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0],</div><div class="line"><a name="l01448"></a><span class="lineno"> 1448</span>  weightTensor.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0] * weightTensor.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1],</div><div class="line"><a name="l01449"></a><span class="lineno"> 1449</span>  outputHeight,</div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span>  outputWidth },</div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span>  DataType::Float32);</div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>  <span class="keywordflow">break</span>;</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> </div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span>  <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputHeight, weightHeight, desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</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>, padding);</div><div class="line"><a name="l01456"></a><span class="lineno"> 1456</span>  <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputWidth, weightWidth, desc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</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>, padding);</div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span> </div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a78367a5054c92d435f4f5c7e10ec65b8">AddDepthwiseConvolution2dLayer</a>(desc,</div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span>  weightTensor,</div><div class="line"><a name="l01460"></a><span class="lineno"> 1460</span>  <a class="code" href="structarmnn_1_1_empty_optional.xhtml">EmptyOptional</a>(),</div><div class="line"><a name="l01461"></a><span class="lineno"> 1461</span>  nodeDef.name().c_str());</div><div class="line"><a name="l01462"></a><span class="lineno"> 1462</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>(outputInfo);</div><div class="line"><a name="l01463"></a><span class="lineno"> 1463</span>  inputSlot.<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="l01464"></a><span class="lineno"> 1464</span> </div><div class="line"><a name="l01465"></a><span class="lineno"> 1465</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l01466"></a><span class="lineno"> 1466</span> }</div><div class="line"><a name="l01467"></a><span class="lineno"> 1467</span> </div><div class="line"><a name="l01468"></a><span class="lineno"><a class="line" href="namespacearmnn_tf_parser.xhtml#a22ac203831113ee3e429746f6055aa73"> 1468</a></span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn_tf_parser.xhtml#a22ac203831113ee3e429746f6055aa73">OutputShapeOfExpandDims</a>(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo)</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>  BOOST_ASSERT(nodeDef.op() == <span class="stringliteral">"ExpandDims"</span>);</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>  <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="l01473"></a><span class="lineno"> 1473</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</span>  boost::str(</div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span>  boost::format(</div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span>  <span class="stringliteral">"Unsupported number of dimensions: %1% for input shape for ExpandDims %2% %3%"</span>)</div><div class="line"><a name="l01477"></a><span class="lineno"> 1477</span>  % inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()</div><div class="line"><a name="l01478"></a><span class="lineno"> 1478</span>  % nodeDef.name()</div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l01482"></a><span class="lineno"> 1482</span>  std::int32_t expandDim = ReadMandatoryNodeInt32Attribute(nodeDef, <span class="stringliteral">"Tdim"</span>);</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>  std::int32_t inputDimSize = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><int32_t>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>());</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> </div><div class="line"><a name="l01487"></a><span class="lineno"> 1487</span>  <span class="comment">// expandDim operation requires: -1-input.dims() <= dim <= input.dims()</span></div><div class="line"><a name="l01488"></a><span class="lineno"> 1488</span>  <span class="keywordflow">if</span> (expandDim >= -1 - inputDimSize && expandDim <= inputDimSize)</div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span>  {</div><div class="line"><a name="l01490"></a><span class="lineno"> 1490</span>  <span class="comment">// add current input shape to outputDims</span></div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</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="l01492"></a><span class="lineno"> 1492</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="l01493"></a><span class="lineno"> 1493</span>  outputDims.push_back(currentDimension);</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="comment">// insert a dimension of 1 at index 'expandDim' of inputs shape</span></div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span>  <span class="keywordflow">if</span> (expandDim >= 0)</div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span>  {</div><div class="line"><a name="l01499"></a><span class="lineno"> 1499</span>  <span class="keyword">auto</span> getPosition = std::next(outputDims.begin() + 0, expandDim);</div><div class="line"><a name="l01500"></a><span class="lineno"> 1500</span>  outputDims.insert(getPosition, 1);</div><div class="line"><a name="l01501"></a><span class="lineno"> 1501</span>  }</div><div class="line"><a name="l01502"></a><span class="lineno"> 1502</span> </div><div class="line"><a name="l01503"></a><span class="lineno"> 1503</span>  <span class="comment">// if negative number for 'expandDim' then count backwards from the last element</span></div><div class="line"><a name="l01504"></a><span class="lineno"> 1504</span>  <span class="comment">// and insert 1 dimension at index 'expandDim'</span></div><div class="line"><a name="l01505"></a><span class="lineno"> 1505</span>  <span class="keywordflow">if</span> (expandDim < 0)</div><div class="line"><a name="l01506"></a><span class="lineno"> 1506</span>  {</div><div class="line"><a name="l01507"></a><span class="lineno"> 1507</span>  <span class="keywordtype">int</span> outputDimSize = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">int</span>>(outputDims.size() + 1);</div><div class="line"><a name="l01508"></a><span class="lineno"> 1508</span>  <span class="keyword">auto</span> getPosition = std::next(outputDims.begin() + outputDimSize, expandDim);</div><div class="line"><a name="l01509"></a><span class="lineno"> 1509</span>  outputDims.insert(getPosition, 1);</div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</span>  }</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">else</span></div><div class="line"><a name="l01513"></a><span class="lineno"> 1513</span>  {</div><div class="line"><a name="l01514"></a><span class="lineno"> 1514</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l01515"></a><span class="lineno"> 1515</span>  boost::str(</div><div class="line"><a name="l01516"></a><span class="lineno"> 1516</span>  boost::format(</div><div class="line"><a name="l01517"></a><span class="lineno"> 1517</span>  <span class="stringliteral">"Cannot expand dimension %1% in input tensor with %2% dimension %3%"</span>)</div><div class="line"><a name="l01518"></a><span class="lineno"> 1518</span>  % expandDim</div><div class="line"><a name="l01519"></a><span class="lineno"> 1519</span>  % inputDimSize</div><div class="line"><a name="l01520"></a><span class="lineno"> 1520</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span>  <span class="keywordflow">if</span> (outputDims.size() > 4)</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="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span>  boost::str(</div><div class="line"><a name="l01527"></a><span class="lineno"> 1527</span>  boost::format(</div><div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>  <span class="stringliteral">"Unsupported number of dimensions: %1% for output shape for ExpandDims %2% %3%"</span>)</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>  % outputDims.size()</div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span>  % nodeDef.name()</div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l01534"></a><span class="lineno"> 1534</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="l01535"></a><span class="lineno"> 1535</span>  outputDims.data());</div><div class="line"><a name="l01536"></a><span class="lineno"> 1536</span> </div><div class="line"><a name="l01537"></a><span class="lineno"> 1537</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outTensorInfo = inputTensorInfo;</div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span>  outTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(outShape);</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="keywordflow">return</span> outTensorInfo;</div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span> }</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> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseExpandDims(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span> {</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 1);</div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span> </div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& prevLayerOutputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = prevLayerOutputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo;</div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>  outputInfo = <a class="code" href="namespacearmnn_tf_parser.xhtml#a22ac203831113ee3e429746f6055aa73">OutputShapeOfExpandDims</a>(nodeDef, inputTensorInfo);</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>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">ReshapeDescriptor</a> reshapeDesc;</div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>  reshapeDesc.<a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#ac77b89eb982f9d745730c90fcbdddba4">AddReshapeLayer</a>(reshapeDesc, nodeDef.name().c_str());</div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>  prevLayerOutputSlot.<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="l01558"></a><span class="lineno"> 1558</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>(outputInfo);</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>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span> }</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span> </div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseFusedBatchNorm(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span> {</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 5);</div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span> </div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span>  <span class="keywordflow">if</span> (!HasParsedConstTensor<float>(inputs[1].m_IndexedValue->GetNode().name()))</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span>  boost::str(</div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span>  boost::format(</div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span>  <span class="stringliteral">"ArmNN only supports FusedBatchNormalization layers with constant scale. "</span></div><div class="line"><a name="l01575"></a><span class="lineno"> 1575</span>  <span class="stringliteral">"Input %1%. Node %2% %3%"</span>)</div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span>  % inputs[1].m_IndexedValue->GetNode().name()</div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span>  % nodeDef.name()</div><div class="line"><a name="l01578"></a><span class="lineno"> 1578</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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>  ParsedConstTfOperation<float>* scaleNode =</div><div class="line"><a name="l01581"></a><span class="lineno"> 1581</span>  boost::polymorphic_downcast<ParsedConstTfOperation<float> *>(inputs[1].m_IndexedValue);</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>  <span class="keywordflow">if</span> (!HasParsedConstTensor<float>(inputs[2].m_IndexedValue->GetNode().name()))</div><div class="line"><a name="l01584"></a><span class="lineno"> 1584</span>  {</div><div class="line"><a name="l01585"></a><span class="lineno"> 1585</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01586"></a><span class="lineno"> 1586</span>  boost::str(</div><div class="line"><a name="l01587"></a><span class="lineno"> 1587</span>  boost::format(</div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</span>  <span class="stringliteral">"ArmNN only supports FusedBatchNormalization layers with constant offset. "</span></div><div class="line"><a name="l01589"></a><span class="lineno"> 1589</span>  <span class="stringliteral">"Input %1%. Node %2% %3%"</span>)</div><div class="line"><a name="l01590"></a><span class="lineno"> 1590</span>  % inputs[2].m_IndexedValue->GetNode().name()</div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>  % nodeDef.name()</div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span>  }</div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>  ParsedConstTfOperation<float>* offsetNode =</div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</span>  boost::polymorphic_downcast<ParsedConstTfOperation<float> *>(inputs[2].m_IndexedValue);</div><div class="line"><a name="l01596"></a><span class="lineno"> 1596</span> </div><div class="line"><a name="l01597"></a><span class="lineno"> 1597</span>  <span class="keywordflow">if</span> (!HasParsedConstTensor<float>(inputs[3].m_IndexedValue->GetNode().name()))</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="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01600"></a><span class="lineno"> 1600</span>  boost::str(</div><div class="line"><a name="l01601"></a><span class="lineno"> 1601</span>  boost::format(</div><div class="line"><a name="l01602"></a><span class="lineno"> 1602</span>  <span class="stringliteral">"ArmNN only supports FusedBatchNormalization layers with constant mean. "</span></div><div class="line"><a name="l01603"></a><span class="lineno"> 1603</span>  <span class="stringliteral">"Input %1%. Node %2% %3%"</span>)</div><div class="line"><a name="l01604"></a><span class="lineno"> 1604</span>  % inputs[3].m_IndexedValue->GetNode().name()</div><div class="line"><a name="l01605"></a><span class="lineno"> 1605</span>  % nodeDef.name()</div><div class="line"><a name="l01606"></a><span class="lineno"> 1606</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01607"></a><span class="lineno"> 1607</span>  }</div><div class="line"><a name="l01608"></a><span class="lineno"> 1608</span>  ParsedConstTfOperation<float>* meanNode =</div><div class="line"><a name="l01609"></a><span class="lineno"> 1609</span>  boost::polymorphic_downcast<ParsedConstTfOperation<float> *>(inputs[3].m_IndexedValue);</div><div class="line"><a name="l01610"></a><span class="lineno"> 1610</span> </div><div class="line"><a name="l01611"></a><span class="lineno"> 1611</span>  <span class="keywordflow">if</span> (!HasParsedConstTensor<float>(inputs[4].m_IndexedValue->GetNode().name()))</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="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01614"></a><span class="lineno"> 1614</span>  boost::str(</div><div class="line"><a name="l01615"></a><span class="lineno"> 1615</span>  boost::format(</div><div class="line"><a name="l01616"></a><span class="lineno"> 1616</span>  <span class="stringliteral">"ArmNN only supports FusedBatchNormalization layers with constant variance. "</span></div><div class="line"><a name="l01617"></a><span class="lineno"> 1617</span>  <span class="stringliteral">"Input %1%. Node %2% %3%"</span>)</div><div class="line"><a name="l01618"></a><span class="lineno"> 1618</span>  % inputs[4].m_IndexedValue->GetNode().name()</div><div class="line"><a name="l01619"></a><span class="lineno"> 1619</span>  % nodeDef.name()</div><div class="line"><a name="l01620"></a><span class="lineno"> 1620</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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>  ParsedConstTfOperation<float>* varianceNode =</div><div class="line"><a name="l01623"></a><span class="lineno"> 1623</span>  boost::polymorphic_downcast<ParsedConstTfOperation<float> *>(inputs[4].m_IndexedValue);</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">const</span> std::string dataFormat = ReadOptionalNodeStringAttribute(nodeDef, <span class="stringliteral">"data_format"</span>, <span class="stringliteral">"NHWC"</span>);</div><div class="line"><a name="l01626"></a><span class="lineno"> 1626</span>  <a class="code" href="_tf_parser_8cpp.xhtml#a3fb047570644cae325aa88d3cd7bb96e">CHECK_DATA_FORMAT</a>(nodeDef, dataFormat, <span class="stringliteral">"FusedBatchNorm"</span>);</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>  <span class="comment">// The descriptor only has the epsilon attribute.</span></div><div class="line"><a name="l01629"></a><span class="lineno"> 1629</span>  <a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml">BatchNormalizationDescriptor</a> desc;</div><div class="line"><a name="l01630"></a><span class="lineno"> 1630</span>  desc.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">m_Eps</a> = ReadMandatoryNodeFloatAttribute(nodeDef, <span class="stringliteral">"epsilon"</span>);</div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span>  desc.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataFormat == <span class="stringliteral">"NHWC"</span> ? DataLayout::NHWC : DataLayout::NCHW;</div><div class="line"><a name="l01632"></a><span class="lineno"> 1632</span> </div><div class="line"><a name="l01633"></a><span class="lineno"> 1633</span>  <span class="comment">// Data for the parsed tensor args (scale, offset, mean, variance) must be stored</span></div><div class="line"><a name="l01634"></a><span class="lineno"> 1634</span>  <span class="comment">// locally until the layer is added.</span></div><div class="line"><a name="l01635"></a><span class="lineno"> 1635</span>  std::vector<float> scaleTensorData;</div><div class="line"><a name="l01636"></a><span class="lineno"> 1636</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> scaleTensor = scaleNode->GetConstTensor(scaleTensorData);</div><div class="line"><a name="l01637"></a><span class="lineno"> 1637</span> </div><div class="line"><a name="l01638"></a><span class="lineno"> 1638</span>  std::vector<float> offsetTensorData;</div><div class="line"><a name="l01639"></a><span class="lineno"> 1639</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> offsetTensor = offsetNode->GetConstTensor(offsetTensorData);</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>  std::vector<float> meanTensorData;</div><div class="line"><a name="l01642"></a><span class="lineno"> 1642</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> meanTensor = meanNode->GetConstTensor(meanTensorData);</div><div class="line"><a name="l01643"></a><span class="lineno"> 1643</span> </div><div class="line"><a name="l01644"></a><span class="lineno"> 1644</span>  std::vector<float> varianceTensorData;</div><div class="line"><a name="l01645"></a><span class="lineno"> 1645</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> varianceTensor = varianceNode->GetConstTensor(varianceTensorData);</div><div class="line"><a name="l01646"></a><span class="lineno"> 1646</span> </div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a8d1067e754512c434da1238b67ad26ea">AddBatchNormalizationLayer</a>(desc,</div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span>  meanTensor,</div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span>  varianceTensor,</div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span>  offsetTensor,</div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span>  scaleTensor,</div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>  nodeDef.name().c_str());</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_i_output_slot.xhtml">IOutputSlot</a>& inputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span> </div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</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>(inputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>());</div><div class="line"><a name="l01657"></a><span class="lineno"> 1657</span>  inputSlot.<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="l01658"></a><span class="lineno"> 1658</span> </div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l01660"></a><span class="lineno"> 1660</span> }</div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</span> </div><div class="line"><a name="l01662"></a><span class="lineno"> 1662</span> <span class="keywordtype">bool</span> TfParser::IsSupportedLeakyReluPattern(<span class="keyword">const</span> tensorflow::NodeDef& mulNodeDef,</div><div class="line"><a name="l01663"></a><span class="lineno"> 1663</span>  <span class="keywordtype">size_t</span> alphaLayerIndex,</div><div class="line"><a name="l01664"></a><span class="lineno"> 1664</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_tf_parser_1_1_with_output_tensor_index.xhtml">OutputOfParsedTfOperation</a>& otherOp,</div><div class="line"><a name="l01665"></a><span class="lineno"> 1665</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>** outputOfLeakyRelu,</div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a> & desc)</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="keyword">const</span> tensorflow::NodeDef& otherNodeDef = otherOp.<a class="code" href="structarmnn_tf_parser_1_1_with_output_tensor_index.xhtml#a08f2876bc5d60ed9c711ac7c26747305">m_IndexedValue</a>->GetNode();</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>  <span class="comment">// Verifying all these assumptions hold:</span></div><div class="line"><a name="l01671"></a><span class="lineno"> 1671</span>  <span class="comment">//</span></div><div class="line"><a name="l01672"></a><span class="lineno"> 1672</span>  <span class="comment">// 1, the mulNodeDef is an elementwise multiplication node "Mul"</span></div><div class="line"><a name="l01673"></a><span class="lineno"> 1673</span>  <span class="comment">// 2, the alphaLayerIndex selects a constant node from the inputs of the "Mul" node</span></div><div class="line"><a name="l01674"></a><span class="lineno"> 1674</span>  <span class="comment">// 3, the inputLayerIndex selects a layer which has the same name as otherNodeDef</span></div><div class="line"><a name="l01675"></a><span class="lineno"> 1675</span>  <span class="comment">//</span></div><div class="line"><a name="l01676"></a><span class="lineno"> 1676</span> </div><div class="line"><a name="l01677"></a><span class="lineno"> 1677</span>  <span class="keywordflow">if</span> (mulNodeDef.op() == <span class="stringliteral">"Mul"</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">size_t</span> otherLayerIndex = (alphaLayerIndex == 0 ? 1 : 0);</div><div class="line"><a name="l01680"></a><span class="lineno"> 1680</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(mulNodeDef, 2);</div><div class="line"><a name="l01681"></a><span class="lineno"> 1681</span> </div><div class="line"><a name="l01682"></a><span class="lineno"> 1682</span>  BOOST_ASSERT(inputs.size() == 2);</div><div class="line"><a name="l01683"></a><span class="lineno"> 1683</span>  BOOST_ASSERT((otherLayerIndex == 0 || alphaLayerIndex == 0));</div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span>  BOOST_ASSERT((otherLayerIndex == 1 || alphaLayerIndex == 1));</div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span>  BOOST_ASSERT(((otherLayerIndex + alphaLayerIndex) == 1));</div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span> </div><div class="line"><a name="l01687"></a><span class="lineno"> 1687</span>  <span class="keywordflow">if</span> (inputs[otherLayerIndex].m_IndexedValue->GetNode().name() == otherNodeDef.name())</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="keywordflow">if</span> (HasParsedConstTensor<float>(inputs[alphaLayerIndex].m_IndexedValue->GetNode().name()))</div><div class="line"><a name="l01690"></a><span class="lineno"> 1690</span>  {</div><div class="line"><a name="l01691"></a><span class="lineno"> 1691</span>  ParsedConstTfOperation<float>* alpha =</div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span>  boost::polymorphic_downcast<ParsedConstTfOperation<float> *>(</div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span>  inputs[alphaLayerIndex].m_IndexedValue);</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>  std::vector<float> const_data;</div><div class="line"><a name="l01696"></a><span class="lineno"> 1696</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> const_tensor = alpha->GetConstTensor(const_data);</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>  <span class="keywordflow">if</span> (const_data.size() == 1)</div><div class="line"><a name="l01699"></a><span class="lineno"> 1699</span>  {</div><div class="line"><a name="l01700"></a><span class="lineno"> 1700</span>  desc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::LeakyReLu;</div><div class="line"><a name="l01701"></a><span class="lineno"> 1701</span>  desc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = const_data[0];</div><div class="line"><a name="l01702"></a><span class="lineno"> 1702</span> </div><div class="line"><a name="l01703"></a><span class="lineno"> 1703</span>  *outputOfLeakyRelu = &(otherOp.<a class="code" href="structarmnn_tf_parser_1_1_with_output_tensor_index.xhtml#a08f2876bc5d60ed9c711ac7c26747305">m_IndexedValue</a>->ResolveArmnnOutputSlot(otherOp.<a class="code" href="structarmnn_tf_parser_1_1_with_output_tensor_index.xhtml#a271b1a398c11fb4bf8603119041562c9">m_Index</a>));</div><div class="line"><a name="l01704"></a><span class="lineno"> 1704</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</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>  }</div><div class="line"><a name="l01707"></a><span class="lineno"> 1707</span>  }</div><div class="line"><a name="l01708"></a><span class="lineno"> 1708</span>  }</div><div class="line"><a name="l01709"></a><span class="lineno"> 1709</span>  <span class="keywordflow">return</span> <span class="keyword">false</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> </div><div class="line"><a name="l01712"></a><span class="lineno"> 1712</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseMaximum(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l01714"></a><span class="lineno"> 1714</span> {</div><div class="line"><a name="l01715"></a><span class="lineno"> 1715</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l01716"></a><span class="lineno"> 1716</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2);</div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span>  <span class="keywordflow">if</span> (inputs.size() != 2)</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>  boost::str(</div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span>  boost::format(</div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span>  <span class="stringliteral">"Maximum expects two inputs!. Got %1% for Node %2% %3%"</span>)</div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>  % inputs.size()</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span>  % nodeDef.name()</div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01726"></a><span class="lineno"> 1726</span>  }</div><div class="line"><a name="l01727"></a><span class="lineno"> 1727</span> </div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span>  <span class="keyword">auto</span> inputNode0 = inputs[0].m_IndexedValue->GetNode();</div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span>  <span class="keyword">auto</span> inputNode1 = inputs[1].m_IndexedValue->GetNode();</div><div class="line"><a name="l01730"></a><span class="lineno"> 1730</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* outputOfLeakyRelu = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l01731"></a><span class="lineno"> 1731</span> </div><div class="line"><a name="l01732"></a><span class="lineno"> 1732</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> desc;</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>  <span class="comment">// A max node may be part of a LeakyRelu, with one input as a multiplication with a scalar constant,</span></div><div class="line"><a name="l01735"></a><span class="lineno"> 1735</span>  <span class="comment">// i.e. one of the four possible scenarios:</span></div><div class="line"><a name="l01736"></a><span class="lineno"> 1736</span>  <span class="comment">// 1, max(mul(a, x), x)</span></div><div class="line"><a name="l01737"></a><span class="lineno"> 1737</span>  <span class="comment">// 2, max(mul(x, a), x)</span></div><div class="line"><a name="l01738"></a><span class="lineno"> 1738</span>  <span class="comment">// 3, max(x, mul(a, x))</span></div><div class="line"><a name="l01739"></a><span class="lineno"> 1739</span>  <span class="comment">// 4, max(x, mul(x, a))</span></div><div class="line"><a name="l01740"></a><span class="lineno"> 1740</span>  <span class="comment">// These are handled by an activation layer.</span></div><div class="line"><a name="l01741"></a><span class="lineno"> 1741</span> </div><div class="line"><a name="l01742"></a><span class="lineno"> 1742</span>  <span class="keywordflow">if</span> (IsSupportedLeakyReluPattern(inputNode0, 0, inputs[1], &outputOfLeakyRelu, desc) ||</div><div class="line"><a name="l01743"></a><span class="lineno"> 1743</span>  IsSupportedLeakyReluPattern(inputNode0, 1, inputs[1], &outputOfLeakyRelu, desc) ||</div><div class="line"><a name="l01744"></a><span class="lineno"> 1744</span>  IsSupportedLeakyReluPattern(inputNode1, 0, inputs[0], &outputOfLeakyRelu, desc) ||</div><div class="line"><a name="l01745"></a><span class="lineno"> 1745</span>  IsSupportedLeakyReluPattern(inputNode1, 1, inputs[0], &outputOfLeakyRelu, desc))</div><div class="line"><a name="l01746"></a><span class="lineno"> 1746</span>  {</div><div class="line"><a name="l01747"></a><span class="lineno"> 1747</span>  BOOST_ASSERT(outputOfLeakyRelu != <span class="keyword">nullptr</span>);</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#aea068f6094e1c3bfcdf8167b68112632">AddActivationLayer</a>(desc, nodeDef.name().c_str());</div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span>  outputOfLeakyRelu-><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="l01751"></a><span class="lineno"> 1751</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>(outputOfLeakyRelu-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>());</div><div class="line"><a name="l01752"></a><span class="lineno"> 1752</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l01753"></a><span class="lineno"> 1753</span>  }</div><div class="line"><a name="l01754"></a><span class="lineno"> 1754</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01755"></a><span class="lineno"> 1755</span>  {</div><div class="line"><a name="l01756"></a><span class="lineno"> 1756</span>  <span class="comment">// Anything else is just a maximum layer.</span></div><div class="line"><a name="l01757"></a><span class="lineno"> 1757</span> </div><div class="line"><a name="l01758"></a><span class="lineno"> 1758</span>  <span class="keywordflow">return</span> AddMaximumLayer(nodeDef);</div><div class="line"><a name="l01759"></a><span class="lineno"> 1759</span>  }</div><div class="line"><a name="l01760"></a><span class="lineno"> 1760</span> }</div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span> </div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span> std::pair<armnn::IOutputSlot*, armnn::IOutputSlot*> TfParser::ProcessElementwiseInputSlots(</div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span>  <span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> std::string& layerName)</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<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2);</div><div class="line"><a name="l01766"></a><span class="lineno"> 1766</span> </div><div class="line"><a name="l01767"></a><span class="lineno"> 1767</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input0Slot = &inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l01768"></a><span class="lineno"> 1768</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input1Slot = &inputs[1].m_IndexedValue->ResolveArmnnOutputSlot(inputs[1].m_Index);</div><div class="line"><a name="l01769"></a><span class="lineno"> 1769</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input0Dim = input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l01770"></a><span class="lineno"> 1770</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input1Dim = input1Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span> </div><div class="line"><a name="l01772"></a><span class="lineno"> 1772</span>  <span class="keywordflow">if</span> (input0Dim != input1Dim)</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>  <span class="comment">// broadcasting where input0 and input1 have different number of dimensions</span></div><div class="line"><a name="l01775"></a><span class="lineno"> 1775</span>  <span class="comment">// is only supported for 1D and 4D tensors pair</span></div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>  <span class="keywordflow">if</span> (input0Dim == 1 && input1Dim == 4)</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>  input0Slot = AddBroadcastReshapeLayer(input1Slot, input0Slot, <span class="keyword">true</span>, *m_Network, nodeDef);</div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span>  }</div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (input0Dim == 4 && input1Dim == 1)</div><div class="line"><a name="l01781"></a><span class="lineno"> 1781</span>  {</div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span>  input1Slot = AddBroadcastReshapeLayer(input0Slot, input1Slot, <span class="keyword">true</span>, *m_Network, nodeDef);</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="keywordflow">else</span></div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span>  {</div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span>  boost::str(</div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span>  boost::format(<span class="stringliteral">"Unsupported broadcast configuration for %1% operation %2% %3%"</span>)</div><div class="line"><a name="l01789"></a><span class="lineno"> 1789</span>  % layerName</div><div class="line"><a name="l01790"></a><span class="lineno"> 1790</span>  % nodeDef.name()</div><div class="line"><a name="l01791"></a><span class="lineno"> 1791</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span>  }</div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span>  }</div><div class="line"><a name="l01794"></a><span class="lineno"> 1794</span>  <span class="keywordflow">return</span> {input0Slot, input1Slot};</div><div class="line"><a name="l01795"></a><span class="lineno"> 1795</span> }</div><div class="line"><a name="l01796"></a><span class="lineno"> 1796</span> </div><div class="line"><a name="l01797"></a><span class="lineno"> 1797</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ProcessComparisonLayer(</div><div class="line"><a name="l01798"></a><span class="lineno"> 1798</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input0Slot,</div><div class="line"><a name="l01799"></a><span class="lineno"> 1799</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input1Slot,</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>* <span class="keyword">const</span> layer,</div><div class="line"><a name="l01801"></a><span class="lineno"> 1801</span>  <span class="keyword">const</span> tensorflow::NodeDef& nodeDef)</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>  input0Slot-><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="l01804"></a><span class="lineno"> 1804</span>  input1Slot-><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>(1));</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo = input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l01807"></a><span class="lineno"> 1807</span>  outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a71975fcec1464d639f1a78f73164d1bd">SetDataType</a>(DataType::Boolean);</div><div class="line"><a name="l01808"></a><span class="lineno"> 1808</span>  std::vector<unsigned int> outputShape;</div><div class="line"><a name="l01809"></a><span class="lineno"> 1809</span> </div><div class="line"><a name="l01810"></a><span class="lineno"> 1810</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& input0Shape = input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l01811"></a><span class="lineno"> 1811</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& input1Shape = input1Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</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="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < input0Shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); i++)</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>  outputShape.push_back(std::max(input0Shape[i], input1Shape[i]));</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>  outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(input0Shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(), outputShape.data()));</div><div class="line"><a name="l01819"></a><span class="lineno"> 1819</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>(outputInfo);</div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span> </div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</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> </div><div class="line"><a name="l01824"></a><span class="lineno"> 1824</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ProcessElementwiseLayer(</div><div class="line"><a name="l01825"></a><span class="lineno"> 1825</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input0Slot,</div><div class="line"><a name="l01826"></a><span class="lineno"> 1826</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input1Slot,</div><div class="line"><a name="l01827"></a><span class="lineno"> 1827</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer,</div><div class="line"><a name="l01828"></a><span class="lineno"> 1828</span>  <span class="keyword">const</span> tensorflow::NodeDef& nodeDef)</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>  input0Slot-><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="l01831"></a><span class="lineno"> 1831</span>  input1Slot-><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>(1));</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo = input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>  std::vector<unsigned int> outputShape;</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>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& input0Shape = input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l01837"></a><span class="lineno"> 1837</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& input1Shape = input1Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l01838"></a><span class="lineno"> 1838</span> </div><div class="line"><a name="l01839"></a><span class="lineno"> 1839</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < input0Shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); i++)</div><div class="line"><a name="l01840"></a><span class="lineno"> 1840</span>  {</div><div class="line"><a name="l01841"></a><span class="lineno"> 1841</span>  outputShape.push_back(std::max(input0Shape[i], input1Shape[i]));</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> </div><div class="line"><a name="l01844"></a><span class="lineno"> 1844</span>  outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(input0Shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(), outputShape.data()));</div><div class="line"><a name="l01845"></a><span class="lineno"> 1845</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>(outputInfo);</div><div class="line"><a name="l01846"></a><span class="lineno"> 1846</span> </div><div class="line"><a name="l01847"></a><span class="lineno"> 1847</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l01848"></a><span class="lineno"> 1848</span> }</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> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseGather(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l01851"></a><span class="lineno"> 1851</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l01852"></a><span class="lineno"> 1852</span> {</div><div class="line"><a name="l01853"></a><span class="lineno"> 1853</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l01854"></a><span class="lineno"> 1854</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2);</div><div class="line"><a name="l01855"></a><span class="lineno"> 1855</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& params = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l01856"></a><span class="lineno"> 1856</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& indices = inputs[1].m_IndexedValue->ResolveArmnnOutputSlot(inputs[1].m_Index);</div><div class="line"><a name="l01857"></a><span class="lineno"> 1857</span> </div><div class="line"><a name="l01858"></a><span class="lineno"> 1858</span>  <span class="comment">// Infer shape of output tensor</span></div><div class="line"><a name="l01859"></a><span class="lineno"> 1859</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> paramsDim = params.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l01860"></a><span class="lineno"> 1860</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> indicesDim = indices.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l01861"></a><span class="lineno"> 1861</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputDim = paramsDim - 1 + indicesDim;</div><div class="line"><a name="l01862"></a><span class="lineno"> 1862</span> </div><div class="line"><a name="l01863"></a><span class="lineno"> 1863</span>  std::vector<unsigned int> dimSizes;</div><div class="line"><a name="l01864"></a><span class="lineno"> 1864</span> </div><div class="line"><a name="l01865"></a><span class="lineno"> 1865</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < indicesDim; ++i)</div><div class="line"><a name="l01866"></a><span class="lineno"> 1866</span>  {</div><div class="line"><a name="l01867"></a><span class="lineno"> 1867</span>  dimSizes.push_back(indices.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i]);</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">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 1; i < paramsDim; ++i)</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>  dimSizes.push_back(params.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i]);</div><div class="line"><a name="l01872"></a><span class="lineno"> 1872</span>  }</div><div class="line"><a name="l01873"></a><span class="lineno"> 1873</span> </div><div class="line"><a name="l01874"></a><span class="lineno"> 1874</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inferredShape = <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(outputDim, dimSizes.data());</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="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inferredOutputInfo(inferredShape, params.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>());</div><div class="line"><a name="l01877"></a><span class="lineno"> 1877</span> </div><div class="line"><a name="l01878"></a><span class="lineno"> 1878</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a8440d2a2afd3eb3526212081c9016830">AddGatherLayer</a>(nodeDef.name().c_str());</div><div class="line"><a name="l01879"></a><span class="lineno"> 1879</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>(inferredOutputInfo);</div><div class="line"><a name="l01880"></a><span class="lineno"> 1880</span> </div><div class="line"><a name="l01881"></a><span class="lineno"> 1881</span>  params.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer->GetInputSlot(0));</div><div class="line"><a name="l01882"></a><span class="lineno"> 1882</span>  indices.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(layer->GetInputSlot(1));</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>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</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> </div><div class="line"><a name="l01887"></a><span class="lineno"> 1887</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseGreater(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l01888"></a><span class="lineno"> 1888</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l01889"></a><span class="lineno"> 1889</span> {</div><div class="line"><a name="l01890"></a><span class="lineno"> 1890</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l01891"></a><span class="lineno"> 1891</span>  std::pair<armnn::IOutputSlot*, armnn::IOutputSlot*> inputLayers = ProcessElementwiseInputSlots(nodeDef, <span class="stringliteral">"Greater"</span>);</div><div class="line"><a name="l01892"></a><span class="lineno"> 1892</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input0Slot = inputLayers.first;</div><div class="line"><a name="l01893"></a><span class="lineno"> 1893</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input1Slot = inputLayers.second;</div><div class="line"><a name="l01894"></a><span class="lineno"> 1894</span> </div><div class="line"><a name="l01895"></a><span class="lineno"> 1895</span>  <a class="code" href="structarmnn_1_1_comparison_descriptor.xhtml">ComparisonDescriptor</a> descriptor(ComparisonOperation::Greater);</div><div class="line"><a name="l01896"></a><span class="lineno"> 1896</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#ac3be1bcc0fa5ffaf04a4f1d20d0ab7f4">AddComparisonLayer</a>(descriptor, nodeDef.name().c_str());</div><div class="line"><a name="l01897"></a><span class="lineno"> 1897</span> </div><div class="line"><a name="l01898"></a><span class="lineno"> 1898</span>  <span class="keywordflow">return</span> ProcessComparisonLayer(input0Slot, input1Slot, layer, nodeDef);</div><div class="line"><a name="l01899"></a><span class="lineno"> 1899</span> }</div><div class="line"><a name="l01900"></a><span class="lineno"> 1900</span> </div><div class="line"><a name="l01901"></a><span class="lineno"> 1901</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseEqual(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l01902"></a><span class="lineno"> 1902</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</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>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l01905"></a><span class="lineno"> 1905</span>  std::pair<armnn::IOutputSlot*, armnn::IOutputSlot*> inputLayers = ProcessElementwiseInputSlots(nodeDef, <span class="stringliteral">"Equal"</span>);</div><div class="line"><a name="l01906"></a><span class="lineno"> 1906</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input0Slot = inputLayers.first;</div><div class="line"><a name="l01907"></a><span class="lineno"> 1907</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input1Slot = inputLayers.second;</div><div class="line"><a name="l01908"></a><span class="lineno"> 1908</span> </div><div class="line"><a name="l01909"></a><span class="lineno"> 1909</span>  <a class="code" href="structarmnn_1_1_comparison_descriptor.xhtml">ComparisonDescriptor</a> descriptor(ComparisonOperation::Equal);</div><div class="line"><a name="l01910"></a><span class="lineno"> 1910</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#ac3be1bcc0fa5ffaf04a4f1d20d0ab7f4">AddComparisonLayer</a>(descriptor, nodeDef.name().c_str());</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>  <span class="keywordflow">return</span> ProcessComparisonLayer(input0Slot, input1Slot, layer, nodeDef);</div><div class="line"><a name="l01913"></a><span class="lineno"> 1913</span> }</div><div class="line"><a name="l01914"></a><span class="lineno"> 1914</span> </div><div class="line"><a name="l01915"></a><span class="lineno"> 1915</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseMinimum(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l01916"></a><span class="lineno"> 1916</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l01917"></a><span class="lineno"> 1917</span> {</div><div class="line"><a name="l01918"></a><span class="lineno"> 1918</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l01919"></a><span class="lineno"> 1919</span>  std::pair<armnn::IOutputSlot*, armnn::IOutputSlot*> inputLayers = ProcessElementwiseInputSlots(nodeDef, <span class="stringliteral">"Minimum"</span>);</div><div class="line"><a name="l01920"></a><span class="lineno"> 1920</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input0Slot = inputLayers.first;</div><div class="line"><a name="l01921"></a><span class="lineno"> 1921</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input1Slot = inputLayers.second;</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_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a4cc12e3bd9ffe196cc8b351f25a104e3">AddMinimumLayer</a>(nodeDef.name().c_str());</div><div class="line"><a name="l01924"></a><span class="lineno"> 1924</span> </div><div class="line"><a name="l01925"></a><span class="lineno"> 1925</span>  <span class="keywordflow">return</span> ProcessElementwiseLayer(input0Slot, input1Slot, layer, nodeDef);</div><div class="line"><a name="l01926"></a><span class="lineno"> 1926</span> }</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> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseSub(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l01929"></a><span class="lineno"> 1929</span> {</div><div class="line"><a name="l01930"></a><span class="lineno"> 1930</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l01931"></a><span class="lineno"> 1931</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2);</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>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input0Slot = &inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l01934"></a><span class="lineno"> 1934</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input1Slot = &inputs[1].m_IndexedValue->ResolveArmnnOutputSlot(inputs[1].m_Index);</div><div class="line"><a name="l01935"></a><span class="lineno"> 1935</span> </div><div class="line"><a name="l01936"></a><span class="lineno"> 1936</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& input0Info = input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l01937"></a><span class="lineno"> 1937</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& input1Info = input1Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l01938"></a><span class="lineno"> 1938</span> </div><div class="line"><a name="l01939"></a><span class="lineno"> 1939</span>  <span class="keywordflow">if</span> (input0Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() == 1)</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>  <span class="keyword">const</span> <span class="keywordtype">bool</span> isNHWC = <span class="keyword">true</span>;</div><div class="line"><a name="l01942"></a><span class="lineno"> 1942</span>  input0Slot = AddBroadcastReshapeLayer(input1Slot, input0Slot, isNHWC, *m_Network, nodeDef);</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> </div><div class="line"><a name="l01945"></a><span class="lineno"> 1945</span>  <span class="keywordflow">if</span> (input1Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() == 1)</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="keyword">const</span> <span class="keywordtype">bool</span> isNHWC = <span class="keyword">true</span>;</div><div class="line"><a name="l01948"></a><span class="lineno"> 1948</span>  input1Slot = AddBroadcastReshapeLayer(input0Slot, input1Slot, isNHWC, *m_Network, nodeDef);</div><div class="line"><a name="l01949"></a><span class="lineno"> 1949</span>  }</div><div class="line"><a name="l01950"></a><span class="lineno"> 1950</span> </div><div class="line"><a name="l01951"></a><span class="lineno"> 1951</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#ab6d332d9c4b4f04c23f40f04f7f56d0d">AddSubtractionLayer</a>(nodeDef.name().c_str());</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>  input0Slot-><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="l01954"></a><span class="lineno"> 1954</span>  input1Slot-><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>(1));</div><div class="line"><a name="l01955"></a><span class="lineno"> 1955</span> </div><div class="line"><a name="l01956"></a><span class="lineno"> 1956</span>  <span class="keywordflow">if</span> (input0Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() == 1)</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>  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>(input1Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>());</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>  <span class="keywordflow">else</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>  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>(input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>());</div><div class="line"><a name="l01963"></a><span class="lineno"> 1963</span>  }</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>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</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> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseStack(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l01969"></a><span class="lineno"> 1969</span> {</div><div class="line"><a name="l01970"></a><span class="lineno"> 1970</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l01971"></a><span class="lineno"> 1971</span>  std::vector<OutputOfConstNodeDef> nodes = GetTfInputNodes(nodeDef);</div><div class="line"><a name="l01972"></a><span class="lineno"> 1972</span> </div><div class="line"><a name="l01973"></a><span class="lineno"> 1973</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numInputs = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(nodes.size());</div><div class="line"><a name="l01974"></a><span class="lineno"> 1974</span>  <span class="keywordflow">if</span> (numInputs < 1)</div><div class="line"><a name="l01975"></a><span class="lineno"> 1975</span>  {</div><div class="line"><a name="l01976"></a><span class="lineno"> 1976</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01977"></a><span class="lineno"> 1977</span>  boost::str(</div><div class="line"><a name="l01978"></a><span class="lineno"> 1978</span>  boost::format(</div><div class="line"><a name="l01979"></a><span class="lineno"> 1979</span>  <span class="stringliteral">"Pack/Stack expects at least one input. Got %1% for Node %2% %3%"</span>)</div><div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>  % numInputs</div><div class="line"><a name="l01981"></a><span class="lineno"> 1981</span>  % nodeDef.name()</div><div class="line"><a name="l01982"></a><span class="lineno"> 1982</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01983"></a><span class="lineno"> 1983</span>  }</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>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, numInputs);</div><div class="line"><a name="l01986"></a><span class="lineno"> 1986</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="l01987"></a><span class="lineno"> 1987</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input0Slot = &inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l01988"></a><span class="lineno"> 1988</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l01989"></a><span class="lineno"> 1989</span>  <span class="keyword">auto</span> numDimensions = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>().<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l01990"></a><span class="lineno"> 1990</span> </div><div class="line"><a name="l01991"></a><span class="lineno"> 1991</span>  <span class="comment">// validate axis</span></div><div class="line"><a name="l01992"></a><span class="lineno"> 1992</span>  int32_t axis = ReadMandatoryNodeInt32Attribute(nodeDef, <span class="stringliteral">"axis"</span>);</div><div class="line"><a name="l01993"></a><span class="lineno"> 1993</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> sNumDimensions = (<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(numDimensions) + 1);</div><div class="line"><a name="l01994"></a><span class="lineno"> 1994</span>  <span class="keywordflow">if</span> (!(axis < sNumDimensions && axis >= -sNumDimensions))</div><div class="line"><a name="l01995"></a><span class="lineno"> 1995</span>  {</div><div class="line"><a name="l01996"></a><span class="lineno"> 1996</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01997"></a><span class="lineno"> 1997</span>  boost::str(</div><div class="line"><a name="l01998"></a><span class="lineno"> 1998</span>  boost::format(</div><div class="line"><a name="l01999"></a><span class="lineno"> 1999</span>  <span class="stringliteral">"Axis index is not in range. Got %1% for Node %2% %3%"</span>)</div><div class="line"><a name="l02000"></a><span class="lineno"> 2000</span>  % axis</div><div class="line"><a name="l02001"></a><span class="lineno"> 2001</span>  % nodeDef.name()</div><div class="line"><a name="l02002"></a><span class="lineno"> 2002</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02003"></a><span class="lineno"> 2003</span>  }</div><div class="line"><a name="l02004"></a><span class="lineno"> 2004</span> </div><div class="line"><a name="l02005"></a><span class="lineno"> 2005</span>  <span class="keywordflow">if</span> (axis < 0)</div><div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>  {</div><div class="line"><a name="l02007"></a><span class="lineno"> 2007</span>  axis = <span class="keyword">static_cast<</span>int32_t<span class="keyword">></span>(numDimensions) + axis + 1;</div><div class="line"><a name="l02008"></a><span class="lineno"> 2008</span>  }</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>  <a class="code" href="structarmnn_1_1_stack_descriptor.xhtml">StackDescriptor</a> stackDescriptor;</div><div class="line"><a name="l02011"></a><span class="lineno"> 2011</span>  stackDescriptor.<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>(axis);</div><div class="line"><a name="l02012"></a><span class="lineno"> 2012</span>  stackDescriptor.<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>(numInputs);</div><div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>  stackDescriptor.<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="l02014"></a><span class="lineno"> 2014</span> </div><div class="line"><a name="l02015"></a><span class="lineno"> 2015</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> supportedNumDims = 4;</div><div class="line"><a name="l02016"></a><span class="lineno"> 2016</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> viewIndex = 0; viewIndex < numInputs; ++viewIndex)</div><div class="line"><a name="l02017"></a><span class="lineno"> 2017</span>  {</div><div class="line"><a name="l02018"></a><span class="lineno"> 2018</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& inputSlot = inputs[viewIndex].m_IndexedValue->ResolveArmnnOutputSlot(inputs[viewIndex].m_Index);</div><div class="line"><a name="l02019"></a><span class="lineno"> 2019</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = inputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</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>  <span class="comment">// Double check dimensions of the tensors</span></div><div class="line"><a name="l02022"></a><span class="lineno"> 2022</span>  <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() >= supportedNumDims)</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">armnn::ParseException</a>(</div><div class="line"><a name="l02025"></a><span class="lineno"> 2025</span>  boost::str(</div><div class="line"><a name="l02026"></a><span class="lineno"> 2026</span>  boost::format(</div><div class="line"><a name="l02027"></a><span class="lineno"> 2027</span>  <span class="stringliteral">"The number of dimensions: %1% for input tensors of the "</span></div><div class="line"><a name="l02028"></a><span class="lineno"> 2028</span>  <span class="stringliteral">"Pack/Stack op. Number of dimensions should be less than %2% %3%"</span>)</div><div class="line"><a name="l02029"></a><span class="lineno"> 2029</span>  % inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()</div><div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>  % supportedNumDims</div><div class="line"><a name="l02031"></a><span class="lineno"> 2031</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02032"></a><span class="lineno"> 2032</span>  }</div><div class="line"><a name="l02033"></a><span class="lineno"> 2033</span>  }</div><div class="line"><a name="l02034"></a><span class="lineno"> 2034</span> </div><div class="line"><a name="l02035"></a><span class="lineno"> 2035</span>  std::vector<unsigned int> outputDimensions;</div><div class="line"><a name="l02036"></a><span class="lineno"> 2036</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < stackDescriptor.<a class="code" href="structarmnn_1_1_stack_descriptor.xhtml#a2bea87b470268bb0b73457c3733dbc04">m_InputShape</a>.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); ++i)</div><div class="line"><a name="l02037"></a><span class="lineno"> 2037</span>  {</div><div class="line"><a name="l02038"></a><span class="lineno"> 2038</span>  outputDimensions.push_back(stackDescriptor.<a class="code" href="structarmnn_1_1_stack_descriptor.xhtml#a2bea87b470268bb0b73457c3733dbc04">m_InputShape</a>[i]);</div><div class="line"><a name="l02039"></a><span class="lineno"> 2039</span>  }</div><div class="line"><a name="l02040"></a><span class="lineno"> 2040</span>  outputDimensions.insert(outputDimensions.begin() + axis, numInputs);</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>  <span class="comment">// add Stack Layer</span></div><div class="line"><a name="l02043"></a><span class="lineno"> 2043</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#afaa808f44f0b8332ec0bd54f4fea47c0">AddStackLayer</a>(stackDescriptor, nodeDef.name().c_str());</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>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> viewIndex = 0; viewIndex < numInputs; ++viewIndex)</div><div class="line"><a name="l02046"></a><span class="lineno"> 2046</span>  {</div><div class="line"><a name="l02047"></a><span class="lineno"> 2047</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& inputSlot = inputs[viewIndex].m_IndexedValue->ResolveArmnnOutputSlot(inputs[viewIndex].m_Index);</div><div class="line"><a name="l02048"></a><span class="lineno"> 2048</span>  inputSlot.<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>(viewIndex));</div><div class="line"><a name="l02049"></a><span class="lineno"> 2049</span>  }</div><div class="line"><a name="l02050"></a><span class="lineno"> 2050</span> </div><div class="line"><a name="l02051"></a><span class="lineno"> 2051</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>(</div><div class="line"><a name="l02052"></a><span class="lineno"> 2052</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(static_cast<uint32_t>(outputDimensions.size()),</div><div class="line"><a name="l02053"></a><span class="lineno"> 2053</span>  outputDimensions.data(),</div><div class="line"><a name="l02054"></a><span class="lineno"> 2054</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()));</div><div class="line"><a name="l02055"></a><span class="lineno"> 2055</span> </div><div class="line"><a name="l02056"></a><span class="lineno"> 2056</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l02057"></a><span class="lineno"> 2057</span> }</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> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseTranspose(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l02060"></a><span class="lineno"> 2060</span> {</div><div class="line"><a name="l02061"></a><span class="lineno"> 2061</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02062"></a><span class="lineno"> 2062</span> </div><div class="line"><a name="l02063"></a><span class="lineno"> 2063</span>  <span class="keyword">auto</span> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2);</div><div class="line"><a name="l02064"></a><span class="lineno"> 2064</span>  <span class="keyword">const</span> <span class="keyword">auto</span> inputCount = inputs.size();</div><div class="line"><a name="l02065"></a><span class="lineno"> 2065</span> </div><div class="line"><a name="l02066"></a><span class="lineno"> 2066</span>  <span class="keywordflow">if</span> (inputCount != 2)</div><div class="line"><a name="l02067"></a><span class="lineno"> 2067</span>  {</div><div class="line"><a name="l02068"></a><span class="lineno"> 2068</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02069"></a><span class="lineno"> 2069</span>  boost::str(</div><div class="line"><a name="l02070"></a><span class="lineno"> 2070</span>  boost::format(</div><div class="line"><a name="l02071"></a><span class="lineno"> 2071</span>  <span class="stringliteral">"The number of given input is %1%. It should be two for Transpose op."</span></div><div class="line"><a name="l02072"></a><span class="lineno"> 2072</span>  <span class="stringliteral">"Node %2% %3%"</span>)</div><div class="line"><a name="l02073"></a><span class="lineno"> 2073</span>  % inputCount</div><div class="line"><a name="l02074"></a><span class="lineno"> 2074</span>  % nodeDef.name()</div><div class="line"><a name="l02075"></a><span class="lineno"> 2075</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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>  <span class="keyword">auto</span>* input0Slot = &inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</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>  <span class="keyword">const</span> <span class="keyword">auto</span> constInput = inputs[GetConstInputIndex(inputs)];</div><div class="line"><a name="l02081"></a><span class="lineno"> 2081</span>  <span class="keyword">auto</span>* permuteVectorInput =</div><div class="line"><a name="l02082"></a><span class="lineno"> 2082</span>  boost::polymorphic_downcast<ParsedConstTfOperation<int32_t>*>(constInput.m_IndexedValue);</div><div class="line"><a name="l02083"></a><span class="lineno"> 2083</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& permuteVectorInfo = permuteVectorInput-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l02084"></a><span class="lineno"> 2084</span> </div><div class="line"><a name="l02085"></a><span class="lineno"> 2085</span>  std::vector<int32_t> permuteVectorData;</div><div class="line"><a name="l02086"></a><span class="lineno"> 2086</span>  permuteVectorInput->GetConstTensor(permuteVectorData);</div><div class="line"><a name="l02087"></a><span class="lineno"> 2087</span> </div><div class="line"><a name="l02088"></a><span class="lineno"> 2088</span>  std::vector<unsigned int> armnnPermuteVectorData(permuteVectorData.begin(), permuteVectorData.end());</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="keyword">const</span> <span class="keyword">auto</span> permutationVector = <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a>(armnnPermuteVectorData.data(), permuteVectorInfo.GetNumElements());</div><div class="line"><a name="l02091"></a><span class="lineno"> 2091</span>  <span class="keyword">const</span> <span class="keyword">auto</span> desc = <a class="code" href="structarmnn_1_1_transpose_descriptor.xhtml">TransposeDescriptor</a>(permutationVector);</div><div class="line"><a name="l02092"></a><span class="lineno"> 2092</span> </div><div class="line"><a name="l02093"></a><span class="lineno"> 2093</span>  <span class="keyword">auto</span>* layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a4f6070c1337d40f1e98988acee015c7d">AddTransposeLayer</a>(desc, nodeDef.name().c_str());</div><div class="line"><a name="l02094"></a><span class="lineno"> 2094</span>  BOOST_ASSERT(layer);</div><div class="line"><a name="l02095"></a><span class="lineno"> 2095</span> </div><div class="line"><a name="l02096"></a><span class="lineno"> 2096</span>  input0Slot-><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="l02097"></a><span class="lineno"> 2097</span> </div><div class="line"><a name="l02098"></a><span class="lineno"> 2098</span>  <span class="keyword">const</span> <span class="keyword">auto</span>& input0Info = input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l02099"></a><span class="lineno"> 2099</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo {input0Info};</div><div class="line"><a name="l02100"></a><span class="lineno"> 2100</span>  outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(<a class="code" href="namespacearmnn_utils.xhtml#a428a9a6ffdf0e8d723b50c038c56c336">armnnUtils::TransposeTensorShape</a>(input0Info.GetShape(), desc.m_DimMappings));</div><div class="line"><a name="l02101"></a><span class="lineno"> 2101</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>(outputInfo);</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="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l02104"></a><span class="lineno"> 2104</span> }</div><div class="line"><a name="l02105"></a><span class="lineno"> 2105</span> </div><div class="line"><a name="l02106"></a><span class="lineno"><a class="line" href="namespacearmnn_tf_parser.xhtml#ae5488f1478c62281c5e937e79ebcd145"> 2106</a></span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#ae5488f1478c62281c5e937e79ebcd145">CheckPaddingTensor</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>& paddingTensor,</div><div class="line"><a name="l02107"></a><span class="lineno"> 2107</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo,</div><div class="line"><a name="l02108"></a><span class="lineno"> 2108</span>  <span class="keyword">const</span> std::string& nodeName)</div><div class="line"><a name="l02109"></a><span class="lineno"> 2109</span> {</div><div class="line"><a name="l02110"></a><span class="lineno"> 2110</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> rank = paddingTensor.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0];</div><div class="line"><a name="l02111"></a><span class="lineno"> 2111</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> expectedRank = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l02112"></a><span class="lineno"> 2112</span>  <span class="keywordflow">if</span> (rank != expectedRank)</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">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02115"></a><span class="lineno"> 2115</span>  boost::str(</div><div class="line"><a name="l02116"></a><span class="lineno"> 2116</span>  boost::format(</div><div class="line"><a name="l02117"></a><span class="lineno"> 2117</span>  <span class="stringliteral">"Expected the padding tensor to be of rank %1 not %2 on Node %3 %4."</span>)</div><div class="line"><a name="l02118"></a><span class="lineno"> 2118</span>  % expectedRank</div><div class="line"><a name="l02119"></a><span class="lineno"> 2119</span>  % rank</div><div class="line"><a name="l02120"></a><span class="lineno"> 2120</span>  % nodeName</div><div class="line"><a name="l02121"></a><span class="lineno"> 2121</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> second = paddingTensor.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l02124"></a><span class="lineno"> 2124</span>  <span class="keywordflow">if</span> (second != 2)</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02127"></a><span class="lineno"> 2127</span>  boost::str(</div><div class="line"><a name="l02128"></a><span class="lineno"> 2128</span>  boost::format(</div><div class="line"><a name="l02129"></a><span class="lineno"> 2129</span>  <span class="stringliteral">"Expected the padding tensor to be of dimensions [%1, 2] not [%1, %2] on Node %3 %4."</span>)</div><div class="line"><a name="l02130"></a><span class="lineno"> 2130</span>  % rank</div><div class="line"><a name="l02131"></a><span class="lineno"> 2131</span>  % second</div><div class="line"><a name="l02132"></a><span class="lineno"> 2132</span>  % nodeName</div><div class="line"><a name="l02133"></a><span class="lineno"> 2133</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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="keywordflow">return</span> rank;</div><div class="line"><a name="l02136"></a><span class="lineno"> 2136</span> }</div><div class="line"><a name="l02137"></a><span class="lineno"> 2137</span> </div><div class="line"><a name="l02138"></a><span class="lineno"><a class="line" href="namespacearmnn_tf_parser.xhtml#a9c18860db8b032de579c5ad94cbae5d0"> 2138</a></span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn_tf_parser.xhtml#a9c18860db8b032de579c5ad94cbae5d0">CalculatePaddedOutputTensorInfo</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo,</div><div class="line"><a name="l02139"></a><span class="lineno"> 2139</span>  <span class="keyword">const</span> std::vector<std::pair<unsigned int, unsigned int>>& padList)</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>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDims = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l02142"></a><span class="lineno"> 2142</span>  std::vector<unsigned int> outDims;</div><div class="line"><a name="l02143"></a><span class="lineno"> 2143</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < numDims; ++i)</div><div class="line"><a name="l02144"></a><span class="lineno"> 2144</span>  {</div><div class="line"><a name="l02145"></a><span class="lineno"> 2145</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimSize = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i];</div><div class="line"><a name="l02146"></a><span class="lineno"> 2146</span>  <span class="keyword">const</span> std::pair<unsigned int, unsigned int>& dimPadding = padList[i];</div><div class="line"><a name="l02147"></a><span class="lineno"> 2147</span>  dimSize += dimPadding.first;</div><div class="line"><a name="l02148"></a><span class="lineno"> 2148</span>  dimSize += dimPadding.second;</div><div class="line"><a name="l02149"></a><span class="lineno"> 2149</span>  outDims.push_back(dimSize);</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="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> paddedTensorInfo = inputTensorInfo;</div><div class="line"><a name="l02152"></a><span class="lineno"> 2152</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outDimsSize = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(outDims.size());</div><div class="line"><a name="l02153"></a><span class="lineno"> 2153</span>  paddedTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{ outDimsSize, outDims.data() });</div><div class="line"><a name="l02154"></a><span class="lineno"> 2154</span>  <span class="keywordflow">return</span> paddedTensorInfo;</div><div class="line"><a name="l02155"></a><span class="lineno"> 2155</span> }</div><div class="line"><a name="l02156"></a><span class="lineno"> 2156</span> </div><div class="line"><a name="l02157"></a><span class="lineno"> 2157</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParsePad(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l02158"></a><span class="lineno"> 2158</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l02159"></a><span class="lineno"> 2159</span> {</div><div class="line"><a name="l02160"></a><span class="lineno"> 2160</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02161"></a><span class="lineno"> 2161</span>  <span class="comment">// input consists of:</span></div><div class="line"><a name="l02162"></a><span class="lineno"> 2162</span>  <span class="comment">// input[0] the tensor which will be padded</span></div><div class="line"><a name="l02163"></a><span class="lineno"> 2163</span>  <span class="comment">// input[1] the tensor holding the padding values</span></div><div class="line"><a name="l02164"></a><span class="lineno"> 2164</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2);</div><div class="line"><a name="l02165"></a><span class="lineno"> 2165</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& previousLayerOutputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l02166"></a><span class="lineno"> 2166</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = previousLayerOutputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l02167"></a><span class="lineno"> 2167</span>  <span class="keywordflow">if</span> (!HasParsedConstTensor<int32_t>(inputs[1].m_IndexedValue))</div><div class="line"><a name="l02168"></a><span class="lineno"> 2168</span>  {</div><div class="line"><a name="l02169"></a><span class="lineno"> 2169</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02170"></a><span class="lineno"> 2170</span>  boost::str(</div><div class="line"><a name="l02171"></a><span class="lineno"> 2171</span>  boost::format(</div><div class="line"><a name="l02172"></a><span class="lineno"> 2172</span>  <span class="stringliteral">"ArmNN only supports Pad with constant padding. "</span></div><div class="line"><a name="l02173"></a><span class="lineno"> 2173</span>  <span class="stringliteral">"Input %1%. Node %2% %3%"</span>)</div><div class="line"><a name="l02174"></a><span class="lineno"> 2174</span>  % inputs[1].m_IndexedValue->GetNode().name()</div><div class="line"><a name="l02175"></a><span class="lineno"> 2175</span>  % nodeDef.name()</div><div class="line"><a name="l02176"></a><span class="lineno"> 2176</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02177"></a><span class="lineno"> 2177</span> </div><div class="line"><a name="l02178"></a><span class="lineno"> 2178</span>  }</div><div class="line"><a name="l02179"></a><span class="lineno"> 2179</span>  ParsedConstTfOperation<int32_t>* paddingTensorOp =</div><div class="line"><a name="l02180"></a><span class="lineno"> 2180</span>  boost::polymorphic_downcast<ParsedConstTfOperation<int32_t>*>(inputs[1].m_IndexedValue);</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>  std::vector<int32_t> paddingTensorData;</div><div class="line"><a name="l02183"></a><span class="lineno"> 2183</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> paddingTensor = paddingTensorOp->GetConstTensor(paddingTensorData);</div><div class="line"><a name="l02184"></a><span class="lineno"> 2184</span>  <span class="comment">// paddings is an integer tensor with shape [n, 2], where n is the rank of tensor</span></div><div class="line"><a name="l02185"></a><span class="lineno"> 2185</span>  <span class="comment">// and should match the rank of the input tensor that is being padded.</span></div><div class="line"><a name="l02186"></a><span class="lineno"> 2186</span>  <span class="comment">// For each dimension D of input, paddings[D, 0] indicates how many values to add</span></div><div class="line"><a name="l02187"></a><span class="lineno"> 2187</span>  <span class="comment">// before the contents of tensor in that dimension, and paddings[D, 1] indicates how</span></div><div class="line"><a name="l02188"></a><span class="lineno"> 2188</span>  <span class="comment">// many values to add after the contents of tensor in that dimension</span></div><div class="line"><a name="l02189"></a><span class="lineno"> 2189</span>  <span class="comment">// This needs to be translated into a padList for ACL</span></div><div class="line"><a name="l02190"></a><span class="lineno"> 2190</span>  std::vector<std::pair<unsigned int, unsigned int>> padList;</div><div class="line"><a name="l02191"></a><span class="lineno"> 2191</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> rank = <a class="code" href="namespacearmnn_tf_parser.xhtml#ae5488f1478c62281c5e937e79ebcd145">CheckPaddingTensor</a>(paddingTensor, inputTensorInfo, nodeDef.name());</div><div class="line"><a name="l02192"></a><span class="lineno"> 2192</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < rank; ++i)</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>  std::pair<unsigned int, unsigned int> paddingForDim;</div><div class="line"><a name="l02195"></a><span class="lineno"> 2195</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> j = 0; j < 2; j++)</div><div class="line"><a name="l02196"></a><span class="lineno"> 2196</span>  {</div><div class="line"><a name="l02197"></a><span class="lineno"> 2197</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> index = (i * 2) + j;</div><div class="line"><a name="l02198"></a><span class="lineno"> 2198</span>  <span class="keywordtype">int</span> paddingAmount = paddingTensorData[index];</div><div class="line"><a name="l02199"></a><span class="lineno"> 2199</span>  <span class="comment">// make sure we can cast to an unsigned value</span></div><div class="line"><a name="l02200"></a><span class="lineno"> 2200</span>  <span class="keywordflow">if</span> (paddingAmount < 0)</div><div class="line"><a name="l02201"></a><span class="lineno"> 2201</span>  {</div><div class="line"><a name="l02202"></a><span class="lineno"> 2202</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02203"></a><span class="lineno"> 2203</span>  boost::str(</div><div class="line"><a name="l02204"></a><span class="lineno"> 2204</span>  boost::format(</div><div class="line"><a name="l02205"></a><span class="lineno"> 2205</span>  <span class="stringliteral">"Negative amount %1 specified at [%2, %3] of padding tensor on Node %4 %5."</span>)</div><div class="line"><a name="l02206"></a><span class="lineno"> 2206</span>  % paddingAmount</div><div class="line"><a name="l02207"></a><span class="lineno"> 2207</span>  % i</div><div class="line"><a name="l02208"></a><span class="lineno"> 2208</span>  % j</div><div class="line"><a name="l02209"></a><span class="lineno"> 2209</span>  % nodeDef.name()</div><div class="line"><a name="l02210"></a><span class="lineno"> 2210</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02211"></a><span class="lineno"> 2211</span>  }</div><div class="line"><a name="l02212"></a><span class="lineno"> 2212</span>  <span class="keywordflow">if</span> (j == 0)</div><div class="line"><a name="l02213"></a><span class="lineno"> 2213</span>  {</div><div class="line"><a name="l02214"></a><span class="lineno"> 2214</span>  paddingForDim.first = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(paddingAmount);</div><div class="line"><a name="l02215"></a><span class="lineno"> 2215</span>  }</div><div class="line"><a name="l02216"></a><span class="lineno"> 2216</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l02217"></a><span class="lineno"> 2217</span>  {</div><div class="line"><a name="l02218"></a><span class="lineno"> 2218</span>  paddingForDim.second = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(paddingAmount);</div><div class="line"><a name="l02219"></a><span class="lineno"> 2219</span>  }</div><div class="line"><a name="l02220"></a><span class="lineno"> 2220</span>  }</div><div class="line"><a name="l02221"></a><span class="lineno"> 2221</span>  padList.push_back(paddingForDim);</div><div class="line"><a name="l02222"></a><span class="lineno"> 2222</span>  }</div><div class="line"><a name="l02223"></a><span class="lineno"> 2223</span>  <a class="code" href="structarmnn_1_1_pad_descriptor.xhtml">PadDescriptor</a> padDescriptor(padList);</div><div class="line"><a name="l02224"></a><span class="lineno"> 2224</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a07485f1477554d32e43edc39502ac419">AddPadLayer</a>(padDescriptor, nodeDef.name().c_str());</div><div class="line"><a name="l02225"></a><span class="lineno"> 2225</span>  previousLayerOutputSlot.<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="l02226"></a><span class="lineno"> 2226</span>  <span class="comment">// Use the padding to calculate the new output tensor shape</span></div><div class="line"><a name="l02227"></a><span class="lineno"> 2227</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_tf_parser.xhtml#a9c18860db8b032de579c5ad94cbae5d0">CalculatePaddedOutputTensorInfo</a>(inputTensorInfo, padList);</div><div class="line"><a name="l02228"></a><span class="lineno"> 2228</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="l02229"></a><span class="lineno"> 2229</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l02230"></a><span class="lineno"> 2230</span> }</div><div class="line"><a name="l02231"></a><span class="lineno"> 2231</span> </div><div class="line"><a name="l02232"></a><span class="lineno"> 2232</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseConcat(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l02233"></a><span class="lineno"> 2233</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l02234"></a><span class="lineno"> 2234</span> {</div><div class="line"><a name="l02235"></a><span class="lineno"> 2235</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02236"></a><span class="lineno"> 2236</span>  std::vector<OutputOfConstNodeDef> nodes = GetTfInputNodes(nodeDef);</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="comment">// In tensorflow, we have the last input of the Concat layer as the axis for concatenation.</span></div><div class="line"><a name="l02239"></a><span class="lineno"> 2239</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numInputs = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(nodes.size());</div><div class="line"><a name="l02240"></a><span class="lineno"> 2240</span> </div><div class="line"><a name="l02241"></a><span class="lineno"> 2241</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, numInputs);</div><div class="line"><a name="l02242"></a><span class="lineno"> 2242</span> </div><div class="line"><a name="l02243"></a><span class="lineno"> 2243</span>  <span class="comment">// Constant tensor index</span></div><div class="line"><a name="l02244"></a><span class="lineno"> 2244</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> index = GetConstInputIndex(inputs);</div><div class="line"><a name="l02245"></a><span class="lineno"> 2245</span>  <span class="comment">// Get the axis tensor data</span></div><div class="line"><a name="l02246"></a><span class="lineno"> 2246</span>  ParsedConstTfOperation<int32_t>* shapeNode =</div><div class="line"><a name="l02247"></a><span class="lineno"> 2247</span>  boost::polymorphic_downcast<ParsedConstTfOperation<int32_t>*>(inputs[index].m_IndexedValue);</div><div class="line"><a name="l02248"></a><span class="lineno"> 2248</span> </div><div class="line"><a name="l02249"></a><span class="lineno"> 2249</span>  std::vector<int32_t> axisTensorData;</div><div class="line"><a name="l02250"></a><span class="lineno"> 2250</span>  shapeNode->GetConstTensor(axisTensorData);</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">// This concatDim indicates the data format: 3 is the NHWC, 1 is the NCHW.</span></div><div class="line"><a name="l02253"></a><span class="lineno"> 2253</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> concatDim = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(axisTensorData[0]);</div><div class="line"><a name="l02254"></a><span class="lineno"> 2254</span> </div><div class="line"><a name="l02255"></a><span class="lineno"> 2255</span>  <span class="comment">// Armnn supports concatenation along the channel dimension for data formats NHWC and NCHW.</span></div><div class="line"><a name="l02256"></a><span class="lineno"> 2256</span>  <span class="keywordflow">if</span> (concatDim == 0 || concatDim == 2)</div><div class="line"><a name="l02257"></a><span class="lineno"> 2257</span>  {</div><div class="line"><a name="l02258"></a><span class="lineno"> 2258</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02259"></a><span class="lineno"> 2259</span>  boost::str(</div><div class="line"><a name="l02260"></a><span class="lineno"> 2260</span>  boost::format(</div><div class="line"><a name="l02261"></a><span class="lineno"> 2261</span>  <span class="stringliteral">"Dimension %1% for concatenation is not supported by Armnn. "</span></div><div class="line"><a name="l02262"></a><span class="lineno"> 2262</span>  <span class="stringliteral">"Node %2% %3%"</span>)</div><div class="line"><a name="l02263"></a><span class="lineno"> 2263</span>  % concatDim</div><div class="line"><a name="l02264"></a><span class="lineno"> 2264</span>  % nodeDef.name()</div><div class="line"><a name="l02265"></a><span class="lineno"> 2265</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l02268"></a><span class="lineno"> 2268</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> supportedNumDims = 4;</div><div class="line"><a name="l02269"></a><span class="lineno"> 2269</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numConcatViews = numInputs - 1;</div><div class="line"><a name="l02270"></a><span class="lineno"> 2270</span>  <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a> concatDescriptor(static_cast<uint32_t>(numConcatViews), supportedNumDims);</div><div class="line"><a name="l02271"></a><span class="lineno"> 2271</span>  concatDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a5b192c5fcd96a0f75542524cf646b355">SetConcatAxis</a>(concatDim);</div><div class="line"><a name="l02272"></a><span class="lineno"> 2272</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> mergeDims(supportedNumDims);</div><div class="line"><a name="l02273"></a><span class="lineno"> 2273</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> mergeDim = 0;</div><div class="line"><a name="l02274"></a><span class="lineno"> 2274</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> viewIndex = 0; viewIndex < numConcatViews; ++viewIndex)</div><div class="line"><a name="l02275"></a><span class="lineno"> 2275</span>  {</div><div class="line"><a name="l02276"></a><span class="lineno"> 2276</span>  <span class="comment">// Need to double check whether it should be</span></div><div class="line"><a name="l02277"></a><span class="lineno"> 2277</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& inputSlot = inputs[viewIndex].m_IndexedValue->ResolveArmnnOutputSlot(inputs[viewIndex].m_Index);</div><div class="line"><a name="l02278"></a><span class="lineno"> 2278</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = inputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l02279"></a><span class="lineno"> 2279</span> </div><div class="line"><a name="l02280"></a><span class="lineno"> 2280</span>  <span class="comment">// Double check dimensions of the tensors</span></div><div class="line"><a name="l02281"></a><span class="lineno"> 2281</span>  <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() != supportedNumDims)</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">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">armnn::ParseException</a>(</div><div class="line"><a name="l02284"></a><span class="lineno"> 2284</span>  boost::str(</div><div class="line"><a name="l02285"></a><span class="lineno"> 2285</span>  boost::format(</div><div class="line"><a name="l02286"></a><span class="lineno"> 2286</span>  <span class="stringliteral">"The number of dimensions: %1% for input tensors of the "</span></div><div class="line"><a name="l02287"></a><span class="lineno"> 2287</span>  <span class="stringliteral">"concatenation op should be %2% %3%"</span>)</div><div class="line"><a name="l02288"></a><span class="lineno"> 2288</span>  % inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()</div><div class="line"><a name="l02289"></a><span class="lineno"> 2289</span>  % supportedNumDims</div><div class="line"><a name="l02290"></a><span class="lineno"> 2290</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l02293"></a><span class="lineno"> 2293</span>  <span class="comment">// Copy the input tensor shape to mergeDimSizes and initialize the view origin coordinates for the current input</span></div><div class="line"><a name="l02294"></a><span class="lineno"> 2294</span>  mergeDims = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l02295"></a><span class="lineno"> 2295</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>* viewOrigin = <span class="keyword">const_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>*<span class="keyword">></span>(concatDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#ab78e6fe963508c1ac5c00d04bb3361a3">GetViewOrigin</a>(viewIndex));</div><div class="line"><a name="l02296"></a><span class="lineno"> 2296</span>  std::fill(viewOrigin, viewOrigin + supportedNumDims, 0);</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>  <span class="comment">// Update the view origin coordinates and the merge dimension value</span></div><div class="line"><a name="l02299"></a><span class="lineno"> 2299</span>  concatDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(viewIndex, concatDim, mergeDim);</div><div class="line"><a name="l02300"></a><span class="lineno"> 2300</span>  mergeDim += mergeDims[concatDim];</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> </div><div class="line"><a name="l02303"></a><span class="lineno"> 2303</span>  <span class="comment">// Update the output shape</span></div><div class="line"><a name="l02304"></a><span class="lineno"> 2304</span>  mergeDims[concatDim] = mergeDim;</div><div class="line"><a name="l02305"></a><span class="lineno"> 2305</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a> *layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#aef27f787e8a2ee19c4052261f963f28e">AddConcatLayer</a>(concatDescriptor, nodeDef.name().c_str());</div><div class="line"><a name="l02306"></a><span class="lineno"> 2306</span> </div><div class="line"><a name="l02307"></a><span class="lineno"> 2307</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>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(mergeDims, DataType::Float32));</div><div class="line"><a name="l02308"></a><span class="lineno"> 2308</span> </div><div class="line"><a name="l02309"></a><span class="lineno"> 2309</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> viewIndex = 0; viewIndex < numConcatViews; ++viewIndex)</div><div class="line"><a name="l02310"></a><span class="lineno"> 2310</span>  {</div><div class="line"><a name="l02311"></a><span class="lineno"> 2311</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& inputSlot = inputs[viewIndex].m_IndexedValue->ResolveArmnnOutputSlot(inputs[viewIndex].m_Index);</div><div class="line"><a name="l02312"></a><span class="lineno"> 2312</span>  inputSlot.<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>(viewIndex));</div><div class="line"><a name="l02313"></a><span class="lineno"> 2313</span>  }</div><div class="line"><a name="l02314"></a><span class="lineno"> 2314</span> </div><div class="line"><a name="l02315"></a><span class="lineno"> 2315</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l02316"></a><span class="lineno"> 2316</span> }</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="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseShape(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l02319"></a><span class="lineno"> 2319</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l02320"></a><span class="lineno"> 2320</span> {</div><div class="line"><a name="l02321"></a><span class="lineno"> 2321</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02322"></a><span class="lineno"> 2322</span>  <span class="comment">// Note: the Shape layer is handled in a special way, because:</span></div><div class="line"><a name="l02323"></a><span class="lineno"> 2323</span>  <span class="comment">// 1. ARMNN doesn't support int32 tensors which it outputs.</span></div><div class="line"><a name="l02324"></a><span class="lineno"> 2324</span>  <span class="comment">// 2. ARMNN works with statically shaped tensors which are known at parse time.</span></div><div class="line"><a name="l02325"></a><span class="lineno"> 2325</span>  <span class="comment">// 3. because of 1. and 2. we treat the output of Shape as a temporary const int32</span></div><div class="line"><a name="l02326"></a><span class="lineno"> 2326</span>  <span class="comment">// tensor which may be used as an input to other ops, most likely a Reshape.</span></div><div class="line"><a name="l02327"></a><span class="lineno"> 2327</span> </div><div class="line"><a name="l02328"></a><span class="lineno"> 2328</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">tensorflow::DataType</a> tfDataType = ReadMandatoryNodeTypeAttribute(nodeDef, <span class="stringliteral">"out_type"</span>);</div><div class="line"><a name="l02329"></a><span class="lineno"> 2329</span>  <span class="keywordflow">if</span> (tfDataType != tensorflow::DT_INT32)</div><div class="line"><a name="l02330"></a><span class="lineno"> 2330</span>  {</div><div class="line"><a name="l02331"></a><span class="lineno"> 2331</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02332"></a><span class="lineno"> 2332</span>  boost::str(</div><div class="line"><a name="l02333"></a><span class="lineno"> 2333</span>  boost::format(</div><div class="line"><a name="l02334"></a><span class="lineno"> 2334</span>  <span class="stringliteral">"Armnn only supports DT_INT32 as out_type. Got %1% for Node %2% %3%"</span>)</div><div class="line"><a name="l02335"></a><span class="lineno"> 2335</span>  % tensorflow::DataType_Name(tfDataType)</div><div class="line"><a name="l02336"></a><span class="lineno"> 2336</span>  % nodeDef.name()</div><div class="line"><a name="l02337"></a><span class="lineno"> 2337</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02338"></a><span class="lineno"> 2338</span>  }</div><div class="line"><a name="l02339"></a><span class="lineno"> 2339</span> </div><div class="line"><a name="l02340"></a><span class="lineno"> 2340</span>  <span class="keyword">const</span> std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 1);</div><div class="line"><a name="l02341"></a><span class="lineno"> 2341</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& prevLayerOutputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l02342"></a><span class="lineno"> 2342</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& prevLayerTensorInfo = prevLayerOutputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l02343"></a><span class="lineno"> 2343</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> prevLayerDimensions = prevLayerTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l02344"></a><span class="lineno"> 2344</span> </div><div class="line"><a name="l02345"></a><span class="lineno"> 2345</span>  std::vector<int32_t> shapeTensorData;</div><div class="line"><a name="l02346"></a><span class="lineno"> 2346</span>  shapeTensorData.reserve(prevLayerDimensions);</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>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i=0; i<prevLayerDimensions; ++i)</div><div class="line"><a name="l02349"></a><span class="lineno"> 2349</span>  {</div><div class="line"><a name="l02350"></a><span class="lineno"> 2350</span>  shapeTensorData.push_back(static_cast<int32_t>(prevLayerTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i]));</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> </div><div class="line"><a name="l02353"></a><span class="lineno"> 2353</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> shapeTensorInfo(1, &prevLayerDimensions, DataType::Signed32);</div><div class="line"><a name="l02354"></a><span class="lineno"> 2354</span> </div><div class="line"><a name="l02355"></a><span class="lineno"> 2355</span>  <span class="keywordflow">return</span> std::make_unique<ParsedConstTfOperation<int32_t>>(<span class="keyword">this</span>,</div><div class="line"><a name="l02356"></a><span class="lineno"> 2356</span>  nodeDef,</div><div class="line"><a name="l02357"></a><span class="lineno"> 2357</span>  &shapeTensorData[0],</div><div class="line"><a name="l02358"></a><span class="lineno"> 2358</span>  shapeTensorInfo);</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> </div><div class="line"><a name="l02361"></a><span class="lineno"> 2361</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseReshape(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l02362"></a><span class="lineno"> 2362</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l02363"></a><span class="lineno"> 2363</span> {</div><div class="line"><a name="l02364"></a><span class="lineno"> 2364</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02365"></a><span class="lineno"> 2365</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2);</div><div class="line"><a name="l02366"></a><span class="lineno"> 2366</span>  ParsedTfOperation* inputNode = inputs[0].m_IndexedValue;</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="keywordflow">if</span> (!HasParsedConstTensor<int32_t>(inputs[1].m_IndexedValue->GetNode().name()))</div><div class="line"><a name="l02369"></a><span class="lineno"> 2369</span>  {</div><div class="line"><a name="l02370"></a><span class="lineno"> 2370</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02371"></a><span class="lineno"> 2371</span>  boost::str(</div><div class="line"><a name="l02372"></a><span class="lineno"> 2372</span>  boost::format(</div><div class="line"><a name="l02373"></a><span class="lineno"> 2373</span>  <span class="stringliteral">"ArmNN only supports Reshape layers with constant shapes. "</span></div><div class="line"><a name="l02374"></a><span class="lineno"> 2374</span>  <span class="stringliteral">"Input %1% Node %2% %3%"</span>)</div><div class="line"><a name="l02375"></a><span class="lineno"> 2375</span>  % inputs[1].m_IndexedValue->GetNode().name()</div><div class="line"><a name="l02376"></a><span class="lineno"> 2376</span>  % nodeDef.name()</div><div class="line"><a name="l02377"></a><span class="lineno"> 2377</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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>  ParsedConstTfOperation<int32_t>* shapeNode =</div><div class="line"><a name="l02380"></a><span class="lineno"> 2380</span>  boost::polymorphic_downcast<ParsedConstTfOperation<int32_t>*>(inputs[1].m_IndexedValue);</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>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>& prevLayerOutputSlot = inputNode->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l02383"></a><span class="lineno"> 2383</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = prevLayerOutputSlot.GetTensorInfo();</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>  std::vector<int32_t> shapeTensorData;</div><div class="line"><a name="l02386"></a><span class="lineno"> 2386</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> shapeTensor = shapeNode->GetConstTensor(shapeTensorData);</div><div class="line"><a name="l02387"></a><span class="lineno"> 2387</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = PrepareReshape(inputTensorInfo, shapeTensorData);</div><div class="line"><a name="l02388"></a><span class="lineno"> 2388</span> </div><div class="line"><a name="l02389"></a><span class="lineno"> 2389</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> targetShape = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l02390"></a><span class="lineno"> 2390</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">ReshapeDescriptor</a> reshapeDesc;</div><div class="line"><a name="l02391"></a><span class="lineno"> 2391</span>  reshapeDesc.<a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = targetShape;</div><div class="line"><a name="l02392"></a><span class="lineno"> 2392</span> </div><div class="line"><a name="l02393"></a><span class="lineno"> 2393</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#ac77b89eb982f9d745730c90fcbdddba4">AddReshapeLayer</a>(reshapeDesc, nodeDef.name().c_str());</div><div class="line"><a name="l02394"></a><span class="lineno"> 2394</span>  prevLayerOutputSlot.Connect(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l02395"></a><span class="lineno"> 2395</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="l02396"></a><span class="lineno"> 2396</span> </div><div class="line"><a name="l02397"></a><span class="lineno"> 2397</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l02398"></a><span class="lineno"> 2398</span> }</div><div class="line"><a name="l02399"></a><span class="lineno"> 2399</span> </div><div class="line"><a name="l02400"></a><span class="lineno"> 2400</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseResizeBilinear(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l02401"></a><span class="lineno"> 2401</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l02402"></a><span class="lineno"> 2402</span> {</div><div class="line"><a name="l02403"></a><span class="lineno"> 2403</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02404"></a><span class="lineno"> 2404</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2);</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>  <span class="keywordflow">if</span> (!HasParsedConstTensor<int32_t>(inputs[1].m_IndexedValue->GetNode().name()))</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02409"></a><span class="lineno"> 2409</span>  boost::str(</div><div class="line"><a name="l02410"></a><span class="lineno"> 2410</span>  boost::format(</div><div class="line"><a name="l02411"></a><span class="lineno"> 2411</span>  <span class="stringliteral">"ArmNN only supports ResizeBilinear layers with constant sizes. "</span></div><div class="line"><a name="l02412"></a><span class="lineno"> 2412</span>  <span class="stringliteral">"Input %1%. Node %2% %3%"</span>)</div><div class="line"><a name="l02413"></a><span class="lineno"> 2413</span>  % inputs[1].m_IndexedValue->GetNode().name()</div><div class="line"><a name="l02414"></a><span class="lineno"> 2414</span>  % nodeDef.name()</div><div class="line"><a name="l02415"></a><span class="lineno"> 2415</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02416"></a><span class="lineno"> 2416</span>  }</div><div class="line"><a name="l02417"></a><span class="lineno"> 2417</span>  ParsedConstTfOperation<int32_t>* sizeNode =</div><div class="line"><a name="l02418"></a><span class="lineno"> 2418</span>  boost::polymorphic_downcast<ParsedConstTfOperation<int32_t>*>(inputs[1].m_IndexedValue);</div><div class="line"><a name="l02419"></a><span class="lineno"> 2419</span> </div><div class="line"><a name="l02420"></a><span class="lineno"> 2420</span>  <span class="comment">// Checks the align_corners attribute is not set.</span></div><div class="line"><a name="l02421"></a><span class="lineno"> 2421</span>  <span class="keywordflow">if</span> (ReadOptionalNodeBoolAttribute(nodeDef, <span class="stringliteral">"align_corners"</span>, <span class="keyword">false</span>))</div><div class="line"><a name="l02422"></a><span class="lineno"> 2422</span>  {</div><div class="line"><a name="l02423"></a><span class="lineno"> 2423</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02424"></a><span class="lineno"> 2424</span>  boost::str(</div><div class="line"><a name="l02425"></a><span class="lineno"> 2425</span>  boost::format(</div><div class="line"><a name="l02426"></a><span class="lineno"> 2426</span>  <span class="stringliteral">"ArmNN only supports ResizeBilinear layers with align_corners set to false. "</span></div><div class="line"><a name="l02427"></a><span class="lineno"> 2427</span>  <span class="stringliteral">"Node %1% %2%"</span>)</div><div class="line"><a name="l02428"></a><span class="lineno"> 2428</span>  % nodeDef.name()</div><div class="line"><a name="l02429"></a><span class="lineno"> 2429</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02430"></a><span class="lineno"> 2430</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>  <span class="comment">// Data for the parsed tensor args (size) must be stored locally.</span></div><div class="line"><a name="l02433"></a><span class="lineno"> 2433</span>  std::vector<int32_t> sizeTensorData;</div><div class="line"><a name="l02434"></a><span class="lineno"> 2434</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> sizeTensor = sizeNode->GetConstTensor(sizeTensorData);</div><div class="line"><a name="l02435"></a><span class="lineno"> 2435</span> </div><div class="line"><a name="l02436"></a><span class="lineno"> 2436</span>  <span class="comment">// The descriptor only has target height and width attributes, which we get from the size tensor.</span></div><div class="line"><a name="l02437"></a><span class="lineno"> 2437</span>  <a class="code" href="structarmnn_1_1_resize_descriptor.xhtml">ResizeDescriptor</a> desc;</div><div class="line"><a name="l02438"></a><span class="lineno"> 2438</span>  desc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a869254cb56968986a78a79e1d6d4a86b">m_Method</a> = <a class="code" href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f">armnn::ResizeMethod::Bilinear</a>;</div><div class="line"><a name="l02439"></a><span class="lineno"> 2439</span>  desc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a46c3fa15c46fb0d1dcdc24d0ea5cb5cd">m_TargetHeight</a> = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span> (sizeTensorData[0]);</div><div class="line"><a name="l02440"></a><span class="lineno"> 2440</span>  desc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">m_TargetWidth</a> = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span> (sizeTensorData[1]);</div><div class="line"><a name="l02441"></a><span class="lineno"> 2441</span>  desc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a617aeb663e1535568864c23f5d988dd8">AddResizeLayer</a>(desc, nodeDef.name().c_str());</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>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& inputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l02446"></a><span class="lineno"> 2446</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = inputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l02447"></a><span class="lineno"> 2447</span>  <span class="comment">// The input shape is always in BHWC format, this will be swizzled below; for now,</span></div><div class="line"><a name="l02448"></a><span class="lineno"> 2448</span>  <span class="comment">// get the batch and channels to make up the ArmNN output shape with the target size.</span></div><div class="line"><a name="l02449"></a><span class="lineno"> 2449</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outBatch = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0];</div><div class="line"><a name="l02450"></a><span class="lineno"> 2450</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outChannels = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3];</div><div class="line"><a name="l02451"></a><span class="lineno"> 2451</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outHeight = desc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a46c3fa15c46fb0d1dcdc24d0ea5cb5cd">m_TargetHeight</a>;</div><div class="line"><a name="l02452"></a><span class="lineno"> 2452</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outWidth = desc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">m_TargetWidth</a>;</div><div class="line"><a name="l02453"></a><span class="lineno"> 2453</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outShape({outBatch, outHeight, outWidth, outChannels });</div><div class="line"><a name="l02454"></a><span class="lineno"> 2454</span>  <span class="comment">// The output DataType is always Float32, regardless of the input DataType.</span></div><div class="line"><a name="l02455"></a><span class="lineno"> 2455</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo(outShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l02456"></a><span class="lineno"> 2456</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="l02457"></a><span class="lineno"> 2457</span> </div><div class="line"><a name="l02458"></a><span class="lineno"> 2458</span>  inputSlot.<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="l02459"></a><span class="lineno"> 2459</span> </div><div class="line"><a name="l02460"></a><span class="lineno"> 2460</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l02461"></a><span class="lineno"> 2461</span> }</div><div class="line"><a name="l02462"></a><span class="lineno"> 2462</span> </div><div class="line"><a name="l02463"></a><span class="lineno"><a class="line" href="namespacearmnn_tf_parser.xhtml#a6e06adf62d53562032e738b89f3eb37c"> 2463</a></span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn_tf_parser.xhtml#a6e06adf62d53562032e738b89f3eb37c">OutputShapeOfSqueeze</a>(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo)</div><div class="line"><a name="l02464"></a><span class="lineno"> 2464</span> {</div><div class="line"><a name="l02465"></a><span class="lineno"> 2465</span>  BOOST_ASSERT(nodeDef.op() == <span class="stringliteral">"Squeeze"</span>);</div><div class="line"><a name="l02466"></a><span class="lineno"> 2466</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">tensorflow::DataType</a> tfDataType = ReadMandatoryNodeTypeAttribute(nodeDef, <span class="stringliteral">"T"</span>);</div><div class="line"><a name="l02467"></a><span class="lineno"> 2467</span> </div><div class="line"><a name="l02468"></a><span class="lineno"> 2468</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> type;</div><div class="line"><a name="l02469"></a><span class="lineno"> 2469</span>  <span class="keywordflow">if</span> (tfDataType == tensorflow::DT_FLOAT)</div><div class="line"><a name="l02470"></a><span class="lineno"> 2470</span>  {</div><div class="line"><a name="l02471"></a><span class="lineno"> 2471</span>  type = DataType::Float32;</div><div class="line"><a name="l02472"></a><span class="lineno"> 2472</span>  }</div><div class="line"><a name="l02473"></a><span class="lineno"> 2473</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (tfDataType == tensorflow::DT_INT32)</div><div class="line"><a name="l02474"></a><span class="lineno"> 2474</span>  {</div><div class="line"><a name="l02475"></a><span class="lineno"> 2475</span>  type = DataType::Signed32;</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l02478"></a><span class="lineno"> 2478</span>  {</div><div class="line"><a name="l02479"></a><span class="lineno"> 2479</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02480"></a><span class="lineno"> 2480</span>  boost::str(</div><div class="line"><a name="l02481"></a><span class="lineno"> 2481</span>  boost::format(<span class="stringliteral">"Unsupported DataType %1% for Squeeze operation %2% %3%"</span>)</div><div class="line"><a name="l02482"></a><span class="lineno"> 2482</span>  % tensorflow::DataType_Name(tfDataType)</div><div class="line"><a name="l02483"></a><span class="lineno"> 2483</span>  % nodeDef.name()</div><div class="line"><a name="l02484"></a><span class="lineno"> 2484</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02485"></a><span class="lineno"> 2485</span>  }</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> </div><div class="line"><a name="l02488"></a><span class="lineno"> 2488</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="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>(</div><div class="line"><a name="l02491"></a><span class="lineno"> 2491</span>  boost::str(</div><div class="line"><a name="l02492"></a><span class="lineno"> 2492</span>  boost::format(</div><div class="line"><a name="l02493"></a><span class="lineno"> 2493</span>  <span class="stringliteral">"Unsupported number of dimensions: %1% for input shape for Squeeze %2% %3%"</span>)</div><div class="line"><a name="l02494"></a><span class="lineno"> 2494</span>  % inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()</div><div class="line"><a name="l02495"></a><span class="lineno"> 2495</span>  % nodeDef.name()</div><div class="line"><a name="l02496"></a><span class="lineno"> 2496</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l02499"></a><span class="lineno"> 2499</span>  std::vector<uint32_t> squeezeDims = ReadOptionalNodeUint32ListAttribute(nodeDef, <span class="stringliteral">"squeeze_dims"</span>);</div><div class="line"><a name="l02500"></a><span class="lineno"> 2500</span>  <span class="keyword">static</span> <span class="keyword">const</span> uint32_t dimensionSequence[] = { 0, 1, 2, 3 };</div><div class="line"><a name="l02501"></a><span class="lineno"> 2501</span> </div><div class="line"><a name="l02502"></a><span class="lineno"> 2502</span>  <span class="keywordflow">if</span> (squeezeDims.empty())</div><div class="line"><a name="l02503"></a><span class="lineno"> 2503</span>  {</div><div class="line"><a name="l02504"></a><span class="lineno"> 2504</span>  squeezeDims.assign(dimensionSequence,</div><div class="line"><a name="l02505"></a><span class="lineno"> 2505</span>  dimensionSequence+inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>());</div><div class="line"><a name="l02506"></a><span class="lineno"> 2506</span>  }</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>  std::vector<uint32_t> outputDims;</div><div class="line"><a name="l02509"></a><span class="lineno"> 2509</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="l02510"></a><span class="lineno"> 2510</span>  {</div><div class="line"><a name="l02511"></a><span class="lineno"> 2511</span>  <span class="keywordtype">bool</span> skipSqueeze = (std::find(squeezeDims.begin(), squeezeDims.end(), i) == squeezeDims.end());</div><div class="line"><a name="l02512"></a><span class="lineno"> 2512</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="l02513"></a><span class="lineno"> 2513</span>  <span class="keywordflow">if</span> (skipSqueeze || currentDimension != 1)</div><div class="line"><a name="l02514"></a><span class="lineno"> 2514</span>  {</div><div class="line"><a name="l02515"></a><span class="lineno"> 2515</span>  outputDims.push_back(currentDimension);</div><div class="line"><a name="l02516"></a><span class="lineno"> 2516</span>  }</div><div class="line"><a name="l02517"></a><span class="lineno"> 2517</span>  }</div><div class="line"><a name="l02518"></a><span class="lineno"> 2518</span> </div><div class="line"><a name="l02519"></a><span class="lineno"> 2519</span>  <span class="keywordflow">if</span> (outputDims.size() > 4)</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02522"></a><span class="lineno"> 2522</span>  boost::str(</div><div class="line"><a name="l02523"></a><span class="lineno"> 2523</span>  boost::format(</div><div class="line"><a name="l02524"></a><span class="lineno"> 2524</span>  <span class="stringliteral">"Unsupported number of dimensions: %1% for output shape for Squeeze %2% %3%"</span>)</div><div class="line"><a name="l02525"></a><span class="lineno"> 2525</span>  % outputDims.size()</div><div class="line"><a name="l02526"></a><span class="lineno"> 2526</span>  % nodeDef.name()</div><div class="line"><a name="l02527"></a><span class="lineno"> 2527</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l02530"></a><span class="lineno"> 2530</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="l02531"></a><span class="lineno"> 2531</span>  outputDims.data());</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outTensorInfo = inputTensorInfo;</div><div class="line"><a name="l02534"></a><span class="lineno"> 2534</span>  outTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(outShape);</div><div class="line"><a name="l02535"></a><span class="lineno"> 2535</span>  outTensorInfo.SetDataType(type);</div><div class="line"><a name="l02536"></a><span class="lineno"> 2536</span> </div><div class="line"><a name="l02537"></a><span class="lineno"> 2537</span>  <span class="keywordflow">return</span> outTensorInfo;</div><div class="line"><a name="l02538"></a><span class="lineno"> 2538</span> }</div><div class="line"><a name="l02539"></a><span class="lineno"> 2539</span> </div><div class="line"><a name="l02540"></a><span class="lineno"> 2540</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseSqueeze(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l02541"></a><span class="lineno"> 2541</span> {</div><div class="line"><a name="l02542"></a><span class="lineno"> 2542</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02543"></a><span class="lineno"> 2543</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 1);</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>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& prevLayerOutputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l02546"></a><span class="lineno"> 2546</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = prevLayerOutputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l02547"></a><span class="lineno"> 2547</span> </div><div class="line"><a name="l02548"></a><span class="lineno"> 2548</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo;</div><div class="line"><a name="l02549"></a><span class="lineno"> 2549</span>  outputInfo = <a class="code" href="namespacearmnn_tf_parser.xhtml#a6e06adf62d53562032e738b89f3eb37c">OutputShapeOfSqueeze</a>(nodeDef, inputTensorInfo);</div><div class="line"><a name="l02550"></a><span class="lineno"> 2550</span> </div><div class="line"><a name="l02551"></a><span class="lineno"> 2551</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">ReshapeDescriptor</a> reshapeDesc;</div><div class="line"><a name="l02552"></a><span class="lineno"> 2552</span>  reshapeDesc.<a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l02553"></a><span class="lineno"> 2553</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#ac77b89eb982f9d745730c90fcbdddba4">AddReshapeLayer</a>(reshapeDesc, nodeDef.name().c_str());</div><div class="line"><a name="l02554"></a><span class="lineno"> 2554</span>  prevLayerOutputSlot.<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="l02555"></a><span class="lineno"> 2555</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>(outputInfo);</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>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l02558"></a><span class="lineno"> 2558</span> }</div><div class="line"><a name="l02559"></a><span class="lineno"> 2559</span> </div><div class="line"><a name="l02560"></a><span class="lineno"> 2560</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseLrn(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</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="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02563"></a><span class="lineno"> 2563</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 1);</div><div class="line"><a name="l02564"></a><span class="lineno"> 2564</span> </div><div class="line"><a name="l02565"></a><span class="lineno"> 2565</span>  <a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml">NormalizationDescriptor</a> normalizationDescriptor;</div><div class="line"><a name="l02566"></a><span class="lineno"> 2566</span>  normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a05945f080edf694b631960728b87aadb">m_NormMethodType</a> = NormalizationAlgorithmMethod::LocalBrightness;</div><div class="line"><a name="l02567"></a><span class="lineno"> 2567</span>  normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#afe1f0f09d49ad2befc01f8789187b7dd">m_NormChannelType</a> = NormalizationAlgorithmChannel::Across;</div><div class="line"><a name="l02568"></a><span class="lineno"> 2568</span>  normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a174279be57d7596eeb04c6b7f7510f99">m_Alpha</a> = ReadMandatoryNodeFloatAttribute(nodeDef, <span class="stringliteral">"alpha"</span>);</div><div class="line"><a name="l02569"></a><span class="lineno"> 2569</span>  normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = ReadMandatoryNodeFloatAttribute(nodeDef, <span class="stringliteral">"beta"</span>);</div><div class="line"><a name="l02570"></a><span class="lineno"> 2570</span>  normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a8526ea7cf860d8e7f8340e9f9354f9f0">m_K</a> = ReadMandatoryNodeFloatAttribute(nodeDef, <span class="stringliteral">"bias"</span>);</div><div class="line"><a name="l02571"></a><span class="lineno"> 2571</span>  normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#aa70c05f1aad12fbd9d9ec43ea4557b03">m_NormSize</a> = ReadMandatoryNodeUint32Attribute(nodeDef, <span class="stringliteral">"depth_radius"</span>);</div><div class="line"><a name="l02572"></a><span class="lineno"> 2572</span>  normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l02573"></a><span class="lineno"> 2573</span> </div><div class="line"><a name="l02574"></a><span class="lineno"> 2574</span>  <span class="comment">// The window size must be an odd value. For a window size of (2 * n + 1), TensorFlow defines depth_radius = n.</span></div><div class="line"><a name="l02575"></a><span class="lineno"> 2575</span>  normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#aa70c05f1aad12fbd9d9ec43ea4557b03">m_NormSize</a> = normalizationDescriptor.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#aa70c05f1aad12fbd9d9ec43ea4557b03">m_NormSize</a> * 2 + 1;</div><div class="line"><a name="l02576"></a><span class="lineno"> 2576</span> </div><div class="line"><a name="l02577"></a><span class="lineno"> 2577</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& prevLayerOutputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l02578"></a><span class="lineno"> 2578</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a74dac9efbb6dbd1982a45af1805eb4e0">AddNormalizationLayer</a>(normalizationDescriptor,</div><div class="line"><a name="l02579"></a><span class="lineno"> 2579</span>  nodeDef.name().c_str());</div><div class="line"><a name="l02580"></a><span class="lineno"> 2580</span>  prevLayerOutputSlot.<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="l02581"></a><span class="lineno"> 2581</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>(prevLayerOutputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>());</div><div class="line"><a name="l02582"></a><span class="lineno"> 2582</span> </div><div class="line"><a name="l02583"></a><span class="lineno"> 2583</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l02584"></a><span class="lineno"> 2584</span> }</div><div class="line"><a name="l02585"></a><span class="lineno"> 2585</span> <span class="comment"></span></div><div class="line"><a name="l02586"></a><span class="lineno"> 2586</span> <span class="comment">/// An ParsedTfOperation for a MatMul node.</span></div><div class="line"><a name="l02587"></a><span class="lineno"> 2587</span> <span class="comment">/// Creation of the armnn FullyConnected layer is deferred until it is actually needed, because</span></div><div class="line"><a name="l02588"></a><span class="lineno"> 2588</span> <span class="comment">/// MatMul nodes are often used for the first part of a biased FullyConnected (MatMul followed</span></div><div class="line"><a name="l02589"></a><span class="lineno"> 2589</span> <span class="comment">/// by Add) and in these cases armnn doesn't need a separate layer for the MatMul.</span></div><div class="line"><a name="l02590"></a><span class="lineno"> 2590</span> <span class="comment">///</span></div><div class="line"><a name="l02591"></a><span class="lineno"> 2591</span> <span class="comment"></span><span class="keyword">class </span><a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml#a97caa75ebdb49fc10250742b33d29ae7">ParsedMatMulTfOperation</a> : <span class="keyword">public</span> DeferredSingleLayerParsedTfOperation</div><div class="line"><a name="l02592"></a><span class="lineno"> 2592</span> {</div><div class="line"><a name="l02593"></a><span class="lineno"> 2593</span> <span class="keyword">public</span>:</div><div class="line"><a name="l02594"></a><span class="lineno"> 2594</span>  <a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml#a97caa75ebdb49fc10250742b33d29ae7">ParsedMatMulTfOperation</a>(<a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml">TfParser</a>* parser, <span class="keyword">const</span> tensorflow::NodeDef& node)</div><div class="line"><a name="l02595"></a><span class="lineno"> 2595</span>  : DeferredSingleLayerParsedTfOperation(parser, node)</div><div class="line"><a name="l02596"></a><span class="lineno"> 2596</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="keywordtype">void</span> CreateLayerDeferred()<span class="keyword"> override</span></div><div class="line"><a name="l02600"></a><span class="lineno"> 2600</span> <span class="keyword"> </span>{</div><div class="line"><a name="l02601"></a><span class="lineno"> 2601</span>  BOOST_ASSERT(<a class="code" href="_subgraph_view_selector_8cpp.xhtml#ae7fc37b88ff10e9294ed1c72f2a25ac7">m_Layer</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02602"></a><span class="lineno"> 2602</span>  <a class="code" href="_subgraph_view_selector_8cpp.xhtml#ae7fc37b88ff10e9294ed1c72f2a25ac7">m_Layer</a> = m_Parser->AddFullyConnectedLayer(m_Node, <span class="keyword">nullptr</span>, m_Node.name().c_str());</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> };</div><div class="line"><a name="l02605"></a><span class="lineno"> 2605</span> </div><div class="line"><a name="l02606"></a><span class="lineno"> 2606</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseMatMul(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l02607"></a><span class="lineno"> 2607</span> {</div><div class="line"><a name="l02608"></a><span class="lineno"> 2608</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02609"></a><span class="lineno"> 2609</span> </div><div class="line"><a name="l02610"></a><span class="lineno"> 2610</span>  <span class="comment">// Defers the creation of the layer (see ParsedMatMulTfOperation).</span></div><div class="line"><a name="l02611"></a><span class="lineno"> 2611</span>  <span class="keywordflow">return</span> std::make_unique<ParsedMatMulTfOperation>(<span class="keyword">this</span>, nodeDef);</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> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseMean(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l02615"></a><span class="lineno"> 2615</span> {</div><div class="line"><a name="l02616"></a><span class="lineno"> 2616</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02617"></a><span class="lineno"> 2617</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2);</div><div class="line"><a name="l02618"></a><span class="lineno"> 2618</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& inputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l02619"></a><span class="lineno"> 2619</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = inputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l02620"></a><span class="lineno"> 2620</span> </div><div class="line"><a name="l02621"></a><span class="lineno"> 2621</span>  <span class="keywordflow">if</span> (inputs.size() != 2)</div><div class="line"><a name="l02622"></a><span class="lineno"> 2622</span>  {</div><div class="line"><a name="l02623"></a><span class="lineno"> 2623</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02624"></a><span class="lineno"> 2624</span>  boost::str(boost::format(<span class="stringliteral">"Mean expects two inputs!. Got %1% for Node %2% %3%"</span>)</div><div class="line"><a name="l02625"></a><span class="lineno"> 2625</span>  % inputs.size()</div><div class="line"><a name="l02626"></a><span class="lineno"> 2626</span>  % nodeDef.name()</div><div class="line"><a name="l02627"></a><span class="lineno"> 2627</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02628"></a><span class="lineno"> 2628</span>  }</div><div class="line"><a name="l02629"></a><span class="lineno"> 2629</span> </div><div class="line"><a name="l02630"></a><span class="lineno"> 2630</span>  <span class="keywordtype">bool</span> keepDims = ReadMandatoryNodeBoolAttribute(nodeDef, <span class="stringliteral">"keep_dims"</span>);</div><div class="line"><a name="l02631"></a><span class="lineno"> 2631</span> </div><div class="line"><a name="l02632"></a><span class="lineno"> 2632</span>  ParsedConstTfOperation<int32_t>* axisNode =</div><div class="line"><a name="l02633"></a><span class="lineno"> 2633</span>  boost::polymorphic_downcast<ParsedConstTfOperation<int32_t>*>(inputs[1].m_IndexedValue);</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>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& axisTensorInfo = axisNode->GetTensorInfo();</div><div class="line"><a name="l02636"></a><span class="lineno"> 2636</span> </div><div class="line"><a name="l02637"></a><span class="lineno"> 2637</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> axisTensor(axisTensorInfo, axisNode->GetStorage());</div><div class="line"><a name="l02638"></a><span class="lineno"> 2638</span>  <span class="keyword">const</span> <span class="keywordtype">int</span>* axisData = <span class="keyword">static_cast<</span><span class="keyword">const </span><span class="keywordtype">int</span>*<span class="keyword">></span>(axisTensor.GetMemoryArea());</div><div class="line"><a name="l02639"></a><span class="lineno"> 2639</span> </div><div class="line"><a name="l02640"></a><span class="lineno"> 2640</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l02641"></a><span class="lineno"> 2641</span>  <a class="code" href="structarmnn_1_1_mean_descriptor.xhtml">MeanDescriptor</a> meanDescriptor;</div><div class="line"><a name="l02642"></a><span class="lineno"> 2642</span>  meanDescriptor.<a class="code" href="structarmnn_1_1_mean_descriptor.xhtml#a28e0548abfc4e79c48f29a3d11a062e9">m_KeepDims</a> = keepDims;</div><div class="line"><a name="l02643"></a><span class="lineno"> 2643</span> </div><div class="line"><a name="l02644"></a><span class="lineno"> 2644</span>  <span class="comment">// Negative axis values are supported so that the process requires</span></div><div class="line"><a name="l02645"></a><span class="lineno"> 2645</span>  <span class="comment">// to convert them into the corresponding positive ones.</span></div><div class="line"><a name="l02646"></a><span class="lineno"> 2646</span>  <span class="comment">// Duplicate values are also removed.</span></div><div class="line"><a name="l02647"></a><span class="lineno"> 2647</span>  std::vector<int> rawAxisVector(axisData, axisData + axisTensorInfo.GetNumElements());</div><div class="line"><a name="l02648"></a><span class="lineno"> 2648</span>  std::set<unsigned int> positiveAxisSet;</div><div class="line"><a name="l02649"></a><span class="lineno"> 2649</span>  <span class="keywordtype">int</span> rank = <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>());</div><div class="line"><a name="l02650"></a><span class="lineno"> 2650</span> </div><div class="line"><a name="l02651"></a><span class="lineno"> 2651</span>  std::transform(rawAxisVector.begin(), rawAxisVector.end(),</div><div class="line"><a name="l02652"></a><span class="lineno"> 2652</span>  std::inserter(positiveAxisSet, positiveAxisSet.begin()),</div><div class="line"><a name="l02653"></a><span class="lineno"> 2653</span>  [rank](<span class="keywordtype">int</span> i) -> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> { <span class="keywordflow">return</span> <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>((i + rank) % rank); });</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>  <a class="code" href="namespacearmnn_utils.xhtml#aac34adc5b96d744ae53eac580213f855">CalculateReducedOutputTensoInfo</a>(inputTensorInfo, positiveAxisSet, keepDims, outputTensorInfo);</div><div class="line"><a name="l02656"></a><span class="lineno"> 2656</span> </div><div class="line"><a name="l02657"></a><span class="lineno"> 2657</span>  <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() > positiveAxisSet.size())</div><div class="line"><a name="l02658"></a><span class="lineno"> 2658</span>  {</div><div class="line"><a name="l02659"></a><span class="lineno"> 2659</span>  meanDescriptor.<a class="code" href="structarmnn_1_1_mean_descriptor.xhtml#a1f0d67b087c491248bd1cde3ff995a95">m_Axis</a>.assign(positiveAxisSet.begin(), positiveAxisSet.end());</div><div class="line"><a name="l02660"></a><span class="lineno"> 2660</span>  }</div><div class="line"><a name="l02661"></a><span class="lineno"> 2661</span> </div><div class="line"><a name="l02662"></a><span class="lineno"> 2662</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a8262e9e6fc869a9c9782115a6a552f36">AddMeanLayer</a>(meanDescriptor, nodeDef.name().c_str());</div><div class="line"><a name="l02663"></a><span class="lineno"> 2663</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="l02664"></a><span class="lineno"> 2664</span>  inputSlot.<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="l02665"></a><span class="lineno"> 2665</span> </div><div class="line"><a name="l02666"></a><span class="lineno"> 2666</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l02667"></a><span class="lineno"> 2667</span> }</div><div class="line"><a name="l02668"></a><span class="lineno"> 2668</span> <span class="comment"></span></div><div class="line"><a name="l02669"></a><span class="lineno"> 2669</span> <span class="comment">/// An ParsedTfOperation for a Mul node.</span></div><div class="line"><a name="l02670"></a><span class="lineno"> 2670</span> <span class="comment">/// Creation of the armnn Mul layer is deferred until it is actually needed, because Mul nodes</span></div><div class="line"><a name="l02671"></a><span class="lineno"> 2671</span> <span class="comment">/// are also used for the first part of a leaky relu activation function (Mul followed by Maximum)</span></div><div class="line"><a name="l02672"></a><span class="lineno"> 2672</span> <span class="comment">/// and in these cases armnn doesn't need a separate layer for the Mul.</span></div><div class="line"><a name="l02673"></a><span class="lineno"> 2673</span> <span class="comment">///</span></div><div class="line"><a name="l02674"></a><span class="lineno"> 2674</span> <span class="comment"></span><span class="keyword">class </span><a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml#ac28271f7220cd595de55dbb7f99f4a63">ParsedMulTfOperation</a> : <span class="keyword">public</span> DeferredSingleLayerParsedTfOperation</div><div class="line"><a name="l02675"></a><span class="lineno"> 2675</span> {</div><div class="line"><a name="l02676"></a><span class="lineno"> 2676</span> <span class="keyword">public</span>:</div><div class="line"><a name="l02677"></a><span class="lineno"> 2677</span>  <a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml#ac28271f7220cd595de55dbb7f99f4a63">ParsedMulTfOperation</a>(<a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml">TfParser</a>* parser, <span class="keyword">const</span> tensorflow::NodeDef& node)</div><div class="line"><a name="l02678"></a><span class="lineno"> 2678</span>  : DeferredSingleLayerParsedTfOperation(parser, node)</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>  }</div><div class="line"><a name="l02681"></a><span class="lineno"> 2681</span> </div><div class="line"><a name="l02682"></a><span class="lineno"> 2682</span>  <span class="keywordtype">void</span> CreateLayerDeferred()<span class="keyword"> override</span></div><div class="line"><a name="l02683"></a><span class="lineno"> 2683</span> <span class="keyword"> </span>{</div><div class="line"><a name="l02684"></a><span class="lineno"> 2684</span>  BOOST_ASSERT(<a class="code" href="_subgraph_view_selector_8cpp.xhtml#ae7fc37b88ff10e9294ed1c72f2a25ac7">m_Layer</a> == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l02685"></a><span class="lineno"> 2685</span>  <a class="code" href="_subgraph_view_selector_8cpp.xhtml#ae7fc37b88ff10e9294ed1c72f2a25ac7">m_Layer</a> = m_Parser->AddMultiplicationLayer(m_Node);</div><div class="line"><a name="l02686"></a><span class="lineno"> 2686</span>  }</div><div class="line"><a name="l02687"></a><span class="lineno"> 2687</span> };</div><div class="line"><a name="l02688"></a><span class="lineno"> 2688</span> </div><div class="line"><a name="l02689"></a><span class="lineno"> 2689</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseMul(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l02690"></a><span class="lineno"> 2690</span> {</div><div class="line"><a name="l02691"></a><span class="lineno"> 2691</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02692"></a><span class="lineno"> 2692</span> </div><div class="line"><a name="l02693"></a><span class="lineno"> 2693</span>  <span class="keywordflow">return</span> std::make_unique<ParsedMulTfOperation>(<span class="keyword">this</span>, nodeDef);</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> </div><div class="line"><a name="l02696"></a><span class="lineno"> 2696</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParsePlaceholder(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l02697"></a><span class="lineno"> 2697</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</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>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02700"></a><span class="lineno"> 2700</span> </div><div class="line"><a name="l02701"></a><span class="lineno"> 2701</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 0);</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="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> layerId = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a>>(m_NetworkInputsBindingInfo.size());</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>  <span class="keyword">auto</span> it = m_InputShapes.find(nodeDef.name());</div><div class="line"><a name="l02706"></a><span class="lineno"> 2706</span>  <span class="keywordflow">if</span> (it == m_InputShapes.end())</div><div class="line"><a name="l02707"></a><span class="lineno"> 2707</span>  {</div><div class="line"><a name="l02708"></a><span class="lineno"> 2708</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l02709"></a><span class="lineno"> 2709</span>  boost::str(</div><div class="line"><a name="l02710"></a><span class="lineno"> 2710</span>  boost::format(</div><div class="line"><a name="l02711"></a><span class="lineno"> 2711</span>  <span class="stringliteral">"Missing input shape for Placeholder '%1%' %2%"</span>)</div><div class="line"><a name="l02712"></a><span class="lineno"> 2712</span>  % nodeDef.name()</div><div class="line"><a name="l02713"></a><span class="lineno"> 2713</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l02714"></a><span class="lineno"> 2714</span>  }</div><div class="line"><a name="l02715"></a><span class="lineno"> 2715</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> tensorInfo(it->second, DataType::Float32);</div><div class="line"><a name="l02716"></a><span class="lineno"> 2716</span> </div><div class="line"><a name="l02717"></a><span class="lineno"> 2717</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a87d5ec72def73ca14bd2987a024bd569">AddInputLayer</a>(layerId, nodeDef.name().c_str());</div><div class="line"><a name="l02718"></a><span class="lineno"> 2718</span> </div><div class="line"><a name="l02719"></a><span class="lineno"> 2719</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="l02720"></a><span class="lineno"> 2720</span> </div><div class="line"><a name="l02721"></a><span class="lineno"> 2721</span>  TrackInputBinding(layer, layerId, tensorInfo);</div><div class="line"><a name="l02722"></a><span class="lineno"> 2722</span> </div><div class="line"><a name="l02723"></a><span class="lineno"> 2723</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l02724"></a><span class="lineno"> 2724</span> }</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="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseRealDiv(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l02727"></a><span class="lineno"> 2727</span> {</div><div class="line"><a name="l02728"></a><span class="lineno"> 2728</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02729"></a><span class="lineno"> 2729</span>  <span class="keywordflow">return</span> AddRealDivLayer(nodeDef);</div><div class="line"><a name="l02730"></a><span class="lineno"> 2730</span> }</div><div class="line"><a name="l02731"></a><span class="lineno"> 2731</span> </div><div class="line"><a name="l02732"></a><span class="lineno"> 2732</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseRelu(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l02733"></a><span class="lineno"> 2733</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l02734"></a><span class="lineno"> 2734</span> {</div><div class="line"><a name="l02735"></a><span class="lineno"> 2735</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02736"></a><span class="lineno"> 2736</span> </div><div class="line"><a name="l02737"></a><span class="lineno"> 2737</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDesc;</div><div class="line"><a name="l02738"></a><span class="lineno"> 2738</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::ReLu;</div><div class="line"><a name="l02739"></a><span class="lineno"> 2739</span>  <span class="keywordflow">return</span> AddActivationLayer(nodeDef, activationDesc);</div><div class="line"><a name="l02740"></a><span class="lineno"> 2740</span> }</div><div class="line"><a name="l02741"></a><span class="lineno"> 2741</span> </div><div class="line"><a name="l02742"></a><span class="lineno"> 2742</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseRelu6(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l02743"></a><span class="lineno"> 2743</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l02744"></a><span class="lineno"> 2744</span> {</div><div class="line"><a name="l02745"></a><span class="lineno"> 2745</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02746"></a><span class="lineno"> 2746</span> </div><div class="line"><a name="l02747"></a><span class="lineno"> 2747</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDesc;</div><div class="line"><a name="l02748"></a><span class="lineno"> 2748</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::BoundedReLu;</div><div class="line"><a name="l02749"></a><span class="lineno"> 2749</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 6.0f;</div><div class="line"><a name="l02750"></a><span class="lineno"> 2750</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = 0.0f;</div><div class="line"><a name="l02751"></a><span class="lineno"> 2751</span> </div><div class="line"><a name="l02752"></a><span class="lineno"> 2752</span>  <span class="keywordflow">return</span> AddActivationLayer(nodeDef, activationDesc);</div><div class="line"><a name="l02753"></a><span class="lineno"> 2753</span> }</div><div class="line"><a name="l02754"></a><span class="lineno"> 2754</span> </div><div class="line"><a name="l02755"></a><span class="lineno"> 2755</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseSigmoid(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l02756"></a><span class="lineno"> 2756</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</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>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02759"></a><span class="lineno"> 2759</span> </div><div class="line"><a name="l02760"></a><span class="lineno"> 2760</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDesc;</div><div class="line"><a name="l02761"></a><span class="lineno"> 2761</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::Sigmoid;</div><div class="line"><a name="l02762"></a><span class="lineno"> 2762</span> </div><div class="line"><a name="l02763"></a><span class="lineno"> 2763</span>  <span class="keywordflow">return</span> AddActivationLayer(nodeDef, activationDesc);</div><div class="line"><a name="l02764"></a><span class="lineno"> 2764</span> }</div><div class="line"><a name="l02765"></a><span class="lineno"> 2765</span> </div><div class="line"><a name="l02766"></a><span class="lineno"> 2766</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseRsqrt(<span class="keyword">const</span> tensorflow::NodeDef &nodeDef,</div><div class="line"><a name="l02767"></a><span class="lineno"> 2767</span>  <span class="keyword">const</span> tensorflow::GraphDef &graphDef)</div><div class="line"><a name="l02768"></a><span class="lineno"> 2768</span> {</div><div class="line"><a name="l02769"></a><span class="lineno"> 2769</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02770"></a><span class="lineno"> 2770</span> </div><div class="line"><a name="l02771"></a><span class="lineno"> 2771</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 1);</div><div class="line"><a name="l02772"></a><span class="lineno"> 2772</span> </div><div class="line"><a name="l02773"></a><span class="lineno"> 2773</span>  <a class="code" href="structarmnn_1_1_elementwise_unary_descriptor.xhtml">ElementwiseUnaryDescriptor</a> descriptor(UnaryOperation::Rsqrt);</div><div class="line"><a name="l02774"></a><span class="lineno"> 2774</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a5bf8e0c150c7e6f8085c0767c6ab1914">AddElementwiseUnaryLayer</a>(descriptor, nodeDef.name().c_str());</div><div class="line"><a name="l02775"></a><span class="lineno"> 2775</span> </div><div class="line"><a name="l02776"></a><span class="lineno"> 2776</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& prevLayerOutputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l02777"></a><span class="lineno"> 2777</span>  prevLayerOutputSlot.<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="l02778"></a><span class="lineno"> 2778</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>(prevLayerOutputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>());</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="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l02781"></a><span class="lineno"> 2781</span> }</div><div class="line"><a name="l02782"></a><span class="lineno"> 2782</span> </div><div class="line"><a name="l02783"></a><span class="lineno"> 2783</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseSoftmax(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l02784"></a><span class="lineno"> 2784</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</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>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02787"></a><span class="lineno"> 2787</span> </div><div class="line"><a name="l02788"></a><span class="lineno"> 2788</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 1);</div><div class="line"><a name="l02789"></a><span class="lineno"> 2789</span> </div><div class="line"><a name="l02790"></a><span class="lineno"> 2790</span>  <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">SoftmaxDescriptor</a> softmaxDescriptor;</div><div class="line"><a name="l02791"></a><span class="lineno"> 2791</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a53949668a151924c4aad83b176db1080">AddSoftmaxLayer</a>(softmaxDescriptor, nodeDef.name().c_str());</div><div class="line"><a name="l02792"></a><span class="lineno"> 2792</span> </div><div class="line"><a name="l02793"></a><span class="lineno"> 2793</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& prevLayerSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l02794"></a><span class="lineno"> 2794</span>  prevLayerSlot.<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="l02795"></a><span class="lineno"> 2795</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>(prevLayerSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>());</div><div class="line"><a name="l02796"></a><span class="lineno"> 2796</span> </div><div class="line"><a name="l02797"></a><span class="lineno"> 2797</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l02798"></a><span class="lineno"> 2798</span> }</div><div class="line"><a name="l02799"></a><span class="lineno"> 2799</span> </div><div class="line"><a name="l02800"></a><span class="lineno"> 2800</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseSplit(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l02801"></a><span class="lineno"> 2801</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l02802"></a><span class="lineno"> 2802</span> {</div><div class="line"><a name="l02803"></a><span class="lineno"> 2803</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</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>  std::vector<OutputOfConstNodeDef> nodes = GetTfInputNodes(nodeDef);</div><div class="line"><a name="l02806"></a><span class="lineno"> 2806</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numInputs = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(nodes.size());</div><div class="line"><a name="l02807"></a><span class="lineno"> 2807</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, numInputs);</div><div class="line"><a name="l02808"></a><span class="lineno"> 2808</span> </div><div class="line"><a name="l02809"></a><span class="lineno"> 2809</span>  <span class="comment">// Constant tensor index</span></div><div class="line"><a name="l02810"></a><span class="lineno"> 2810</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> index = GetConstInputIndex(inputs);</div><div class="line"><a name="l02811"></a><span class="lineno"> 2811</span>  <span class="comment">// Get the axis tensor data</span></div><div class="line"><a name="l02812"></a><span class="lineno"> 2812</span>  ParsedConstTfOperation<int32_t>* shapeNode =</div><div class="line"><a name="l02813"></a><span class="lineno"> 2813</span>  boost::polymorphic_downcast<ParsedConstTfOperation<int32_t>*>(inputs[index].m_IndexedValue);</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>  std::vector<int32_t> axisTensorData;</div><div class="line"><a name="l02816"></a><span class="lineno"> 2816</span>  shapeNode->GetConstTensor(axisTensorData);</div><div class="line"><a name="l02817"></a><span class="lineno"> 2817</span> </div><div class="line"><a name="l02818"></a><span class="lineno"> 2818</span>  <span class="comment">// This splitDim indicates the data format: 3 is the NHWC, 1 is the NCHW.</span></div><div class="line"><a name="l02819"></a><span class="lineno"> 2819</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitDim = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(axisTensorData[0]);</div><div class="line"><a name="l02820"></a><span class="lineno"> 2820</span> </div><div class="line"><a name="l02821"></a><span class="lineno"> 2821</span>  <span class="comment">// Armnn supports split along the channel dimension for data formats NHWC and NCHW.</span></div><div class="line"><a name="l02822"></a><span class="lineno"> 2822</span>  <span class="keywordflow">if</span> (splitDim == 0 || splitDim == 2)</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="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">armnn::ParseException</a>(</div><div class="line"><a name="l02825"></a><span class="lineno"> 2825</span>  boost::str(</div><div class="line"><a name="l02826"></a><span class="lineno"> 2826</span>  boost::format(</div><div class="line"><a name="l02827"></a><span class="lineno"> 2827</span>  <span class="stringliteral">"Dimension %1% for split is not supported by Armnn. "</span></div><div class="line"><a name="l02828"></a><span class="lineno"> 2828</span>  <span class="stringliteral">"Node %2% %3%"</span>)</div><div class="line"><a name="l02829"></a><span class="lineno"> 2829</span>  % splitDim</div><div class="line"><a name="l02830"></a><span class="lineno"> 2830</span>  % nodeDef.name()</div><div class="line"><a name="l02831"></a><span class="lineno"> 2831</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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="comment">// As Armnn only supports splitter outputs of the same shape, therefore num_split will be limited to an integer.</span></div><div class="line"><a name="l02835"></a><span class="lineno"> 2835</span>  uint32_t num_split = ReadMandatoryNodeUint32Attribute(nodeDef, <span class="stringliteral">"num_split"</span>);</div><div class="line"><a name="l02836"></a><span class="lineno"> 2836</span> </div><div class="line"><a name="l02837"></a><span class="lineno"> 2837</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& inputSlot = inputs[1 - index].m_IndexedValue->ResolveArmnnOutputSlot(inputs[1 - index].m_Index);</div><div class="line"><a name="l02838"></a><span class="lineno"> 2838</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = inputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l02839"></a><span class="lineno"> 2839</span> </div><div class="line"><a name="l02840"></a><span class="lineno"> 2840</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> supportedNumDims = 4;</div><div class="line"><a name="l02841"></a><span class="lineno"> 2841</span>  <span class="keyword">auto</span> inputDimSize = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l02842"></a><span class="lineno"> 2842</span> </div><div class="line"><a name="l02843"></a><span class="lineno"> 2843</span>  <span class="keywordflow">if</span> (inputDimSize != supportedNumDims)</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">armnn::ParseException</a>(</div><div class="line"><a name="l02846"></a><span class="lineno"> 2846</span>  boost::str(</div><div class="line"><a name="l02847"></a><span class="lineno"> 2847</span>  boost::format(</div><div class="line"><a name="l02848"></a><span class="lineno"> 2848</span>  <span class="stringliteral">"The number of dimensions: %1% for input tensors of the "</span></div><div class="line"><a name="l02849"></a><span class="lineno"> 2849</span>  <span class="stringliteral">"split op should be %2% %3%"</span>)</div><div class="line"><a name="l02850"></a><span class="lineno"> 2850</span>  % inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()</div><div class="line"><a name="l02851"></a><span class="lineno"> 2851</span>  % supportedNumDims</div><div class="line"><a name="l02852"></a><span class="lineno"> 2852</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l02855"></a><span class="lineno"> 2855</span>  std::vector<unsigned int> splitterDimSizes(inputDimSize);</div><div class="line"><a name="l02856"></a><span class="lineno"> 2856</span> </div><div class="line"><a name="l02857"></a><span class="lineno"> 2857</span>  <span class="comment">// Add current input shape to splitterDimSizes</span></div><div class="line"><a name="l02858"></a><span class="lineno"> 2858</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="l02859"></a><span class="lineno"> 2859</span>  {</div><div class="line"><a name="l02860"></a><span class="lineno"> 2860</span>  splitterDimSizes[i] = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i];</div><div class="line"><a name="l02861"></a><span class="lineno"> 2861</span>  }</div><div class="line"><a name="l02862"></a><span class="lineno"> 2862</span> </div><div class="line"><a name="l02863"></a><span class="lineno"> 2863</span>  <span class="keywordflow">if</span> (splitterDimSizes[splitDim] % num_split != 0)</div><div class="line"><a name="l02864"></a><span class="lineno"> 2864</span>  {</div><div class="line"><a name="l02865"></a><span class="lineno"> 2865</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="l02866"></a><span class="lineno"> 2866</span>  }</div><div class="line"><a name="l02867"></a><span class="lineno"> 2867</span>  splitterDimSizes[splitDim] /= num_split;</div><div class="line"><a name="l02868"></a><span class="lineno"> 2868</span> </div><div class="line"><a name="l02869"></a><span class="lineno"> 2869</span>  <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">SplitterDescriptor</a> splitDesc(num_split);</div><div class="line"><a name="l02870"></a><span class="lineno"> 2870</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> g = 0; g < num_split; ++g)</div><div class="line"><a name="l02871"></a><span class="lineno"> 2871</span>  {</div><div class="line"><a name="l02872"></a><span class="lineno"> 2872</span>  <span class="comment">// Set the size of the views.</span></div><div class="line"><a name="l02873"></a><span class="lineno"> 2873</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="l02874"></a><span class="lineno"> 2874</span>  {</div><div class="line"><a name="l02875"></a><span class="lineno"> 2875</span>  splitDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">SetViewSize</a>(g, dimIdx, splitterDimSizes[dimIdx]);</div><div class="line"><a name="l02876"></a><span class="lineno"> 2876</span>  }</div><div class="line"><a name="l02877"></a><span class="lineno"> 2877</span>  splitDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">SetViewOriginCoord</a>(g, splitDim, splitterDimSizes[splitDim] * g);</div><div class="line"><a name="l02878"></a><span class="lineno"> 2878</span>  }</div><div class="line"><a name="l02879"></a><span class="lineno"> 2879</span> </div><div class="line"><a name="l02880"></a><span class="lineno"> 2880</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a> *layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a3a2dbac031f1a0b1b323916b1c7f61d2">AddSplitterLayer</a>(splitDesc, nodeDef.name().c_str());</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>  inputSlot.<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="l02883"></a><span class="lineno"> 2883</span> </div><div class="line"><a name="l02884"></a><span class="lineno"> 2884</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>(splitterDimSizes.size()),</div><div class="line"><a name="l02885"></a><span class="lineno"> 2885</span>  splitterDimSizes.data());</div><div class="line"><a name="l02886"></a><span class="lineno"> 2886</span> </div><div class="line"><a name="l02887"></a><span class="lineno"> 2887</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>(); ++i)</div><div class="line"><a name="l02888"></a><span class="lineno"> 2888</span>  {</div><div class="line"><a name="l02889"></a><span class="lineno"> 2889</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="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(outShape, inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()));</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="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l02893"></a><span class="lineno"> 2893</span> }</div><div class="line"><a name="l02894"></a><span class="lineno"> 2894</span> </div><div class="line"><a name="l02895"></a><span class="lineno"> 2895</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseSoftplus(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l02896"></a><span class="lineno"> 2896</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l02897"></a><span class="lineno"> 2897</span> {</div><div class="line"><a name="l02898"></a><span class="lineno"> 2898</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02899"></a><span class="lineno"> 2899</span> </div><div class="line"><a name="l02900"></a><span class="lineno"> 2900</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDesc;</div><div class="line"><a name="l02901"></a><span class="lineno"> 2901</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::SoftReLu;</div><div class="line"><a name="l02902"></a><span class="lineno"> 2902</span> </div><div class="line"><a name="l02903"></a><span class="lineno"> 2903</span>  <span class="keywordflow">return</span> AddActivationLayer(nodeDef, activationDesc);</div><div class="line"><a name="l02904"></a><span class="lineno"> 2904</span> }</div><div class="line"><a name="l02905"></a><span class="lineno"> 2905</span> </div><div class="line"><a name="l02906"></a><span class="lineno"> 2906</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseStridedSlice(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l02907"></a><span class="lineno"> 2907</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l02908"></a><span class="lineno"> 2908</span> {</div><div class="line"><a name="l02909"></a><span class="lineno"> 2909</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02910"></a><span class="lineno"> 2910</span> </div><div class="line"><a name="l02911"></a><span class="lineno"> 2911</span>  std::vector<OutputOfConstNodeDef> nodes = GetTfInputNodes(nodeDef);</div><div class="line"><a name="l02912"></a><span class="lineno"> 2912</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numInputs = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(nodes.size());</div><div class="line"><a name="l02913"></a><span class="lineno"> 2913</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, numInputs);</div><div class="line"><a name="l02914"></a><span class="lineno"> 2914</span> </div><div class="line"><a name="l02915"></a><span class="lineno"> 2915</span>  ParsedConstTfOperation<int32_t>* beginNode =</div><div class="line"><a name="l02916"></a><span class="lineno"> 2916</span>  boost::polymorphic_downcast<ParsedConstTfOperation<int32_t> *>(inputs[1].m_IndexedValue);</div><div class="line"><a name="l02917"></a><span class="lineno"> 2917</span>  std::vector<int32_t> beginTensorData;</div><div class="line"><a name="l02918"></a><span class="lineno"> 2918</span>  beginNode->GetConstTensor(beginTensorData);</div><div class="line"><a name="l02919"></a><span class="lineno"> 2919</span> </div><div class="line"><a name="l02920"></a><span class="lineno"> 2920</span>  ParsedConstTfOperation<int32_t>* endNode =</div><div class="line"><a name="l02921"></a><span class="lineno"> 2921</span>  boost::polymorphic_downcast<ParsedConstTfOperation<int32_t> *>(inputs[2].m_IndexedValue);</div><div class="line"><a name="l02922"></a><span class="lineno"> 2922</span>  std::vector<int32_t> endTensorData;</div><div class="line"><a name="l02923"></a><span class="lineno"> 2923</span>  endNode->GetConstTensor(endTensorData);</div><div class="line"><a name="l02924"></a><span class="lineno"> 2924</span> </div><div class="line"><a name="l02925"></a><span class="lineno"> 2925</span>  ParsedConstTfOperation<int32_t>* stridesNode =</div><div class="line"><a name="l02926"></a><span class="lineno"> 2926</span>  boost::polymorphic_downcast<ParsedConstTfOperation<int32_t> *>(inputs[3].m_IndexedValue);</div><div class="line"><a name="l02927"></a><span class="lineno"> 2927</span>  std::vector<int32_t> stridesTensorData;</div><div class="line"><a name="l02928"></a><span class="lineno"> 2928</span>  stridesNode->GetConstTensor(stridesTensorData);</div><div class="line"><a name="l02929"></a><span class="lineno"> 2929</span> </div><div class="line"><a name="l02930"></a><span class="lineno"> 2930</span>  <a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml">StridedSliceDescriptor</a> desc;</div><div class="line"><a name="l02931"></a><span class="lineno"> 2931</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a118fe06b7c2599da60398ee311ede923">m_Begin</a> = beginTensorData;</div><div class="line"><a name="l02932"></a><span class="lineno"> 2932</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#aa68194dd6258ab5b04123005a066ea25">m_End</a> = endTensorData;</div><div class="line"><a name="l02933"></a><span class="lineno"> 2933</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a0d53caff836b84204adbd1c28752a201">m_Stride</a> = stridesTensorData;</div><div class="line"><a name="l02934"></a><span class="lineno"> 2934</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a61081be1483984e33db452c75d569f51">m_BeginMask</a> = ReadMandatoryNodeInt32Attribute(nodeDef, <span class="stringliteral">"begin_mask"</span>);</div><div class="line"><a name="l02935"></a><span class="lineno"> 2935</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#ac37e49c0d6e6e54f9d2015d0f11f8ee7">m_EndMask</a> = ReadMandatoryNodeInt32Attribute(nodeDef, <span class="stringliteral">"end_mask"</span>);</div><div class="line"><a name="l02936"></a><span class="lineno"> 2936</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#af996d82c47e43a16f4c8faa6c6b3e030">m_EllipsisMask</a> = ReadMandatoryNodeInt32Attribute(nodeDef, <span class="stringliteral">"ellipsis_mask"</span>);</div><div class="line"><a name="l02937"></a><span class="lineno"> 2937</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a7c91eda2b331d607bae92cd8ebf50bb9">m_NewAxisMask</a> = ReadMandatoryNodeInt32Attribute(nodeDef, <span class="stringliteral">"new_axis_mask"</span>);</div><div class="line"><a name="l02938"></a><span class="lineno"> 2938</span>  desc.<a class="code" href="structarmnn_1_1_strided_slice_descriptor.xhtml#a6d0384878432cfc9652b7ae8bc59506f">m_ShrinkAxisMask</a> = ReadMandatoryNodeInt32Attribute(nodeDef, <span class="stringliteral">"shrink_axis_mask"</span>);</div><div class="line"><a name="l02939"></a><span class="lineno"> 2939</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="l02940"></a><span class="lineno"> 2940</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a348f95b60998a987ba20a58bfc720590">AddStridedSliceLayer</a>(desc, nodeDef.name().c_str());</div><div class="line"><a name="l02941"></a><span class="lineno"> 2941</span> </div><div class="line"><a name="l02942"></a><span class="lineno"> 2942</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& prevLayerSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l02943"></a><span class="lineno"> 2943</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = prevLayerSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l02946"></a><span class="lineno"> 2946</span>  <a class="code" href="namespacearmnn_utils.xhtml#a9300450bab29bb951d6f8755b7d9d3a8">CalculateStridedSliceOutputTensorInfo</a>(inputTensorInfo, desc, outputTensorInfo);</div><div class="line"><a name="l02947"></a><span class="lineno"> 2947</span> </div><div class="line"><a name="l02948"></a><span class="lineno"> 2948</span>  prevLayerSlot.<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="l02949"></a><span class="lineno"> 2949</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="l02950"></a><span class="lineno"> 2950</span> </div><div class="line"><a name="l02951"></a><span class="lineno"> 2951</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</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> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseTanh(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l02955"></a><span class="lineno"> 2955</span> {</div><div class="line"><a name="l02956"></a><span class="lineno"> 2956</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02957"></a><span class="lineno"> 2957</span> </div><div class="line"><a name="l02958"></a><span class="lineno"> 2958</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> activationDesc;</div><div class="line"><a name="l02959"></a><span class="lineno"> 2959</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = ActivationFunction::TanH;</div><div class="line"><a name="l02960"></a><span class="lineno"> 2960</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l02961"></a><span class="lineno"> 2961</span>  activationDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = 1.0f;</div><div class="line"><a name="l02962"></a><span class="lineno"> 2962</span> </div><div class="line"><a name="l02963"></a><span class="lineno"> 2963</span>  <span class="keywordflow">return</span> AddActivationLayer(nodeDef, activationDesc);</div><div class="line"><a name="l02964"></a><span class="lineno"> 2964</span> }</div><div class="line"><a name="l02965"></a><span class="lineno"> 2965</span> </div><div class="line"><a name="l02966"></a><span class="lineno"> 2966</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::AddActivationLayer(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l02967"></a><span class="lineno"> 2967</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a>& activationDesc)</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::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 1);</div><div class="line"><a name="l02970"></a><span class="lineno"> 2970</span> </div><div class="line"><a name="l02971"></a><span class="lineno"> 2971</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#aea068f6094e1c3bfcdf8167b68112632">AddActivationLayer</a>(activationDesc, nodeDef.name().c_str());</div><div class="line"><a name="l02972"></a><span class="lineno"> 2972</span> </div><div class="line"><a name="l02973"></a><span class="lineno"> 2973</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& prevLayerOutputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l02974"></a><span class="lineno"> 2974</span>  prevLayerOutputSlot.<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="l02975"></a><span class="lineno"> 2975</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>(prevLayerOutputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>());</div><div class="line"><a name="l02976"></a><span class="lineno"> 2976</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l02977"></a><span class="lineno"> 2977</span> }</div><div class="line"><a name="l02978"></a><span class="lineno"> 2978</span> </div><div class="line"><a name="l02979"></a><span class="lineno"> 2979</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseMaxPool(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l02980"></a><span class="lineno"> 2980</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l02981"></a><span class="lineno"> 2981</span> {</div><div class="line"><a name="l02982"></a><span class="lineno"> 2982</span>  <span class="keywordflow">return</span> ParsePooling2d(nodeDef, graphDef, PoolingAlgorithm::Max);</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"> 2985</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParseAvgPool(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l02986"></a><span class="lineno"> 2986</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l02987"></a><span class="lineno"> 2987</span> {</div><div class="line"><a name="l02988"></a><span class="lineno"> 2988</span>  <span class="keywordflow">return</span> ParsePooling2d(nodeDef, graphDef, PoolingAlgorithm::Average);</div><div class="line"><a name="l02989"></a><span class="lineno"> 2989</span> }</div><div class="line"><a name="l02990"></a><span class="lineno"> 2990</span> </div><div class="line"><a name="l02991"></a><span class="lineno"> 2991</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::ParsePooling2d(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef,</div><div class="line"><a name="l02992"></a><span class="lineno"> 2992</span>  <span class="keyword">const</span> tensorflow::GraphDef& graphDef, <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718">PoolingAlgorithm</a> pooltype)</div><div class="line"><a name="l02993"></a><span class="lineno"> 2993</span> {</div><div class="line"><a name="l02994"></a><span class="lineno"> 2994</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graphDef);</div><div class="line"><a name="l02995"></a><span class="lineno"> 2995</span> </div><div class="line"><a name="l02996"></a><span class="lineno"> 2996</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 1);</div><div class="line"><a name="l02997"></a><span class="lineno"> 2997</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& inputSlot = inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l02998"></a><span class="lineno"> 2998</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo = inputSlot.<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</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>  <span class="keywordflow">if</span> (inputs.size() != 1)</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03003"></a><span class="lineno"> 3003</span>  boost::str(</div><div class="line"><a name="l03004"></a><span class="lineno"> 3004</span>  boost::format(</div><div class="line"><a name="l03005"></a><span class="lineno"> 3005</span>  <span class="stringliteral">"2D Pooling expects one input!. Got %1% for Node %2% %3%"</span>)</div><div class="line"><a name="l03006"></a><span class="lineno"> 3006</span>  % inputs.size()</div><div class="line"><a name="l03007"></a><span class="lineno"> 3007</span>  % nodeDef.name()</div><div class="line"><a name="l03008"></a><span class="lineno"> 3008</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03009"></a><span class="lineno"> 3009</span>  }</div><div class="line"><a name="l03010"></a><span class="lineno"> 3010</span> </div><div class="line"><a name="l03011"></a><span class="lineno"> 3011</span>  std::string paddingString = ReadMandatoryNodeStringAttribute(nodeDef, <span class="stringliteral">"padding"</span>);</div><div class="line"><a name="l03012"></a><span class="lineno"> 3012</span>  std::string dataFormat = ReadMandatoryNodeStringAttribute(nodeDef, <span class="stringliteral">"data_format"</span>);</div><div class="line"><a name="l03013"></a><span class="lineno"> 3013</span>  std::vector<uint32_t> strides = ReadMandatoryNodeUint32ListAttribute(nodeDef, <span class="stringliteral">"strides"</span>);</div><div class="line"><a name="l03014"></a><span class="lineno"> 3014</span>  std::vector<uint32_t> ksize = ReadMandatoryNodeUint32ListAttribute(nodeDef, <span class="stringliteral">"ksize"</span>); <span class="comment">// size of pool windows</span></div><div class="line"><a name="l03015"></a><span class="lineno"> 3015</span> </div><div class="line"><a name="l03016"></a><span class="lineno"> 3016</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> pooling2dDescriptor;</div><div class="line"><a name="l03017"></a><span class="lineno"> 3017</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = pooltype;</div><div class="line"><a name="l03018"></a><span class="lineno"> 3018</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = PaddingMethod::Exclude;</div><div class="line"><a name="l03019"></a><span class="lineno"> 3019</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#affb5b68b3eba3ed45a06c7cde7781962">m_OutputShapeRounding</a> = OutputShapeRounding::Floor;</div><div class="line"><a name="l03020"></a><span class="lineno"> 3020</span> </div><div class="line"><a name="l03021"></a><span class="lineno"> 3021</span>  <a class="code" href="_tf_parser_8cpp.xhtml#a3fb047570644cae325aa88d3cd7bb96e">CHECK_DATA_FORMAT</a>(nodeDef, dataFormat, <span class="stringliteral">"Pooling2D"</span>);</div><div class="line"><a name="l03022"></a><span class="lineno"> 3022</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout = dataFormat == <span class="stringliteral">"NHWC"</span> ? DataLayout::NHWC : DataLayout::NCHW;</div><div class="line"><a name="l03023"></a><span class="lineno"> 3023</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l03024"></a><span class="lineno"> 3024</span>  <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">DataLayoutIndexed</a> dataLayoutIndexed(dataLayout);</div><div class="line"><a name="l03025"></a><span class="lineno"> 3025</span> </div><div class="line"><a name="l03026"></a><span class="lineno"> 3026</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strides[dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>()];</div><div class="line"><a name="l03027"></a><span class="lineno"> 3027</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strides[dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>()];</div><div class="line"><a name="l03028"></a><span class="lineno"> 3028</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = ksize[dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>()];</div><div class="line"><a name="l03029"></a><span class="lineno"> 3029</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = ksize[dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>()];</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>  uint32_t inputHeight = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>()];</div><div class="line"><a name="l03032"></a><span class="lineno"> 3032</span>  uint32_t inputWidth = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>()];</div><div class="line"><a name="l03033"></a><span class="lineno"> 3033</span> </div><div class="line"><a name="l03034"></a><span class="lineno"> 3034</span>  <span class="keywordtype">bool</span> padding = <span class="keyword">false</span>;</div><div class="line"><a name="l03035"></a><span class="lineno"> 3035</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo;</div><div class="line"><a name="l03036"></a><span class="lineno"> 3036</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = 0;</div><div class="line"><a name="l03037"></a><span class="lineno"> 3037</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = 0;</div><div class="line"><a name="l03038"></a><span class="lineno"> 3038</span> </div><div class="line"><a name="l03039"></a><span class="lineno"> 3039</span>  <a class="code" href="_tf_parser_8cpp.xhtml#aab838eb7734e531bb5be6f6dece673bf">CHECK_PADDING_TYPE</a>(nodeDef, paddingString);</div><div class="line"><a name="l03040"></a><span class="lineno"> 3040</span> </div><div class="line"><a name="l03041"></a><span class="lineno"> 3041</span>  <span class="keywordflow">if</span> (paddingString == <span class="stringliteral">"SAME"</span>)</div><div class="line"><a name="l03042"></a><span class="lineno"> 3042</span>  {</div><div class="line"><a name="l03043"></a><span class="lineno"> 3043</span>  padding = <span class="keyword">true</span>;</div><div class="line"><a name="l03044"></a><span class="lineno"> 3044</span> </div><div class="line"><a name="l03045"></a><span class="lineno"> 3045</span>  outputHeight = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(ceil(static_cast<float>(inputHeight) /</div><div class="line"><a name="l03046"></a><span class="lineno"> 3046</span>  static_cast<float>(pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>)));</div><div class="line"><a name="l03047"></a><span class="lineno"> 3047</span>  outputWidth = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(ceil(static_cast<float>(inputWidth) /</div><div class="line"><a name="l03048"></a><span class="lineno"> 3048</span>  static_cast<float>(pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>)));</div><div class="line"><a name="l03049"></a><span class="lineno"> 3049</span>  }</div><div class="line"><a name="l03050"></a><span class="lineno"> 3050</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (paddingString == <span class="stringliteral">"VALID"</span>)</div><div class="line"><a name="l03051"></a><span class="lineno"> 3051</span>  {</div><div class="line"><a name="l03052"></a><span class="lineno"> 3052</span>  padding = <span class="keyword">false</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"> 3054</span>  outputHeight = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(ceil(</div><div class="line"><a name="l03055"></a><span class="lineno"> 3055</span>  static_cast<float>(inputHeight - pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> + 1) /</div><div class="line"><a name="l03056"></a><span class="lineno"> 3056</span>  static_cast<float>(pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>)));</div><div class="line"><a name="l03057"></a><span class="lineno"> 3057</span>  outputWidth = <span class="keyword">static_cast<</span>uint32_t<span class="keyword">></span>(ceil(</div><div class="line"><a name="l03058"></a><span class="lineno"> 3058</span>  static_cast<float>(inputWidth - pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> + 1) /</div><div class="line"><a name="l03059"></a><span class="lineno"> 3059</span>  static_cast<float>(pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>)));</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> </div><div class="line"><a name="l03062"></a><span class="lineno"> 3062</span>  <span class="keywordflow">switch</span> (dataLayout)</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>  <span class="keywordflow">case</span> DataLayout::NHWC:</div><div class="line"><a name="l03065"></a><span class="lineno"> 3065</span>  outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0],</div><div class="line"><a name="l03066"></a><span class="lineno"> 3066</span>  outputHeight,</div><div class="line"><a name="l03067"></a><span class="lineno"> 3067</span>  outputWidth,</div><div class="line"><a name="l03068"></a><span class="lineno"> 3068</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3] },</div><div class="line"><a name="l03069"></a><span class="lineno"> 3069</span>  DataType::Float32);</div><div class="line"><a name="l03070"></a><span class="lineno"> 3070</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l03071"></a><span class="lineno"> 3071</span>  <span class="keywordflow">case</span> DataLayout::NCHW:</div><div class="line"><a name="l03072"></a><span class="lineno"> 3072</span>  outputInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0],</div><div class="line"><a name="l03073"></a><span class="lineno"> 3073</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1],</div><div class="line"><a name="l03074"></a><span class="lineno"> 3074</span>  outputHeight,</div><div class="line"><a name="l03075"></a><span class="lineno"> 3075</span>  outputWidth },</div><div class="line"><a name="l03076"></a><span class="lineno"> 3076</span>  DataType::Float32);</div><div class="line"><a name="l03077"></a><span class="lineno"> 3077</span>  <span class="keywordflow">break</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>  <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputWidth, pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a>, pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>,</div><div class="line"><a name="l03081"></a><span class="lineno"> 3081</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>, pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>, padding);</div><div class="line"><a name="l03082"></a><span class="lineno"> 3082</span>  <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputHeight, pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a>, pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>,</div><div class="line"><a name="l03083"></a><span class="lineno"> 3083</span>  pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>, pooling2dDescriptor.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>, padding);</div><div class="line"><a name="l03084"></a><span class="lineno"> 3084</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a4ec92bca4e51755105abb89e1878585f">AddPooling2dLayer</a>(pooling2dDescriptor, nodeDef.name().c_str());</div><div class="line"><a name="l03087"></a><span class="lineno"> 3087</span>  <span class="keywordflow">if</span> (layer == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l03088"></a><span class="lineno"> 3088</span>  {</div><div class="line"><a name="l03089"></a><span class="lineno"> 3089</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03090"></a><span class="lineno"> 3090</span>  boost::str(</div><div class="line"><a name="l03091"></a><span class="lineno"> 3091</span>  boost::format(</div><div class="line"><a name="l03092"></a><span class="lineno"> 3092</span>  <span class="stringliteral">"Failed to add pooling2d layer for %1% %2%"</span>)</div><div class="line"><a name="l03093"></a><span class="lineno"> 3093</span>  % nodeDef.name()</div><div class="line"><a name="l03094"></a><span class="lineno"> 3094</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l03097"></a><span class="lineno"> 3097</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>(outputInfo);</div><div class="line"><a name="l03098"></a><span class="lineno"> 3098</span> </div><div class="line"><a name="l03099"></a><span class="lineno"> 3099</span>  inputSlot.<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="l03100"></a><span class="lineno"> 3100</span> </div><div class="line"><a name="l03101"></a><span class="lineno"> 3101</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l03102"></a><span class="lineno"> 3102</span> }</div><div class="line"><a name="l03103"></a><span class="lineno"> 3103</span> </div><div class="line"><a name="l03104"></a><span class="lineno"> 3104</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::AddAdditionLayer(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keywordtype">bool</span> isBiasAdd)</div><div class="line"><a name="l03105"></a><span class="lineno"> 3105</span> {</div><div class="line"><a name="l03106"></a><span class="lineno"> 3106</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2);</div><div class="line"><a name="l03107"></a><span class="lineno"> 3107</span> </div><div class="line"><a name="l03108"></a><span class="lineno"> 3108</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input0Slot = &inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l03109"></a><span class="lineno"> 3109</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input1Slot = &inputs[1].m_IndexedValue->ResolveArmnnOutputSlot(inputs[1].m_Index);</div><div class="line"><a name="l03110"></a><span class="lineno"> 3110</span> </div><div class="line"><a name="l03111"></a><span class="lineno"> 3111</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& input0Info = input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l03112"></a><span class="lineno"> 3112</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& input1Info = input1Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l03113"></a><span class="lineno"> 3113</span> </div><div class="line"><a name="l03114"></a><span class="lineno"> 3114</span>  <span class="keywordflow">if</span> (isBiasAdd)</div><div class="line"><a name="l03115"></a><span class="lineno"> 3115</span>  {</div><div class="line"><a name="l03116"></a><span class="lineno"> 3116</span>  <span class="comment">// BiasAdd takes bias as a 1D tensor. We need to add a reshape layer to create a 4D tensor</span></div><div class="line"><a name="l03117"></a><span class="lineno"> 3117</span>  <span class="comment">// with the same data in the correct dimension for broadcast in addition.</span></div><div class="line"><a name="l03118"></a><span class="lineno"> 3118</span>  <span class="keywordflow">if</span>(input1Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() != 1)</div><div class="line"><a name="l03119"></a><span class="lineno"> 3119</span>  {</div><div class="line"><a name="l03120"></a><span class="lineno"> 3120</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03121"></a><span class="lineno"> 3121</span>  boost::str(</div><div class="line"><a name="l03122"></a><span class="lineno"> 3122</span>  boost::format(</div><div class="line"><a name="l03123"></a><span class="lineno"> 3123</span>  <span class="stringliteral">"Unsupported bias for BiasAdd. It should be a 1D vector. "</span></div><div class="line"><a name="l03124"></a><span class="lineno"> 3124</span>  <span class="stringliteral">"Got %1% dimensions for input %2%. Node %3% %4%"</span>)</div><div class="line"><a name="l03125"></a><span class="lineno"> 3125</span>  % input1Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()</div><div class="line"><a name="l03126"></a><span class="lineno"> 3126</span>  % inputs[1].m_IndexedValue->GetNode().name()</div><div class="line"><a name="l03127"></a><span class="lineno"> 3127</span>  % nodeDef.name()</div><div class="line"><a name="l03128"></a><span class="lineno"> 3128</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03129"></a><span class="lineno"> 3129</span>  }</div><div class="line"><a name="l03130"></a><span class="lineno"> 3130</span> </div><div class="line"><a name="l03131"></a><span class="lineno"> 3131</span>  <span class="keyword">const</span> std::string dataFormat = ReadMandatoryNodeStringAttribute(nodeDef, <span class="stringliteral">"data_format"</span>);</div><div class="line"><a name="l03132"></a><span class="lineno"> 3132</span> </div><div class="line"><a name="l03133"></a><span class="lineno"> 3133</span>  <a class="code" href="_tf_parser_8cpp.xhtml#a3fb047570644cae325aa88d3cd7bb96e">CHECK_DATA_FORMAT</a>(nodeDef, dataFormat, <span class="stringliteral">"BiasAdd"</span>);</div><div class="line"><a name="l03134"></a><span class="lineno"> 3134</span>  input1Slot = AddBroadcastReshapeLayer(input0Slot, input1Slot, dataFormat == <span class="stringliteral">"NHWC"</span>, *m_Network, nodeDef);</div><div class="line"><a name="l03135"></a><span class="lineno"> 3135</span>  }</div><div class="line"><a name="l03136"></a><span class="lineno"> 3136</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l03137"></a><span class="lineno"> 3137</span>  {</div><div class="line"><a name="l03138"></a><span class="lineno"> 3138</span>  <span class="keywordflow">if</span> (input0Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() == 1)</div><div class="line"><a name="l03139"></a><span class="lineno"> 3139</span>  {</div><div class="line"><a name="l03140"></a><span class="lineno"> 3140</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> isNHWC = <span class="keyword">true</span>;</div><div class="line"><a name="l03141"></a><span class="lineno"> 3141</span>  input0Slot = AddBroadcastReshapeLayer(input1Slot, input0Slot, isNHWC, *m_Network, nodeDef);</div><div class="line"><a name="l03142"></a><span class="lineno"> 3142</span>  }</div><div class="line"><a name="l03143"></a><span class="lineno"> 3143</span> </div><div class="line"><a name="l03144"></a><span class="lineno"> 3144</span>  <span class="keywordflow">if</span> (input1Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() == 1)</div><div class="line"><a name="l03145"></a><span class="lineno"> 3145</span>  {</div><div class="line"><a name="l03146"></a><span class="lineno"> 3146</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> isNHWC = <span class="keyword">true</span>;</div><div class="line"><a name="l03147"></a><span class="lineno"> 3147</span>  input1Slot = AddBroadcastReshapeLayer(input0Slot, input1Slot, isNHWC, *m_Network, nodeDef);</div><div class="line"><a name="l03148"></a><span class="lineno"> 3148</span>  }</div><div class="line"><a name="l03149"></a><span class="lineno"> 3149</span>  }</div><div class="line"><a name="l03150"></a><span class="lineno"> 3150</span> </div><div class="line"><a name="l03151"></a><span class="lineno"> 3151</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a4812e0137ee610310d23059efed2cb84">AddAdditionLayer</a>(nodeDef.name().c_str());</div><div class="line"><a name="l03152"></a><span class="lineno"> 3152</span> </div><div class="line"><a name="l03153"></a><span class="lineno"> 3153</span>  input0Slot-><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="l03154"></a><span class="lineno"> 3154</span>  input1Slot-><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>(1));</div><div class="line"><a name="l03155"></a><span class="lineno"> 3155</span> </div><div class="line"><a name="l03156"></a><span class="lineno"> 3156</span>  <span class="keywordflow">if</span> (input0Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() == input1Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div><div class="line"><a name="l03157"></a><span class="lineno"> 3157</span>  {</div><div class="line"><a name="l03158"></a><span class="lineno"> 3158</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& input0Shape = input0Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l03159"></a><span class="lineno"> 3159</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& input1Shape = input1Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l03160"></a><span class="lineno"> 3160</span> </div><div class="line"><a name="l03161"></a><span class="lineno"> 3161</span>  std::vector<unsigned int> outputShape;</div><div class="line"><a name="l03162"></a><span class="lineno"> 3162</span>  outputShape.reserve(input0Shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>());</div><div class="line"><a name="l03163"></a><span class="lineno"> 3163</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(input0Info);</div><div class="line"><a name="l03164"></a><span class="lineno"> 3164</span> </div><div class="line"><a name="l03165"></a><span class="lineno"> 3165</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < input0Shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); i++)</div><div class="line"><a name="l03166"></a><span class="lineno"> 3166</span>  {</div><div class="line"><a name="l03167"></a><span class="lineno"> 3167</span>  outputShape.push_back(std::max(input0Shape[i], input1Shape[i]));</div><div class="line"><a name="l03168"></a><span class="lineno"> 3168</span>  }</div><div class="line"><a name="l03169"></a><span class="lineno"> 3169</span> </div><div class="line"><a name="l03170"></a><span class="lineno"> 3170</span>  outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(input0Shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(), outputShape.data()));</div><div class="line"><a name="l03171"></a><span class="lineno"> 3171</span> </div><div class="line"><a name="l03172"></a><span class="lineno"> 3172</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>(outputInfo);</div><div class="line"><a name="l03173"></a><span class="lineno"> 3173</span>  }</div><div class="line"><a name="l03174"></a><span class="lineno"> 3174</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (input0Info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() == 1 && isBiasAdd == <span class="keyword">false</span>)</div><div class="line"><a name="l03175"></a><span class="lineno"> 3175</span>  {</div><div class="line"><a name="l03176"></a><span class="lineno"> 3176</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>(input1Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>());</div><div class="line"><a name="l03177"></a><span class="lineno"> 3177</span>  }</div><div class="line"><a name="l03178"></a><span class="lineno"> 3178</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l03179"></a><span class="lineno"> 3179</span>  {</div><div class="line"><a name="l03180"></a><span class="lineno"> 3180</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>(input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>());</div><div class="line"><a name="l03181"></a><span class="lineno"> 3181</span>  }</div><div class="line"><a name="l03182"></a><span class="lineno"> 3182</span> </div><div class="line"><a name="l03183"></a><span class="lineno"> 3183</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l03184"></a><span class="lineno"> 3184</span> }</div><div class="line"><a name="l03185"></a><span class="lineno"> 3185</span> </div><div class="line"><a name="l03186"></a><span class="lineno"> 3186</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::AddRealDivLayer(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef)</div><div class="line"><a name="l03187"></a><span class="lineno"> 3187</span> {</div><div class="line"><a name="l03188"></a><span class="lineno"> 3188</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2);</div><div class="line"><a name="l03189"></a><span class="lineno"> 3189</span> </div><div class="line"><a name="l03190"></a><span class="lineno"> 3190</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a82a5bc0d24f4c4eb1fbf793e156a5193">AddDivisionLayer</a>(nodeDef.name().c_str());</div><div class="line"><a name="l03191"></a><span class="lineno"> 3191</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input0Slot = &inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l03192"></a><span class="lineno"> 3192</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input1Slot = &inputs[1].m_IndexedValue->ResolveArmnnOutputSlot(inputs[1].m_Index);</div><div class="line"><a name="l03193"></a><span class="lineno"> 3193</span> </div><div class="line"><a name="l03194"></a><span class="lineno"> 3194</span>  <span class="keyword">auto</span> <span class="keyword">const</span> input0NumDims = input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l03195"></a><span class="lineno"> 3195</span>  <span class="keyword">auto</span> <span class="keyword">const</span> input1NumDims = input1Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l03196"></a><span class="lineno"> 3196</span> </div><div class="line"><a name="l03197"></a><span class="lineno"> 3197</span> </div><div class="line"><a name="l03198"></a><span class="lineno"> 3198</span>  <span class="keywordflow">if</span> (input0NumDims < input1NumDims)</div><div class="line"><a name="l03199"></a><span class="lineno"> 3199</span>  {</div><div class="line"><a name="l03200"></a><span class="lineno"> 3200</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> isNHWC = <span class="keyword">true</span>;</div><div class="line"><a name="l03201"></a><span class="lineno"> 3201</span>  input0Slot = AddBroadcastReshapeLayer(input1Slot, input0Slot, isNHWC, *m_Network, nodeDef);</div><div class="line"><a name="l03202"></a><span class="lineno"> 3202</span>  }</div><div class="line"><a name="l03203"></a><span class="lineno"> 3203</span>  <span class="keywordflow">if</span> (input1NumDims < input0NumDims)</div><div class="line"><a name="l03204"></a><span class="lineno"> 3204</span>  {</div><div class="line"><a name="l03205"></a><span class="lineno"> 3205</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> isNHWC = <span class="keyword">true</span>;</div><div class="line"><a name="l03206"></a><span class="lineno"> 3206</span>  input1Slot = AddBroadcastReshapeLayer(input0Slot, input1Slot, isNHWC, *m_Network, nodeDef);</div><div class="line"><a name="l03207"></a><span class="lineno"> 3207</span>  }</div><div class="line"><a name="l03208"></a><span class="lineno"> 3208</span> </div><div class="line"><a name="l03209"></a><span class="lineno"> 3209</span>  input0Slot-><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="l03210"></a><span class="lineno"> 3210</span>  input1Slot-><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>(1));</div><div class="line"><a name="l03211"></a><span class="lineno"> 3211</span> </div><div class="line"><a name="l03212"></a><span class="lineno"> 3212</span>  <span class="keywordflow">if</span> (input0NumDims < input1NumDims)</div><div class="line"><a name="l03213"></a><span class="lineno"> 3213</span>  {</div><div class="line"><a name="l03214"></a><span class="lineno"> 3214</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>(input1Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>());</div><div class="line"><a name="l03215"></a><span class="lineno"> 3215</span>  }</div><div class="line"><a name="l03216"></a><span class="lineno"> 3216</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l03217"></a><span class="lineno"> 3217</span>  {</div><div class="line"><a name="l03218"></a><span class="lineno"> 3218</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>(input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>());</div><div class="line"><a name="l03219"></a><span class="lineno"> 3219</span> </div><div class="line"><a name="l03220"></a><span class="lineno"> 3220</span>  }</div><div class="line"><a name="l03221"></a><span class="lineno"> 3221</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l03222"></a><span class="lineno"> 3222</span> }</div><div class="line"><a name="l03223"></a><span class="lineno"> 3223</span> </div><div class="line"><a name="l03224"></a><span class="lineno"> 3224</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> TfParser::AddMaximumLayer(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef)</div><div class="line"><a name="l03225"></a><span class="lineno"> 3225</span> {</div><div class="line"><a name="l03226"></a><span class="lineno"> 3226</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2);</div><div class="line"><a name="l03227"></a><span class="lineno"> 3227</span> </div><div class="line"><a name="l03228"></a><span class="lineno"> 3228</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input0Slot = &inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l03229"></a><span class="lineno"> 3229</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input1Slot = &inputs[1].m_IndexedValue->ResolveArmnnOutputSlot(inputs[1].m_Index);</div><div class="line"><a name="l03230"></a><span class="lineno"> 3230</span> </div><div class="line"><a name="l03231"></a><span class="lineno"> 3231</span>  <span class="keyword">auto</span> <span class="keyword">const</span> input0NumDims = input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l03232"></a><span class="lineno"> 3232</span>  <span class="keyword">auto</span> <span class="keyword">const</span> input1NumDims = input1Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l03233"></a><span class="lineno"> 3233</span> </div><div class="line"><a name="l03234"></a><span class="lineno"> 3234</span>  <span class="keywordflow">if</span> (input0NumDims < input1NumDims)</div><div class="line"><a name="l03235"></a><span class="lineno"> 3235</span>  {</div><div class="line"><a name="l03236"></a><span class="lineno"> 3236</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> isNHWC = <span class="keyword">true</span>;</div><div class="line"><a name="l03237"></a><span class="lineno"> 3237</span>  input0Slot = AddBroadcastReshapeLayer(input1Slot, input0Slot, isNHWC, *m_Network, nodeDef);</div><div class="line"><a name="l03238"></a><span class="lineno"> 3238</span>  }</div><div class="line"><a name="l03239"></a><span class="lineno"> 3239</span>  <span class="keywordflow">if</span> (input1NumDims < input0NumDims)</div><div class="line"><a name="l03240"></a><span class="lineno"> 3240</span>  {</div><div class="line"><a name="l03241"></a><span class="lineno"> 3241</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> isNHWC = <span class="keyword">true</span>;</div><div class="line"><a name="l03242"></a><span class="lineno"> 3242</span>  input1Slot = AddBroadcastReshapeLayer(input0Slot, input1Slot, isNHWC, *m_Network, nodeDef);</div><div class="line"><a name="l03243"></a><span class="lineno"> 3243</span>  }</div><div class="line"><a name="l03244"></a><span class="lineno"> 3244</span> </div><div class="line"><a name="l03245"></a><span class="lineno"> 3245</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#afb8d4577c796ffdd213428cd285734b1">AddMaximumLayer</a>(nodeDef.name().c_str());</div><div class="line"><a name="l03246"></a><span class="lineno"> 3246</span> </div><div class="line"><a name="l03247"></a><span class="lineno"> 3247</span>  input0Slot-><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="l03248"></a><span class="lineno"> 3248</span>  input1Slot-><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>(1));</div><div class="line"><a name="l03249"></a><span class="lineno"> 3249</span> </div><div class="line"><a name="l03250"></a><span class="lineno"> 3250</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo = input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>();</div><div class="line"><a name="l03251"></a><span class="lineno"> 3251</span>  std::vector<unsigned int> outputShape;</div><div class="line"><a name="l03252"></a><span class="lineno"> 3252</span> </div><div class="line"><a name="l03253"></a><span class="lineno"> 3253</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& input0Shape = input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l03254"></a><span class="lineno"> 3254</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& input1Shape = input1Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l03255"></a><span class="lineno"> 3255</span> </div><div class="line"><a name="l03256"></a><span class="lineno"> 3256</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < input0Shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); i++)</div><div class="line"><a name="l03257"></a><span class="lineno"> 3257</span>  {</div><div class="line"><a name="l03258"></a><span class="lineno"> 3258</span>  outputShape.push_back(std::max(input0Shape[i], input1Shape[i]));</div><div class="line"><a name="l03259"></a><span class="lineno"> 3259</span>  }</div><div class="line"><a name="l03260"></a><span class="lineno"> 3260</span> </div><div class="line"><a name="l03261"></a><span class="lineno"> 3261</span>  outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(input0Shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(), outputShape.data()));</div><div class="line"><a name="l03262"></a><span class="lineno"> 3262</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>(outputInfo);</div><div class="line"><a name="l03263"></a><span class="lineno"> 3263</span> </div><div class="line"><a name="l03264"></a><span class="lineno"> 3264</span>  <span class="keywordflow">return</span> std::make_unique<SingleLayerParsedTfOperation>(<span class="keyword">this</span>, nodeDef, layer);</div><div class="line"><a name="l03265"></a><span class="lineno"> 3265</span> }</div><div class="line"><a name="l03266"></a><span class="lineno"> 3266</span> </div><div class="line"><a name="l03267"></a><span class="lineno"> 3267</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* TfParser::AddMultiplicationLayer(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef)</div><div class="line"><a name="l03268"></a><span class="lineno"> 3268</span> {</div><div class="line"><a name="l03269"></a><span class="lineno"> 3269</span>  std::vector<OutputOfParsedTfOperation> inputs = GetInputParsedTfOperationsChecked(nodeDef, 2);</div><div class="line"><a name="l03270"></a><span class="lineno"> 3270</span> </div><div class="line"><a name="l03271"></a><span class="lineno"> 3271</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#ae24e82cf1ae2a71c5cd976edfb192fc0">AddMultiplicationLayer</a>(nodeDef.name().c_str());</div><div class="line"><a name="l03272"></a><span class="lineno"> 3272</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input0Slot = &inputs[0].m_IndexedValue->ResolveArmnnOutputSlot(inputs[0].m_Index);</div><div class="line"><a name="l03273"></a><span class="lineno"> 3273</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>* input1Slot = &inputs[1].m_IndexedValue->ResolveArmnnOutputSlot(inputs[1].m_Index);</div><div class="line"><a name="l03274"></a><span class="lineno"> 3274</span> </div><div class="line"><a name="l03275"></a><span class="lineno"> 3275</span>  <span class="keyword">auto</span> <span class="keyword">const</span> input0NumDims = input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l03276"></a><span class="lineno"> 3276</span>  <span class="keyword">auto</span> <span class="keyword">const</span> input1NumDims = input1Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>().<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l03277"></a><span class="lineno"> 3277</span> </div><div class="line"><a name="l03278"></a><span class="lineno"> 3278</span>  <span class="keywordflow">if</span> (input0NumDims < input1NumDims)</div><div class="line"><a name="l03279"></a><span class="lineno"> 3279</span>  {</div><div class="line"><a name="l03280"></a><span class="lineno"> 3280</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> isNHWC = <span class="keyword">true</span>;</div><div class="line"><a name="l03281"></a><span class="lineno"> 3281</span>  input0Slot = AddBroadcastReshapeLayer(input1Slot, input0Slot, isNHWC, *m_Network, nodeDef);</div><div class="line"><a name="l03282"></a><span class="lineno"> 3282</span>  }</div><div class="line"><a name="l03283"></a><span class="lineno"> 3283</span>  <span class="keywordflow">if</span> (input1NumDims < input0NumDims)</div><div class="line"><a name="l03284"></a><span class="lineno"> 3284</span>  {</div><div class="line"><a name="l03285"></a><span class="lineno"> 3285</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> isNHWC = <span class="keyword">true</span>;</div><div class="line"><a name="l03286"></a><span class="lineno"> 3286</span>  input1Slot = AddBroadcastReshapeLayer(input0Slot, input1Slot, isNHWC, *m_Network, nodeDef);</div><div class="line"><a name="l03287"></a><span class="lineno"> 3287</span>  }</div><div class="line"><a name="l03288"></a><span class="lineno"> 3288</span> </div><div class="line"><a name="l03289"></a><span class="lineno"> 3289</span>  input0Slot-><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="l03290"></a><span class="lineno"> 3290</span>  input1Slot-><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>(1));</div><div class="line"><a name="l03291"></a><span class="lineno"> 3291</span> </div><div class="line"><a name="l03292"></a><span class="lineno"> 3292</span>  <span class="keywordflow">if</span> (input0NumDims < input1NumDims)</div><div class="line"><a name="l03293"></a><span class="lineno"> 3293</span>  {</div><div class="line"><a name="l03294"></a><span class="lineno"> 3294</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>(input1Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>());</div><div class="line"><a name="l03295"></a><span class="lineno"> 3295</span>  }</div><div class="line"><a name="l03296"></a><span class="lineno"> 3296</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l03297"></a><span class="lineno"> 3297</span>  {</div><div class="line"><a name="l03298"></a><span class="lineno"> 3298</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>(input0Slot-><a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">GetTensorInfo</a>());</div><div class="line"><a name="l03299"></a><span class="lineno"> 3299</span>  }</div><div class="line"><a name="l03300"></a><span class="lineno"> 3300</span>  <span class="keywordflow">return</span> layer;</div><div class="line"><a name="l03301"></a><span class="lineno"> 3301</span> }</div><div class="line"><a name="l03302"></a><span class="lineno"> 3302</span> </div><div class="line"><a name="l03303"></a><span class="lineno"> 3303</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* TfParser::AddFullyConnectedLayer(<span class="keyword">const</span> tensorflow::NodeDef& matMulNodeDef,</div><div class="line"><a name="l03304"></a><span class="lineno"> 3304</span>  <span class="keyword">const</span> tensorflow::NodeDef* addNodeDef, <span class="keyword">const</span> <span class="keywordtype">char</span>* armnnLayerName)</div><div class="line"><a name="l03305"></a><span class="lineno"> 3305</span> {</div><div class="line"><a name="l03306"></a><span class="lineno"> 3306</span>  <span class="comment">// Finds bias const (if applicable).</span></div><div class="line"><a name="l03307"></a><span class="lineno"> 3307</span>  ParsedConstTfOperation<float>* biasNode = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l03308"></a><span class="lineno"> 3308</span>  <span class="keywordflow">if</span> (addNodeDef != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l03309"></a><span class="lineno"> 3309</span>  {</div><div class="line"><a name="l03310"></a><span class="lineno"> 3310</span>  std::vector<OutputOfParsedTfOperation> addInputs = GetInputParsedTfOperationsChecked(*addNodeDef, 2);</div><div class="line"><a name="l03311"></a><span class="lineno"> 3311</span>  <span class="comment">// Finds our inputs.</span></div><div class="line"><a name="l03312"></a><span class="lineno"> 3312</span>  <span class="keywordflow">if</span> (HasParsedConstTensor<float>(addInputs[0].m_IndexedValue->GetNode().name()))</div><div class="line"><a name="l03313"></a><span class="lineno"> 3313</span>  {</div><div class="line"><a name="l03314"></a><span class="lineno"> 3314</span>  biasNode = boost::polymorphic_downcast<ParsedConstTfOperation<float>*>(addInputs[0].m_IndexedValue);</div><div class="line"><a name="l03315"></a><span class="lineno"> 3315</span>  }</div><div class="line"><a name="l03316"></a><span class="lineno"> 3316</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (HasParsedConstTensor<float>(addInputs[1].m_IndexedValue->GetNode().name()))</div><div class="line"><a name="l03317"></a><span class="lineno"> 3317</span>  {</div><div class="line"><a name="l03318"></a><span class="lineno"> 3318</span>  biasNode = boost::polymorphic_downcast<ParsedConstTfOperation<float>*>(addInputs[1].m_IndexedValue);</div><div class="line"><a name="l03319"></a><span class="lineno"> 3319</span>  }</div><div class="line"><a name="l03320"></a><span class="lineno"> 3320</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l03321"></a><span class="lineno"> 3321</span>  {</div><div class="line"><a name="l03322"></a><span class="lineno"> 3322</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03323"></a><span class="lineno"> 3323</span>  boost::str(</div><div class="line"><a name="l03324"></a><span class="lineno"> 3324</span>  boost::format(</div><div class="line"><a name="l03325"></a><span class="lineno"> 3325</span>  <span class="stringliteral">"ArmNN only supports fully connected layers with constant bias. "</span></div><div class="line"><a name="l03326"></a><span class="lineno"> 3326</span>  <span class="stringliteral">"Inputs %1% and %2%. AddNode %3%. MatMulNode %4% %5%"</span>)</div><div class="line"><a name="l03327"></a><span class="lineno"> 3327</span>  % addInputs[0].m_IndexedValue->GetNode().name()</div><div class="line"><a name="l03328"></a><span class="lineno"> 3328</span>  % addInputs[1].m_IndexedValue->GetNode().name()</div><div class="line"><a name="l03329"></a><span class="lineno"> 3329</span>  % addNodeDef->name()</div><div class="line"><a name="l03330"></a><span class="lineno"> 3330</span>  % matMulNodeDef.name()</div><div class="line"><a name="l03331"></a><span class="lineno"> 3331</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03332"></a><span class="lineno"> 3332</span>  }</div><div class="line"><a name="l03333"></a><span class="lineno"> 3333</span>  }</div><div class="line"><a name="l03334"></a><span class="lineno"> 3334</span> </div><div class="line"><a name="l03335"></a><span class="lineno"> 3335</span>  <span class="comment">// Finds matmul inputs.</span></div><div class="line"><a name="l03336"></a><span class="lineno"> 3336</span>  ParsedConstTfOperation<float>* weightNode = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l03337"></a><span class="lineno"> 3337</span>  ParsedTfOperation* inputNode = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l03338"></a><span class="lineno"> 3338</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputIdx = 0;</div><div class="line"><a name="l03339"></a><span class="lineno"> 3339</span>  std::vector<OutputOfParsedTfOperation> mulInputs = GetInputParsedTfOperationsChecked(matMulNodeDef, 2);</div><div class="line"><a name="l03340"></a><span class="lineno"> 3340</span>  <span class="keywordflow">if</span> (HasParsedConstTensor<float>(mulInputs[0].m_IndexedValue->GetNode().name()))</div><div class="line"><a name="l03341"></a><span class="lineno"> 3341</span>  {</div><div class="line"><a name="l03342"></a><span class="lineno"> 3342</span>  weightNode = boost::polymorphic_downcast<ParsedConstTfOperation<float>*>(mulInputs[0].m_IndexedValue);</div><div class="line"><a name="l03343"></a><span class="lineno"> 3343</span>  inputNode = mulInputs[1].m_IndexedValue;</div><div class="line"><a name="l03344"></a><span class="lineno"> 3344</span>  inputIdx = mulInputs[1].m_Index;</div><div class="line"><a name="l03345"></a><span class="lineno"> 3345</span>  }</div><div class="line"><a name="l03346"></a><span class="lineno"> 3346</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (HasParsedConstTensor<float>(mulInputs[1].m_IndexedValue->GetNode().name()))</div><div class="line"><a name="l03347"></a><span class="lineno"> 3347</span>  {</div><div class="line"><a name="l03348"></a><span class="lineno"> 3348</span>  weightNode = boost::polymorphic_downcast<ParsedConstTfOperation<float>*>(mulInputs[1].m_IndexedValue);</div><div class="line"><a name="l03349"></a><span class="lineno"> 3349</span>  inputNode = mulInputs[0].m_IndexedValue;</div><div class="line"><a name="l03350"></a><span class="lineno"> 3350</span>  inputIdx = mulInputs[0].m_Index;</div><div class="line"><a name="l03351"></a><span class="lineno"> 3351</span>  }</div><div class="line"><a name="l03352"></a><span class="lineno"> 3352</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l03353"></a><span class="lineno"> 3353</span>  {</div><div class="line"><a name="l03354"></a><span class="lineno"> 3354</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03355"></a><span class="lineno"> 3355</span>  boost::str(</div><div class="line"><a name="l03356"></a><span class="lineno"> 3356</span>  boost::format(</div><div class="line"><a name="l03357"></a><span class="lineno"> 3357</span>  <span class="stringliteral">"ArmNN only supports fully connected layers with constant weights. "</span></div><div class="line"><a name="l03358"></a><span class="lineno"> 3358</span>  <span class="stringliteral">"Inputs %1% and %2%. MatMulNode %3% %4%"</span>)</div><div class="line"><a name="l03359"></a><span class="lineno"> 3359</span>  % mulInputs[0].m_IndexedValue->GetNode().name()</div><div class="line"><a name="l03360"></a><span class="lineno"> 3360</span>  % mulInputs[1].m_IndexedValue->GetNode().name()</div><div class="line"><a name="l03361"></a><span class="lineno"> 3361</span>  % matMulNodeDef.name()</div><div class="line"><a name="l03362"></a><span class="lineno"> 3362</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03363"></a><span class="lineno"> 3363</span>  }</div><div class="line"><a name="l03364"></a><span class="lineno"> 3364</span> </div><div class="line"><a name="l03365"></a><span class="lineno"> 3365</span>  std::vector<float> weightTensorData;</div><div class="line"><a name="l03366"></a><span class="lineno"> 3366</span>  <span class="comment">// Handles weight.</span></div><div class="line"><a name="l03367"></a><span class="lineno"> 3367</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights = weightNode->GetConstTensor(weightTensorData);</div><div class="line"><a name="l03368"></a><span class="lineno"> 3368</span> </div><div class="line"><a name="l03369"></a><span class="lineno"> 3369</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a> desc;</div><div class="line"><a name="l03370"></a><span class="lineno"> 3370</span>  desc.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = addNodeDef != <span class="keyword">nullptr</span>;</div><div class="line"><a name="l03371"></a><span class="lineno"> 3371</span> </div><div class="line"><a name="l03372"></a><span class="lineno"> 3372</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l03373"></a><span class="lineno"> 3373</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a> optionalBiases;</div><div class="line"><a name="l03374"></a><span class="lineno"> 3374</span>  std::vector<float> biasTensorData;</div><div class="line"><a name="l03375"></a><span class="lineno"> 3375</span>  <span class="comment">// Makes the layer.</span></div><div class="line"><a name="l03376"></a><span class="lineno"> 3376</span>  <span class="keywordflow">if</span> (addNodeDef != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l03377"></a><span class="lineno"> 3377</span>  {</div><div class="line"><a name="l03378"></a><span class="lineno"> 3378</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> biases = biasNode->GetConstTensor(biasTensorData);</div><div class="line"><a name="l03379"></a><span class="lineno"> 3379</span> </div><div class="line"><a name="l03380"></a><span class="lineno"> 3380</span>  <span class="keywordflow">if</span> (weights.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1] != biases.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0])</div><div class="line"><a name="l03381"></a><span class="lineno"> 3381</span>  {</div><div class="line"><a name="l03382"></a><span class="lineno"> 3382</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03383"></a><span class="lineno"> 3383</span>  boost::str(</div><div class="line"><a name="l03384"></a><span class="lineno"> 3384</span>  boost::format(</div><div class="line"><a name="l03385"></a><span class="lineno"> 3385</span>  <span class="stringliteral">"Shape of matmul weights and bias do not match. "</span></div><div class="line"><a name="l03386"></a><span class="lineno"> 3386</span>  <span class="stringliteral">"AddNode %1%. MatMulNode %2% %3%"</span>)</div><div class="line"><a name="l03387"></a><span class="lineno"> 3387</span>  % addNodeDef->name()</div><div class="line"><a name="l03388"></a><span class="lineno"> 3388</span>  % matMulNodeDef.name()</div><div class="line"><a name="l03389"></a><span class="lineno"> 3389</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03390"></a><span class="lineno"> 3390</span>  }</div><div class="line"><a name="l03391"></a><span class="lineno"> 3391</span> </div><div class="line"><a name="l03392"></a><span class="lineno"> 3392</span>  optionalBiases = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(biases);</div><div class="line"><a name="l03393"></a><span class="lineno"> 3393</span>  }</div><div class="line"><a name="l03394"></a><span class="lineno"> 3394</span>  layer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#a4839e4ec3f07974c57ca2c856b40cd57">AddFullyConnectedLayer</a>(desc, weights, optionalBiases, armnnLayerName);</div><div class="line"><a name="l03395"></a><span class="lineno"> 3395</span> </div><div class="line"><a name="l03396"></a><span class="lineno"> 3396</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l03397"></a><span class="lineno"> 3397</span> </div><div class="line"><a name="l03398"></a><span class="lineno"> 3398</span>  inputNode->ResolveArmnnOutputSlot(inputIdx).Connect(layer->GetInputSlot(0));</div><div class="line"><a name="l03399"></a><span class="lineno"> 3399</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batches = inputNode->ResolveArmnnOutputSlot(inputIdx).GetTensorInfo().GetShape()[0];</div><div class="line"><a name="l03400"></a><span class="lineno"> 3400</span> </div><div class="line"><a name="l03401"></a><span class="lineno"> 3401</span>  <span class="comment">// Handles output.</span></div><div class="line"><a name="l03402"></a><span class="lineno"> 3402</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo({ batches, weights.<a class="code" href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1] }, DataType::Float32);</div><div class="line"><a name="l03403"></a><span class="lineno"> 3403</span>  layer->GetOutputSlot(0).SetTensorInfo(outputInfo);</div><div class="line"><a name="l03404"></a><span class="lineno"> 3404</span>  <span class="keywordflow">return</span> layer;</div><div class="line"><a name="l03405"></a><span class="lineno"> 3405</span> }</div><div class="line"><a name="l03406"></a><span class="lineno"> 3406</span> </div><div class="line"><a name="l03407"></a><span class="lineno"> 3407</span> <span class="keywordtype">void</span> TfParser::LoadNodeDef(<span class="keyword">const</span> tensorflow::NodeDef& nodeDef, <span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l03408"></a><span class="lineno"> 3408</span> {</div><div class="line"><a name="l03409"></a><span class="lineno"> 3409</span>  <span class="comment">// Gets the type of the node (assume float).</span></div><div class="line"><a name="l03410"></a><span class="lineno"> 3410</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">tensorflow::DataType</a> type = tensorflow::DT_FLOAT;</div><div class="line"><a name="l03411"></a><span class="lineno"> 3411</span>  <span class="keywordflow">if</span> (nodeDef.attr().count(<span class="stringliteral">"T"</span>) != 0)</div><div class="line"><a name="l03412"></a><span class="lineno"> 3412</span>  {</div><div class="line"><a name="l03413"></a><span class="lineno"> 3413</span>  <span class="keyword">auto</span> attr = nodeDef.attr().at(<span class="stringliteral">"T"</span>);</div><div class="line"><a name="l03414"></a><span class="lineno"> 3414</span>  type = attr.type();</div><div class="line"><a name="l03415"></a><span class="lineno"> 3415</span>  }</div><div class="line"><a name="l03416"></a><span class="lineno"> 3416</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (nodeDef.attr().count(<span class="stringliteral">"dtype"</span>) != 0)</div><div class="line"><a name="l03417"></a><span class="lineno"> 3417</span>  {</div><div class="line"><a name="l03418"></a><span class="lineno"> 3418</span>  <span class="keyword">auto</span> attr = nodeDef.attr().at(<span class="stringliteral">"dtype"</span>);</div><div class="line"><a name="l03419"></a><span class="lineno"> 3419</span>  type = attr.type();</div><div class="line"><a name="l03420"></a><span class="lineno"> 3420</span>  }</div><div class="line"><a name="l03421"></a><span class="lineno"> 3421</span> </div><div class="line"><a name="l03422"></a><span class="lineno"> 3422</span>  <span class="keywordflow">if</span> ((type != tensorflow::DT_FLOAT && type != tensorflow::DT_INT32) && nodeDef.op() != <span class="stringliteral">"Const"</span>)</div><div class="line"><a name="l03423"></a><span class="lineno"> 3423</span>  {</div><div class="line"><a name="l03424"></a><span class="lineno"> 3424</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03425"></a><span class="lineno"> 3425</span>  boost::str(</div><div class="line"><a name="l03426"></a><span class="lineno"> 3426</span>  boost::format(</div><div class="line"><a name="l03427"></a><span class="lineno"> 3427</span>  <span class="stringliteral">"Currently only FLOAT and INT32 are supported for tensorflow nodes (apart from Const). "</span></div><div class="line"><a name="l03428"></a><span class="lineno"> 3428</span>  <span class="stringliteral">"Got %1% for Node %2% %3%"</span>)</div><div class="line"><a name="l03429"></a><span class="lineno"> 3429</span>  % tensorflow::DataType_Name(type)</div><div class="line"><a name="l03430"></a><span class="lineno"> 3430</span>  % nodeDef.name()</div><div class="line"><a name="l03431"></a><span class="lineno"> 3431</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03432"></a><span class="lineno"> 3432</span>  }</div><div class="line"><a name="l03433"></a><span class="lineno"> 3433</span> </div><div class="line"><a name="l03434"></a><span class="lineno"> 3434</span>  <span class="keyword">const</span> std::string& operation = nodeDef.op();</div><div class="line"><a name="l03435"></a><span class="lineno"> 3435</span>  <span class="keyword">auto</span> itControlInput = std::find(m_ControlInputs.begin(), m_ControlInputs.end(), operation);</div><div class="line"><a name="l03436"></a><span class="lineno"> 3436</span>  <span class="keywordflow">if</span> (itControlInput != m_ControlInputs.end())</div><div class="line"><a name="l03437"></a><span class="lineno"> 3437</span>  {</div><div class="line"><a name="l03438"></a><span class="lineno"> 3438</span>  <span class="comment">// We currently allow Control Input from TensorFlow graph but we ignore them from ArmNN graph.</span></div><div class="line"><a name="l03439"></a><span class="lineno"> 3439</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l03440"></a><span class="lineno"> 3440</span>  }</div><div class="line"><a name="l03441"></a><span class="lineno"> 3441</span>  <span class="keyword">auto</span> it = ms_OperationNameToParsingFunctions.find(operation);</div><div class="line"><a name="l03442"></a><span class="lineno"> 3442</span>  <span class="keywordflow">if</span> (it != ms_OperationNameToParsingFunctions.end())</div><div class="line"><a name="l03443"></a><span class="lineno"> 3443</span>  {</div><div class="line"><a name="l03444"></a><span class="lineno"> 3444</span>  <span class="keyword">auto</span> func = it->second;</div><div class="line"><a name="l03445"></a><span class="lineno"> 3445</span>  <a class="code" href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">ParsedTfOperationPtr</a> parsedTfOperation = (this->*func)(nodeDef, graphDef);</div><div class="line"><a name="l03446"></a><span class="lineno"> 3446</span>  ParsedTfOperation* parsedTfOperationRaw = parsedTfOperation.get();</div><div class="line"><a name="l03447"></a><span class="lineno"> 3447</span> </div><div class="line"><a name="l03448"></a><span class="lineno"> 3448</span>  <span class="comment">// Stores the parsed operation so that dependent layers can connect to it.</span></div><div class="line"><a name="l03449"></a><span class="lineno"> 3449</span>  <span class="keyword">auto</span> it = m_ParsedTfOperations.find(nodeDef.name());</div><div class="line"><a name="l03450"></a><span class="lineno"> 3450</span>  <span class="keywordflow">if</span> (it != m_ParsedTfOperations.end())</div><div class="line"><a name="l03451"></a><span class="lineno"> 3451</span>  {</div><div class="line"><a name="l03452"></a><span class="lineno"> 3452</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(boost::format(<span class="stringliteral">"Name %1% used by more than one node"</span>) % nodeDef.name()));</div><div class="line"><a name="l03453"></a><span class="lineno"> 3453</span>  }</div><div class="line"><a name="l03454"></a><span class="lineno"> 3454</span>  m_ParsedTfOperations[nodeDef.name()] = std::move(parsedTfOperation);</div><div class="line"><a name="l03455"></a><span class="lineno"> 3455</span> </div><div class="line"><a name="l03456"></a><span class="lineno"> 3456</span>  <span class="comment">// If this node was requested as an output from the network, then adds an ArmNN output layer.</span></div><div class="line"><a name="l03457"></a><span class="lineno"> 3457</span>  <span class="keywordflow">if</span> (std::find(m_RequestedOutputs.begin(), m_RequestedOutputs.end(), nodeDef.name()) !=</div><div class="line"><a name="l03458"></a><span class="lineno"> 3458</span>  m_RequestedOutputs.end())</div><div class="line"><a name="l03459"></a><span class="lineno"> 3459</span>  {</div><div class="line"><a name="l03460"></a><span class="lineno"> 3460</span>  <span class="keyword">auto</span> outId = ParseOutputId(nodeDef.name());</div><div class="line"><a name="l03461"></a><span class="lineno"> 3461</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> layerId = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a>>(m_NetworkOutputsBindingInfo.size());</div><div class="line"><a name="l03462"></a><span class="lineno"> 3462</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">IOutputSlot</a>& prevSlot = parsedTfOperationRaw->ResolveArmnnOutputSlot(outId.m_Index);</div><div class="line"><a name="l03463"></a><span class="lineno"> 3463</span> </div><div class="line"><a name="l03464"></a><span class="lineno"> 3464</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> tensorInfo = prevSlot.GetTensorInfo();</div><div class="line"><a name="l03465"></a><span class="lineno"> 3465</span> </div><div class="line"><a name="l03466"></a><span class="lineno"> 3466</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* outputLayer = m_Network-><a class="code" href="classarmnn_1_1_i_network.xhtml#ad8582fba2ebeb65da43a56bc22d4f88b">AddOutputLayer</a>(layerId, nodeDef.name().c_str());</div><div class="line"><a name="l03467"></a><span class="lineno"> 3467</span> </div><div class="line"><a name="l03468"></a><span class="lineno"> 3468</span>  prevSlot.Connect(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l03469"></a><span class="lineno"> 3469</span> </div><div class="line"><a name="l03470"></a><span class="lineno"> 3470</span>  TrackOutputBinding(outputLayer, layerId, tensorInfo);</div><div class="line"><a name="l03471"></a><span class="lineno"> 3471</span>  }</div><div class="line"><a name="l03472"></a><span class="lineno"> 3472</span>  }</div><div class="line"><a name="l03473"></a><span class="lineno"> 3473</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l03474"></a><span class="lineno"> 3474</span>  {</div><div class="line"><a name="l03475"></a><span class="lineno"> 3475</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03476"></a><span class="lineno"> 3476</span>  boost::str(</div><div class="line"><a name="l03477"></a><span class="lineno"> 3477</span>  boost::format(</div><div class="line"><a name="l03478"></a><span class="lineno"> 3478</span>  <span class="stringliteral">"Unsupported operation %1% in tensorflow::GraphDef %2%"</span>)</div><div class="line"><a name="l03479"></a><span class="lineno"> 3479</span>  % operation</div><div class="line"><a name="l03480"></a><span class="lineno"> 3480</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03481"></a><span class="lineno"> 3481</span>  }</div><div class="line"><a name="l03482"></a><span class="lineno"> 3482</span> }</div><div class="line"><a name="l03483"></a><span class="lineno"> 3483</span> </div><div class="line"><a name="l03484"></a><span class="lineno"> 3484</span> <span class="keywordtype">void</span> TfParser::LoadGraphDef(<span class="keyword">const</span> tensorflow::GraphDef& graphDef)</div><div class="line"><a name="l03485"></a><span class="lineno"> 3485</span> {</div><div class="line"><a name="l03486"></a><span class="lineno"> 3486</span>  <span class="comment">// Adds all nodes to our map.</span></div><div class="line"><a name="l03487"></a><span class="lineno"> 3487</span>  m_NodesByName.clear();</div><div class="line"><a name="l03488"></a><span class="lineno"> 3488</span>  m_NetworkInputsBindingInfo.clear();</div><div class="line"><a name="l03489"></a><span class="lineno"> 3489</span>  m_NetworkOutputsBindingInfo.clear();</div><div class="line"><a name="l03490"></a><span class="lineno"> 3490</span> </div><div class="line"><a name="l03491"></a><span class="lineno"> 3491</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < graphDef.node_size(); ++i)</div><div class="line"><a name="l03492"></a><span class="lineno"> 3492</span>  {</div><div class="line"><a name="l03493"></a><span class="lineno"> 3493</span>  <span class="keyword">const</span> tensorflow::NodeDef& node = graphDef.node(i);</div><div class="line"><a name="l03494"></a><span class="lineno"> 3494</span>  m_NodesByName[node.name()] = &node;</div><div class="line"><a name="l03495"></a><span class="lineno"> 3495</span>  }</div><div class="line"><a name="l03496"></a><span class="lineno"> 3496</span> </div><div class="line"><a name="l03497"></a><span class="lineno"> 3497</span>  <span class="comment">// Checks that the input nodes the user has requested exist.</span></div><div class="line"><a name="l03498"></a><span class="lineno"> 3498</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& pair : m_InputShapes)</div><div class="line"><a name="l03499"></a><span class="lineno"> 3499</span>  {</div><div class="line"><a name="l03500"></a><span class="lineno"> 3500</span>  <span class="keyword">const</span> std::string& requestedInputName = pair.first;</div><div class="line"><a name="l03501"></a><span class="lineno"> 3501</span>  <span class="keyword">auto</span> nodeIt = m_NodesByName.find(requestedInputName);</div><div class="line"><a name="l03502"></a><span class="lineno"> 3502</span>  <span class="keywordflow">if</span> (nodeIt == m_NodesByName.end())</div><div class="line"><a name="l03503"></a><span class="lineno"> 3503</span>  {</div><div class="line"><a name="l03504"></a><span class="lineno"> 3504</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03505"></a><span class="lineno"> 3505</span>  boost::str(</div><div class="line"><a name="l03506"></a><span class="lineno"> 3506</span>  boost::format(</div><div class="line"><a name="l03507"></a><span class="lineno"> 3507</span>  <span class="stringliteral">"Couldn't find requested input node '%1%' in graph %2%"</span>)</div><div class="line"><a name="l03508"></a><span class="lineno"> 3508</span>  % requestedInputName</div><div class="line"><a name="l03509"></a><span class="lineno"> 3509</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03510"></a><span class="lineno"> 3510</span>  }</div><div class="line"><a name="l03511"></a><span class="lineno"> 3511</span>  }</div><div class="line"><a name="l03512"></a><span class="lineno"> 3512</span> </div><div class="line"><a name="l03513"></a><span class="lineno"> 3513</span>  <span class="comment">// Finds the output nodes the user requested.</span></div><div class="line"><a name="l03514"></a><span class="lineno"> 3514</span>  std::vector<const tensorflow::NodeDef*> targetNodes;</div><div class="line"><a name="l03515"></a><span class="lineno"> 3515</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> std::string& requestedOutputName : m_RequestedOutputs)</div><div class="line"><a name="l03516"></a><span class="lineno"> 3516</span>  {</div><div class="line"><a name="l03517"></a><span class="lineno"> 3517</span>  <span class="keyword">auto</span> nodeIt = m_NodesByName.find(requestedOutputName);</div><div class="line"><a name="l03518"></a><span class="lineno"> 3518</span>  <span class="keywordflow">if</span> (nodeIt == m_NodesByName.end())</div><div class="line"><a name="l03519"></a><span class="lineno"> 3519</span>  {</div><div class="line"><a name="l03520"></a><span class="lineno"> 3520</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03521"></a><span class="lineno"> 3521</span>  boost::str(</div><div class="line"><a name="l03522"></a><span class="lineno"> 3522</span>  boost::format(</div><div class="line"><a name="l03523"></a><span class="lineno"> 3523</span>  <span class="stringliteral">"Couldn't find requested output node '%1%' in graph %2%"</span>)</div><div class="line"><a name="l03524"></a><span class="lineno"> 3524</span>  % requestedOutputName</div><div class="line"><a name="l03525"></a><span class="lineno"> 3525</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03526"></a><span class="lineno"> 3526</span>  }</div><div class="line"><a name="l03527"></a><span class="lineno"> 3527</span>  targetNodes.push_back(nodeIt->second);</div><div class="line"><a name="l03528"></a><span class="lineno"> 3528</span>  }</div><div class="line"><a name="l03529"></a><span class="lineno"> 3529</span> </div><div class="line"><a name="l03530"></a><span class="lineno"> 3530</span>  <span class="comment">// Sorts them into a linear ordering such that all inputs of a node are before the node itself.</span></div><div class="line"><a name="l03531"></a><span class="lineno"> 3531</span>  std::vector<const tensorflow::NodeDef*> sortedNodes;</div><div class="line"><a name="l03532"></a><span class="lineno"> 3532</span>  <span class="keywordflow">if</span> (!armnnUtils::GraphTopologicalSort<const tensorflow::NodeDef*>(</div><div class="line"><a name="l03533"></a><span class="lineno"> 3533</span>  targetNodes,</div><div class="line"><a name="l03534"></a><span class="lineno"> 3534</span>  [<span class="keyword">this</span>](<span class="keyword">const</span> tensorflow::NodeDef* node)</div><div class="line"><a name="l03535"></a><span class="lineno"> 3535</span>  {</div><div class="line"><a name="l03536"></a><span class="lineno"> 3536</span>  <span class="keyword">auto</span> outputs = GetTfInputNodes(*node);</div><div class="line"><a name="l03537"></a><span class="lineno"> 3537</span>  std::vector<const tensorflow::NodeDef*> nodesOnly;</div><div class="line"><a name="l03538"></a><span class="lineno"> 3538</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span> & o : outputs) {</div><div class="line"><a name="l03539"></a><span class="lineno"> 3539</span>  nodesOnly.push_back(o.m_IndexedValue);</div><div class="line"><a name="l03540"></a><span class="lineno"> 3540</span>  }</div><div class="line"><a name="l03541"></a><span class="lineno"> 3541</span>  <span class="keywordflow">return</span> nodesOnly;</div><div class="line"><a name="l03542"></a><span class="lineno"> 3542</span>  },</div><div class="line"><a name="l03543"></a><span class="lineno"> 3543</span>  sortedNodes))</div><div class="line"><a name="l03544"></a><span class="lineno"> 3544</span>  {</div><div class="line"><a name="l03545"></a><span class="lineno"> 3545</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03546"></a><span class="lineno"> 3546</span>  boost::str(</div><div class="line"><a name="l03547"></a><span class="lineno"> 3547</span>  boost::format(</div><div class="line"><a name="l03548"></a><span class="lineno"> 3548</span>  <span class="stringliteral">"Cycle detected in graph %1%"</span>)</div><div class="line"><a name="l03549"></a><span class="lineno"> 3549</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03550"></a><span class="lineno"> 3550</span>  }</div><div class="line"><a name="l03551"></a><span class="lineno"> 3551</span> </div><div class="line"><a name="l03552"></a><span class="lineno"> 3552</span>  <span class="comment">// Parses each node in order, knowing that all inputs of a node will be processed before the node itself.</span></div><div class="line"><a name="l03553"></a><span class="lineno"> 3553</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& it : sortedNodes)</div><div class="line"><a name="l03554"></a><span class="lineno"> 3554</span>  {</div><div class="line"><a name="l03555"></a><span class="lineno"> 3555</span>  <span class="keyword">const</span> tensorflow::NodeDef& currentNode = *it;</div><div class="line"><a name="l03556"></a><span class="lineno"> 3556</span>  LoadNodeDef(currentNode, graphDef);</div><div class="line"><a name="l03557"></a><span class="lineno"> 3557</span>  }</div><div class="line"><a name="l03558"></a><span class="lineno"> 3558</span> }</div><div class="line"><a name="l03559"></a><span class="lineno"> 3559</span> </div><div class="line"><a name="l03560"></a><span class="lineno"><a class="line" href="classarmnn_tf_parser_1_1_tf_parser.xhtml#ae2d544957c50461d305b2517581c86d0"> 3560</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml#ae2d544957c50461d305b2517581c86d0">TfParser::CreateNetworkFromTextFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile,</div><div class="line"><a name="l03561"></a><span class="lineno"> 3561</span>  <span class="keyword">const</span> std::map<std::string, TensorShape>& inputShapes,</div><div class="line"><a name="l03562"></a><span class="lineno"> 3562</span>  <span class="keyword">const</span> std::vector<std::string>& requestedOutputs)</div><div class="line"><a name="l03563"></a><span class="lineno"> 3563</span> {</div><div class="line"><a name="l03564"></a><span class="lineno"> 3564</span>  FILE* fd = fopen(graphFile, <span class="stringliteral">"r"</span>);</div><div class="line"><a name="l03565"></a><span class="lineno"> 3565</span> </div><div class="line"><a name="l03566"></a><span class="lineno"> 3566</span>  <span class="keywordflow">if</span> (fd == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l03567"></a><span class="lineno"> 3567</span>  {</div><div class="line"><a name="l03568"></a><span class="lineno"> 3568</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_file_not_found_exception.xhtml">FileNotFoundException</a>(</div><div class="line"><a name="l03569"></a><span class="lineno"> 3569</span>  boost::str(</div><div class="line"><a name="l03570"></a><span class="lineno"> 3570</span>  boost::format(</div><div class="line"><a name="l03571"></a><span class="lineno"> 3571</span>  <span class="stringliteral">"Graph file %1% failed to open %2%"</span>)</div><div class="line"><a name="l03572"></a><span class="lineno"> 3572</span>  % graphFile</div><div class="line"><a name="l03573"></a><span class="lineno"> 3573</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03574"></a><span class="lineno"> 3574</span>  }</div><div class="line"><a name="l03575"></a><span class="lineno"> 3575</span> </div><div class="line"><a name="l03576"></a><span class="lineno"> 3576</span>  <span class="comment">// Parses the file into a message.</span></div><div class="line"><a name="l03577"></a><span class="lineno"> 3577</span>  tensorflow::GraphDef graphDef;</div><div class="line"><a name="l03578"></a><span class="lineno"> 3578</span>  <span class="keyword">auto</span> input = <span class="keyword">new</span> google::protobuf::io::FileInputStream(fileno(fd));</div><div class="line"><a name="l03579"></a><span class="lineno"> 3579</span>  <span class="keywordtype">bool</span> success = google::protobuf::TextFormat::Parse(input, &graphDef);</div><div class="line"><a name="l03580"></a><span class="lineno"> 3580</span>  <span class="keyword">delete</span> input;</div><div class="line"><a name="l03581"></a><span class="lineno"> 3581</span>  fclose(fd);</div><div class="line"><a name="l03582"></a><span class="lineno"> 3582</span> </div><div class="line"><a name="l03583"></a><span class="lineno"> 3583</span>  <span class="keywordflow">if</span> (!success)</div><div class="line"><a name="l03584"></a><span class="lineno"> 3584</span>  {</div><div class="line"><a name="l03585"></a><span class="lineno"> 3585</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03586"></a><span class="lineno"> 3586</span>  boost::str(</div><div class="line"><a name="l03587"></a><span class="lineno"> 3587</span>  boost::format(</div><div class="line"><a name="l03588"></a><span class="lineno"> 3588</span>  <span class="stringliteral">"Failed to parse graph file %1%"</span>)</div><div class="line"><a name="l03589"></a><span class="lineno"> 3589</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03590"></a><span class="lineno"> 3590</span>  }</div><div class="line"><a name="l03591"></a><span class="lineno"> 3591</span> </div><div class="line"><a name="l03592"></a><span class="lineno"> 3592</span>  <span class="keywordflow">return</span> CreateNetworkFromGraphDef(graphDef, inputShapes, requestedOutputs);</div><div class="line"><a name="l03593"></a><span class="lineno"> 3593</span> }</div><div class="line"><a name="l03594"></a><span class="lineno"> 3594</span> </div><div class="line"><a name="l03595"></a><span class="lineno"><a class="line" href="classarmnn_tf_parser_1_1_tf_parser.xhtml#acd82aa5171feb1c852506964f3c5254b"> 3595</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml#acd82aa5171feb1c852506964f3c5254b">TfParser::CreateNetworkFromString</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* protoText,</div><div class="line"><a name="l03596"></a><span class="lineno"> 3596</span>  <span class="keyword">const</span> std::map<std::string, TensorShape>& inputShapes,</div><div class="line"><a name="l03597"></a><span class="lineno"> 3597</span>  <span class="keyword">const</span> std::vector<std::string>& requestedOutputs)</div><div class="line"><a name="l03598"></a><span class="lineno"> 3598</span> {</div><div class="line"><a name="l03599"></a><span class="lineno"> 3599</span>  <span class="comment">// Parses the string into a message.</span></div><div class="line"><a name="l03600"></a><span class="lineno"> 3600</span>  tensorflow::GraphDef graphDef;</div><div class="line"><a name="l03601"></a><span class="lineno"> 3601</span>  <span class="keywordtype">bool</span> success = google::protobuf::TextFormat::ParseFromString(protoText, &graphDef);</div><div class="line"><a name="l03602"></a><span class="lineno"> 3602</span> </div><div class="line"><a name="l03603"></a><span class="lineno"> 3603</span>  <span class="keywordflow">if</span> (!success)</div><div class="line"><a name="l03604"></a><span class="lineno"> 3604</span>  {</div><div class="line"><a name="l03605"></a><span class="lineno"> 3605</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03606"></a><span class="lineno"> 3606</span>  boost::str(</div><div class="line"><a name="l03607"></a><span class="lineno"> 3607</span>  boost::format(</div><div class="line"><a name="l03608"></a><span class="lineno"> 3608</span>  <span class="stringliteral">"Failed to parse graph file %1%"</span>)</div><div class="line"><a name="l03609"></a><span class="lineno"> 3609</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03610"></a><span class="lineno"> 3610</span>  }</div><div class="line"><a name="l03611"></a><span class="lineno"> 3611</span> </div><div class="line"><a name="l03612"></a><span class="lineno"> 3612</span>  <span class="keywordflow">return</span> CreateNetworkFromGraphDef(graphDef, inputShapes, requestedOutputs);</div><div class="line"><a name="l03613"></a><span class="lineno"> 3613</span> }</div><div class="line"><a name="l03614"></a><span class="lineno"> 3614</span> </div><div class="line"><a name="l03615"></a><span class="lineno"><a class="line" href="classarmnn_tf_parser_1_1_tf_parser.xhtml#afb0edadd00c78430efbdc02844ef379a"> 3615</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml#afb0edadd00c78430efbdc02844ef379a">TfParser::CreateNetworkFromBinaryFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile,</div><div class="line"><a name="l03616"></a><span class="lineno"> 3616</span>  <span class="keyword">const</span> std::map<std::string, TensorShape>& inputShapes,</div><div class="line"><a name="l03617"></a><span class="lineno"> 3617</span>  <span class="keyword">const</span> std::vector<std::string>& requestedOutputs)</div><div class="line"><a name="l03618"></a><span class="lineno"> 3618</span> {</div><div class="line"><a name="l03619"></a><span class="lineno"> 3619</span>  FILE* fd = fopen(graphFile, <span class="stringliteral">"rb"</span>);</div><div class="line"><a name="l03620"></a><span class="lineno"> 3620</span> </div><div class="line"><a name="l03621"></a><span class="lineno"> 3621</span>  <span class="keywordflow">if</span> (fd == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l03622"></a><span class="lineno"> 3622</span>  {</div><div class="line"><a name="l03623"></a><span class="lineno"> 3623</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_file_not_found_exception.xhtml">FileNotFoundException</a>(</div><div class="line"><a name="l03624"></a><span class="lineno"> 3624</span>  boost::str(</div><div class="line"><a name="l03625"></a><span class="lineno"> 3625</span>  boost::format(</div><div class="line"><a name="l03626"></a><span class="lineno"> 3626</span>  <span class="stringliteral">"Graph file %1% failed to open %2%"</span>)</div><div class="line"><a name="l03627"></a><span class="lineno"> 3627</span>  % graphFile</div><div class="line"><a name="l03628"></a><span class="lineno"> 3628</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03629"></a><span class="lineno"> 3629</span>  }</div><div class="line"><a name="l03630"></a><span class="lineno"> 3630</span> </div><div class="line"><a name="l03631"></a><span class="lineno"> 3631</span>  <span class="comment">// Parses the file into a message.</span></div><div class="line"><a name="l03632"></a><span class="lineno"> 3632</span>  tensorflow::GraphDef graphDef;</div><div class="line"><a name="l03633"></a><span class="lineno"> 3633</span> </div><div class="line"><a name="l03634"></a><span class="lineno"> 3634</span>  google::protobuf::io::FileInputStream inStream(fileno(fd));</div><div class="line"><a name="l03635"></a><span class="lineno"> 3635</span>  google::protobuf::io::CodedInputStream codedStream(&inStream);</div><div class="line"><a name="l03636"></a><span class="lineno"> 3636</span>  codedStream.SetTotalBytesLimit(INT_MAX, INT_MAX);</div><div class="line"><a name="l03637"></a><span class="lineno"> 3637</span>  <span class="keywordtype">bool</span> success = graphDef.ParseFromCodedStream(&codedStream);</div><div class="line"><a name="l03638"></a><span class="lineno"> 3638</span>  fclose(fd);</div><div class="line"><a name="l03639"></a><span class="lineno"> 3639</span> </div><div class="line"><a name="l03640"></a><span class="lineno"> 3640</span>  <span class="keywordflow">if</span> (!success)</div><div class="line"><a name="l03641"></a><span class="lineno"> 3641</span>  {</div><div class="line"><a name="l03642"></a><span class="lineno"> 3642</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03643"></a><span class="lineno"> 3643</span>  boost::str(</div><div class="line"><a name="l03644"></a><span class="lineno"> 3644</span>  boost::format(</div><div class="line"><a name="l03645"></a><span class="lineno"> 3645</span>  <span class="stringliteral">"Failed to parse protobuf file %1% %2%"</span>)</div><div class="line"><a name="l03646"></a><span class="lineno"> 3646</span>  % graphFile</div><div class="line"><a name="l03647"></a><span class="lineno"> 3647</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03648"></a><span class="lineno"> 3648</span>  }</div><div class="line"><a name="l03649"></a><span class="lineno"> 3649</span> </div><div class="line"><a name="l03650"></a><span class="lineno"> 3650</span>  <span class="keywordflow">return</span> CreateNetworkFromGraphDef(graphDef, inputShapes, requestedOutputs);</div><div class="line"><a name="l03651"></a><span class="lineno"> 3651</span> }</div><div class="line"><a name="l03652"></a><span class="lineno"> 3652</span> </div><div class="line"><a name="l03653"></a><span class="lineno"> 3653</span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> TfParser::CreateNetworkFromGraphDef(<span class="keyword">const</span> tensorflow::GraphDef& graphDef,</div><div class="line"><a name="l03654"></a><span class="lineno"> 3654</span>  <span class="keyword">const</span> std::map<std::string, TensorShape>& inputShapes,</div><div class="line"><a name="l03655"></a><span class="lineno"> 3655</span>  <span class="keyword">const</span> std::vector<std::string>& requestedOutputs)</div><div class="line"><a name="l03656"></a><span class="lineno"> 3656</span> {</div><div class="line"><a name="l03657"></a><span class="lineno"> 3657</span>  m_Network = INetwork::Create();</div><div class="line"><a name="l03658"></a><span class="lineno"> 3658</span> </div><div class="line"><a name="l03659"></a><span class="lineno"> 3659</span>  m_InputShapes = inputShapes;</div><div class="line"><a name="l03660"></a><span class="lineno"> 3660</span>  <span class="keywordflow">if</span> (requestedOutputs.size() == 0)</div><div class="line"><a name="l03661"></a><span class="lineno"> 3661</span>  {</div><div class="line"><a name="l03662"></a><span class="lineno"> 3662</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03663"></a><span class="lineno"> 3663</span>  boost::str(</div><div class="line"><a name="l03664"></a><span class="lineno"> 3664</span>  boost::format(</div><div class="line"><a name="l03665"></a><span class="lineno"> 3665</span>  <span class="stringliteral">"requestedOutputs must have at least one entry %1%"</span>)</div><div class="line"><a name="l03666"></a><span class="lineno"> 3666</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03667"></a><span class="lineno"> 3667</span>  }</div><div class="line"><a name="l03668"></a><span class="lineno"> 3668</span>  m_RequestedOutputs = requestedOutputs;</div><div class="line"><a name="l03669"></a><span class="lineno"> 3669</span> </div><div class="line"><a name="l03670"></a><span class="lineno"> 3670</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l03671"></a><span class="lineno"> 3671</span>  {</div><div class="line"><a name="l03672"></a><span class="lineno"> 3672</span>  LoadGraphDef(graphDef);</div><div class="line"><a name="l03673"></a><span class="lineno"> 3673</span>  }</div><div class="line"><a name="l03674"></a><span class="lineno"> 3674</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="l03675"></a><span class="lineno"> 3675</span>  {</div><div class="line"><a name="l03676"></a><span class="lineno"> 3676</span>  Cleanup();</div><div class="line"><a name="l03677"></a><span class="lineno"> 3677</span>  <span class="keywordflow">throw</span> e;</div><div class="line"><a name="l03678"></a><span class="lineno"> 3678</span>  }</div><div class="line"><a name="l03679"></a><span class="lineno"> 3679</span> </div><div class="line"><a name="l03680"></a><span class="lineno"> 3680</span>  Cleanup();</div><div class="line"><a name="l03681"></a><span class="lineno"> 3681</span> </div><div class="line"><a name="l03682"></a><span class="lineno"> 3682</span>  <span class="keywordflow">return</span> std::move(m_Network);</div><div class="line"><a name="l03683"></a><span class="lineno"> 3683</span> }</div><div class="line"><a name="l03684"></a><span class="lineno"> 3684</span> </div><div class="line"><a name="l03685"></a><span class="lineno"> 3685</span> <span class="keywordtype">void</span> TfParser::Cleanup()</div><div class="line"><a name="l03686"></a><span class="lineno"> 3686</span> {</div><div class="line"><a name="l03687"></a><span class="lineno"> 3687</span>  <span class="comment">// Cleanup, in case we reuse this parser.</span></div><div class="line"><a name="l03688"></a><span class="lineno"> 3688</span>  m_InputShapes.clear();</div><div class="line"><a name="l03689"></a><span class="lineno"> 3689</span>  m_RequestedOutputs.clear();</div><div class="line"><a name="l03690"></a><span class="lineno"> 3690</span>  m_NodesByName.clear();</div><div class="line"><a name="l03691"></a><span class="lineno"> 3691</span>  m_ParsedTfOperations.clear();</div><div class="line"><a name="l03692"></a><span class="lineno"> 3692</span> }</div><div class="line"><a name="l03693"></a><span class="lineno"> 3693</span> </div><div class="line"><a name="l03694"></a><span class="lineno"><a class="line" href="classarmnn_tf_parser_1_1_tf_parser.xhtml#aba39201ebaeb0738f15a14b3c8da1f5a"> 3694</a></span> <a class="code" href="namespacearmnn_tf_parser.xhtml#a9084adbf804022c874039ad40d1939e9">BindingPointInfo</a> <a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml#aba39201ebaeb0738f15a14b3c8da1f5a">TfParser::GetNetworkInputBindingInfo</a>(<span class="keyword">const</span> std::string& name)<span class="keyword"> const</span></div><div class="line"><a name="l03695"></a><span class="lineno"> 3695</span> <span class="keyword"></span>{</div><div class="line"><a name="l03696"></a><span class="lineno"> 3696</span>  <span class="keywordflow">return</span> GetBindingInfo(name, <span class="stringliteral">"input"</span>, m_NetworkInputsBindingInfo);</div><div class="line"><a name="l03697"></a><span class="lineno"> 3697</span> }</div><div class="line"><a name="l03698"></a><span class="lineno"> 3698</span> </div><div class="line"><a name="l03699"></a><span class="lineno"><a class="line" href="classarmnn_tf_parser_1_1_tf_parser.xhtml#aee8c8fa7de3c87392791d9f8dd90655f"> 3699</a></span> <a class="code" href="namespacearmnn_tf_parser.xhtml#a9084adbf804022c874039ad40d1939e9">BindingPointInfo</a> <a class="code" href="classarmnn_tf_parser_1_1_tf_parser.xhtml#aee8c8fa7de3c87392791d9f8dd90655f">TfParser::GetNetworkOutputBindingInfo</a>(<span class="keyword">const</span> std::string& name)<span class="keyword"> const</span></div><div class="line"><a name="l03700"></a><span class="lineno"> 3700</span> <span class="keyword"></span>{</div><div class="line"><a name="l03701"></a><span class="lineno"> 3701</span>  <span class="keywordflow">return</span> GetBindingInfo(name, <span class="stringliteral">"output"</span>, m_NetworkOutputsBindingInfo);</div><div class="line"><a name="l03702"></a><span class="lineno"> 3702</span> }</div><div class="line"><a name="l03703"></a><span class="lineno"> 3703</span> </div><div class="line"><a name="l03704"></a><span class="lineno"> 3704</span> std::pair<LayerBindingId, TensorInfo> TfParser::GetBindingInfo(<span class="keyword">const</span> std::string& layerName,</div><div class="line"><a name="l03705"></a><span class="lineno"> 3705</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* bindingPointDesc,</div><div class="line"><a name="l03706"></a><span class="lineno"> 3706</span>  <span class="keyword">const</span> std::unordered_map<std::string, BindingPointInfo>& nameToBindingInfo)</div><div class="line"><a name="l03707"></a><span class="lineno"> 3707</span> {</div><div class="line"><a name="l03708"></a><span class="lineno"> 3708</span>  <span class="keyword">auto</span> it = nameToBindingInfo.find(layerName);</div><div class="line"><a name="l03709"></a><span class="lineno"> 3709</span>  <span class="keywordflow">if</span> (it == nameToBindingInfo.end())</div><div class="line"><a name="l03710"></a><span class="lineno"> 3710</span>  {</div><div class="line"><a name="l03711"></a><span class="lineno"> 3711</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l03712"></a><span class="lineno"> 3712</span>  boost::str(</div><div class="line"><a name="l03713"></a><span class="lineno"> 3713</span>  boost::format(</div><div class="line"><a name="l03714"></a><span class="lineno"> 3714</span>  <span class="stringliteral">"Unknown %1% '%2%' %3%"</span>)</div><div class="line"><a name="l03715"></a><span class="lineno"> 3715</span>  % bindingPointDesc</div><div class="line"><a name="l03716"></a><span class="lineno"> 3716</span>  % layerName</div><div class="line"><a name="l03717"></a><span class="lineno"> 3717</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03718"></a><span class="lineno"> 3718</span>  }</div><div class="line"><a name="l03719"></a><span class="lineno"> 3719</span>  <span class="keywordflow">return</span> it->second;</div><div class="line"><a name="l03720"></a><span class="lineno"> 3720</span> }</div><div class="line"><a name="l03721"></a><span class="lineno"> 3721</span> </div><div class="line"><a name="l03722"></a><span class="lineno"> 3722</span> <span class="keywordtype">void</span> TfParser::TrackInputBinding(<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer, <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> <span class="keywordtype">id</span>, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensorInfo)</div><div class="line"><a name="l03723"></a><span class="lineno"> 3723</span> {</div><div class="line"><a name="l03724"></a><span class="lineno"> 3724</span>  <span class="keywordflow">return</span> TrackBindingPoint(layer, <span class="keywordtype">id</span>, tensorInfo, <span class="stringliteral">"input"</span>, m_NetworkInputsBindingInfo);</div><div class="line"><a name="l03725"></a><span class="lineno"> 3725</span> }</div><div class="line"><a name="l03726"></a><span class="lineno"> 3726</span> </div><div class="line"><a name="l03727"></a><span class="lineno"> 3727</span> <span class="keywordtype">void</span> TfParser::TrackOutputBinding(<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer, <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> <span class="keywordtype">id</span>, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensorInfo)</div><div class="line"><a name="l03728"></a><span class="lineno"> 3728</span> {</div><div class="line"><a name="l03729"></a><span class="lineno"> 3729</span>  <span class="keywordflow">return</span> TrackBindingPoint(layer, <span class="keywordtype">id</span>, tensorInfo, <span class="stringliteral">"output"</span>, m_NetworkOutputsBindingInfo);</div><div class="line"><a name="l03730"></a><span class="lineno"> 3730</span> }</div><div class="line"><a name="l03731"></a><span class="lineno"> 3731</span> </div><div class="line"><a name="l03732"></a><span class="lineno"> 3732</span> <span class="keywordtype">void</span> TfParser::TrackBindingPoint(<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer,</div><div class="line"><a name="l03733"></a><span class="lineno"> 3733</span>  <a class="code" href="namespacearmnn.xhtml#ab8cf8f9fb6792e654c2d8d8382f6f01b">LayerBindingId</a> <span class="keywordtype">id</span>,</div><div class="line"><a name="l03734"></a><span class="lineno"> 3734</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensorInfo,</div><div class="line"><a name="l03735"></a><span class="lineno"> 3735</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* bindingPointDesc,</div><div class="line"><a name="l03736"></a><span class="lineno"> 3736</span>  std::unordered_map<std::string, BindingPointInfo>& nameToBindingInfo)</div><div class="line"><a name="l03737"></a><span class="lineno"> 3737</span> {</div><div class="line"><a name="l03738"></a><span class="lineno"> 3738</span>  <span class="keyword">const</span> std::string layerName = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#afcc1c3a20bd2860e0ddd21674389246f">GetName</a>();</div><div class="line"><a name="l03739"></a><span class="lineno"> 3739</span>  <span class="keyword">auto</span> it = nameToBindingInfo.find(layerName);</div><div class="line"><a name="l03740"></a><span class="lineno"> 3740</span>  <span class="keywordflow">if</span> (it == nameToBindingInfo.end())</div><div class="line"><a name="l03741"></a><span class="lineno"> 3741</span>  {</div><div class="line"><a name="l03742"></a><span class="lineno"> 3742</span>  nameToBindingInfo[layerName] = std::make_pair(<span class="keywordtype">id</span>, tensorInfo);</div><div class="line"><a name="l03743"></a><span class="lineno"> 3743</span>  }</div><div class="line"><a name="l03744"></a><span class="lineno"> 3744</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l03745"></a><span class="lineno"> 3745</span>  {</div><div class="line"><a name="l03746"></a><span class="lineno"> 3746</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l03747"></a><span class="lineno"> 3747</span>  boost::str(</div><div class="line"><a name="l03748"></a><span class="lineno"> 3748</span>  boost::format(</div><div class="line"><a name="l03749"></a><span class="lineno"> 3749</span>  <span class="stringliteral">"Id %1% used by more than one %2% layer %3%"</span>)</div><div class="line"><a name="l03750"></a><span class="lineno"> 3750</span>  % <span class="keywordtype">id</span></div><div class="line"><a name="l03751"></a><span class="lineno"> 3751</span>  % bindingPointDesc</div><div class="line"><a name="l03752"></a><span class="lineno"> 3752</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l03753"></a><span class="lineno"> 3753</span>  }</div><div class="line"><a name="l03754"></a><span class="lineno"> 3754</span> }</div><div class="line"><a name="l03755"></a><span class="lineno"> 3755</span> </div><div class="line"><a name="l03756"></a><span class="lineno"> 3756</span> } <span class="comment">// namespace armnnTfParser</span></div><div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Convolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00428">Descriptors.hpp:428</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::Convolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00438">Descriptors.hpp:438</a></div></div> +<div class="ttc" id="namespacearmnn_tf_parser_xhtml_af7cec8b9a69e02f18a5de38502675d94"><div class="ttname"><a href="namespacearmnn_tf_parser.xhtml#af7cec8b9a69e02f18a5de38502675d94">armnnTfParser::ITfParserPtr</a></div><div class="ttdeci">std::unique_ptr< ITfParser, void(*)(ITfParser *parser)> ITfParserPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_tf_parser_8hpp_source.xhtml#l00022">ITfParser.hpp:22</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a8440d2a2afd3eb3526212081c9016830"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a8440d2a2afd3eb3526212081c9016830">armnn::INetwork::AddGatherLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddGatherLayer(const char *name=nullptr)=0</div><div class="ttdoc">Add Gather layer to the network. </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_network_xhtml_ac3be1bcc0fa5ffaf04a4f1d20d0ab7f4"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#ac3be1bcc0fa5ffaf04a4f1d20d0ab7f4">armnn::INetwork::AddComparisonLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddComparisonLayer(const ComparisonDescriptor &comparisonDescriptor, const char *name=nullptr)=0</div><div class="ttdoc">Add a Comparison layer to the network. </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="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="_data_layout_indexed_8hpp_xhtml"><div class="ttname"><a href="_data_layout_indexed_8hpp.xhtml">DataLayoutIndexed.hpp</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="classarmnn_1_1_i_network_xhtml_a8262e9e6fc869a9c9782115a6a552f36"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a8262e9e6fc869a9c9782115a6a552f36">armnn::INetwork::AddMeanLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddMeanLayer(const MeanDescriptor &meanDescriptor, const char *name=nullptr)=0</div><div class="ttdoc">Add a Mean layer to the network. </div></div> +<div class="ttc" id="_transpose_8hpp_xhtml"><div class="ttname"><a href="_transpose_8hpp.xhtml">Transpose.hpp</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="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00049">Types.hpp:49</a></div></div> +<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml_a414e6f95548e6f7a01d5028b55ad3941"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">armnnUtils::DataLayoutIndexed::GetWidthIndex</a></div><div class="ttdeci">unsigned int GetWidthIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00025">DataLayoutIndexed.hpp:25</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a8526ea7cf860d8e7f8340e9f9354f9f0"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a8526ea7cf860d8e7f8340e9f9354f9f0">armnn::NormalizationDescriptor::m_K</a></div><div class="ttdeci">float m_K</div><div class="ttdoc">Kappa value used for the across channel normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00585">Descriptors.hpp:585</a></div></div> +<div class="ttc" id="classarmnn_tf_parser_1_1_tf_parser_xhtml_ac28271f7220cd595de55dbb7f99f4a63"><div class="ttname"><a href="classarmnn_tf_parser_1_1_tf_parser.xhtml#ac28271f7220cd595de55dbb7f99f4a63">armnnTfParser::TfParser::ParsedMulTfOperation</a></div><div class="ttdeci">friend class ParsedMulTfOperation</div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8hpp_source.xhtml#l00098">TfParser.hpp:98</a></div></div> +<div class="ttc" id="namespacearmnn_tf_parser_xhtml_ad85fe4a9bf2aff90c53bc2f50c8931e6"><div class="ttname"><a href="namespacearmnn_tf_parser.xhtml#ad85fe4a9bf2aff90c53bc2f50c8931e6">armnnTfParser::OutputOfParsedTfOperation</a></div><div class="ttdeci">WithOutputTensorIndex< ParsedTfOperation * > OutputOfParsedTfOperation</div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8hpp_source.xhtml#l00060">TfParser.hpp:60</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="namespacearmnn_tf_parser_xhtml_a9084adbf804022c874039ad40d1939e9"><div class="ttname"><a href="namespacearmnn_tf_parser.xhtml#a9084adbf804022c874039ad40d1939e9">armnnTfParser::BindingPointInfo</a></div><div class="ttdeci">armnn::BindingPointInfo BindingPointInfo</div><div class="ttdef"><b>Definition:</b> <a href="_i_tf_parser_8hpp_source.xhtml#l00019">ITfParser.hpp:19</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_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="namespacearmnn_tf_parser_xhtml_abcf8e5fd95ba7e7bd8cd36fc24974223"><div class="ttname"><a href="namespacearmnn_tf_parser.xhtml#abcf8e5fd95ba7e7bd8cd36fc24974223">armnnTfParser::OutputId</a></div><div class="ttdeci">WithOutputTensorIndex< std::string > OutputId</div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8hpp_source.xhtml#l00062">TfParser.hpp:62</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="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="classarmnn_1_1_i_network_xhtml_a53949668a151924c4aad83b176db1080"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a53949668a151924c4aad83b176db1080">armnn::INetwork::AddSoftmaxLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddSoftmaxLayer(const SoftmaxDescriptor &softmaxDescriptor, const char *name=nullptr)=0</div><div class="ttdoc">Adds a softmax layer to the network. </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_comparison_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_comparison_descriptor.xhtml">armnn::ComparisonDescriptor</a></div><div class="ttdoc">A ComparisonDescriptor for the ComparisonLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00062">Descriptors.hpp:62</a></div></div> +<div class="ttc" id="structarmnn_1_1_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="classarmnn_1_1_i_network_xhtml_a4cc12e3bd9ffe196cc8b351f25a104e3"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a4cc12e3bd9ffe196cc8b351f25a104e3">armnn::INetwork::AddMinimumLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddMinimumLayer(const char *name=nullptr)=0</div><div class="ttdoc">Add a Minimum layer to the network. </div></div> +<div class="ttc" id="structarmnn_tf_parser_1_1_with_output_tensor_index_xhtml_a271b1a398c11fb4bf8603119041562c9"><div class="ttname"><a href="structarmnn_tf_parser_1_1_with_output_tensor_index.xhtml#a271b1a398c11fb4bf8603119041562c9">armnnTfParser::WithOutputTensorIndex::m_Index</a></div><div class="ttdeci">unsigned int m_Index</div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8hpp_source.xhtml#l00049">TfParser.hpp:49</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="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="structarmnn_1_1_normalization_descriptor_xhtml_a174279be57d7596eeb04c6b7f7510f99"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a174279be57d7596eeb04c6b7f7510f99">armnn::NormalizationDescriptor::m_Alpha</a></div><div class="ttdeci">float m_Alpha</div><div class="ttdoc">Alpha value for the normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00581">Descriptors.hpp:581</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_1_1_i_network_xhtml_a07485f1477554d32e43edc39502ac419"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a07485f1477554d32e43edc39502ac419">armnn::INetwork::AddPadLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddPadLayer(const PadDescriptor &padDescriptor, const char *name=nullptr)=0</div><div class="ttdoc">Adds a fully pad layer to the network. </div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml_aac34adc5b96d744ae53eac580213f855"><div class="ttname"><a href="namespacearmnn_utils.xhtml#aac34adc5b96d744ae53eac580213f855">armnnUtils::CalculateReducedOutputTensoInfo</a></div><div class="ttdeci">void CalculateReducedOutputTensoInfo(const armnn::TensorInfo &inputTensorInfo, const std::set< unsigned int > &axisSet, bool keepDims, armnn::TensorInfo &outputTensorInfo)</div><div class="ttdoc">Creates a tensor info after reducing the dimensions mentioned in axisData. </div><div class="ttdef"><b>Definition:</b> <a href="_parser_helper_8cpp_source.xhtml#l00054">ParserHelper.cpp:54</a></div></div> +<div class="ttc" id="classarmnn_1_1_base_tensor_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_base_tensor.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::BaseTensor::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00169">Tensor.hpp:169</a></div></div> +<div class="ttc" id="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="classarmnn_1_1_i_network_xhtml_aea068f6094e1c3bfcdf8167b68112632"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#aea068f6094e1c3bfcdf8167b68112632">armnn::INetwork::AddActivationLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddActivationLayer(const ActivationDescriptor &activationDescriptor, const char *name=nullptr)=0</div><div class="ttdoc">Adds an activation layer to the network. </div></div> +<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml_a11c821c7524251004a72ed13c510853c"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">armnn::BatchNormalizationDescriptor::m_Eps</a></div><div class="ttdeci">float m_Eps</div><div class="ttdoc">Value to add to the variance. Used to avoid dividing by zero. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00623">Descriptors.hpp:623</a></div></div> +<div class="ttc" id="structarmnn_1_1_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_batch_normalization_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::BatchNormalizationDescriptor::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#l00625">Descriptors.hpp:625</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a5bf8e0c150c7e6f8085c0767c6ab1914"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a5bf8e0c150c7e6f8085c0767c6ab1914">armnn::INetwork::AddElementwiseUnaryLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddElementwiseUnaryLayer(const ElementwiseUnaryDescriptor &elementwiseUnaryDescriptor, const char *name=nullptr)=0</div><div class="ttdoc">Add an ElementwiseUnary layer to the network. </div></div> +<div class="ttc" id="namespacearmnn_tf_parser_xhtml_ae5488f1478c62281c5e937e79ebcd145"><div class="ttname"><a href="namespacearmnn_tf_parser.xhtml#ae5488f1478c62281c5e937e79ebcd145">armnnTfParser::CheckPaddingTensor</a></div><div class="ttdeci">unsigned int CheckPaddingTensor(const ConstTensor &paddingTensor, const TensorInfo &inputTensorInfo, const std::string &nodeName)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8cpp_source.xhtml#l02106">TfParser.cpp:2106</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="classarmnn_tf_parser_1_1_tf_parser_xhtml_aee8c8fa7de3c87392791d9f8dd90655f"><div class="ttname"><a href="classarmnn_tf_parser_1_1_tf_parser.xhtml#aee8c8fa7de3c87392791d9f8dd90655f">armnnTfParser::TfParser::GetNetworkOutputBindingInfo</a></div><div class="ttdeci">virtual BindingPointInfo GetNetworkOutputBindingInfo(const std::string &name) const override</div><div class="ttdoc">Retrieves binding info (layer id and tensor info) for the network output identified by the given laye...</div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8cpp_source.xhtml#l03699">TfParser.cpp:3699</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml">armnn::INetwork</a></div><div class="ttdoc">Main network class which provides the interface for building up a neural network. ...</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00105">INetwork.hpp:105</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a8d1067e754512c434da1238b67ad26ea"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a8d1067e754512c434da1238b67ad26ea">armnn::INetwork::AddBatchNormalizationLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddBatchNormalizationLayer(const BatchNormalizationDescriptor &desc, const ConstTensor &mean, const ConstTensor &variance, const ConstTensor &beta, const ConstTensor &gamma, const char *name=nullptr)=0</div><div class="ttdoc">Adds a batch normalization layer to the network. </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_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_normalization_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::NormalizationDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00587">Descriptors.hpp:587</a></div></div> +<div class="ttc" id="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="namespacearmnn_tf_parser_xhtml"><div class="ttname"><a href="namespacearmnn_tf_parser.xhtml">armnnTfParser</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_tf_parser_8hpp_source.xhtml#l00016">ITfParser.hpp:16</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="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="classarmnn_tf_parser_1_1_tf_parser_xhtml_ad3c8cd69190956793af7af503dc495cd"><div class="ttname"><a href="classarmnn_tf_parser_1_1_tf_parser.xhtml#ad3c8cd69190956793af7af503dc495cd">armnnTfParser::TfParser::ParsedConstTfOperation</a></div><div class="ttdeci">friend class ParsedConstTfOperation</div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8hpp_source.xhtml#l00096">TfParser.hpp:96</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="namespacearmnn_utils_xhtml_a12124184ac6aec018beb98b9715330c7"><div class="ttname"><a href="namespacearmnn_utils.xhtml#a12124184ac6aec018beb98b9715330c7">armnnUtils::NHWCToArmNN</a></div><div class="ttdeci">const armnn::PermutationVector NHWCToArmNN</div><div class="ttdef"><b>Definition:</b> <a href="_parser_helper_8cpp_source.xhtml#l00016">ParserHelper.cpp:16</a></div></div> +<div class="ttc" id="classarmnn_1_1_layer_xhtml_a1594bddc87d6477df300317658f566bb"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a1594bddc87d6477df300317658f566bb">armnn::Layer::GetNumOutputSlots</a></div><div class="ttdeci">unsigned int GetNumOutputSlots() const override</div><div class="ttdoc">Returns the number of connectable output slots. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00308">Layer.hpp:308</a></div></div> +<div class="ttc" id="_tf_parser_8hpp_xhtml"><div class="ttname"><a href="_tf_parser_8hpp.xhtml">TfParser.hpp</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_network_xhtml_a74dac9efbb6dbd1982a45af1805eb4e0"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a74dac9efbb6dbd1982a45af1805eb4e0">armnn::INetwork::AddNormalizationLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddNormalizationLayer(const NormalizationDescriptor &normalizationDescriptor, const char *name=nullptr)=0</div><div class="ttdoc">Adds a normalization layer to the network. </div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a4839e4ec3f07974c57ca2c856b40cd57"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a4839e4ec3f07974c57ca2c856b40cd57">armnn::INetwork::AddFullyConnectedLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddFullyConnectedLayer(const FullyConnectedDescriptor &fullyConnectedDescriptor, const ConstTensor &weights, const Optional< ConstTensor > &biases, const char *name=nullptr)=0</div><div class="ttdoc">Adds a fully connected layer to the network. </div></div> +<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml_a61c00316c443adc233c24e85c6c5b740"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">armnnUtils::DataLayoutIndexed::GetHeightIndex</a></div><div class="ttdeci">unsigned int GetHeightIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00024">DataLayoutIndexed.hpp:24</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="structarmnn_1_1_normalization_descriptor_xhtml_a05945f080edf694b631960728b87aadb"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a05945f080edf694b631960728b87aadb">armnn::NormalizationDescriptor::m_NormMethodType</a></div><div class="ttdeci">NormalizationAlgorithmMethod m_NormMethodType</div><div class="ttdoc">Normalization method algorithm to use (LocalBrightness, LocalContrast). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00577">Descriptors.hpp:577</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_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="classarmnn_1_1_i_network_xhtml_a073e2f61f527d7d3801c26bdbd37dd7e"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a073e2f61f527d7d3801c26bdbd37dd7e">armnn::INetwork::AddConvolution2dLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddConvolution2dLayer(const Convolution2dDescriptor &convolution2dDescriptor, const ConstTensor &weights, const Optional< ConstTensor > &biases, const char *name=nullptr)=0</div><div class="ttdoc">Adds a 2D convolution layer to the network. </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_1_1_i_network_xhtml_ad8582fba2ebeb65da43a56bc22d4f88b"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#ad8582fba2ebeb65da43a56bc22d4f88b">armnn::INetwork::AddOutputLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddOutputLayer(LayerBindingId id, const char *name=nullptr)=0</div><div class="ttdoc">Adds an output layer to the network. </div></div> +<div class="ttc" id="namespacearmnn_tf_parser_xhtml_aa36bf288c19fe35767bb6e059636f405"><div class="ttname"><a href="namespacearmnn_tf_parser.xhtml#aa36bf288c19fe35767bb6e059636f405">armnnTfParser::ParsedTfOperationPtr</a></div><div class="ttdeci">std::unique_ptr< ParsedTfOperation > ParsedTfOperationPtr</div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8hpp_source.xhtml#l00035">TfParser.hpp:35</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a4812e0137ee610310d23059efed2cb84"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a4812e0137ee610310d23059efed2cb84">armnn::INetwork::AddAdditionLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddAdditionLayer(const char *name=nullptr)=0</div><div class="ttdoc">Adds an addition layer to the network. </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_pad_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pad_descriptor.xhtml">armnn::PadDescriptor</a></div><div class="ttdoc">A PadDescriptor for the PadLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00901">Descriptors.hpp:901</a></div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml_af3c74017185773dd61d8ca6662d65d43"><div class="ttname"><a href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a></div><div class="ttdeci">void Permute(const armnn::TensorShape &dstShape, const armnn::PermutationVector &mappings, const void *src, void *dst, size_t dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.xhtml#l00121">Permute.cpp:121</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_aef27f787e8a2ee19c4052261f963f28e"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#aef27f787e8a2ee19c4052261f963f28e">armnn::INetwork::AddConcatLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddConcatLayer(const ConcatDescriptor &concatDescriptor, const char *name=nullptr)=0</div><div class="ttdoc">Adds a concatenation layer to the network. </div></div> +<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml_ab78e6fe963508c1ac5c00d04bb3361a3"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml#ab78e6fe963508c1ac5c00d04bb3361a3">armnn::OriginsDescriptor::GetViewOrigin</a></div><div class="ttdeci">const uint32_t * GetViewOrigin(uint32_t idx) const</div><div class="ttdoc">Return the view origin at the int value idx. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00189">Descriptors.cpp:189</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="classarmnn_1_1_i_network_xhtml_afaa808f44f0b8332ec0bd54f4fea47c0"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#afaa808f44f0b8332ec0bd54f4fea47c0">armnn::INetwork::AddStackLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddStackLayer(const StackDescriptor &descriptor, const char *name=nullptr)=0</div><div class="ttdoc">Adds a stack layer to the network. </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="namespacearmnn_tf_parser_xhtml_a4c8735480b01dbd0f75c63377fe054e9"><div class="ttname"><a href="namespacearmnn_tf_parser.xhtml#a4c8735480b01dbd0f75c63377fe054e9">armnnTfParser::OutputOfConstNodeDef</a></div><div class="ttdeci">WithOutputTensorIndex< const tensorflow::NodeDef * > OutputOfConstNodeDef</div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8hpp_source.xhtml#l00061">TfParser.hpp:61</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="_permute_8hpp_xhtml"><div class="ttname"><a href="_permute_8hpp.xhtml">Permute.hpp</a></div></div> +<div class="ttc" id="_subgraph_view_selector_8cpp_xhtml_ae7fc37b88ff10e9294ed1c72f2a25ac7"><div class="ttname"><a href="_subgraph_view_selector_8cpp.xhtml#ae7fc37b88ff10e9294ed1c72f2a25ac7">m_Layer</a></div><div class="ttdeci">Layer * m_Layer</div><div class="ttdef"><b>Definition:</b> <a href="_subgraph_view_selector_8cpp_source.xhtml#l00242">SubgraphViewSelector.cpp:242</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_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_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::DepthwiseConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00478">Descriptors.hpp:478</a></div></div> +<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml_aae0893695f5803a3517985c7cb1ccb2e"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">armnn::ViewsDescriptor::SetViewSize</a></div><div class="ttdeci">Status SetViewSize(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">Set the size of the views. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00307">Descriptors.cpp:307</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a617aeb663e1535568864c23f5d988dd8"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a617aeb663e1535568864c23f5d988dd8">armnn::INetwork::AddResizeLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddResizeLayer(const ResizeDescriptor &resizeDescriptor, const char *name=nullptr)=0</div><div class="ttdoc">Adds a resize layer to the network. </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="classarmnn_1_1_i_network_xhtml_a4ec92bca4e51755105abb89e1878585f"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a4ec92bca4e51755105abb89e1878585f">armnn::INetwork::AddPooling2dLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddPooling2dLayer(const Pooling2dDescriptor &pooling2dDescriptor, const char *name=nullptr)=0</div><div class="ttdoc">Adds a pooling layer to the network. </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="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="classarmnn_utils_1_1_data_layout_indexed_xhtml"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml">armnnUtils::DataLayoutIndexed</a></div><div class="ttdoc">Provides access to the appropriate indexes for Channels, Height and Width based on DataLayout...</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00017">DataLayoutIndexed.hpp:17</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="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="classarmnn_1_1_i_network_xhtml_a3a2dbac031f1a0b1b323916b1c7f61d2"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a3a2dbac031f1a0b1b323916b1c7f61d2">armnn::INetwork::AddSplitterLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddSplitterLayer(const ViewsDescriptor &splitterDescriptor, const char *name=nullptr)=0</div><div class="ttdoc">Adds a splitter layer to the network. </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="structarmnn_tf_parser_1_1_with_output_tensor_index_xhtml"><div class="ttname"><a href="structarmnn_tf_parser_1_1_with_output_tensor_index.xhtml">armnnTfParser::WithOutputTensorIndex</a></div><div class="ttdoc">WithOutputTensorIndex wraps a value and an index. </div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8hpp_source.xhtml#l00046">TfParser.hpp:46</a></div></div> +<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00199">Tensor.hpp:199</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_adcf5037208faac36c0788239a073f75c"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">armnn::ResizeDescriptor::m_TargetWidth</a></div><div class="ttdeci">uint32_t m_TargetWidth</div><div class="ttdoc">Target width value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00744">Descriptors.hpp:744</a></div></div> +<div class="ttc" id="classarmnn_tf_parser_1_1_tf_parser_xhtml_acd82aa5171feb1c852506964f3c5254b"><div class="ttname"><a href="classarmnn_tf_parser_1_1_tf_parser.xhtml#acd82aa5171feb1c852506964f3c5254b">armnnTfParser::TfParser::CreateNetworkFromString</a></div><div class="ttdeci">virtual armnn::INetworkPtr CreateNetworkFromString(const char *protoText, const std::map< std::string, armnn::TensorShape > &inputShapes, const std::vector< std::string > &requestedOutputs) override</div><div class="ttdoc">Creates the network directly from protobuf text in a string. Useful for debugging/testing. </div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8cpp_source.xhtml#l03595">TfParser.cpp:3595</a></div></div> +<div class="ttc" id="_graph_topological_sort_8hpp_xhtml"><div class="ttname"><a href="_graph_topological_sort_8hpp.xhtml">GraphTopologicalSort.hpp</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_tf_parser_1_1_with_output_tensor_index_xhtml_a08f2876bc5d60ed9c711ac7c26747305"><div class="ttname"><a href="structarmnn_tf_parser_1_1_with_output_tensor_index.xhtml#a08f2876bc5d60ed9c711ac7c26747305">armnnTfParser::WithOutputTensorIndex::m_IndexedValue</a></div><div class="ttdeci">T m_IndexedValue</div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8hpp_source.xhtml#l00048">TfParser.hpp:48</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="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_tf_parser_1_1_tf_parser_xhtml_a97caa75ebdb49fc10250742b33d29ae7"><div class="ttname"><a href="classarmnn_tf_parser_1_1_tf_parser.xhtml#a97caa75ebdb49fc10250742b33d29ae7">armnnTfParser::TfParser::ParsedMatMulTfOperation</a></div><div class="ttdeci">friend class ParsedMatMulTfOperation</div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8hpp_source.xhtml#l00097">TfParser.hpp:97</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="namespacearmnn_tf_parser_xhtml_a0540bb475d62bab024eebe8685181845"><div class="ttname"><a href="namespacearmnn_tf_parser.xhtml#a0540bb475d62bab024eebe8685181845">armnnTfParser::CalculateSamePadding</a></div><div class="ttdeci">void CalculateSamePadding(uint32_t inputSize, uint32_t stride, uint32_t filterSize, bool samePadding, uint32_t *paddingFront, uint32_t *paddingBack)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8cpp_source.xhtml#l00405">TfParser.cpp:405</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a71975fcec1464d639f1a78f73164d1bd"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a71975fcec1464d639f1a78f73164d1bd">armnn::TensorInfo::SetDataType</a></div><div class="ttdeci">void SetDataType(DataType type)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00096">Tensor.hpp:96</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_resize_descriptor_xhtml_a46c3fa15c46fb0d1dcdc24d0ea5cb5cd"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a46c3fa15c46fb0d1dcdc24d0ea5cb5cd">armnn::ResizeDescriptor::m_TargetHeight</a></div><div class="ttdeci">uint32_t m_TargetHeight</div><div class="ttdoc">Target height value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00746">Descriptors.hpp:746</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a78367a5054c92d435f4f5c7e10ec65b8"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a78367a5054c92d435f4f5c7e10ec65b8">armnn::INetwork::AddDepthwiseConvolution2dLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddDepthwiseConvolution2dLayer(const DepthwiseConvolution2dDescriptor &convolution2dDescriptor, const ConstTensor &weights, const Optional< ConstTensor > &biases, const char *name=nullptr)=0</div><div class="ttdoc">Adds a 2D depthwise convolution layer to the network. </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="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="namespacearmnn_utils_xhtml_a9300450bab29bb951d6f8755b7d9d3a8"><div class="ttname"><a href="namespacearmnn_utils.xhtml#a9300450bab29bb951d6f8755b7d9d3a8">armnnUtils::CalculateStridedSliceOutputTensorInfo</a></div><div class="ttdeci">void CalculateStridedSliceOutputTensorInfo(const armnn::TensorInfo &inputTensorInfo, const armnn::StridedSliceDescriptor &desc, armnn::TensorInfo &outputTensorInfo)</div><div class="ttdoc">Create output tensor info for a StridedSlice operator. </div><div class="ttdef"><b>Definition:</b> <a href="_parser_helper_8cpp_source.xhtml#l00105">ParserHelper.cpp:105</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_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_normalization_descriptor_xhtml_afe1f0f09d49ad2befc01f8789187b7dd"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#afe1f0f09d49ad2befc01f8789187b7dd">armnn::NormalizationDescriptor::m_NormChannelType</a></div><div class="ttdeci">NormalizationAlgorithmChannel m_NormChannelType</div><div class="ttdoc">Normalization channel algorithm to use (Across, Within). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00575">Descriptors.hpp:575</a></div></div> +<div class="ttc" id="namespacearmnn_tf_parser_xhtml_a3d934e14ca544ba7af4fe562def8a986"><div class="ttname"><a href="namespacearmnn_tf_parser.xhtml#a3d934e14ca544ba7af4fe562def8a986">armnnTfParser::ConvertTfTensorDataType</a></div><div class="ttdeci">DataType ConvertTfTensorDataType(const tensorflow::DataType tfDataType, const tensorflow::NodeDef &nodeDef)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8cpp_source.xhtml#l00933">TfParser.cpp:933</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="_tf_parser_8cpp_xhtml_a3fb047570644cae325aa88d3cd7bb96e"><div class="ttname"><a href="_tf_parser_8cpp.xhtml#a3fb047570644cae325aa88d3cd7bb96e">CHECK_DATA_FORMAT</a></div><div class="ttdeci">#define CHECK_DATA_FORMAT(NODE_DEF, FORMAT, NODE_TYPE)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8cpp_source.xhtml#l00314">TfParser.cpp:314</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a348f95b60998a987ba20a58bfc720590"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a348f95b60998a987ba20a58bfc720590">armnn::INetwork::AddStridedSliceLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddStridedSliceLayer(const StridedSliceDescriptor &stridedSliceDescriptor, const char *name=nullptr)=0</div><div class="ttdoc">Adds a strided slice layer to the network. </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_1_1_i_network_xhtml_ac77b89eb982f9d745730c90fcbdddba4"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#ac77b89eb982f9d745730c90fcbdddba4">armnn::INetwork::AddReshapeLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddReshapeLayer(const ReshapeDescriptor &reshapeDescriptor, const char *name=nullptr)=0</div><div class="ttdoc">Adds a reshape layer to the network. </div></div> +<div class="ttc" id="structarmnn_1_1_elementwise_unary_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_elementwise_unary_descriptor.xhtml">armnn::ElementwiseUnaryDescriptor</a></div><div class="ttdoc">A ElementwiseUnaryDescriptor for the ElementwiseUnaryLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00082">Descriptors.hpp:82</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="classarmnn_tf_parser_1_1_i_tf_parser_xhtml"><div class="ttname"><a href="classarmnn_tf_parser_1_1_i_tf_parser.xhtml">armnnTfParser::ITfParser</a></div><div class="ttdoc">Parses a directed acyclic graph from a tensorflow protobuf file. </div><div class="ttdef"><b>Definition:</b> <a href="_i_tf_parser_8hpp_source.xhtml#l00025">ITfParser.hpp:25</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_afb8d4577c796ffdd213428cd285734b1"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#afb8d4577c796ffdd213428cd285734b1">armnn::INetwork::AddMaximumLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddMaximumLayer(const char *name=nullptr)=0</div><div class="ttdoc">Add a Maximum layer to the network. </div></div> +<div class="ttc" id="_descriptors_8hpp_xhtml"><div class="ttname"><a href="_descriptors_8hpp.xhtml">Descriptors.hpp</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f"><div class="ttname"><a href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f">armnn::ResizeMethod::Bilinear</a></div></div> +<div class="ttc" id="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="classarmnn_tf_parser_1_1_tf_parser_xhtml_ae2d544957c50461d305b2517581c86d0"><div class="ttname"><a href="classarmnn_tf_parser_1_1_tf_parser.xhtml#ae2d544957c50461d305b2517581c86d0">armnnTfParser::TfParser::CreateNetworkFromTextFile</a></div><div class="ttdeci">virtual armnn::INetworkPtr CreateNetworkFromTextFile(const char *graphFile, const std::map< std::string, armnn::TensorShape > &inputShapes, const std::vector< std::string > &requestedOutputs) override</div><div class="ttdoc">Creates the network from a protobuf text file on the disk. </div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8cpp_source.xhtml#l03560">TfParser.cpp:3560</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_parser_1_1_tf_parser_xhtml"><div class="ttname"><a href="classarmnn_tf_parser_1_1_tf_parser.xhtml">armnnTfParser::TfParser</a></div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8hpp_source.xhtml#l00064">TfParser.hpp:64</a></div></div> +<div class="ttc" id="classarmnn_1_1_output_slot_xhtml_a7e5c5771d741dd5473989047a9314728"><div class="ttname"><a href="classarmnn_1_1_output_slot.xhtml#a7e5c5771d741dd5473989047a9314728">armnn::OutputSlot::SetTensorInfo</a></div><div class="ttdeci">void SetTensorInfo(const TensorInfo &tensorInfo) override</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00058">Layer.cpp:58</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a6ec9e0eb66d7d6a01240492a0b18104c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">armnn::IConnectableLayer::GetInputSlot</a></div><div class="ttdeci">virtual const IInputSlot & GetInputSlot(unsigned int index) const =0</div><div class="ttdoc">Get a const input slot handle by slot index. </div></div> +<div class="ttc" id="structarmnn_1_1_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="namespacearmnn_utils_xhtml"><div class="ttname"><a href="namespacearmnn_utils.xhtml">armnnUtils</a></div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00013">DataLayoutIndexed.hpp:13</a></div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml_acee63cd08da47910fc166a1990988fa8"><div class="ttname"><a href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a></div><div class="ttdeci">armnn::TensorInfo GetTensorInfo(unsigned int numberOfBatches, unsigned int numberOfChannels, unsigned int height, unsigned int width, const armnn::DataLayout dataLayout, const armnn::DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_utils_8cpp_source.xhtml#l00038">TensorUtils.cpp:38</a></div></div> +<div class="ttc" id="classarmnn_1_1_layer_xhtml_a0e36688a43c35668d8db5257274c68fe"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a0e36688a43c35668d8db5257274c68fe">armnn::Layer::GetOutputSlot</a></div><div class="ttdeci">const OutputSlot & GetOutputSlot(unsigned int index=0) const override</div><div class="ttdoc">Get the const output slot handle by slot index. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00312">Layer.hpp:312</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_transpose_descriptor.xhtml">armnn::TransposeDescriptor</a></div><div class="ttdoc">A TransposeDescriptor for the TransposeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01123">Descriptors.hpp:1123</a></div></div> +<div class="ttc" id="structarmnn_1_1_strided_slice_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_strided_slice_descriptor.xhtml">armnn::StridedSliceDescriptor</a></div><div class="ttdoc">A StridedSliceDescriptor for the StridedSliceLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l01002">Descriptors.hpp:1002</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a9943775a364fc4ab53b85ac88f311886"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a9943775a364fc4ab53b85ac88f311886">armnn::IOutputSlot::GetTensorInfo</a></div><div class="ttdeci">virtual const TensorInfo & GetTensorInfo() const =0</div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a80ac4eda2e7f2757ec9dd96fc96dbd16"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">armnn::IConnectableLayer::GetOutputSlot</a></div><div class="ttdeci">virtual const IOutputSlot & GetOutputSlot(unsigned int index) const =0</div><div class="ttdoc">Get the const output slot handle by slot index. </div></div> +<div class="ttc" id="classarmnn_1_1_layer_xhtml_a7ddf0cf6f620d59c10e63495ace795d0"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a7ddf0cf6f620d59c10e63495ace795d0">armnn::Layer::GetName</a></div><div class="ttdeci">const char * GetName() const override</div><div class="ttdoc">Returns the name of the layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00305">Layer.hpp:305</a></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="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="namespacearmnn_utils_xhtml_a428a9a6ffdf0e8d723b50c038c56c336"><div class="ttname"><a href="namespacearmnn_utils.xhtml#a428a9a6ffdf0e8d723b50c038c56c336">armnnUtils::TransposeTensorShape</a></div><div class="ttdeci">armnn::TensorShape TransposeTensorShape(const armnn::TensorShape &srcShape, const armnn::PermutationVector &mappings)</div><div class="ttdef"><b>Definition:</b> <a href="armnn_utils_2_transpose_8cpp_source.xhtml#l00098">Transpose.cpp:98</a></div></div> +<div class="ttc" id="_tf_parser_8cpp_xhtml_aab838eb7734e531bb5be6f6dece673bf"><div class="ttname"><a href="_tf_parser_8cpp.xhtml#aab838eb7734e531bb5be6f6dece673bf">CHECK_PADDING_TYPE</a></div><div class="ttdeci">#define CHECK_PADDING_TYPE(NODE_DEF, PADDING)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8cpp_source.xhtml#l00328">TfParser.cpp:328</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_pooling2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml">armnn::Pooling2dDescriptor</a></div><div class="ttdoc">A Pooling2dDescriptor for the Pooling2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00313">Descriptors.hpp:313</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml">armnn::NormalizationDescriptor</a></div><div class="ttdoc">A NormalizationDescriptor for the NormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00551">Descriptors.hpp:551</a></div></div> +<div class="ttc" id="classarmnn_tf_parser_1_1_tf_parser_xhtml_aba39201ebaeb0738f15a14b3c8da1f5a"><div class="ttname"><a href="classarmnn_tf_parser_1_1_tf_parser.xhtml#aba39201ebaeb0738f15a14b3c8da1f5a">armnnTfParser::TfParser::GetNetworkInputBindingInfo</a></div><div class="ttdeci">virtual BindingPointInfo GetNetworkInputBindingInfo(const std::string &name) const override</div><div class="ttdoc">Retrieves 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_parser_8cpp_source.xhtml#l03694">TfParser.cpp:3694</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::ResizeDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00751">Descriptors.hpp:751</a></div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml_a59cbccbfbae7633020d200f8c23fe69e"><div class="ttname"><a href="namespacearmnn_utils.xhtml#a59cbccbfbae7633020d200f8c23fe69e">armnnUtils::ArmNNToNHWC</a></div><div class="ttdeci">const armnn::PermutationVector ArmNNToNHWC</div><div class="ttdef"><b>Definition:</b> <a href="_parser_helper_8cpp_source.xhtml#l00017">ParserHelper.cpp:17</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="classarmnn_1_1_i_network_xhtml_a82a5bc0d24f4c4eb1fbf793e156a5193"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a82a5bc0d24f4c4eb1fbf793e156a5193">armnn::INetwork::AddDivisionLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddDivisionLayer(const char *name=nullptr)=0</div><div class="ttdoc">Adds a division layer to the network. </div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a87d5ec72def73ca14bd2987a024bd569"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a87d5ec72def73ca14bd2987a024bd569">armnn::INetwork::AddInputLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddInputLayer(LayerBindingId id, const char *name=nullptr)=0</div><div class="ttdoc">Adds an input layer to the network. </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="structarmnn_1_1_normalization_descriptor_xhtml_a8275d51ef9a584feb95726ea0522f6e5"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">armnn::NormalizationDescriptor::m_Beta</a></div><div class="ttdeci">float m_Beta</div><div class="ttdoc">Beta value for the normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00583">Descriptors.hpp:583</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_aa70c05f1aad12fbd9d9ec43ea4557b03"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#aa70c05f1aad12fbd9d9ec43ea4557b03">armnn::NormalizationDescriptor::m_NormSize</a></div><div class="ttdeci">uint32_t m_NormSize</div><div class="ttdoc">Depth radius value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00579">Descriptors.hpp:579</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_parser_1_1_tf_parser_xhtml_afb0edadd00c78430efbdc02844ef379a"><div class="ttname"><a href="classarmnn_tf_parser_1_1_tf_parser.xhtml#afb0edadd00c78430efbdc02844ef379a">armnnTfParser::TfParser::CreateNetworkFromBinaryFile</a></div><div class="ttdeci">virtual armnn::INetworkPtr CreateNetworkFromBinaryFile(const char *graphFile, const std::map< std::string, armnn::TensorShape > &inputShapes, const std::vector< std::string > &requestedOutputs) override</div><div class="ttdoc">Creates the network from a protobuf binary file on the disk. </div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8cpp_source.xhtml#l03615">TfParser.cpp:3615</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="structarmnn_1_1_batch_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml">armnn::BatchNormalizationDescriptor</a></div><div class="ttdoc">A BatchNormalizationDescriptor for the BatchNormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00610">Descriptors.hpp:610</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Convolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00422">Descriptors.hpp:422</a></div></div> +<div class="ttc" id="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="structarmnn_1_1_origins_descriptor_xhtml_a2b125117aa61f9baf3a9cb8658aa61a2"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml#a2b125117aa61f9baf3a9cb8658aa61a2">armnn::OriginsDescriptor::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#l00159">Descriptors.cpp:159</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_aa02b9e06fb20fa3c13da0427e6ee5ab2"><div class="ttname"><a href="namespacearmnn.xhtml#aa02b9e06fb20fa3c13da0427e6ee5ab2">armnn::GetDataTypeSize</a></div><div class="ttdeci">constexpr unsigned int GetDataTypeSize(DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8hpp_source.xhtml#l00115">TypesUtils.hpp:115</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a4f6070c1337d40f1e98988acee015c7d"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a4f6070c1337d40f1e98988acee015c7d">armnn::INetwork::AddTransposeLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddTransposeLayer(const TransposeDescriptor &transposeDescriptor, const char *name=nullptr)=0</div><div class="ttdoc">Adds a transpose layer to the network. </div></div> +<div class="ttc" id="namespacearmnn_tf_parser_xhtml_a9c18860db8b032de579c5ad94cbae5d0"><div class="ttname"><a href="namespacearmnn_tf_parser.xhtml#a9c18860db8b032de579c5ad94cbae5d0">armnnTfParser::CalculatePaddedOutputTensorInfo</a></div><div class="ttdeci">TensorInfo CalculatePaddedOutputTensorInfo(const TensorInfo &inputTensorInfo, const std::vector< std::pair< unsigned int, unsigned int >> &padList)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8cpp_source.xhtml#l02138">TfParser.cpp:2138</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::DepthwiseConvolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00476">Descriptors.hpp:476</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_ae24e82cf1ae2a71c5cd976edfb192fc0"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#ae24e82cf1ae2a71c5cd976edfb192fc0">armnn::INetwork::AddMultiplicationLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddMultiplicationLayer(const char *name=nullptr)=0</div><div class="ttdoc">Adds a multiplication layer to the network. </div></div> +<div class="ttc" id="namespacearmnn_tf_parser_xhtml_a22ac203831113ee3e429746f6055aa73"><div class="ttname"><a href="namespacearmnn_tf_parser.xhtml#a22ac203831113ee3e429746f6055aa73">armnnTfParser::OutputShapeOfExpandDims</a></div><div class="ttdeci">TensorInfo OutputShapeOfExpandDims(const tensorflow::NodeDef &nodeDef, TensorInfo inputTensorInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8cpp_source.xhtml#l01468">TfParser.cpp:1468</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_ab6d332d9c4b4f04c23f40f04f7f56d0d"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#ab6d332d9c4b4f04c23f40f04f7f56d0d">armnn::INetwork::AddSubtractionLayer</a></div><div class="ttdeci">virtual IConnectableLayer * AddSubtractionLayer(const char *name=nullptr)=0</div><div class="ttdoc">Adds a subtraction layer to the network. </div></div> +<div class="ttc" id="namespacearmnn_tf_parser_xhtml_a6e06adf62d53562032e738b89f3eb37c"><div class="ttname"><a href="namespacearmnn_tf_parser.xhtml#a6e06adf62d53562032e738b89f3eb37c">armnnTfParser::OutputShapeOfSqueeze</a></div><div class="ttdeci">TensorInfo OutputShapeOfSqueeze(const tensorflow::NodeDef &nodeDef, TensorInfo inputTensorInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_tf_parser_8cpp_source.xhtml#l02463">TfParser.cpp:2463</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_50aba35e23cba3c8856f8642cc06d5dc.xhtml">armnnTfParser</a></li><li class="navelem"><a class="el" href="_tf_parser_8cpp.xhtml">TfParser.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> |