diff options
Diffstat (limited to '20.02/_onnx_parser_8cpp_source.xhtml')
| -rw-r--r-- | 20.02/_onnx_parser_8cpp_source.xhtml | 205 |
1 files changed, 205 insertions, 0 deletions
diff --git a/20.02/_onnx_parser_8cpp_source.xhtml b/20.02/_onnx_parser_8cpp_source.xhtml new file mode 100644 index 0000000000..9a7df6e759 --- /dev/null +++ b/20.02/_onnx_parser_8cpp_source.xhtml @@ -0,0 +1,205 @@ +<!-- 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/armnnOnnxParser/OnnxParser.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('_onnx_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">OnnxParser.cpp</div> </div> +</div><!--header--> +<div class="contents"> +<a href="_onnx_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> <span class="preprocessor">#include "<a class="code" href="_onnx_parser_8hpp.xhtml">OnnxParser.hpp</a>"</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> </div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include <<a class="code" href="_descriptors_8hpp.xhtml">armnn/Descriptors.hpp</a>></span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <<a class="code" href="_utils_8hpp.xhtml">armnn/Utils.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_verification_helpers_8hpp.xhtml">VerificationHelpers.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 <boost/format.hpp></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <boost/numeric/conversion/cast.hpp></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> </div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <google/protobuf/text_format.h></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <google/protobuf/io/zero_copy_stream_impl.h></span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <numeric></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="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> </div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="keyword">namespace </span><a class="code" href="namespacearmnn_onnx_parser.xhtml">armnnOnnxParser</a></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> {</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> {</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="keywordtype">void</span> CheckValidDataType(std::initializer_list<onnx::TensorProto::DataType> validInputTypes,</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">onnx::TensorProto::DataType</a> actualValue,</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keyword">const</span> <span class="keywordtype">char</span>* validExpr,</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  std::string nodeName,</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  std::string tensorName,</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_check_location.xhtml">armnn::CheckLocation</a>& location)</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> {</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="keywordtype">bool</span> isValid = std::any_of(validInputTypes.begin(),</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  validInputTypes.end(),</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  [&actualValue](<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">onnx::TensorProto::DataType</a> x) { <span class="keywordflow">return</span> x == actualValue; } );</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keywordflow">if</span> (!isValid)</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  {</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  boost::str(</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  boost::format(<span class="stringliteral">"Datatype %1% is not valid for tensor '%2%' of node '%3%', not in {%4%}. %5%"</span>) %</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  onnx::TensorProto::DataType_Name(actualValue) %</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  tensorName %</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  nodeName %</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  validExpr %</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  location.<a class="code" href="structarmnn_1_1_check_location.xhtml#a5e3562cda960da001597e7dd5679b140">AsString</a>()));</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  }</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> }</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> </div><div class="line"><a name="l00048"></a><span class="lineno"><a class="line" href="_onnx_parser_8cpp.xhtml#a71cae957feb9162183d6f62fd549ffe1"> 48</a></span> <span class="preprocessor">#define CHECK_VALID_DATATYPE(NODE, TENSOR, ACTUAL, ...) \</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> <span class="preprocessor">CheckValidDataType({__VA_ARGS__}, ACTUAL, #__VA_ARGS__, NODE, TENSOR, CHECK_LOCATION())</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> </div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <span class="keyword">using</span> StrTypeListPair = std::pair<const char*, std::initializer_list<onnx::TensorProto::DataType>>;</div><div class="line"><a name="l00052"></a><span class="lineno"><a class="line" href="_onnx_parser_8cpp.xhtml#a5426a7adb280d1739cc6d66fe9ac1b9c"> 52</a></span> <span class="preprocessor">#define STR_LIST(...) StrTypeListPair(#__VA_ARGS__, {__VA_ARGS__})</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> </div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> <span class="keyword">template</span> <<span class="keyword">typename</span> Callable></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> <span class="keywordtype">void</span> ReadMandatoryNodeAttributeImpl(<span class="keyword">const</span> onnx::NodeProto& node,</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keyword">const</span> std::string& attribName,</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  onnx::AttributeProto::AttributeType expectedType,</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  Callable callable)</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> {</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keyword">auto</span> attribs = node.attribute();</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keywordtype">int</span> attriNum = 0;</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordflow">while</span> (attriNum < node.attribute_size())</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  {</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="keywordflow">if</span> (attribs.Get(attriNum).name() == attribName)</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  {</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="keywordflow">if</span> (attribs.Get(attriNum).type() == expectedType)</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  {</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  callable(attribs.Get(attriNum));</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  {</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(boost::format(</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="stringliteral">"Attribute %1% of node %2% expected to have %3% as onnx::AttributeProto::AttributeType, "</span></div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="stringliteral">"but found %4% instead %5%"</span>)</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  % attribName</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  % node.name()</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  % onnx::AttributeProto::AttributeType_Name(expectedType)</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  % onnx::AttributeProto::AttributeType_Name(attribs.Get(attriNum).type())</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>  <span class="keywordflow">break</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>  ++attriNum;</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  }</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keywordflow">if</span> (attriNum == node.attribute_size())</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  {</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(boost::format(<span class="stringliteral">"Could not find required attribute %1% in node %2% %3%"</span>)</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  % attribName % node.name() % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  }</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> }</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> </div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> <span class="keyword">template</span> <<span class="keyword">typename</span> Callable></div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> <span class="keywordtype">void</span> ReadOptionalNodeAttributeImpl(<span class="keyword">const</span> onnx::NodeProto& node,</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keyword">const</span> std::string& attribName,</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  onnx::AttributeProto::AttributeType expectedType,</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  Callable callable)</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> {</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keyword">auto</span> attribs = node.attribute();</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> attriNum = 0; attriNum < node.attribute_size(); ++attriNum)</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  {</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keywordflow">if</span> (attribs.Get(attriNum).name() == attribName)</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  {</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keywordflow">if</span> (attribs.Get(attriNum).type() == expectedType)</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  {</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  callable(attribs.Get(attriNum));</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  }</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  {</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(boost::format(</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="stringliteral">"Attribute %1% of node %2% expected to have %3% as onnx::AttributeProto::AttributeType, "</span></div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="stringliteral">"but found %4% instead %5%"</span>)</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  % attribName</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  % node.name()</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  % onnx::AttributeProto::AttributeType_Name(expectedType)</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  % onnx::AttributeProto::AttributeType_Name(attribs.Get(attriNum).type())</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  }</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>  }</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> </div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> std::vector<uint32_t> ReadMandatoryNodeUint32ListAttribute(<span class="keyword">const</span> onnx::NodeProto& node,</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keyword">const</span> std::string& name)</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> {</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  std::vector<uint32_t> attriList;</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  ReadMandatoryNodeAttributeImpl(node, name, onnx::AttributeProto::INTS,</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  [&attriList](<span class="keyword">const</span> onnx::AttributeProto& attrValue)</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  {</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> attriNum = 0; attriNum < attrValue.ints_size(); ++attriNum)</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  {</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  attriList.push_back(<a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_verification_helpers_8hpp.xhtml#aa693ef8620e450b6362938828002f2a6">CHECKED_INT32</a>(attrValue.ints().Get(attriNum))));</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  }</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  });</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="keywordflow">return</span> attriList;</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> }</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> </div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> uint32_t ReadOptionalNodeUint32Attribute(<span class="keyword">const</span> onnx::NodeProto& node,</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keyword">const</span> std::string& name,</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keyword">const</span> uint32_t defaultVal = 0u)</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>  uint32_t attribValue = defaultVal;</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::INT,</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  [&attribValue](<span class="keyword">const</span> onnx::AttributeProto& attrValue)</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>  attribValue = <a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_verification_helpers_8hpp.xhtml#aa693ef8620e450b6362938828002f2a6">CHECKED_INT32</a>((attrValue.i())));</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  });</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="keywordflow">return</span> attribValue;</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> }</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> </div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> std::vector<uint32_t> ReadOptionalNodeUint32ListAttribute(<span class="keyword">const</span> onnx::NodeProto& node,</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keyword">const</span> std::string& name)</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> {</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  std::vector<uint32_t> attriList;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::INTS,</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  [&attriList](<span class="keyword">const</span> onnx::AttributeProto& attrValue)</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  {</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> attriNum = 0; attriNum < attrValue.ints_size(); ++attriNum)</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>  attriList.push_back(<a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_verification_helpers_8hpp.xhtml#aa693ef8620e450b6362938828002f2a6">CHECKED_INT32</a>(attrValue.ints().Get(attriNum))));</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  }</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  });</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>  <span class="keywordflow">return</span> attriList;</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> </div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> <span class="keywordtype">float</span> ReadOptionalNodeFloatAttribute(<span class="keyword">const</span> onnx::NodeProto& node,</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="keyword">const</span> std::string& name,</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> defaultValue = 0.0f)</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> {</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keywordtype">float</span> attribValue = defaultValue;</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::FLOAT,</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  [&attribValue](<span class="keyword">const</span> onnx::AttributeProto& attrValue)</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  {</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  attribValue = attrValue.f();</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  });</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keywordflow">return</span> attribValue;</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> }</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> </div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> std::string ReadOptionalNodeStringAttribute(<span class="keyword">const</span> onnx::NodeProto& node, <span class="keyword">const</span> std::string& name)</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>  std::string attribValue = <span class="stringliteral">""</span>;</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  ReadOptionalNodeAttributeImpl(node, name, onnx::AttributeProto::STRING,</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  [&attribValue](<span class="keyword">const</span> onnx::AttributeProto& attrValue)</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  {</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  attribValue = attrValue.s();</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  });</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="keywordflow">return</span> attribValue;</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> }</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> </div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(<span class="keyword">const</span> std::string& name, std::vector<unsigned int>& shape, <span class="keywordtype">int</span> data_type)</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> {</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> type;</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <span class="keywordflow">switch</span>(data_type)</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  {</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="keywordflow">case</span> onnx::TensorProto::FLOAT:</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>  type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>;</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  }</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keywordflow">case</span> onnx::TensorProto::INT32:</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <span class="keywordflow">case</span> onnx::TensorProto::INT64:</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  {</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  type = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>;</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  }</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  {</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  boost::str(</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  boost::format(<span class="stringliteral">"'%1%' is not a currently supported datatype for tensor %2%."</span></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="stringliteral">" Supported dataTypes are FLOAT, INT32 and INT64. %3%"</span>) %</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  onnx::TensorProto::DataType_Name(</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  static_cast<onnx::TensorProto::DataType>(data_type)) %</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  name %</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString() ));</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>  }</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> </div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="comment">// To avoid crashes by trivial tensors</span></div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keywordflow">if</span> (shape.empty())</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  {</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(), type);</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  }</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> </div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(static_cast<unsigned int>(shape.size()), shape.data()), type);</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span> }</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> </div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(<span class="keyword">const</span> onnx::ValueInfoProto& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>)</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span> {</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keyword">const</span> onnx::TensorShapeProto onnxShape = info.type().tensor_type().shape();</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  std::vector<unsigned int> shapeDims;</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i < onnxShape.dim_size(); ++i)</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>  shapeDims.push_back(<a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_verification_helpers_8hpp.xhtml#aa693ef8620e450b6362938828002f2a6">CHECKED_INT32</a>(onnxShape.dim(i).dim_value())));</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>  <span class="keywordflow">if</span> (shapeDims.empty())</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>  shapeDims.push_back(1);</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  }</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> </div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(info.name(), shapeDims, info.type().tensor_type().elem_type());</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> </div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(<span class="keyword">const</span> onnx::TensorProto& tensor)</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span> {</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  std::vector<unsigned int> shapeDims;</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> </div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> dim: tensor.dims())</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  {</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  shapeDims.push_back(<a class="code" href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a>(<a class="code" href="_verification_helpers_8hpp.xhtml#aa693ef8620e450b6362938828002f2a6">CHECKED_INT32</a>(dim)));</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> </div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="keywordflow">if</span> (shapeDims.empty())</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  {</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  shapeDims.push_back(1);</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  }</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> </div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(tensor.name(), shapeDims, tensor.data_type());</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> }</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> </div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> std::string TensorInfoAsString(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& info,</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="keyword">const</span> std::string& name,</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">onnx::TensorProto::DataType</a>& type)</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="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape = info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  std::stringstream ss;</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  ss << <span class="stringliteral">"tensor '"</span> << name << <span class="stringliteral">"' contains "</span></div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  << onnx::TensorProto::DataType_Name(type)</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  << <span class="stringliteral">" and has shape ["</span>;</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span> </div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="keywordflow">for</span> (uint32_t i = 0; i < shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - 1; ++i)</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  {</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  ss << shape[i] << <span class="stringliteral">", "</span>;</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  }</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  ss << shape[shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - 1] << <span class="stringliteral">"]"</span>;</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="keywordflow">return</span> ss.str();</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span> }</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="keywordtype">void</span> <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(uint32_t inputSize, uint32_t filterSize, uint32_t stride, uint32_t* paddingFront,</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  uint32_t* paddingBack, <span class="keywordtype">bool</span> isUpper)</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> {</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  uint32_t outputSize = (inputSize + stride - 1) / stride;</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  uint32_t temp = (outputSize - 1) * stride + filterSize;</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  *paddingFront = (temp - inputSize) / 2;</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  *paddingBack = *paddingFront;</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="keywordflow">if</span>((temp - inputSize) % 2 == 1)</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  {</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="keywordflow">if</span> (isUpper)</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  {</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  *paddingBack += 1;</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>  <span class="keywordflow">else</span></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>  *paddingFront += 1;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  }</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  }</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> </div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> ComputeReshapeInfo(<span class="keyword">const</span> onnx::TensorProto& targetShapeTensor,</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inShape,</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="keyword">const</span> std::string& outName)</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> {</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  std::vector<int> targetDims;</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < targetShapeTensor.int64_data_size(); ++i)</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  {</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <span class="keywordtype">int</span> val = <a class="code" href="_verification_helpers_8hpp.xhtml#aa693ef8620e450b6362938828002f2a6">CHECKED_INT32</a>(targetShapeTensor.int64_data(i));</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="keywordflow">if</span>(val == 0)</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  {</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  targetDims.push_back(static_cast<int>(inShape[static_cast<uint>(i)]));</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  }</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  {</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  targetDims.push_back(val);</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  }</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  }</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span> </div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  std::vector<unsigned int> outDims(targetDims.begin(), targetDims.end());</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</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="l00320"></a><span class="lineno"> 320</span>  <span class="keywordflow">if</span> (stretchDim != targetDims.end())</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  {</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <span class="keywordflow">if</span> (std::find(std::next(stretchDim), targetDims.end(), -1) != targetDims.end())</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  {</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  std::stringstream ss;</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  ss << <span class="stringliteral">"[ "</span>;</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <span class="keywordflow">for</span>(uint i = 0; i < targetDims.size() - 1; ++i)</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  {</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  ss << targetDims[i] << <span class="stringliteral">", "</span>;</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  }</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  ss << targetDims[targetDims.size() - 1] << <span class="stringliteral">" ]"</span>;</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span> </div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  boost::format(<span class="stringliteral">"Error during creation of reshaped tensor '%1%'. At most one component of shape can be "</span></div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <span class="stringliteral">" -1 and here, shape is %2% %3%"</span>)</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  % outName</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  % ss.str()</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  }</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span> </div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <span class="keyword">auto</span> targetNumElements = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(std::accumulate(targetDims.begin(), targetDims.end(),</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  -1, std::multiplies<int32_t>()));</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</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="l00343"></a><span class="lineno"> 343</span>  outDims[stretchIndex] = inShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() / targetNumElements;</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  }</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <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>{<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="l00346"></a><span class="lineno"> 346</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(outShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span> }</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span> </div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span> } <span class="comment">//namespace</span></div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span> </div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span> <span class="keyword">const</span> std::map<std::string, OnnxParser::OperationParsingFunction> OnnxParser::m_ParserFunctions = {</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  { <span class="stringliteral">"BatchNormalization"</span>, &OnnxParser::ParseBatchNormalization},</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  { <span class="stringliteral">"GlobalAveragePool"</span>, &OnnxParser::ParseGlobalAveragePool},</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  { <span class="stringliteral">"AveragePool"</span>, &OnnxParser::ParseAveragePool },</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  { <span class="stringliteral">"Constant"</span>, &OnnxParser::ParseConstant },</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  { <span class="stringliteral">"MaxPool"</span>, &OnnxParser::ParseMaxPool },</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  { <span class="stringliteral">"Reshape"</span>, &OnnxParser::ParseReshape },</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  { <span class="stringliteral">"Sigmoid"</span>, &OnnxParser::ParseSigmoid },</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  { <span class="stringliteral">"Tanh"</span>, &OnnxParser::ParseTanh },</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  { <span class="stringliteral">"Relu"</span>, &OnnxParser::ParseRelu },</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  { <span class="stringliteral">"LeakyRelu"</span>, &OnnxParser::ParseLeakyRelu },</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  { <span class="stringliteral">"Conv"</span>, &OnnxParser::ParseConv },</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  { <span class="stringliteral">"Add"</span>, &OnnxParser::ParseAdd },</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span> };</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span> </div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span> <span class="keyword">template</span><<span class="keyword">typename</span> TypePair, <span class="keyword">typename</span> Location></div><div class="line"><a name="l00367"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a74e612d0e7242695de575fb44e7f0762"> 367</a></span> <span class="keywordtype">void</span> OnnxParser::ValidateInputs(<span class="keyword">const</span> onnx::NodeProto& node,</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  TypePair validInputs,</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <span class="keyword">const</span> Location& location)</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span> {</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> input : node.input())</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  {</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  CheckValidDataType(validInputs.second,</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  m_TensorsInfo[input].m_dtype,</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  validInputs.first,</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  node.name(),</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  input,</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  location);</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  }</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span> }</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span> </div><div class="line"><a name="l00382"></a><span class="lineno"><a class="line" href="_onnx_parser_8cpp.xhtml#a0e987f9d4f46b35c9b1ff0cc950dc5b1"> 382</a></span> <span class="preprocessor">#define VALID_INPUTS(NODE, VALID_INPUTS) \</span></div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span> <span class="preprocessor"> OnnxParser::ValidateInputs(NODE, \</span></div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span> <span class="preprocessor"> VALID_INPUTS, \</span></div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span> <span class="preprocessor"> CHECK_LOCATION())</span></div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span> </div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span> std::vector<TensorInfo> OnnxParser::ComputeOutputInfo(std::vector<std::string> outNames,</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer,</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  std::vector<TensorShape> inputShapes)</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span> {</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  BOOST_ASSERT(! outNames.empty());</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keywordtype">bool</span> needCompute = std::any_of(outNames.begin(),</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  outNames.end(),</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  [<span class="keyword">this</span>](std::string name)</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  {</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <span class="keywordflow">return</span> (m_TensorsInfo.count(name) == 0 || m_TensorsInfo[name].m_info == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  });</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  std::vector<TensorInfo> outInfo;</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <span class="comment">//if the output info(s) are not here, we need to compute them</span></div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  std::vector<TensorShape> inferredShapes;</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="keywordflow">if</span>(needCompute)</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  {</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  inferredShapes = layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#aa6e3c075c888e7c16942a468a3aae33c">InferOutputShapes</a>(inputShapes);</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  BOOST_ASSERT(inferredShapes.size() == outNames.size());</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  }</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="keywordflow">for</span> (uint i = 0; i < outNames.size(); ++i)</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  {</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="keywordflow">if</span>(needCompute)</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  {</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  m_TensorsInfo[outNames[i]] = OnnxTensor();</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  m_TensorsInfo[outNames[i]].m_info = std::make_unique<TensorInfo>(</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(inferredShapes[i], <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  }</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  outInfo.push_back(*m_TensorsInfo[outNames[i]].m_info);</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  }</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <span class="keywordflow">return</span> outInfo;</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"><a class="line" href="classarmnn_onnx_parser_1_1_i_onnx_parser.xhtml#a1ae1d4dfe89d26b84d371439d6815bfb"> 419</a></span> <a class="code" href="classarmnn_onnx_parser_1_1_i_onnx_parser.xhtml">IOnnxParser</a>* IOnnxParser::CreateRaw()</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span> {</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keywordflow">return</span> <span class="keyword">new</span> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml">OnnxParser</a>();</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span> }</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span> </div><div class="line"><a name="l00424"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_i_onnx_parser.xhtml#af9b9254fb8a084f0db4f7deff0498b20"> 424</a></span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#ac7dfccab29feeb5f33f1ec0183c1e123">IOnnxParserPtr</a> IOnnxParser::Create()</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="keywordflow">return</span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#ac7dfccab29feeb5f33f1ec0183c1e123">IOnnxParserPtr</a>(CreateRaw(), &IOnnxParser::Destroy);</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span> }</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span> </div><div class="line"><a name="l00429"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_i_onnx_parser.xhtml#a793da4fa60bf13f128c20d8def32c291"> 429</a></span> <span class="keywordtype">void</span> IOnnxParser::Destroy(<a class="code" href="classarmnn_onnx_parser_1_1_i_onnx_parser.xhtml">IOnnxParser</a>* parser)</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span> {</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <span class="keyword">delete</span> parser;</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span> }</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span> </div><div class="line"><a name="l00434"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a1623e04cb9035d7b589eee3611f623fe"> 434</a></span> OnnxParser::OnnxParser()</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  : m_Network(nullptr, nullptr)</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="keywordtype">void</span> OnnxParser::ResetParser()</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>  m_Network = <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a>(<span class="keyword">nullptr</span>, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  m_Graph = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span> }</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span> </div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span> <span class="keywordtype">void</span> OnnxParser::Cleanup()</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span> {</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  m_TensorConnections.clear();</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  m_TensorsInfo.clear();</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  m_OutputsMap.clear();</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  m_OutputsFusedAndUsed.clear();</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> std::pair<ConstTensor, std::unique_ptr<float[]>> OnnxParser::CreateConstTensor(<span class="keyword">const</span> std::string name)</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span> {</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> tensorInfo = *m_TensorsInfo[name].m_info;</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  onnx::TensorProto onnxTensor = *m_TensorsInfo[name].m_tensor;</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span> </div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <span class="keyword">auto</span> srcData = onnxTensor.float_data().data();</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  std::unique_ptr<float[]> tensorData(<span class="keyword">new</span> <span class="keywordtype">float</span>[tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>()]);</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> tensorSizeInBytes = tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abcbdfb544ece4c31d0b37715ad0f3be0">GetNumBytes</a>();</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <span class="comment">// Copy the value list entries into the destination</span></div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keywordflow">if</span> (!onnxTensor.has_raw_data())</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  {</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <span class="keywordflow">if</span>(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() != <span class="keyword">static_cast<</span>uint<span class="keyword">></span>(onnxTensor.float_data_size()))</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  {</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  boost::format(<span class="stringliteral">"The number of data provided (%1%) does not match the tensor '%2%' number of elements"</span></div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <span class="stringliteral">" (%3%) %4%"</span>)</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  % onnxTensor.float_data_size()</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  % name</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  % tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>()</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  }</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  ::memcpy(tensorData.get(), srcData, tensorSizeInBytes);</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">else</span></div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  {</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  ::memcpy(tensorData.get(), onnxTensor.raw_data().c_str(), tensorSizeInBytes);</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  }</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span> </div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  <span class="comment">// Const tensors requires at least a list of values</span></div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <span class="keywordflow">if</span> (tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() == 0)</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  {</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  boost::format(<span class="stringliteral">"No tensor data found for Const tensor '%1%' %2%"</span>)</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  % name</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  }</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <span class="keywordflow">return</span> std::make_pair(<a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(tensorInfo, tensorData.get()), std::move(tensorData));</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> </div><div class="line"><a name="l00492"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a975a79b9b35d51ea81c42c05d245e7c0"> 492</a></span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a975a79b9b35d51ea81c42c05d245e7c0">OnnxParser::LoadModelFromTextFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile)</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span> {</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  FILE* fd = fopen(graphFile, <span class="stringliteral">"r"</span>);</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span> </div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <span class="keywordflow">if</span> (fd == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  {</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_file_not_found_exception.xhtml">FileNotFoundException</a>(boost::str(</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  boost::format(<span class="stringliteral">"Invalid (null) filename %1%"</span>) % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  <span class="comment">// Parse the file into a message</span></div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> modelProto = std::make_unique<onnx::ModelProto>();</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  <span class="keyword">using</span> google::protobuf::io::FileInputStream;</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  std::unique_ptr<FileInputStream> input = std::make_unique<FileInputStream>(fileno(fd));</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  <span class="keywordtype">bool</span> success = google::protobuf::TextFormat::Parse(input.get(), modelProto.get());</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  fclose(fd);</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span> </div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <span class="keywordflow">if</span> (!success)</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  {</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  std::stringstream <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a>;</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  error << <span class="stringliteral">"Failed to parse graph file"</span>;</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  boost::format(<span class="stringliteral">"%1% %2%"</span>) % error.str() % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  }</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  <span class="keywordflow">return</span> modelProto;</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"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a08bac41eb476686564b15063edf1fc04"> 519</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a08bac41eb476686564b15063edf1fc04">OnnxParser::CreateNetworkFromTextFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile)</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>  ResetParser();</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> modelProto = <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a975a79b9b35d51ea81c42c05d245e7c0">LoadModelFromTextFile</a>(graphFile);</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  <span class="keywordflow">return</span> CreateNetworkFromModel(*modelProto);</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> </div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span> </div><div class="line"><a name="l00527"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#acf9c6119ceb99755bc1f86c5a325c796"> 527</a></span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#acf9c6119ceb99755bc1f86c5a325c796">OnnxParser::LoadModelFromBinaryFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile)</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span> {</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  FILE* fd = fopen(graphFile, <span class="stringliteral">"rb"</span>);</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span> </div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <span class="keywordflow">if</span> (fd == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  {</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_file_not_found_exception.xhtml">FileNotFoundException</a>(boost::str(</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  boost::format(<span class="stringliteral">"Invalid (null) filename %1%"</span>) % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  }</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span> </div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <span class="comment">// Parse the file into a message</span></div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> modelProto = std::make_unique<onnx::ModelProto>();</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span> </div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  google::protobuf::io::FileInputStream inStream(fileno(fd));</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  google::protobuf::io::CodedInputStream codedStream(&inStream);</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  codedStream.SetTotalBytesLimit(INT_MAX, INT_MAX);</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <span class="keywordtype">bool</span> success = modelProto.get()->ParseFromCodedStream(&codedStream);</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  fclose(fd);</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span> </div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  <span class="keywordflow">if</span> (!success)</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  {</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  std::stringstream <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a>;</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  error << <span class="stringliteral">"Failed to parse graph file"</span>;</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  boost::format(<span class="stringliteral">"%1% %2%"</span>) % error.str() % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  }</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  <span class="keywordflow">return</span> modelProto;</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> </div><div class="line"><a name="l00557"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a012b24cafd443425314d4f9e06cec6c1"> 557</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a012b24cafd443425314d4f9e06cec6c1">OnnxParser::CreateNetworkFromBinaryFile</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* graphFile)</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span> {</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  ResetParser();</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> modelProto = <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#acf9c6119ceb99755bc1f86c5a325c796">LoadModelFromBinaryFile</a>(graphFile);</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  <span class="keywordflow">return</span> CreateNetworkFromModel(*modelProto);</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> </div><div class="line"><a name="l00564"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a181f87cf45fdc9f040a41c985ce7f8cd"> 564</a></span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a181f87cf45fdc9f040a41c985ce7f8cd">OnnxParser::LoadModelFromString</a>(<span class="keyword">const</span> std::string& protoText)</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span> {</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  <span class="keywordflow">if</span> (protoText == <span class="stringliteral">""</span>)</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  {</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(boost::str(</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  boost::format(<span class="stringliteral">"Invalid (empty) string for model parameter %1%"</span>) % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  }</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <span class="comment">// Parse the string into a message</span></div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> modelProto = std::make_unique<onnx::ModelProto>();</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <span class="keywordtype">bool</span> success = google::protobuf::TextFormat::ParseFromString(protoText, modelProto.get());</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  <span class="keywordflow">if</span> (!success)</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  {</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  std::stringstream <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">error</a>;</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  error << <span class="stringliteral">"Failed to parse graph file"</span>;</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  boost::format(<span class="stringliteral">"%1% %2%"</span>) % error.str() % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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">return</span> modelProto;</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span> }</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span> </div><div class="line"><a name="l00584"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a4cb67bfbe630abf10787ac613d1a31c5"> 584</a></span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a4cb67bfbe630abf10787ac613d1a31c5">OnnxParser::CreateNetworkFromString</a>(<span class="keyword">const</span> std::string& protoText)</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span> {</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  ResetParser();</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">ModelPtr</a> modelProto = <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a181f87cf45fdc9f040a41c985ce7f8cd">LoadModelFromString</a>(protoText);</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  <span class="keywordflow">return</span> CreateNetworkFromModel(*modelProto);</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span> }</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span> </div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> OnnxParser::CreateNetworkFromModel(onnx::ModelProto& model)</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span> {</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  m_Network = INetwork::Create();</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  {</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  m_Graph = std::make_unique<onnx::GraphProto>(*model.mutable_graph());</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  LoadGraph();</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="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="l00600"></a><span class="lineno"> 600</span>  {</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  Cleanup();</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  <span class="keywordflow">throw</span> e;</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>  Cleanup();</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <span class="keywordflow">return</span> std::move(m_Network);</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span> }</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span> </div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span> <span class="keywordtype">void</span> OnnxParser::LoadGraph()</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span> {</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  BOOST_ASSERT(m_Graph.get() != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span> </div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  <span class="comment">//Fill m_TensorsInfo with the shapes and value of every tensor</span></div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  SetupInfo(m_Graph->mutable_output());</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  SetupInfo(m_Graph->mutable_input());</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  SetupInfo(m_Graph->mutable_value_info());</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="keywordflow">for</span> (<span class="keyword">auto</span> tensor : m_Graph->initializer())</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  {</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  m_TensorsInfo[tensor.name()].m_tensor = std::make_unique<const onnx::TensorProto>(tensor);</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  m_TensorsInfo[tensor.name()].m_info = std::make_unique<TensorInfo>(<a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(tensor));</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  m_TensorsInfo[tensor.name()].m_dtype =</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  <span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">onnx::TensorProto::DataType</a><span class="keyword">></span>(tensor.data_type());</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  }</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span> </div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  SetupInputLayers();</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  SetupOutputLayers();</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span> </div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  <span class="comment">//Detect FullyConnected layers with bias and update the FusedAndUsed map acccordingly</span></div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  DetectFullyConnected();</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span> </div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  <span class="comment">//Parsing the graph</span></div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> nodeIndex = 0; nodeIndex < static_cast<size_t>(m_Graph->node_size()); nodeIndex++)</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  {</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  <span class="keyword">auto</span> node = m_Graph->node(static_cast<int>(nodeIndex));</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  <span class="keyword">const</span> std::string& operation = node.op_type();</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span> </div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  <span class="comment">// check which layers we handled already (add and matmul fused as FC)</span></div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <span class="keywordflow">if</span> (operation == <span class="stringliteral">"MatMul"</span> )</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  {</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <span class="keywordflow">if</span>(m_OutputsFusedAndUsed[nodeIndex].inputForNodes != m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes.size())</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  {</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  <span class="comment">//Node which can not be fused as a FullyConnected layer (used in layers as a simple matmul output)</span></div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  AddFullyConnected(node);</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  }</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  }</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (!(m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes.empty()) && operation == <span class="stringliteral">"Add"</span>)</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="keywordtype">int</span> matmulIndex = <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span> (m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes[0]);</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  AddFullyConnected(m_Graph->node(matmulIndex), &node);</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  }</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (m_OutputsFusedAndUsed[nodeIndex].fusedWithNodes.empty()) <span class="comment">//node is not part of a fused layer</span></div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  {</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  <span class="keyword">auto</span> it = m_ParserFunctions.find(operation);</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  <span class="keywordflow">if</span> (it != m_ParserFunctions.end())</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>  <span class="keyword">auto</span> func = it->second;</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  (this->*func)(node);</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  }</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  {</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  boost::format(<span class="stringliteral">"Unsupported operation %1% for node '%2%' %3%"</span>)</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  % operation</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  % node.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>  }</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>  <span class="comment">//Making the connections between outputs and inputs of each layers</span></div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <span class="keyword">auto</span>& tensorCon : m_TensorConnections)</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="keywordflow">if</span> (tensorCon.second.outputSlot != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  {</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> inputSlotIdx = 0; inputSlotIdx < tensorCon.second.inputSlots.size(); ++inputSlotIdx)</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>  tensorCon.second.outputSlot->Connect(*(tensorCon.second.inputSlots[inputSlotIdx]));</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  }</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  }</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  }</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span> }</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span> </div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span> <span class="keywordtype">void</span> OnnxParser::SetupInfo(<span class="keyword">const</span> google::protobuf::RepeatedPtrField<onnx::ValueInfoProto >* list)</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>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> tensor : *list)</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  {</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  m_TensorsInfo[tensor.name()] = OnnxTensor();</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  m_TensorsInfo[tensor.name()].m_info = std::make_unique<TensorInfo>(<a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(tensor));</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  m_TensorsInfo[tensor.name()].m_dtype =</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  <span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">onnx::TensorProto::DataType</a><span class="keyword">></span>(tensor.type().tensor_type().elem_type());</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  }</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span> }</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span> </div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span> <span class="keywordtype">void</span> OnnxParser::DetectFullyConnected()</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span> {</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  m_OutputsFusedAndUsed = std::vector<UsageSummary> (<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(m_Graph->node_size()), UsageSummary());</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <span class="keyword">auto</span> matmulAndConstant = [&](<span class="keyword">const</span> std::string& constInput,</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  <span class="keyword">const</span> std::string& matmulInput,</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  <span class="keywordtype">int</span>& nodeIndex)</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  {</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  <span class="keyword">auto</span> matmulIt = m_OutputsMap.find(matmulInput);</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  <span class="keywordflow">if</span>(matmulIt != m_OutputsMap.end() && matmulIt->second.first->op_type() == <span class="stringliteral">"MatMul"</span></div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  && m_TensorsInfo[constInput].isConstant())</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  {</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  nodeIndex = matmulIt->second.second;</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  }</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  <span class="keywordflow">return</span> <span class="keyword">false</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> </div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> nodeIndex = 0; nodeIndex < m_Graph->node_size(); nodeIndex++)</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  {</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  <span class="keyword">const</span> onnx::NodeProto* node = &m_Graph->node(nodeIndex);</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> std::string& output : node->output())</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>  m_OutputsMap[output] = std::make_pair(node, nodeIndex);</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  }</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span> </div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> std::string& input : node->input()) <span class="comment">//count how many time a node is used as input</span></div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  {</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  <span class="keyword">auto</span> matmulIt = m_OutputsMap.find(input);</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  <span class="keywordflow">if</span>(matmulIt != m_OutputsMap.end()){</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  ++m_OutputsFusedAndUsed[<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(matmulIt->second.second)].inputForNodes; <span class="comment">//node used</span></div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  }</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  }</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span> </div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  <span class="keywordflow">if</span> (node->op_type() == <span class="stringliteral">"Add"</span>)</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  {</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  <span class="keywordtype">int</span> matmulIndex = 0;</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  <span class="keywordflow">if</span> (matmulAndConstant(node->input(0), node->input(1), matmulIndex) ||</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  matmulAndConstant(node->input(1), node->input(0), matmulIndex))</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>  <span class="comment">//matmul and add were fused</span></div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  m_OutputsFusedAndUsed[<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(matmulIndex)].fusedWithNodes</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  .push_back(static_cast<size_t>(nodeIndex));</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span> </div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  m_OutputsFusedAndUsed[<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(nodeIndex)].fusedWithNodes</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  .push_back(static_cast<size_t>(matmulIndex));</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  }</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  }</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  }</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span> </div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> output: m_Graph->output()) { <span class="comment">//Add usages as output of the graph in count of usages</span></div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  <span class="keyword">auto</span> matmulIt = m_OutputsMap.find(output.name());</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  <span class="keywordflow">if</span>(matmulIt != m_OutputsMap.end()){</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  ++m_OutputsFusedAndUsed[<span class="keyword">static_cast<</span><span class="keywordtype">size_t</span><span class="keyword">></span>(matmulIt->second.second)].inputForNodes;</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>  }</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> </div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span> <span class="keyword">template</span><<span class="keyword">typename</span> Location></div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span> <span class="keywordtype">void</span> OnnxParser::GetInputAndParam(<span class="keyword">const</span> onnx::NodeProto& node,</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  std::string* inputName,</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  std::string* constName,</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  <span class="keyword">const</span> Location& location)</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span> {</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  <span class="keywordtype">int</span> cstIndex;</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  <span class="keywordflow">if</span> (m_TensorsInfo[node.input(0)].isConstant())</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  {</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  cstIndex = 0;</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  }</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (m_TensorsInfo[node.input(1)].isConstant())</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>  cstIndex = 1;</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  }</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  <span class="keywordflow">else</span></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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  boost::format(<span class="stringliteral">"One of the input tensors ('%1%' or '%2%') should be constant in node '%3%' %4%"</span>)</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  % node.input(0)</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  % node.input(1)</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  % node.name()</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  % location.AsString()));</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  }</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  <span class="keywordflow">if</span>(constName)</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  {</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  *constName = node.input(cstIndex);</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">if</span>(inputName)</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>  *inputName = node.input(!cstIndex);</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  }</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span> }</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span> </div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span> <span class="keyword">template</span><<span class="keyword">typename</span> Location></div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span> <span class="keywordtype">void</span> OnnxParser::To1DTensor(<span class="keyword">const</span> std::string& name, <span class="keyword">const</span> Location& location)</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span> {</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> shape = m_TensorsInfo[name].m_info->GetShape();</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  std::vector<uint32_t> newShape;</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  <span class="keywordflow">for</span>(uint i = 0; i < shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - 1; ++i)</div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  {</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  <span class="keywordflow">if</span>(shape[i] != 1)</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  {</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  boost::format(<span class="stringliteral">"Only tensors with shape [1, ..., 1, X] can be converted to 1D and %1% %2%"</span>)</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  % TensorInfoAsString(*m_TensorsInfo[name].m_info, name, m_TensorsInfo[name].m_dtype)</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  % location.AsString()));</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  }</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  }</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  newShape.push_back(shape[shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - 1]);</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span> </div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  m_TensorsInfo[name].m_info->SetShape(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(static_cast<unsigned int>(newShape.size()), newShape.data()));</div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span> }</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span> </div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span> <span class="keywordtype">void</span> OnnxParser::AddFullyConnected(<span class="keyword">const</span> onnx::NodeProto& matmulNode, <span class="keyword">const</span> onnx::NodeProto* addNode)</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span> {</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span> </div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  <span class="comment">// find matmul inputs</span></div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  std::string weightName;</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  std::string inputName;</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(static_cast<size_t>(matmulNode.input_size()), 2);</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(static_cast<size_t>(matmulNode.output_size()), 1);</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  <a class="code" href="_onnx_parser_8cpp.xhtml#a0e987f9d4f46b35c9b1ff0cc950dc5b1">VALID_INPUTS</a>(matmulNode, <a class="code" href="_onnx_parser_8cpp.xhtml#a5426a7adb280d1739cc6d66fe9ac1b9c">STR_LIST</a>(onnx::TensorProto::FLOAT));</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>  GetInputAndParam(matmulNode, &inputName, &weightName, <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>());</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span> </div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a> desc;</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  desc.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = addNode != <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span> </div><div class="line"><a name="l00820"></a><span class="lineno"> 820</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="l00821"></a><span class="lineno"> 821</span>  <span class="keywordflow">if</span>(desc.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>)</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>  <span class="comment">// find bias const</span></div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  std::string biasName;</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(static_cast<size_t>(addNode->input_size()), 2);</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(static_cast<size_t>(addNode->output_size()), 1);</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  <a class="code" href="_onnx_parser_8cpp.xhtml#a0e987f9d4f46b35c9b1ff0cc950dc5b1">VALID_INPUTS</a>(*addNode, <a class="code" href="_onnx_parser_8cpp.xhtml#a5426a7adb280d1739cc6d66fe9ac1b9c">STR_LIST</a>(onnx::TensorProto::FLOAT));</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span> </div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  GetInputAndParam(*addNode, <span class="keyword">nullptr</span>, &biasName, <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>());</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span> </div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  <span class="comment">//Output shape is [1, weights[1]] and 1d vec in ONNX can be [1,X] so we convert biases to "armnn" 1D</span></div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  To1DTensor(biasName, <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>());</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> weightInfo = *m_TensorsInfo[weightName].m_info;</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo = *m_TensorsInfo[biasName].m_info;</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">if</span> (weightInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1] != biasInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0])</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="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  boost::format(<span class="stringliteral">"Shape of weights '%1%' and bias of following Add node '%2%' do not match : %3%"</span></div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  <span class="stringliteral">" and %4% ( /!\\ bias should be a 1D tensor) %5%"</span>)</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  % weightName</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  % addNode->name()</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  % TensorInfoAsString(*m_TensorsInfo[weightName].m_info,</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  weightName,</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  m_TensorsInfo[weightName].m_dtype)</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  % TensorInfoAsString(*m_TensorsInfo[biasName].m_info, biasName,</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>  m_TensorsInfo[biasName].m_dtype )</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  }</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  layer = m_Network->AddFullyConnectedLayer(desc,</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  CreateConstTensor(weightName).first,</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(CreateConstTensor(biasName).first),</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  matmulNode.name().c_str());</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span> </div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({addNode->output(0)}, layer,</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  {m_TensorsInfo[inputName].m_info->GetShape(),</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  m_TensorsInfo[weightName].m_info->GetShape()});</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span> </div><div class="line"><a name="l00860"></a><span class="lineno"> 860</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[0]);</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span> </div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  RegisterInputSlots(layer, {inputName});</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  RegisterOutputSlots(layer, {addNode->output(0)});</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="keywordflow">else</span></div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  {</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  layer = m_Network->AddFullyConnectedLayer(desc,</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  CreateConstTensor(weightName).first,</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  <a class="code" href="structarmnn_1_1_empty_optional.xhtml">EmptyOptional</a>(),</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  matmulNode.name().c_str());</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span> </div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({matmulNode.output(0)}, layer,</div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  {m_TensorsInfo[inputName].m_info->GetShape(),</div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  m_TensorsInfo[weightName].m_info->GetShape()});</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</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[0]);</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span> </div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  RegisterInputSlots(layer, {inputName});</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  RegisterOutputSlots(layer, {matmulNode.output(0)});</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> }</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span> </div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span> <span class="keywordtype">void</span> OnnxParser::CreateConstantLayer(<span class="keyword">const</span> std::string& tensorName, <span class="keyword">const</span> std::string& layerName)</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span> {</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span>  <span class="keyword">auto</span> armnnTensor = CreateConstTensor(tensorName);</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span> </div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddConstantLayer(armnnTensor.first, layerName.c_str());</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</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>(armnnTensor.first.GetInfo());</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  RegisterOutputSlots(layer, {tensorName});</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span> }</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span> </div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span> <span class="keywordtype">void</span> OnnxParser::ParseConstant(<span class="keyword">const</span> onnx::NodeProto& node)</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span> {</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(static_cast<size_t>(node.attribute_size()), 1);</div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  <span class="keywordflow">if</span> (!node.attribute(0).has_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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span>  boost::format(<span class="stringliteral">"Value not found for Constant node '%1%' %2%"</span>)</div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  % node.name()</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  }</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  <span class="keyword">const</span> onnx::TensorProto& onnxTensor = node.attribute(0).t();</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>  <span class="comment">//ONNX can have Float16 and double constant nodes but ArmNN only supports float32</span></div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span>  <a class="code" href="_onnx_parser_8cpp.xhtml#a71cae957feb9162183d6f62fd549ffe1">CHECK_VALID_DATATYPE</a>(node.name(), onnxTensor.name(),</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  <span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">onnx::TensorProto::DataType</a><span class="keyword">></span>(onnxTensor.data_type()), onnx::TensorProto::FLOAT);</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span> </div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  <span class="comment">//Register this as a m_ConstParam so we know we can use it as a constant param in future layers.</span></div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  m_TensorsInfo[node.output(0)].m_tensor = std::make_unique<const onnx::TensorProto>(onnxTensor);</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span>  m_TensorsInfo[node.output(0)].m_info = std::make_unique<TensorInfo>(<a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(onnxTensor));</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  m_TensorsInfo[node.output(0)].m_dtype = <span class="keyword">static_cast<</span><a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">onnx::TensorProto::DataType</a><span class="keyword">></span>(onnxTensor.data_type());</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span> </div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span>  CreateConstantLayer(node.output(0), node.name());</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="keywordtype">void</span> OnnxParser::ParseMaxPool(<span class="keyword">const</span> onnx::NodeProto& node)</div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span> {</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> desc;</div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = PoolingAlgorithm::Max;</div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = PaddingMethod::Exclude;</div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>  AddPoolingLayer(node, desc);</div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span> }</div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span> </div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span> <span class="keywordtype">void</span> OnnxParser::ParseGlobalAveragePool(<span class="keyword">const</span> onnx::NodeProto& node)</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>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> desc = <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a>();</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = PoolingAlgorithm::Average;</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span> </div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  <span class="comment">//kernel size is the same as input</span></div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape();</div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = inputShape[3];</div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = inputShape[2];</div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span> </div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddPooling2dLayer(desc, node.name().c_str());</div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span> </div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({node.output(0)}, layer, {inputShape});</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</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[0]);</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span> </div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  RegisterInputSlots(layer, {node.input(0)});</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span> </div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  RegisterOutputSlots(layer, {node.output(0)});</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span> }</div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span> </div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span> <span class="keywordtype">void</span> OnnxParser::ParseAveragePool(<span class="keyword">const</span> onnx::NodeProto& node)</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span> {</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> desc;</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = PoolingAlgorithm::Average;</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>  uint32_t count_include_pad = 0;</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  count_include_pad = ReadOptionalNodeUint32Attribute(node, <span class="stringliteral">"count_include_pad"</span>);</div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  <span class="keywordflow">if</span>(count_include_pad) {</div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a8c29d6ea9b4186d69aad5961c910939c">m_PaddingMethod</a> = PaddingMethod::IgnoreValue;</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span>  }</div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  AddPoolingLayer(node, desc);</div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span> }</div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span> </div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span> <span class="keywordtype">void</span> OnnxParser::AddPoolingLayer(<span class="keyword">const</span> onnx::NodeProto& node, <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a>& desc)</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> </div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(static_cast<size_t>(node.input_size()), 1);</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(static_cast<size_t>(node.output_size()), 1);</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>  <a class="code" href="_onnx_parser_8cpp.xhtml#a0e987f9d4f46b35c9b1ff0cc950dc5b1">VALID_INPUTS</a>(node, <a class="code" href="_onnx_parser_8cpp.xhtml#a5426a7adb280d1739cc6d66fe9ac1b9c">STR_LIST</a>(onnx::TensorProto::FLOAT));</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span> </div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span>  std::vector<uint32_t> kernel_shape = ReadMandatoryNodeUint32ListAttribute(node, <span class="stringliteral">"kernel_shape"</span>); <span class="comment">//size of pool win</span></div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  std::vector<uint32_t> strides = ReadOptionalNodeUint32ListAttribute(node, <span class="stringliteral">"strides"</span>);</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  std::vector<uint32_t> pads = ReadOptionalNodeUint32ListAttribute(node, <span class="stringliteral">"pads"</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>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#affb5b68b3eba3ed45a06c7cde7781962">m_OutputShapeRounding</a> = OutputShapeRounding::Floor;</div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = kernel_shape[1];</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = kernel_shape[0];</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span> </div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span>  <span class="keywordflow">if</span>(strides.empty())</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>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</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="keywordflow">else</span></div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  {</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strides[1];</div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strides[0];</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  }</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="comment">//Check new padding version first</span></div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  <span class="keywordflow">if</span>(pads.empty())</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span>  {</div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  <span class="comment">//Check deprecated version</span></div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  std::string paddingString = ReadOptionalNodeStringAttribute(node, <span class="stringliteral">"auto_pad"</span>);</div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span>  <span class="keywordflow">if</span>(paddingString != <span class="stringliteral">"VALID"</span> && paddingString != <span class="stringliteral">""</span> && paddingString != <span class="stringliteral">"NOTSET"</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>  <span class="keywordtype">bool</span> isUpper;</div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span>  <span class="keywordflow">if</span>( paddingString == <span class="stringliteral">"SAME_LOWER"</span>)</div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span>  {</div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span>  isUpper = <span class="keyword">false</span>;</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  }</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (paddingString == <span class="stringliteral">"SAME_UPPER"</span>)</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>  {</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  isUpper = <span class="keyword">true</span>;</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  {</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>  boost::format(<span class="stringliteral">"Invalid auto_pad attribute for node %1%. "</span></div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  <span class="stringliteral">"Only SAME_UPPER, SAME_LOWER or VALID supported and found %2% %3%"</span>)</div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  % node.name()</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>  % paddingString</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  }</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  <span class="keyword">auto</span> inputInfo = *m_TensorsInfo[node.input(0)].m_info;</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>  uint32_t inputHeight = inputInfo.GetShape()[2];</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>  uint32_t inputWidth = inputInfo.GetShape()[3];</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>  <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputHeight, desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a>, desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>, &desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>, &desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>, isUpper);</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>  <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputWidth, desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a>, desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>, &desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>, &desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>, isUpper);</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>  }</div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>  }</div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>  <span class="keywordflow">else</span></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>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = pads[0];</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = pads[1];</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = pads[2];</div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = pads[3];</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> </div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddPooling2dLayer(desc, node.name().c_str());</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</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="keyword">auto</span> outputInfo = ComputeOutputInfo({node.output(0)}, layer, {m_TensorsInfo[node.input(0)].m_info->GetShape()});</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</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[0]);</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>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>  RegisterInputSlots(layer, {node.input(0)});</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span> </div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span>  RegisterOutputSlots(layer, {node.output(0)});</div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span> }</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="keywordtype">void</span> OnnxParser::CreateReshapeLayer(<span class="keyword">const</span> std::string& inputName,</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  <span class="keyword">const</span> std::string& outputName,</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>  <span class="keyword">const</span> std::string& layerName)</div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span> {</div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = *m_TensorsInfo[outputName].m_info;</div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">ReshapeDescriptor</a> reshapeDesc;</div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span>  reshapeDesc.<a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span> </div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str());</div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</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="l01053"></a><span class="lineno"> 1053</span> </div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>  RegisterInputSlots(layer, {inputName});</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span> </div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>  RegisterOutputSlots(layer, {outputName});</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> </div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span> <span class="keywordtype">void</span> OnnxParser::ParseReshape(<span class="keyword">const</span> onnx::NodeProto& node)</div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span> {</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(static_cast<size_t>(node.input_size()), 2);</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(static_cast<size_t>(node.output_size()), 1);</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>  <a class="code" href="_onnx_parser_8cpp.xhtml#a71cae957feb9162183d6f62fd549ffe1">CHECK_VALID_DATATYPE</a>(node.name(), node.input(0),</div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>  m_TensorsInfo[node.input(0)].m_dtype,</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>  onnx::TensorProto::FLOAT); <span class="comment">//input</span></div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>  <a class="code" href="_onnx_parser_8cpp.xhtml#a71cae957feb9162183d6f62fd549ffe1">CHECK_VALID_DATATYPE</a>(node.name(), node.input(1),</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>  m_TensorsInfo[node.input(1)].m_dtype,</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>  onnx::TensorProto::INT64); <span class="comment">//shape</span></div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span> </div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>  <span class="keywordflow">if</span>(!m_TensorsInfo[node.input(1)].isConstant())</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>  {</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>  boost::format(<span class="stringliteral">"Shape '%1%' should be constant in Reshape layer '%2%' %3%"</span>)</div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>  % node.input(1)</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>  % node.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="keywordflow">if</span>(m_TensorsInfo[node.input(0)].isConstant())</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>  {</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>  <span class="comment">//make a new cst tensor -> move the data to the output tensor (the shape is already good in the output tensor)</span></div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>  <span class="keywordflow">if</span>(m_TensorsInfo.count(node.output(0)) == 0)</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>  {</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>  m_TensorsInfo[node.output(0)] = OnnxTensor();</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  }</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>  m_TensorsInfo[node.output(0)].m_tensor =</div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>  std::make_unique<onnx::TensorProto>(*m_TensorsInfo[node.input(0)].m_tensor);</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="keywordflow">else</span></div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>  {</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape = m_TensorsInfo[node.input(0)].m_info->GetShape();</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>(m_TensorsInfo.count(node.output(0)) == 0 || m_TensorsInfo[node.output(0)].m_info == <span class="keyword">nullptr</span>)</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>  <span class="keyword">auto</span> outInfo = ComputeReshapeInfo(*m_TensorsInfo[node.input(1)].m_tensor, inputShape, node.output(0));</div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>  m_TensorsInfo[node.output(0)].m_info = std::make_unique<TensorInfo>(outInfo);</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>  CreateReshapeLayer(node.input(0), node.output(0), node.name());</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> }</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span> </div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span> <span class="keywordtype">void</span> OnnxParser::ParseActivation(<span class="keyword">const</span> onnx::NodeProto& node, <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9ea">armnn::ActivationFunction</a> func)</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span> {</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(static_cast<size_t>(node.input_size()), 1);</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(static_cast<size_t>(node.output_size()), 1);</div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span> </div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>  <a class="code" href="_onnx_parser_8cpp.xhtml#a0e987f9d4f46b35c9b1ff0cc950dc5b1">VALID_INPUTS</a>(node, <a class="code" href="_onnx_parser_8cpp.xhtml#a5426a7adb280d1739cc6d66fe9ac1b9c">STR_LIST</a>(onnx::TensorProto::FLOAT));</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>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> desc;</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>  desc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = func;</div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span> </div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* <span class="keyword">const</span> layer = m_Network->AddActivationLayer(desc, node.name().c_str());</div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span> </div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span>  <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({ node.output(0)}, layer, {m_TensorsInfo[node.input(0)].m_info->GetShape()});</div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</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[0]);</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span> </div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span>  RegisterInputSlots(layer, {node.input(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="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>  RegisterOutputSlots(layer, {node.output(0)});</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span> }</div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span> </div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span> <span class="keywordtype">void</span> OnnxParser::ParseSigmoid(<span class="keyword">const</span> onnx::NodeProto& node)</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span> {</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>  ParseActivation(node, ActivationFunction::Sigmoid);</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="keywordtype">void</span> OnnxParser::ParseTanh(<span class="keyword">const</span> onnx::NodeProto& node)</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span> {</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>  ParseActivation(node, ActivationFunction::TanH);</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span> }</div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span> </div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span> <span class="keywordtype">void</span> OnnxParser::ParseRelu(<span class="keyword">const</span> onnx::NodeProto& node)</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span> {</div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>  ParseActivation(node, ActivationFunction::ReLu);</div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span> }</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> <span class="keywordtype">void</span> OnnxParser::ParseLeakyRelu(<span class="keyword">const</span> onnx::NodeProto& node)</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span> {</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>  ParseActivation(node, ActivationFunction::LeakyReLu);</div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span> }</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span> </div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span> <span class="keywordtype">void</span> OnnxParser::AddConvLayerWithDepthwiseConv(<span class="keyword">const</span> onnx::NodeProto& node, <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a>& convDesc)</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span> {</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>  BOOST_ASSERT(node.op_type() == <span class="stringliteral">"Conv"</span>);</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>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a> desc;</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  desc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a>;</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>  desc.m_PadRight = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a>;</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>  desc.m_PadTop = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a>;</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>  desc.m_PadBottom = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a>;</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>  desc.m_StrideX = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a>;</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>  desc.m_StrideY = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a>;</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>  desc.m_BiasEnabled = convDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a>;</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span> </div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer;</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>  <span class="keyword">auto</span> weightTensor = CreateConstTensor(node.input(1));</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& weightShape = weightTensor.first.GetShape();</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  weightShape[1] = weightShape[0];</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>  weightShape[0] = 1;</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  m_TensorsInfo[node.input(1)].m_info->SetShape(weightShape);</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span> </div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>  <span class="keywordflow">if</span> (node.input_size() == 3)</div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>  {</div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>  <span class="keywordflow">if</span>(!m_TensorsInfo[node.input(2)].isConstant())</div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  {</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>  boost::format(<span class="stringliteral">"Bias '%1%' should be constant in Conv layer '%2%' %3%"</span>)</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>  % node.input(2)</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  % node.name()</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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>  desc.m_BiasEnabled = <span class="keyword">true</span>;</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>  <span class="keyword">auto</span> biasTensor = CreateConstTensor(node.input(2));</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  layer = m_Network->AddDepthwiseConvolution2dLayer(desc,</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  weightTensor.first,</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(biasTensor.first),</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>  node.name().c_str());</div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>  }</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>  {</div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  layer = m_Network->AddDepthwiseConvolution2dLayer(desc,</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>  weightTensor.first,</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>  <a class="code" href="structarmnn_1_1_empty_optional.xhtml">EmptyOptional</a>(),</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>  node.name().c_str());</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>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span> </div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({ node.output(0) }, layer,</div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  { m_TensorsInfo[node.input(0)].m_info->GetShape(),</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>  m_TensorsInfo[node.input(1)].m_info->GetShape() });</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span> </div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</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[0]);</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>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>  RegisterInputSlots(layer, {node.input(0)});</div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span> </div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>  RegisterOutputSlots(layer, {node.output(0)});</div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span> }</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span> </div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span> <span class="keywordtype">void</span> OnnxParser::ParseConv(<span class="keyword">const</span> onnx::NodeProto& node)</div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span> {</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(static_cast<size_t>(node.input_size()), 2, 3); <span class="comment">//input, weight, (bias)</span></div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(static_cast<size_t>(node.output_size()), 1);</div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span> </div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>  <a class="code" href="_onnx_parser_8cpp.xhtml#a0e987f9d4f46b35c9b1ff0cc950dc5b1">VALID_INPUTS</a>(node, <a class="code" href="_onnx_parser_8cpp.xhtml#a5426a7adb280d1739cc6d66fe9ac1b9c">STR_LIST</a>(onnx::TensorProto::FLOAT));</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span> </div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span>  <span class="keywordflow">if</span>(m_TensorsInfo[node.input(0)].m_info->GetNumDimensions() != 4)</div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span>  {</div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span>  boost::format(<span class="stringliteral">"ArmNN only supports 2D convolution and Conv layer '%1%' input %2% %3%"</span>)</div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span>  % node.name()</div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>  % TensorInfoAsString(*m_TensorsInfo[node.input(0)].m_info, node.input(0),</div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>  m_TensorsInfo[node.input(0)].m_dtype)</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>  }</div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span> </div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>  <span class="keywordflow">if</span>(!m_TensorsInfo[node.input(1)].isConstant())</div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span>  {</div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>  boost::format(<span class="stringliteral">"Weights '%1%' should be constant in Conv layer '%2%' %3%"</span>)</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>  % node.input(1)</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>  % node.name()</div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>  }</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="keyword">auto</span> inputInfo = *m_TensorsInfo[node.input(0)].m_info;</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span> </div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>  std::vector<uint32_t> dilations = ReadOptionalNodeUint32ListAttribute(node, <span class="stringliteral">"dilations"</span>);</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  <span class="keywordflow">if</span> (!dilations.empty())</div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>  {</div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>  std::stringstream ss;</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  ss << <span class="stringliteral">"[ "</span>;</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> dilation : dilations)</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>  ss << dilation << <span class="stringliteral">", "</span>;</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>  <span class="keywordflow">if</span> (dilation != 1u)</div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span>  {</div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>  ss << <span class="stringliteral">"... ]"</span>;</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>  boost::format(<span class="stringliteral">"ArmNN only supports Convolution layers with dilations [1,1], and node '%1%' "</span></div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>  <span class="stringliteral">"has dilatation %2% %3%"</span>)</div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span>  % node.name()</div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  % ss.str()</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>  }</div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>  }</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>  }</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span> </div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> desc;</div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</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="l01262"></a><span class="lineno"> 1262</span> </div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>  std::vector<uint32_t> strides = ReadOptionalNodeUint32ListAttribute(node, <span class="stringliteral">"strides"</span>);</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>  <span class="keywordflow">if</span>(strides.empty())</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>  {</div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span>  }</div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>  {</div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strides[1];</div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strides[0];</div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>  }</div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span> </div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span>  std::vector<uint32_t> pads = ReadOptionalNodeUint32ListAttribute(node, <span class="stringliteral">"pads"</span>);</div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span>  <span class="comment">//Check new padding version first</span></div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span>  <span class="keywordflow">if</span>(pads.empty())</div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>  {</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span>  <span class="comment">//Check deprecated version</span></div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span>  std::string paddingString = ReadOptionalNodeStringAttribute(node, <span class="stringliteral">"auto_pad"</span>);</div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span>  <span class="keywordflow">if</span>(paddingString != <span class="stringliteral">"VALID"</span> && paddingString != <span class="stringliteral">""</span> && paddingString != <span class="stringliteral">"NOTSET"</span>)</div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span>  {</div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span>  <span class="keywordtype">bool</span> isUpper;</div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span>  <span class="keywordflow">if</span>( paddingString == <span class="stringliteral">"SAME_LOWER"</span>)</div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>  {</div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>  isUpper = <span class="keyword">false</span>;</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="keywordflow">else</span> <span class="keywordflow">if</span> (paddingString == <span class="stringliteral">"SAME_UPPER"</span>)</div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>  {</div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>  isUpper = <span class="keyword">true</span>;</div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>  }</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>  {</div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>  boost::format(<span class="stringliteral">"Invalid auto_pad attribute for node %1%. "</span></div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>  <span class="stringliteral">"Only SAME_UPPER, SAME_LOWER or VALID supported and found %2% %3%"</span>)</div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span>  % node.name()</div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>  % paddingString</div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span>  }</div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>  uint32_t inputHeight = inputInfo.GetShape()[2];</div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span>  uint32_t inputWidth = inputInfo.GetShape()[3];</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>  uint32_t weightHeight;</div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span>  uint32_t weightWidth;</div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>  std::vector<uint32_t> kernel_shape = ReadOptionalNodeUint32ListAttribute(node, <span class="stringliteral">"kernel_shape"</span>);</div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>  <span class="keywordflow">if</span> (kernel_shape.empty())</div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</span>  {</div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> weightTensorInfo = *m_TensorsInfo[node.input(1)].m_info;</div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>  weightHeight = weightTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2];</div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span>  weightWidth = weightTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[3];</div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>  }</div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>  {</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span>  weightHeight = kernel_shape[0];</div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>  weightWidth = kernel_shape[1];</div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>  }</div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>  <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>, isUpper);</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>  <a class="code" href="namespacearmnn_tf_parser.xhtml#aa78bf8d20e213dcd13d48072dfa9cd1f">CalcPadding</a>(inputWidth, 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>, isUpper);</div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span>  }</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>  }</div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>  {</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = pads[0];</div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = pads[1];</div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = pads[2];</div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = pads[3];</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span>  }</div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span> </div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>  uint32_t group = ReadOptionalNodeUint32Attribute(node, <span class="stringliteral">"group"</span>, 1);</div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span>  <span class="keywordflow">if</span>(group > 1)</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>  <span class="keywordflow">if</span> (group > inputInfo.GetShape()[1])</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>  {</div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>  boost::str(</div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>  boost::format(</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span>  <span class="stringliteral">"Error parsing Convolution node: %1%. "</span></div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>  <span class="stringliteral">"The 'group'=%2% parameter cannot be larger than the "</span></div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>  <span class="stringliteral">"channel of the input shape=%3% (in NCHW format). %4%"</span>) %</div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span>  node.name() %</div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span>  group %</div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span>  inputInfo.GetShape()[1] %</div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (group == inputInfo.GetShape()[1])</div><div class="line"><a name="l01347"></a><span class="lineno"> 1347</span>  {</div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span>  <span class="comment">// we use a depthwise convolution here, because the number of groups equals to the</span></div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span>  <span class="comment">// input channels</span></div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>  AddConvLayerWithDepthwiseConv(node, desc);</div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>  }</div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span>  {</div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>  <span class="comment">// TODO: split the input by channels into channels/groups separate convolutions</span></div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>  <span class="comment">// and concatenate the results afterwards</span></div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>  boost::format(<span class="stringliteral">"Error parsing Convolution node: %1%. "</span></div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span>  <span class="stringliteral">"The 'group'=%2% parameter should be 1 or be equal to the "</span></div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span>  <span class="stringliteral">"channel of the input shape=%3% (in NCHW format). %4%"</span>) %</div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>  node.name() %</div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>  group %</div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span>  inputInfo.GetShape()[1] %</div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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>  }</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span> </div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* layer;</div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span>  <span class="keyword">auto</span> weightTensor = CreateConstTensor(node.input(1));</div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span> </div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span>  <span class="keywordflow">if</span> (node.input_size() == 3)</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>  <span class="keywordflow">if</span>(!m_TensorsInfo[node.input(2)].isConstant())</div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</span>  {</div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span>  boost::format(<span class="stringliteral">"Bias '%1%' should be constant in Conv layer '%2%' %3%"</span>)</div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span>  % node.input(2)</div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>  % node.name()</div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>  }</div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span>  desc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01382"></a><span class="lineno"> 1382</span>  <span class="keyword">auto</span> biasTensor = CreateConstTensor(node.input(2));</div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span>  layer = m_Network->AddConvolution2dLayer(desc,</div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</span>  weightTensor.first,</div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(biasTensor.first),</div><div class="line"><a name="l01386"></a><span class="lineno"> 1386</span>  node.name().c_str());</div><div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>  }</div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</span>  <span class="keywordflow">else</span></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>  layer = m_Network->AddConvolution2dLayer(desc,</div><div class="line"><a name="l01391"></a><span class="lineno"> 1391</span>  weightTensor.first,</div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span>  <a class="code" href="structarmnn_1_1_empty_optional.xhtml">EmptyOptional</a>(),</div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>  node.name().c_str());</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>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span> </div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span>  <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({ node.output(0) }, layer,</div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span>  { m_TensorsInfo[node.input(0)].m_info->GetShape(),</div><div class="line"><a name="l01399"></a><span class="lineno"> 1399</span>  m_TensorsInfo[node.input(1)].m_info->GetShape() });</div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</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[0]);</div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span> </div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>  <span class="comment">// register the input connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span>  <span class="comment">// only the tensors for the inputs are relevant, exclude the const tensors</span></div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span>  RegisterInputSlots(layer, {node.input(0)});</div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span> </div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>  <span class="comment">// register the output connection slots for the layer, connections are made after all layers have been created</span></div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>  RegisterOutputSlots(layer, {node.output(0)});</div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span> }</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">void</span> OnnxParser::PrependForBroadcast(<span class="keyword">const</span> std::string& outputName,</div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>  <span class="keyword">const</span> std::string& input0,</div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span>  <span class="keyword">const</span> std::string& input1)</div><div class="line"><a name="l01413"></a><span class="lineno"> 1413</span> {</div><div class="line"><a name="l01414"></a><span class="lineno"> 1414</span>  <span class="comment">//input0 should be reshaped to have same number of dim as input1</span></div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(*m_TensorsInfo[input0].m_info);</div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span> </div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> input0Shape = m_TensorsInfo[input0].m_info->GetShape();</div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> input1Shape = m_TensorsInfo[input1].m_info->GetShape();</div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span> </div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>  uint32_t diff = input1Shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - input0Shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>  std::vector<uint32_t> newShape;</div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>  <span class="keywordflow">while</span>(diff > 0)</div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span>  {</div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>  newShape.push_back(1);</div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>  diff--;</div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>  }</div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span>  <span class="keywordflow">for</span> (uint dim = 0; dim < input0Shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(); ++dim)</div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</span>  {</div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span>  newShape.push_back(input0Shape[dim]);</div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span>  }</div><div class="line"><a name="l01431"></a><span class="lineno"> 1431</span>  outputTensorInfo.<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>(static_cast<unsigned int>(newShape.size()), newShape.data()));</div><div class="line"><a name="l01432"></a><span class="lineno"> 1432</span> </div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</span>  <span class="comment">//add the new tensor to m_TensorsInfo</span></div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>  m_TensorsInfo[outputName] = OnnxTensor();</div><div class="line"><a name="l01435"></a><span class="lineno"> 1435</span>  m_TensorsInfo[outputName].m_info = std::make_unique<TensorInfo>(outputTensorInfo);</div><div class="line"><a name="l01436"></a><span class="lineno"> 1436</span> </div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</span>  <span class="comment">//add reshape layer if the parent was not constant...</span></div><div class="line"><a name="l01438"></a><span class="lineno"> 1438</span>  <span class="keywordflow">if</span>( ! m_TensorsInfo[input0].isConstant())</div><div class="line"><a name="l01439"></a><span class="lineno"> 1439</span>  {</div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span>  CreateReshapeLayer(input0, outputName, boost::str(boost::format(<span class="stringliteral">"Add:reshapeOf%1%"</span>) % input0));</div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</span>  }</div><div class="line"><a name="l01442"></a><span class="lineno"> 1442</span>  <span class="keywordflow">else</span> <span class="comment">//make it constant and it will be create in Add</span></div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span>  {</div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>  m_TensorsInfo[outputName].m_tensor = std::make_unique<onnx::TensorProto>(*m_TensorsInfo[input0].m_tensor);</div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span> </div><div class="line"><a name="l01446"></a><span class="lineno"> 1446</span>  }</div><div class="line"><a name="l01447"></a><span class="lineno"> 1447</span> }</div><div class="line"><a name="l01448"></a><span class="lineno"> 1448</span> </div><div class="line"><a name="l01449"></a><span class="lineno"> 1449</span> std::pair<std::string, std::string> OnnxParser::AddPrepareBroadcast(<span class="keyword">const</span> std::string& input0,</div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span>  <span class="keyword">const</span> std::string& input1)</div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span> {</div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>  std::pair<std::string, std::string> inputs = std::make_pair(input0, input1);</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>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> input0Shape = m_TensorsInfo[input0].m_info->GetShape();</div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> input1Shape = m_TensorsInfo[input1].m_info->GetShape();</div><div class="line"><a name="l01456"></a><span class="lineno"> 1456</span> </div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span>  <span class="keywordflow">if</span>(input1Shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() < input0Shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span>  {</div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span>  <span class="keyword">auto</span> outputName = boost::str(boost::format(<span class="stringliteral">"reshape_output_%1%"</span>) % input1);</div><div class="line"><a name="l01460"></a><span class="lineno"> 1460</span>  PrependForBroadcast(outputName, input1, input0);</div><div class="line"><a name="l01461"></a><span class="lineno"> 1461</span>  inputs.second = outputName;</div><div class="line"><a name="l01462"></a><span class="lineno"> 1462</span>  }</div><div class="line"><a name="l01463"></a><span class="lineno"> 1463</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(input0Shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() < input1Shape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</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="keyword">auto</span> outputName = boost::str(boost::format(<span class="stringliteral">"reshape_output_%1%"</span>) % input0);</div><div class="line"><a name="l01466"></a><span class="lineno"> 1466</span>  PrependForBroadcast(outputName, input0, input1);</div><div class="line"><a name="l01467"></a><span class="lineno"> 1467</span>  inputs.first = outputName;</div><div class="line"><a name="l01468"></a><span class="lineno"> 1468</span>  }</div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span>  <span class="keywordflow">return</span> inputs;</div><div class="line"><a name="l01470"></a><span class="lineno"> 1470</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="keywordtype">void</span> OnnxParser::ParseAdd(<span class="keyword">const</span> onnx::NodeProto& node)</div><div class="line"><a name="l01473"></a><span class="lineno"> 1473</span> {</div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(static_cast<size_t>(node.input_size()), 2);</div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(static_cast<size_t>(node.output_size()), 1);</div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span> </div><div class="line"><a name="l01477"></a><span class="lineno"> 1477</span>  <a class="code" href="_onnx_parser_8cpp.xhtml#a0e987f9d4f46b35c9b1ff0cc950dc5b1">VALID_INPUTS</a>(node, <a class="code" href="_onnx_parser_8cpp.xhtml#a5426a7adb280d1739cc6d66fe9ac1b9c">STR_LIST</a>(onnx::TensorProto::FLOAT));</div><div class="line"><a name="l01478"></a><span class="lineno"> 1478</span> </div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>  <span class="comment">// TODO: unify broadcast validation code across layers</span></div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span>  <span class="comment">// tracked by: IVGCVSW-1576</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>  <span class="comment">// Checking broadcast compatibility : only scalar or 1D tensors</span></div><div class="line"><a name="l01483"></a><span class="lineno"> 1483</span>  <span class="keyword">auto</span> inputs = AddPrepareBroadcast(node.input(0), node.input(1));</div><div class="line"><a name="l01484"></a><span class="lineno"> 1484</span>  <span class="keyword">auto</span> input0 = *m_TensorsInfo[inputs.first].m_info;</div><div class="line"><a name="l01485"></a><span class="lineno"> 1485</span>  <span class="keyword">auto</span> input1 = *m_TensorsInfo[inputs.second].m_info;</div><div class="line"><a name="l01486"></a><span class="lineno"> 1486</span>  BOOST_ASSERT(input0.GetNumDimensions() == input1.GetNumDimensions());</div><div class="line"><a name="l01487"></a><span class="lineno"> 1487</span> </div><div class="line"><a name="l01488"></a><span class="lineno"> 1488</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDims = input0.GetNumDimensions();</div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</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="l01490"></a><span class="lineno"> 1490</span>  {</div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dim0 = input0.GetShape()[i];</div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dim1 = input1.GetShape()[i];</div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span>  <span class="keywordflow">if</span> (dim0 != dim1 && dim0 != 1 && dim1 != 1)</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span>  boost::format(<span class="stringliteral">"Broadcast is only supported for scalar or 1D tensors in Add node '%1%'. "</span></div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span>  <span class="stringliteral">"Input dimensions should either match or one should be of size 1 and here, "</span></div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span>  <span class="stringliteral">"%2% and %3% %4%"</span>)</div><div class="line"><a name="l01499"></a><span class="lineno"> 1499</span>  % node.name()</div><div class="line"><a name="l01500"></a><span class="lineno"> 1500</span>  % TensorInfoAsString(*m_TensorsInfo[inputs.first].m_info, inputs.first,</div><div class="line"><a name="l01501"></a><span class="lineno"> 1501</span>  m_TensorsInfo[inputs.first].m_dtype)</div><div class="line"><a name="l01502"></a><span class="lineno"> 1502</span>  % TensorInfoAsString(*m_TensorsInfo[inputs.second].m_info, inputs.second,</div><div class="line"><a name="l01503"></a><span class="lineno"> 1503</span>  m_TensorsInfo[inputs.second].m_dtype)</div><div class="line"><a name="l01504"></a><span class="lineno"> 1504</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01505"></a><span class="lineno"> 1505</span>  }</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> </div><div class="line"><a name="l01508"></a><span class="lineno"> 1508</span> </div><div class="line"><a name="l01509"></a><span class="lineno"> 1509</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddAdditionLayer(node.name().c_str());</div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</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="keyword">auto</span> outputInfo = ComputeOutputInfo({ node.output(0) }, layer,</div><div class="line"><a name="l01513"></a><span class="lineno"> 1513</span>  { m_TensorsInfo[inputs.first].m_info->GetShape(),</div><div class="line"><a name="l01514"></a><span class="lineno"> 1514</span>  m_TensorsInfo[inputs.second].m_info->GetShape() });</div><div class="line"><a name="l01515"></a><span class="lineno"> 1515</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[0]);</div><div class="line"><a name="l01516"></a><span class="lineno"> 1516</span> </div><div class="line"><a name="l01517"></a><span class="lineno"> 1517</span>  <span class="comment">// register the input connection -> for constant inputs, we need to make a newDim constant layer</span></div><div class="line"><a name="l01518"></a><span class="lineno"> 1518</span>  <span class="keywordflow">if</span>(m_TensorsInfo[inputs.first].isConstant()) {</div><div class="line"><a name="l01519"></a><span class="lineno"> 1519</span>  CreateConstantLayer(inputs.first, boost::str(boost::format(<span class="stringliteral">"Add:constant_of_%1%"</span>) % node.input(0)));</div><div class="line"><a name="l01520"></a><span class="lineno"> 1520</span>  }</div><div class="line"><a name="l01521"></a><span class="lineno"> 1521</span>  <span class="keywordflow">if</span>(m_TensorsInfo[inputs.second].isConstant()) {</div><div class="line"><a name="l01522"></a><span class="lineno"> 1522</span>  CreateConstantLayer(inputs.second, boost::str(boost::format(<span class="stringliteral">"Add:constant_of_%1%"</span>) % node.input(1)));</div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span>  }</div><div class="line"><a name="l01524"></a><span class="lineno"> 1524</span>  RegisterInputSlots(layer, {inputs.first, inputs.second});</div><div class="line"><a name="l01525"></a><span class="lineno"> 1525</span> </div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span>  <span class="comment">// register the output connection</span></div><div class="line"><a name="l01527"></a><span class="lineno"> 1527</span>  RegisterOutputSlots(layer, {node.output(0)});</div><div class="line"><a name="l01528"></a><span class="lineno"> 1528</span> }</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span> </div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span> <span class="keywordtype">void</span> OnnxParser::ParseBatchNormalization(<span class="keyword">const</span> onnx::NodeProto& node)</div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span> {</div><div class="line"><a name="l01532"></a><span class="lineno"> 1532</span>  <span class="comment">//IGNORE momentum parameter and spatial parameters</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="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(static_cast<size_t>(node.input_size()), 5);</div><div class="line"><a name="l01535"></a><span class="lineno"> 1535</span>  <a class="code" href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a>(static_cast<size_t>(node.output_size()), 1);</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="_onnx_parser_8cpp.xhtml#a0e987f9d4f46b35c9b1ff0cc950dc5b1">VALID_INPUTS</a>(node, <a class="code" href="_onnx_parser_8cpp.xhtml#a5426a7adb280d1739cc6d66fe9ac1b9c">STR_LIST</a>(onnx::TensorProto::FLOAT));</div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> ind = 1; ind < node.input_size(); ++ind)</div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span>  {</div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span>  <span class="keyword">auto</span> tensor = node.input(ind);</div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span>  <span class="keywordflow">if</span>(! m_TensorsInfo[tensor].isConstant())</div><div class="line"><a name="l01542"></a><span class="lineno"> 1542</span>  {</div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>  boost::format(<span class="stringliteral">"Input tensor '%1%' should be constant in BatchNormalization node '%2%' %3%"</span>)</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span>  % tensor</div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span>  % node.name()</div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span>  }</div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span>  }</div><div class="line"><a name="l01550"></a><span class="lineno"> 1550</span> </div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</span>  <span class="keywordtype">float</span> epsilon = ReadOptionalNodeFloatAttribute(node, <span class="stringliteral">"epsilon"</span>, 1e-5f);</div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>  <a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml">BatchNormalizationDescriptor</a> desc;</div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>  desc.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">m_Eps</a> = epsilon;</div><div class="line"><a name="l01554"></a><span class="lineno"> 1554</span> </div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>  <span class="keyword">auto</span> scaleTensor = CreateConstTensor(node.input(1));</div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span>  <span class="keyword">auto</span> biasTensor = CreateConstTensor(node.input(2));</div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>  <span class="keyword">auto</span> meanTensor = CreateConstTensor(node.input(3));</div><div class="line"><a name="l01558"></a><span class="lineno"> 1558</span>  <span class="keyword">auto</span> varTensor = CreateConstTensor(node.input(4));</div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span> </div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer = m_Network->AddBatchNormalizationLayer(desc,</div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>  meanTensor.first,</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span>  varTensor.first,</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span>  biasTensor.first,</div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span>  scaleTensor.first,</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span>  node.name().c_str());</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span> </div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span>  <span class="keyword">auto</span> outputInfo = ComputeOutputInfo({node.output(0)}, layer, {m_TensorsInfo[node.input(0)].m_info->GetShape()});</div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span>  layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]);</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>  RegisterInputSlots(layer, {node.input(0)}); <span class="comment">//don't register constant inputs</span></div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span> </div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span>  <span class="comment">// register the output connection</span></div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span>  RegisterOutputSlots(layer, {node.output(0)});</div><div class="line"><a name="l01575"></a><span class="lineno"> 1575</span> }</div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span> </div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span> <span class="keywordtype">void</span> OnnxParser::SetupInputLayers()</div><div class="line"><a name="l01578"></a><span class="lineno"> 1578</span> {</div><div class="line"><a name="l01579"></a><span class="lineno"> 1579</span>  <span class="comment">//Find user input and add their layers</span></div><div class="line"><a name="l01580"></a><span class="lineno"> 1580</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> inputIndex = 0; inputIndex < m_Graph->input_size(); ++inputIndex)</div><div class="line"><a name="l01581"></a><span class="lineno"> 1581</span>  {</div><div class="line"><a name="l01582"></a><span class="lineno"> 1582</span>  <span class="keyword">auto</span> input = m_Graph->input(inputIndex);</div><div class="line"><a name="l01583"></a><span class="lineno"> 1583</span>  <span class="keywordflow">if</span> (! m_TensorsInfo[input.name()].isConstant())</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer =</div><div class="line"><a name="l01586"></a><span class="lineno"> 1586</span>  m_Network->AddInputLayer(static_cast<armnn::LayerBindingId>(inputIndex), input.name().c_str());</div><div class="line"><a name="l01587"></a><span class="lineno"> 1587</span>  <span class="keyword">auto</span> tensorInfo = <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(input);</div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</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="l01589"></a><span class="lineno"> 1589</span> </div><div class="line"><a name="l01590"></a><span class="lineno"> 1590</span>  RegisterOutputSlots(layer,{ input.name() });</div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>  }</div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span>  }</div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span> }</div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span> </div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</span> <span class="keywordtype">void</span> OnnxParser::SetupOutputLayers()</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>(m_Graph->output_size() == 0)</div><div class="line"><a name="l01598"></a><span class="lineno"> 1598</span>  {</div><div class="line"><a name="l01599"></a><span class="lineno"> 1599</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(boost::format(<span class="stringliteral">"The given model does not have any outputs %1%"</span>)</div><div class="line"><a name="l01600"></a><span class="lineno"> 1600</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01601"></a><span class="lineno"> 1601</span>  }</div><div class="line"><a name="l01602"></a><span class="lineno"> 1602</span> </div><div class="line"><a name="l01603"></a><span class="lineno"> 1603</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> outputIndex = 0; outputIndex < m_Graph->output_size(); ++outputIndex)</div><div class="line"><a name="l01604"></a><span class="lineno"> 1604</span>  {</div><div class="line"><a name="l01605"></a><span class="lineno"> 1605</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer =</div><div class="line"><a name="l01606"></a><span class="lineno"> 1606</span>  m_Network->AddOutputLayer(static_cast<armnn::LayerBindingId>(outputIndex),</div><div class="line"><a name="l01607"></a><span class="lineno"> 1607</span>  m_Graph->output(outputIndex).name().c_str());</div><div class="line"><a name="l01608"></a><span class="lineno"> 1608</span> </div><div class="line"><a name="l01609"></a><span class="lineno"> 1609</span>  RegisterInputSlots(layer, { m_Graph->output(outputIndex).name() });</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> }</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="keywordtype">void</span> OnnxParser::RegisterInputSlots(<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer, <span class="keyword">const</span> std::vector<std::string>& tensorIds)</div><div class="line"><a name="l01614"></a><span class="lineno"> 1614</span> {</div><div class="line"><a name="l01615"></a><span class="lineno"> 1615</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01616"></a><span class="lineno"> 1616</span>  <span class="keywordflow">if</span> (tensorIds.size() != layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">GetNumInputSlots</a>())</div><div class="line"><a name="l01617"></a><span class="lineno"> 1617</span>  {</div><div class="line"><a name="l01618"></a><span class="lineno"> 1618</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01619"></a><span class="lineno"> 1619</span>  boost::str(boost::format(<span class="stringliteral">"The number of tensor inputs (%1%) does not match the number expected (%2%) %3%"</span>) %</div><div class="line"><a name="l01620"></a><span class="lineno"> 1620</span>  tensorIds.size() %</div><div class="line"><a name="l01621"></a><span class="lineno"> 1621</span>  layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">GetNumInputSlots</a>() %</div><div class="line"><a name="l01622"></a><span class="lineno"> 1622</span>  <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01623"></a><span class="lineno"> 1623</span>  }</div><div class="line"><a name="l01624"></a><span class="lineno"> 1624</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> slotIndex = 0; slotIndex < layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">GetNumInputSlots</a>(); ++slotIndex)</div><div class="line"><a name="l01625"></a><span class="lineno"> 1625</span>  {</div><div class="line"><a name="l01626"></a><span class="lineno"> 1626</span>  std::string tensorId = tensorIds[slotIndex];</div><div class="line"><a name="l01627"></a><span class="lineno"> 1627</span>  <a class="code" href="classarmnn_1_1_i_input_slot.xhtml">armnn::IInputSlot</a>* slot = &(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(slotIndex));</div><div class="line"><a name="l01628"></a><span class="lineno"> 1628</span> </div><div class="line"><a name="l01629"></a><span class="lineno"> 1629</span>  <span class="keyword">auto</span> it = m_TensorConnections.find(tensorId);</div><div class="line"><a name="l01630"></a><span class="lineno"> 1630</span> </div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span>  <span class="keywordflow">if</span> (it == m_TensorConnections.end())</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">//First time seing this tensor, we need to map it</span></div><div class="line"><a name="l01634"></a><span class="lineno"> 1634</span>  m_TensorConnections[tensorId] = TensorSlots();</div><div class="line"><a name="l01635"></a><span class="lineno"> 1635</span>  }</div><div class="line"><a name="l01636"></a><span class="lineno"> 1636</span>  m_TensorConnections[tensorId].inputSlots.push_back(slot);</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> }</div><div class="line"><a name="l01639"></a><span class="lineno"> 1639</span> </div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span> <span class="keywordtype">void</span> OnnxParser::RegisterOutputSlots(<a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* layer, <span class="keyword">const</span> std::vector<std::string>& tensorIds)</div><div class="line"><a name="l01641"></a><span class="lineno"> 1641</span> {</div><div class="line"><a name="l01642"></a><span class="lineno"> 1642</span>  BOOST_ASSERT(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01643"></a><span class="lineno"> 1643</span>  <span class="keywordflow">if</span> (tensorIds.size() != layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>())</div><div class="line"><a name="l01644"></a><span class="lineno"> 1644</span>  {</div><div class="line"><a name="l01645"></a><span class="lineno"> 1645</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(</div><div class="line"><a name="l01646"></a><span class="lineno"> 1646</span>  boost::str(boost::format(<span class="stringliteral">"The number of tensor outputs (%1%) does not match the number expected (%2%) %3% "</span>)</div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span>  % tensorIds.size()</div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span>  % layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>()</div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span>  }</div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span> </div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> slotIndex = 0; slotIndex < layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#ac2dac3b61c94de52093616be4ab17f8d">GetNumOutputSlots</a>(); ++slotIndex)</div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span>  {</div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span>  std::string tensorId = tensorIds[slotIndex];</div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span>  <a class="code" href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a>* slot = &(layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(slotIndex));</div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</span> </div><div class="line"><a name="l01657"></a><span class="lineno"> 1657</span>  <span class="keyword">auto</span> it = m_TensorConnections.find(tensorId);</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">if</span> (it == m_TensorConnections.end())</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>  <span class="comment">//First time seing this tensor, we need to map it</span></div><div class="line"><a name="l01662"></a><span class="lineno"> 1662</span>  m_TensorConnections[tensorId] = TensorSlots();</div><div class="line"><a name="l01663"></a><span class="lineno"> 1663</span>  }</div><div class="line"><a name="l01664"></a><span class="lineno"> 1664</span> </div><div class="line"><a name="l01665"></a><span class="lineno"> 1665</span>  TensorSlots& tensorSlots = m_TensorConnections[tensorId];</div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span> </div><div class="line"><a name="l01667"></a><span class="lineno"> 1667</span>  <span class="comment">// assuming there is only one producer for that tensor</span></div><div class="line"><a name="l01668"></a><span class="lineno"> 1668</span>  <span class="keywordflow">if</span> (tensorSlots.outputSlot != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l01669"></a><span class="lineno"> 1669</span>  {</div><div class="line"><a name="l01670"></a><span class="lineno"> 1670</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(boost::str(</div><div class="line"><a name="l01671"></a><span class="lineno"> 1671</span>  boost::format(<span class="stringliteral">"Another layer has already registered itself as the producer of "</span></div><div class="line"><a name="l01672"></a><span class="lineno"> 1672</span>  <span class="stringliteral">"tensor:%1% %2%"</span>)</div><div class="line"><a name="l01673"></a><span class="lineno"> 1673</span>  % tensorId</div><div class="line"><a name="l01674"></a><span class="lineno"> 1674</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01675"></a><span class="lineno"> 1675</span>  }</div><div class="line"><a name="l01676"></a><span class="lineno"> 1676</span>  tensorSlots.outputSlot = slot;</div><div class="line"><a name="l01677"></a><span class="lineno"> 1677</span>  }</div><div class="line"><a name="l01678"></a><span class="lineno"> 1678</span> }</div><div class="line"><a name="l01679"></a><span class="lineno"> 1679</span> </div><div class="line"><a name="l01680"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#aba39201ebaeb0738f15a14b3c8da1f5a"> 1680</a></span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#a9084adbf804022c874039ad40d1939e9">BindingPointInfo</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#aba39201ebaeb0738f15a14b3c8da1f5a">OnnxParser::GetNetworkInputBindingInfo</a>(<span class="keyword">const</span> std::string& name)<span class="keyword"> const</span></div><div class="line"><a name="l01681"></a><span class="lineno"> 1681</span> <span class="keyword"></span>{</div><div class="line"><a name="l01682"></a><span class="lineno"> 1682</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < m_Graph->input_size(); ++i)</div><div class="line"><a name="l01683"></a><span class="lineno"> 1683</span>  {</div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span>  <span class="keyword">auto</span> input = m_Graph->input(i);</div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span>  <span class="keywordflow">if</span>(input.name() == name)</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">return</span> std::make_pair(static_cast<armnn::LayerBindingId>(i), <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(input));</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>  }</div><div class="line"><a name="l01690"></a><span class="lineno"> 1690</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(boost::str(boost::format(<span class="stringliteral">"The input layer '%1%' does not exist %2%"</span>)</div><div class="line"><a name="l01691"></a><span class="lineno"> 1691</span>  % name % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span> }</div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span> </div><div class="line"><a name="l01694"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#aee8c8fa7de3c87392791d9f8dd90655f"> 1694</a></span> <a class="code" href="namespacearmnn_onnx_parser.xhtml#a9084adbf804022c874039ad40d1939e9">BindingPointInfo</a> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#aee8c8fa7de3c87392791d9f8dd90655f">OnnxParser::GetNetworkOutputBindingInfo</a>(<span class="keyword">const</span> std::string& name)<span class="keyword"> const</span></div><div class="line"><a name="l01695"></a><span class="lineno"> 1695</span> <span class="keyword"></span>{</div><div class="line"><a name="l01696"></a><span class="lineno"> 1696</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i = 0; i < m_Graph->output_size(); ++i)</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="keyword">auto</span> output = m_Graph->output(i);</div><div class="line"><a name="l01699"></a><span class="lineno"> 1699</span>  <span class="keywordflow">if</span>(output.name() == name)</div><div class="line"><a name="l01700"></a><span class="lineno"> 1700</span>  {</div><div class="line"><a name="l01701"></a><span class="lineno"> 1701</span>  <span class="keywordflow">return</span> std::make_pair(static_cast<armnn::LayerBindingId>(i), <a class="code" href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">ToTensorInfo</a>(output));</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>  }</div><div class="line"><a name="l01704"></a><span class="lineno"> 1704</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(boost::str(boost::format(<span class="stringliteral">"The output layer '%1%' does not exist %2%"</span>)</div><div class="line"><a name="l01705"></a><span class="lineno"> 1705</span>  % name % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a7cf8b801043e1eccd5e6db1325eaa4fe"> 1708</a></span> std::vector<std::string> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a7cf8b801043e1eccd5e6db1325eaa4fe">OnnxParser::GetInputs</a>(<a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">ModelPtr</a>& model)</div><div class="line"><a name="l01709"></a><span class="lineno"> 1709</span> {</div><div class="line"><a name="l01710"></a><span class="lineno"> 1710</span>  <span class="keywordflow">if</span>(model == <span class="keyword">nullptr</span>) {</div><div class="line"><a name="l01711"></a><span class="lineno"> 1711</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(boost::str(</div><div class="line"><a name="l01712"></a><span class="lineno"> 1712</span>  boost::format(<span class="stringliteral">"The given model cannot be null %1%"</span>)</div><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</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> </div><div class="line"><a name="l01716"></a><span class="lineno"> 1716</span>  std::vector<std::string> inputNames;</div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span>  std::map<std::string, bool> isConstant;</div><div class="line"><a name="l01718"></a><span class="lineno"> 1718</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> tensor : model->graph().initializer())</div><div class="line"><a name="l01719"></a><span class="lineno"> 1719</span>  {</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>  isConstant[tensor.name()] = <span class="keyword">true</span>;</div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span>  }</div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> input : model->graph().input())</div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>  {</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span>  <span class="keyword">auto</span> it = isConstant.find(input.name());</div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span>  <span class="keywordflow">if</span>(it == isConstant.end())</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>  inputNames.push_back(input.name());</div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span>  }</div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span>  }</div><div class="line"><a name="l01730"></a><span class="lineno"> 1730</span>  <span class="keywordflow">return</span> inputNames;</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> </div><div class="line"><a name="l01733"></a><span class="lineno"><a class="line" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#ad116319e33228bc23ec505887d3eee4d"> 1733</a></span> std::vector<std::string> <a class="code" href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#ad116319e33228bc23ec505887d3eee4d">OnnxParser::GetOutputs</a>(<a class="code" href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">ModelPtr</a>& model)</div><div class="line"><a name="l01734"></a><span class="lineno"> 1734</span> {</div><div class="line"><a name="l01735"></a><span class="lineno"> 1735</span>  <span class="keywordflow">if</span>(model == <span class="keyword">nullptr</span>) {</div><div class="line"><a name="l01736"></a><span class="lineno"> 1736</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(boost::str(</div><div class="line"><a name="l01737"></a><span class="lineno"> 1737</span>  boost::format(<span class="stringliteral">"The given model cannot be null %1%"</span>)</div><div class="line"><a name="l01738"></a><span class="lineno"> 1738</span>  % <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>().AsString()));</div><div class="line"><a name="l01739"></a><span class="lineno"> 1739</span>  }</div><div class="line"><a name="l01740"></a><span class="lineno"> 1740</span> </div><div class="line"><a name="l01741"></a><span class="lineno"> 1741</span>  std::vector<std::string> outputNames;</div><div class="line"><a name="l01742"></a><span class="lineno"> 1742</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> output : model->graph().output())</div><div class="line"><a name="l01743"></a><span class="lineno"> 1743</span>  {</div><div class="line"><a name="l01744"></a><span class="lineno"> 1744</span>  outputNames.push_back(output.name());</div><div class="line"><a name="l01745"></a><span class="lineno"> 1745</span>  }</div><div class="line"><a name="l01746"></a><span class="lineno"> 1746</span>  <span class="keywordflow">return</span> outputNames;</div><div class="line"><a name="l01747"></a><span class="lineno"> 1747</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> } <span class="comment">// namespace armnnOnnxParser</span></div><div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorShape::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00106">Tensor.cpp:106</a></div></div> +<div class="ttc" id="classarmnn_onnx_parser_1_1_onnx_parser_xhtml"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml">armnnOnnxParser::OnnxParser</a></div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8hpp_source.xhtml#l00025">OnnxParser.hpp:25</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Convolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00428">Descriptors.hpp:428</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::Convolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00438">Descriptors.hpp:438</a></div></div> +<div class="ttc" id="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="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00061">INetwork.hpp:61</a></div></div> +<div class="ttc" id="structarmnn_1_1_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="classarmnn_1_1_i_connectable_layer_xhtml_a9c2cba04b6d7ace4fc2a2436b82a5a63"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a9c2cba04b6d7ace4fc2a2436b82a5a63">armnn::IConnectableLayer::GetNumInputSlots</a></div><div class="ttdeci">virtual unsigned int GetNumInputSlots() const =0</div><div class="ttdoc">Returns the number of connectable input slots. </div></div> +<div class="ttc" id="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_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="_utils_8hpp_xhtml"><div class="ttname"><a href="_utils_8hpp.xhtml">Utils.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_optional_xhtml"><div class="ttname"><a href="classarmnn_1_1_optional.xhtml">armnn::Optional</a></div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00270">Optional.hpp:270</a></div></div> +<div class="ttc" id="structarmnn_1_1_check_location_xhtml_a5e3562cda960da001597e7dd5679b140"><div class="ttname"><a href="structarmnn_1_1_check_location.xhtml#a5e3562cda960da001597e7dd5679b140">armnn::CheckLocation::AsString</a></div><div class="ttdeci">std::string AsString() const</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00029">Exceptions.hpp:29</a></div></div> +<div class="ttc" id="structarmnn_1_1_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="classarmnn_onnx_parser_1_1_onnx_parser_xhtml_aee8c8fa7de3c87392791d9f8dd90655f"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#aee8c8fa7de3c87392791d9f8dd90655f">armnnOnnxParser::OnnxParser::GetNetworkOutputBindingInfo</a></div><div class="ttdeci">virtual BindingPointInfo GetNetworkOutputBindingInfo(const std::string &name) const override</div><div class="ttdoc">Retrieve binding info (layer id and tensor info) for the network output identified by the given layer...</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.xhtml#l01694">OnnxParser.cpp:1694</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div> +<div class="ttc" id="structarmnn_1_1_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="classarmnn_onnx_parser_1_1_onnx_parser_xhtml_a08bac41eb476686564b15063edf1fc04"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a08bac41eb476686564b15063edf1fc04">armnnOnnxParser::OnnxParser::CreateNetworkFromTextFile</a></div><div class="ttdeci">virtual armnn::INetworkPtr CreateNetworkFromTextFile(const char *graphFile) override</div><div class="ttdoc">Create the network from a protobuf text file on disk. </div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.xhtml#l00519">OnnxParser.cpp:519</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a></div><div class="ttdoc">A Convolution2dDescriptor for the Convolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00392">Descriptors.hpp:392</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div> +<div class="ttc" id="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_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="classarmnn_1_1_file_not_found_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_file_not_found_exception.xhtml">armnn::FileNotFoundException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00086">Exceptions.hpp:86</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Pooling2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00353">Descriptors.hpp:353</a></div></div> +<div class="ttc" id="namespacearmnn_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="_onnx_parser_8cpp_xhtml_a0e987f9d4f46b35c9b1ff0cc950dc5b1"><div class="ttname"><a href="_onnx_parser_8cpp.xhtml#a0e987f9d4f46b35c9b1ff0cc950dc5b1">VALID_INPUTS</a></div><div class="ttdeci">#define VALID_INPUTS(NODE, VALID_INPUTS)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.xhtml#l00382">OnnxParser.cpp:382</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="classarmnn_onnx_parser_1_1_onnx_parser_xhtml_acf9c6119ceb99755bc1f86c5a325c796"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#acf9c6119ceb99755bc1f86c5a325c796">armnnOnnxParser::OnnxParser::LoadModelFromBinaryFile</a></div><div class="ttdeci">static ModelPtr LoadModelFromBinaryFile(const char *fileName)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.xhtml#l00527">OnnxParser.cpp:527</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="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acb5e100e5a9a3e7f6d1fd97512215282">armnn::BoostLogSeverityMapping::error</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="_onnx_parser_8hpp_xhtml"><div class="ttname"><a href="_onnx_parser_8hpp.xhtml">OnnxParser.hpp</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="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="classarmnn_onnx_parser_1_1_onnx_parser_xhtml_a975a79b9b35d51ea81c42c05d245e7c0"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a975a79b9b35d51ea81c42c05d245e7c0">armnnOnnxParser::OnnxParser::LoadModelFromTextFile</a></div><div class="ttdeci">static ModelPtr LoadModelFromTextFile(const char *fileName)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.xhtml#l00492">OnnxParser.cpp:492</a></div></div> +<div class="ttc" id="classarmnn_onnx_parser_1_1_onnx_parser_xhtml_a012b24cafd443425314d4f9e06cec6c1"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a012b24cafd443425314d4f9e06cec6c1">armnnOnnxParser::OnnxParser::CreateNetworkFromBinaryFile</a></div><div class="ttdeci">virtual armnn::INetworkPtr CreateNetworkFromBinaryFile(const char *graphFile) override</div><div class="ttdoc">Create the network from a protobuf binary file on disk. </div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.xhtml#l00557">OnnxParser.cpp:557</a></div></div> +<div class="ttc" id="structarmnn_1_1_reshape_descriptor_xhtml_a1178f4dafdda81f59c15145ec327f7d9"><div class="ttname"><a href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">armnn::ReshapeDescriptor::m_TargetShape</a></div><div class="ttdeci">TensorShape m_TargetShape</div><div class="ttdoc">Target shape value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00774">Descriptors.hpp:774</a></div></div> +<div class="ttc" id="classarmnn_onnx_parser_1_1_onnx_parser_xhtml_a7cf8b801043e1eccd5e6db1325eaa4fe"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a7cf8b801043e1eccd5e6db1325eaa4fe">armnnOnnxParser::OnnxParser::GetInputs</a></div><div class="ttdeci">static std::vector< std::string > GetInputs(ModelPtr &model)</div><div class="ttdoc">Retrieve inputs names. </div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.xhtml#l01708">OnnxParser.cpp:1708</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a5699e8606c37d18c03910b242cd1b010"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">armnn::Pooling2dDescriptor::m_PoolHeight</a></div><div class="ttdeci">uint32_t m_PoolHeight</div><div class="ttdoc">Pooling height value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00359">Descriptors.hpp:359</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Convolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00426">Descriptors.hpp:426</a></div></div> +<div class="ttc" id="classarmnn_onnx_parser_1_1_onnx_parser_xhtml_ad116319e33228bc23ec505887d3eee4d"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#ad116319e33228bc23ec505887d3eee4d">armnnOnnxParser::OnnxParser::GetOutputs</a></div><div class="ttdeci">static std::vector< std::string > GetOutputs(ModelPtr &model)</div><div class="ttdoc">Retrieve outputs names. </div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.xhtml#l01733">OnnxParser.cpp:1733</a></div></div> +<div class="ttc" id="namespacearmnn_deserializer_xhtml_aa28868b7dc87dc4d957db6c775a591c1"><div class="ttname"><a href="namespacearmnn_deserializer.xhtml#aa28868b7dc87dc4d957db6c775a591c1">armnnDeserializer::ToTensorInfo</a></div><div class="ttdeci">armnn::TensorInfo ToTensorInfo(Deserializer::TensorRawPtr tensorPtr)</div><div class="ttdef"><b>Definition:</b> <a href="_deserializer_8cpp_source.xhtml#l00501">Deserializer.cpp:501</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Convolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00430">Descriptors.hpp:430</a></div></div> +<div class="ttc" id="namespacearmnn_onnx_parser_xhtml_a503ae4f55dae1486e53978657083b35d"><div class="ttname"><a href="namespacearmnn_onnx_parser.xhtml#a503ae4f55dae1486e53978657083b35d">armnnOnnxParser::ModelPtr</a></div><div class="ttdeci">std::unique_ptr< onnx::ModelProto > ModelPtr</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8hpp_source.xhtml#l00023">OnnxParser.hpp:23</a></div></div> +<div class="ttc" id="classarmnn_onnx_parser_1_1_onnx_parser_xhtml_aba39201ebaeb0738f15a14b3c8da1f5a"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#aba39201ebaeb0738f15a14b3c8da1f5a">armnnOnnxParser::OnnxParser::GetNetworkInputBindingInfo</a></div><div class="ttdeci">virtual BindingPointInfo GetNetworkInputBindingInfo(const std::string &name) const override</div><div class="ttdoc">Retrieve binding info (layer id and tensor info) for the network input identified by the given layer ...</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.xhtml#l01680">OnnxParser.cpp:1680</a></div></div> +<div class="ttc" id="_verification_helpers_8hpp_xhtml"><div class="ttname"><a href="_verification_helpers_8hpp.xhtml">VerificationHelpers.hpp</a></div></div> +<div class="ttc" id="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="classarmnn_1_1_i_output_slot_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml">armnn::IOutputSlot</a></div><div class="ttdoc">An output connection slot for a layer. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00037">INetwork.hpp:37</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a></div><div class="ttdoc">A FullyConnectedDescriptor for the FullyConnectedLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00373">Descriptors.hpp:373</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::FullyConnectedDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00386">Descriptors.hpp:386</a></div></div> +<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00199">Tensor.hpp:199</a></div></div> +<div class="ttc" id="structarmnn_1_1_check_location_xhtml"><div class="ttname"><a href="structarmnn_1_1_check_location.xhtml">armnn::CheckLocation</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00014">Exceptions.hpp:14</a></div></div> +<div class="ttc" id="_verification_helpers_8hpp_xhtml_a479b2821a7a2cbb8fa8eb7f60a47065d"><div class="ttname"><a href="_verification_helpers_8hpp.xhtml#a479b2821a7a2cbb8fa8eb7f60a47065d">CHECK_VALID_SIZE</a></div><div class="ttdeci">#define CHECK_VALID_SIZE(ACTUAL,...)</div><div class="ttdef"><b>Definition:</b> <a href="_verification_helpers_8hpp_source.xhtml#l00032">VerificationHelpers.hpp:32</a></div></div> +<div class="ttc" id="classarmnn_onnx_parser_1_1_i_onnx_parser_xhtml"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_i_onnx_parser.xhtml">armnnOnnxParser::IOnnxParser</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_onnx_parser_8hpp_source.xhtml#l00022">IOnnxParser.hpp:22</a></div></div> +<div class="ttc" id="_verification_helpers_8hpp_xhtml_aaef93dc9a69f51b59f3cdd0ff0165927"><div class="ttname"><a href="_verification_helpers_8hpp.xhtml#aaef93dc9a69f51b59f3cdd0ff0165927">CHECKED_NON_NEGATIVE</a></div><div class="ttdeci">#define CHECKED_NON_NEGATIVE(VALUE)</div><div class="ttdef"><b>Definition:</b> <a href="_verification_helpers_8hpp_source.xhtml#l00035">VerificationHelpers.hpp:35</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a37fa39012e90d568df7f774cd6d1e956"><div class="ttname"><a href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">armnn::numeric_cast</a></div><div class="ttdeci">std::enable_if_t< std::is_unsigned< Source >::value &&std::is_unsigned< Dest >::value, Dest > numeric_cast(Source source)</div><div class="ttdef"><b>Definition:</b> <a href="_numeric_cast_8hpp_source.xhtml#l00033">NumericCast.hpp:33</a></div></div> +<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a></div><div class="ttdoc">An ActivationDescriptor for the ActivationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00020">Descriptors.hpp:20</a></div></div> +<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div> +<div class="ttc" id="_exceptions_8hpp_xhtml_aa3be76aec4ce713822a5ea1ecbb7bc61"><div class="ttname"><a href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a></div><div class="ttdeci">#define CHECK_LOCATION()</div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00192">Exceptions.hpp:192</a></div></div> +<div class="ttc" id="structarmnn_1_1_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_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="namespacearmnn_onnx_parser_xhtml"><div class="ttname"><a href="namespacearmnn_onnx_parser.xhtml">armnnOnnxParser</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_onnx_parser_8hpp_source.xhtml#l00014">IOnnxParser.hpp:14</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="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div> +<div class="ttc" id="_descriptors_8hpp_xhtml"><div class="ttname"><a href="_descriptors_8hpp.xhtml">Descriptors.hpp</a></div></div> +<div class="ttc" id="_onnx_parser_8cpp_xhtml_a5426a7adb280d1739cc6d66fe9ac1b9c"><div class="ttname"><a href="_onnx_parser_8cpp.xhtml#a5426a7adb280d1739cc6d66fe9ac1b9c">STR_LIST</a></div><div class="ttdeci">#define STR_LIST(...)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.xhtml#l00052">OnnxParser.cpp:52</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorShape::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00043">Tensor.hpp:43</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_affb5b68b3eba3ed45a06c7cde7781962"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#affb5b68b3eba3ed45a06c7cde7781962">armnn::Pooling2dDescriptor::m_OutputShapeRounding</a></div><div class="ttdeci">OutputShapeRounding m_OutputShapeRounding</div><div class="ttdoc">The rounding method for the output shape. (Floor, Ceiling). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00365">Descriptors.hpp:365</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a6ec9e0eb66d7d6a01240492a0b18104c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">armnn::IConnectableLayer::GetInputSlot</a></div><div class="ttdeci">virtual const IInputSlot & GetInputSlot(unsigned int index) const =0</div><div class="ttdoc">Get a const input slot handle by slot index. </div></div> +<div class="ttc" id="classarmnn_onnx_parser_1_1_onnx_parser_xhtml_a4cb67bfbe630abf10787ac613d1a31c5"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a4cb67bfbe630abf10787ac613d1a31c5">armnnOnnxParser::OnnxParser::CreateNetworkFromString</a></div><div class="ttdeci">virtual armnn::INetworkPtr CreateNetworkFromString(const std::string &protoText) override</div><div class="ttdoc">Create the network directly from protobuf text in a string. Useful for debugging/testing. </div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.xhtml#l00584">OnnxParser.cpp:584</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a80ac4eda2e7f2757ec9dd96fc96dbd16"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">armnn::IConnectableLayer::GetOutputSlot</a></div><div class="ttdeci">virtual const IOutputSlot & GetOutputSlot(unsigned int index) const =0</div><div class="ttdoc">Get the const output slot handle by slot index. </div></div> +<div class="ttc" id="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00101">INetwork.hpp:101</a></div></div> +<div class="ttc" id="classarmnn_onnx_parser_1_1_onnx_parser_xhtml_a181f87cf45fdc9f040a41c985ce7f8cd"><div class="ttname"><a href="classarmnn_onnx_parser_1_1_onnx_parser.xhtml#a181f87cf45fdc9f040a41c985ce7f8cd">armnnOnnxParser::OnnxParser::LoadModelFromString</a></div><div class="ttdeci">static ModelPtr LoadModelFromString(const std::string &inputString)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.xhtml#l00564">OnnxParser.cpp:564</a></div></div> +<div class="ttc" id="_onnx_parser_8cpp_xhtml_a71cae957feb9162183d6f62fd549ffe1"><div class="ttname"><a href="_onnx_parser_8cpp.xhtml#a71cae957feb9162183d6f62fd549ffe1">CHECK_VALID_DATATYPE</a></div><div class="ttdeci">#define CHECK_VALID_DATATYPE(NODE, TENSOR, ACTUAL,...)</div><div class="ttdef"><b>Definition:</b> <a href="_onnx_parser_8cpp_source.xhtml#l00048">OnnxParser.cpp:48</a></div></div> +<div class="ttc" id="namespacearmnn_onnx_parser_xhtml_a9084adbf804022c874039ad40d1939e9"><div class="ttname"><a href="namespacearmnn_onnx_parser.xhtml#a9084adbf804022c874039ad40d1939e9">armnnOnnxParser::BindingPointInfo</a></div><div class="ttdeci">armnn::BindingPointInfo BindingPointInfo</div><div class="ttdef"><b>Definition:</b> <a href="_i_onnx_parser_8hpp_source.xhtml#l00017">IOnnxParser.hpp:17</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_aa6e3c075c888e7c16942a468a3aae33c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#aa6e3c075c888e7c16942a468a3aae33c">armnn::IConnectableLayer::InferOutputShapes</a></div><div class="ttdeci">virtual std::vector< TensorShape > InferOutputShapes(const std::vector< TensorShape > &inputShapes) const =0</div><div class="ttdoc">Infer the shape of the output(s) based on the provided input shape(s) </div></div> +<div class="ttc" id="_verification_helpers_8hpp_xhtml_aa693ef8620e450b6362938828002f2a6"><div class="ttname"><a href="_verification_helpers_8hpp.xhtml#aa693ef8620e450b6362938828002f2a6">CHECKED_INT32</a></div><div class="ttdeci">#define CHECKED_INT32(VALUE)</div><div class="ttdef"><b>Definition:</b> <a href="_verification_helpers_8hpp_source.xhtml#l00030">VerificationHelpers.hpp:30</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml">armnn::Pooling2dDescriptor</a></div><div class="ttdoc">A Pooling2dDescriptor for the Pooling2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00313">Descriptors.hpp:313</a></div></div> +<div class="ttc" id="namespacearmnn_onnx_parser_xhtml_ac7dfccab29feeb5f33f1ec0183c1e123"><div class="ttname"><a href="namespacearmnn_onnx_parser.xhtml#ac7dfccab29feeb5f33f1ec0183c1e123">armnnOnnxParser::IOnnxParserPtr</a></div><div class="ttdeci">std::unique_ptr< IOnnxParser, void(*)(IOnnxParser *parser)> IOnnxParserPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_onnx_parser_8hpp_source.xhtml#l00020">IOnnxParser.hpp:20</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_1_1_i_input_slot_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_input_slot.xhtml">armnn::IInputSlot</a></div><div class="ttdoc">An input connection slot for a layer. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00024">INetwork.hpp:24</a></div></div> +<div class="ttc" id="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_a56297e0f7b215eea46c818cb7528d9ea"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9ea">armnn::ActivationFunction</a></div><div class="ttdeci">ActivationFunction</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00055">Types.hpp:55</a></div></div> +</div><!-- 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_29e3193d5087607956cb928081b12830.xhtml">armnnOnnxParser</a></li><li class="navelem"><a class="el" href="_onnx_parser_8cpp.xhtml">OnnxParser.cpp</a></li> + <li class="footer">Generated on Fri Mar 13 2020 16:09:09 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> |