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diff --git a/20.02/_workload_data_8cpp_source.xhtml b/20.02/_workload_data_8cpp_source.xhtml new file mode 100644 index 0000000000..8382c98d07 --- /dev/null +++ b/20.02/_workload_data_8cpp_source.xhtml @@ -0,0 +1,237 @@ +<!-- 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/backends/backendsCommon/WorkloadData.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('_workload_data_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">WorkloadData.cpp</div> </div> +</div><!--header--> +<div class="contents"> +<a href="_workload_data_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include <<a class="code" href="_workload_data_8hpp.xhtml">backendsCommon/WorkloadData.hpp</a>></span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include <<a class="code" href="_cpu_tensor_handle_8hpp.xhtml">backendsCommon/CpuTensorHandle.hpp</a>></span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <<a class="code" href="_data_layout_indexed_8hpp.xhtml">armnnUtils/DataLayoutIndexed.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_tensor_utils_8hpp.xhtml">armnnUtils/TensorUtils.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 <algorithm></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <iomanip></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <string></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <sstream></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> </div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <boost/format.hpp></span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <boost/numeric/conversion/cast.hpp></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_utils.xhtml">armnnUtils</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.xhtml">armnn</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> </div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00025"></a><span class="lineno"><a class="line" href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad"> 25</a></span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">GetBiasDataType</a>(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> inputDataType)</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> {</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keywordflow">switch</span> (inputDataType)</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  {</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>:</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <span class="keywordflow">case</span> DataType::Float16:</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="keywordflow">return</span> DataType::Float16;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keywordflow">case</span> DataType::Float32:</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keywordflow">return</span> DataType::Float32;</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keywordflow">case</span> DataType::QAsymmS8:</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keywordflow">return</span> DataType::Signed32;</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keywordflow">case</span> DataType::QAsymmU8:</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keywordflow">return</span> DataType::Signed32;</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <span class="keywordflow">case</span> DataType::QSymmS8:</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <span class="keywordflow">return</span> DataType::Signed32;</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keywordflow">case</span> DataType::QSymmS16:</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keywordflow">return</span> DataType::Signed32;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  BOOST_ASSERT_MSG(<span class="keyword">false</span>, <span class="stringliteral">"Invalid input data type"</span>);</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keywordflow">return</span> DataType::Float32;</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"> 48</span> </div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> <span class="keyword">namespace</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> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> <span class="comment">//android ndk does not support std::to_string function.</span></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> std::string to_string(T value)</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> {</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  std::ostringstream os;</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  os << value;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keywordflow">return</span> os.str();</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> }</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> <span class="keywordtype">void</span> ValidatePointer(<span class="keyword">const</span> <span class="keywordtype">void</span>* ptr, std::string <span class="keyword">const</span>& descName, std::string <span class="keyword">const</span>& paramName)</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> {</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordflow">if</span> (!ptr)</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  {</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">": Invalid null pointer. The "</span> +</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  paramName + <span class="stringliteral">" parameter must be set."</span>);</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  }</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> }</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> </div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> <span class="keywordtype">void</span> ValidateTensorShapesMatch(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& first,</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& second,</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  std::string <span class="keyword">const</span>& descName,</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  std::string <span class="keyword">const</span>& firstName,</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  std::string <span class="keyword">const</span>& secondName)</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> {</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keywordflow">if</span> (first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>() != second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>())</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">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">": "</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  + firstName + <span class="stringliteral">" & "</span> + secondName + <span class="stringliteral">" must have identical shapes"</span>);</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  }</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> </div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> <span class="keywordtype">void</span> ValidateNumInputs(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo, std::string <span class="keyword">const</span>& descName, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> expectedSize)</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> {</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size() != expectedSize)</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  {</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName +</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="stringliteral">": Requires exactly "</span> + to_string(expectedSize) + <span class="stringliteral">"input(s). "</span> +</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  to_string(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size()) + <span class="stringliteral">" have been provided."</span>);</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  }</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> }</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> </div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> <span class="keywordtype">void</span> ValidateNumOutputs(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo, std::string <span class="keyword">const</span>& descName, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> expectedSize)</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> {</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size() != expectedSize)</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  {</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName +</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="stringliteral">": Requires exactly "</span> + to_string(expectedSize) + <span class="stringliteral">" output(s). "</span> +</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  to_string(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size()) + <span class="stringliteral">" has been provided."</span>);</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  }</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> </div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> <span class="keywordtype">void</span> ValidateTensorNumDimensions(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensor,</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  std::string <span class="keyword">const</span>& descName,</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDimensions,</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  std::string <span class="keyword">const</span>& tensorName)</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> {</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="keywordflow">if</span> (tensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() != numDimensions)</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  {</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">": Expected "</span> + to_string(numDimensions) + <span class="stringliteral">" but got "</span> +</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  to_string(tensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()) + <span class="stringliteral">" dimensions for "</span> +</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  tensorName + <span class="stringliteral">" tensor."</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> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> <span class="keywordtype">void</span> ValidateTensorNumElements(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensor,</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  std::string <span class="keyword">const</span>& descName,</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numElements,</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  std::string <span class="keyword">const</span>& tensorName)</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> {</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordflow">if</span> (tensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() != numElements)</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  {</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">": Expected "</span> + to_string(numElements) + <span class="stringliteral">" but got "</span> +</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  to_string(tensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>()) + <span class="stringliteral">" elements for "</span> +</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  tensorName + <span class="stringliteral">" tensor."</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> }</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> </div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> <span class="keywordtype">void</span> ValidateTensorNumDimNumElem(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensorInfo,</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDimension,</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numElements,</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  std::string <span class="keyword">const</span>& tensorName)</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> {</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keyword">const</span> std::string functionName{<span class="stringliteral">"ValidateTensorNumDimNumElem"</span>};</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  ValidateTensorNumDimensions(tensorInfo, functionName, numDimension, tensorName);</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  ValidateTensorNumElements(tensorInfo, functionName, numElements, tensorName);</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> }</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> </div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> <span class="keywordtype">void</span> ValidateTensorDataType(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensor, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dataType,</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="keyword">const</span> std::string& descName, std::string <span class="keyword">const</span>& tensorName)</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> {</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordflow">if</span> (tensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != dataType)</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">": Expected data type "</span> + <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(dataType) + <span class="stringliteral">" but got "</span> +</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(tensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()) + <span class="stringliteral">" for "</span> + tensorName + <span class="stringliteral">" tensor."</span>);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  }</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> }</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> </div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> <span class="keywordtype">void</span> ValidPerAxisQuantizedDataType(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensor, <span class="keyword">const</span> std::string& descName, <span class="keyword">const</span> std::string& tensorName)</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> {</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <a class="code" href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keywordflow">if</span> (tensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != DataType::QSymmS8 &&</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  tensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != DataType::QuantizedSymm8PerAxis)</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  {</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName +</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="stringliteral">": Expected data type which supports per-axis quantization scheme but got "</span> +</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(tensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()) + <span class="stringliteral">" for "</span> + tensorName + <span class="stringliteral">" tensor."</span>);</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  }</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> }</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> </div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> <span class="keywordtype">void</span> ValidateTensorQuantizationSpace(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& first,</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& second,</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keyword">const</span> std::string& descName,</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  std::string <span class="keyword">const</span>& firstName,</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  std::string <span class="keyword">const</span>& secondName)</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> {</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordflow">if</span> (!first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a7c00efeb540198b33b8558c76e5cc2dd">IsQuantized</a>() ||</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  !second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a7c00efeb540198b33b8558c76e5cc2dd">IsQuantized</a>())</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  {</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="comment">// Not a quantized type, ignore the validation</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  }</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span> </div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> firstDataType = first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>();</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> secondDataType = second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>();</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> </div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keywordflow">if</span> (firstDataType != secondDataType)</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  {</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">": "</span> + firstName + <span class="stringliteral">" and "</span> + secondName +</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="stringliteral">" must be of the same quantized type, "</span> +</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  firstName + <span class="stringliteral">" is "</span> + <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(firstDataType) + <span class="stringliteral">", "</span> +</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  secondName + <span class="stringliteral">" is "</span> + <a class="code" href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">GetDataTypeName</a>(secondDataType));</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  }</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> </div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="keywordflow">if</span> (!first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a22f377fc4e10dc1773a3f979061e85f1">IsTypeSpaceMatch</a>(second))</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  {</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">": "</span> + firstName + <span class="stringliteral">" and "</span> + secondName +</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="stringliteral">" must have the same quantization space, "</span> +</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  firstName + <span class="stringliteral">" has offset "</span> + to_string(first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>()) +</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <span class="stringliteral">" and scale "</span> + to_string(first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>()) + <span class="stringliteral">", "</span> +</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  secondName + <span class="stringliteral">" has offset "</span> + to_string(second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>()) +</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="stringliteral">" and scale "</span> + to_string(second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>()));</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</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> </div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span> <span class="keywordtype">void</span> ValidateBiasTensorQuantization(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& biasTensor,</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo,</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& weightsTensorInfo,</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keyword">const</span> std::string& descName)</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span> {</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="comment">// Helper lambda function to validate a single bias quantization scale value</span></div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keyword">auto</span> VerifyBiasQuantizationScale = [&descName](<span class="keywordtype">float</span> biasScale, <span class="keywordtype">float</span> expectedScale) -> <span class="keywordtype">void</span></div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  {</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  constexpr <span class="keywordtype">float</span> tolerance = 0.000001f;</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="keywordflow">if</span> (std::abs(biasScale - expectedScale) > tolerance)</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">// Print the float values with extra precision to see very small differences</span></div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  std::stringstream msg;</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  msg << std::setprecision(10) << descName << <span class="stringliteral">": Expected "</span> << expectedScale <<</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="stringliteral">" quantization scale for bias tensor (the product of the input and weight scales), but got "</span> <<</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  biasScale;</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(msg.str(), <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>());</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  }</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  };</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>  <span class="keywordflow">if</span> (biasTensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>() != 0)</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="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">": Expected zero quantization offset for bias tensor but got "</span> +</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  to_string(biasTensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>()));</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  }</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> </div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keywordflow">if</span> (biasTensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#af672d1c9e2a120a18926cb645981fbb7">HasMultipleQuantizationScales</a>())</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  {</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="comment">// Validate per-axis quantization scales</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="keyword">const</span> std::vector<float>& weightScales = weightsTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8bc11f1fa23ef42532f9fdd04d355270">GetQuantizationScales</a>();</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keyword">const</span> std::vector<float>& biasScales = biasTensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8bc11f1fa23ef42532f9fdd04d355270">GetQuantizationScales</a>();</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> </div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keywordflow">if</span> (weightScales.size() != biasScales.size())</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  {</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  std::stringstream msg;</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  msg << descName << <span class="stringliteral">": Expected matchhing number of per-axis quantization scales, but got different "</span></div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  << <span class="stringliteral">"values: weights="</span> << weightScales.size() << <span class="stringliteral">", biases="</span> << biasScales.size();</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(msg.str(), <a class="code" href="_exceptions_8hpp.xhtml#aa3be76aec4ce713822a5ea1ecbb7bc61">CHECK_LOCATION</a>());</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  }</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span> </div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0ul; i < biasScales.size(); ++i)</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  {</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> expectedScale = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>() * weightScales[i];</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  VerifyBiasQuantizationScale(biasScales[i], expectedScale);</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">else</span></div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  {</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="comment">// Validate per-tensor quantization scale</span></div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> expectedScale = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>() * weightsTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>();</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  VerifyBiasQuantizationScale(biasTensor.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(), expectedScale);</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  }</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> }</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> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span> <span class="keywordtype">void</span> ValidateTensors(<span class="keyword">const</span> std::vector<ITensorHandle*>& vec,</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numExpected,</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <span class="keyword">const</span> std::string& descName,</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  <span class="keyword">const</span> std::string& varName)</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> {</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keywordflow">if</span> (vec.empty() && numExpected > 0)</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  {</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">": Invalid empty "</span> + varName + <span class="stringliteral">" array."</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> </div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < numExpected; ++i)</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  {</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keywordflow">if</span> (!vec[i])</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  {</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">": Invalid NULL for "</span> + varName + to_string(i));</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> }</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> </div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> <span class="keywordtype">void</span> ValidateBroadcastTensorShapesMatch(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& first,</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& second,</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& output,</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  std::string <span class="keyword">const</span>& descName,</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  std::string <span class="keyword">const</span>& firstName,</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  std::string <span class="keyword">const</span>& secondName)</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span> {</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="comment">// Tensors must have the same number of dimensions in order to be explicit about which dimensions will get</span></div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="comment">// broadcasted.</span></div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="keywordflow">if</span> (first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() != second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  {</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">": Tensors "</span></div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  + firstName + <span class="stringliteral">" & "</span> + secondName</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  + <span class="stringliteral">" must have the same number of dimensions in order to be broadcasted"</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>  uint32_t numDims = first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  std::vector<uint32_t> outputDims(numDims, 0u);</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="keywordflow">for</span> (uint32_t i = 0; i < numDims; i++)</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  {</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> dimsNotEqual = first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i] != second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i];</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> dimsNotOne = (first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i] != 1) && (second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i] != 1);</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keywordflow">if</span> (dimsNotEqual && dimsNotOne)</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  {</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(<span class="stringliteral">"Broadcasting is not possible for incompatible shapes"</span>);</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  }</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  outputDims[i] = std::max(first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i], second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i]);</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>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> broadcastShape = <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(boost::numeric_cast<unsigned int>(outputDims.size()), outputDims.data());</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="keywordflow">if</span> (broadcastShape != output.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>())</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  {</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">": The tensor shape resulting from adding "</span></div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  + firstName + <span class="stringliteral">" & "</span> + secondName</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  + <span class="stringliteral">" does not match the output shape"</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> </div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span> <span class="keywordtype">void</span> ValidateDataTypes(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>,</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="keyword">const</span> std::vector<armnn::DataType>& supportedTypes,</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  std::string <span class="keyword">const</span>& descName)</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>  <span class="keyword">auto</span> iterator = std::find(supportedTypes.begin(), supportedTypes.end(), info.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>());</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <span class="keywordflow">if</span> (iterator == supportedTypes.end())</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  {</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">": "</span> + <span class="stringliteral">" Tensor type is not supported."</span>);</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  }</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span> }</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span> </div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span> <span class="keywordtype">void</span> ValidateTensorDataTypesMatch(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& first,</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& second,</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  std::string <span class="keyword">const</span>& descName,</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  std::string <span class="keyword">const</span>& firstName,</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  std::string <span class="keyword">const</span>& secondName)</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span> {</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="keywordflow">if</span> (first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>())</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="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">": "</span> + firstName + <span class="stringliteral">" & "</span> + secondName +</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="stringliteral">" must have identical data types."</span>);</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  }</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span> }</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span> </div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span> <span class="keywordtype">void</span> ValidateTensorNumElementsMatch(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& first,</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& second,</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  std::string <span class="keyword">const</span>& descName,</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  std::string <span class="keyword">const</span>& firstName,</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  std::string <span class="keyword">const</span>& secondName)</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span> {</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <span class="keywordflow">if</span> (first.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>() != second.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>())</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  {</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descName + <span class="stringliteral">": "</span> + firstName + <span class="stringliteral">" & "</span> + secondName +</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="stringliteral">" must have the same number of elements."</span>);</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  }</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span> }</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span> </div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span> <span class="keywordtype">void</span> ValidateWeightDataType(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo,</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& weightInfo,</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <span class="keyword">const</span> std::string& descName)</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span> {</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> inputType = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>();</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <span class="keywordflow">if</span> (<a class="code" href="namespacearmnn.xhtml#ad91bc7bfe29186f5d78c28386c6c5309">IsQuantized8BitType</a>(inputType))</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>  <a class="code" href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <span class="keyword">const</span> std::vector<DataType> validTypes =</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  {</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  DataType::QAsymmU8,</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  DataType::QAsymmS8,</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  DataType::QSymmS8,</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  DataType::QuantizedSymm8PerAxis <span class="comment">// deprecated</span></div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  };</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <a class="code" href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span> </div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  ValidateDataTypes(weightInfo, validTypes, descName);</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  }</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  {</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  ValidateTensorDataTypesMatch(inputInfo, weightInfo, descName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"weight"</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"> 382</span> }</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span> </div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span> <span class="keywordtype">void</span> ValidatePerAxisQuantizationDimension(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensorInfo,</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <span class="keyword">const</span> std::string& descName,</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <span class="keyword">const</span> std::string& tensorName)</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span> {</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_optional.xhtml">Optional<unsigned int></a>& quantizationDim = tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b8fc85ce966c035d789cf22db5088a1">GetQuantizationDim</a>();</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="keywordflow">if</span> (!quantizationDim.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>())</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>  <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="l00392"></a><span class="lineno"> 392</span>  boost::format(<span class="stringliteral">"%1%: Quantization dimension for per-axis quantization not set on tensor %2%."</span>)</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  % descName % tensorName));</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  }</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">if</span> (quantizationDim.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>() != 0)</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>  <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="l00399"></a><span class="lineno"> 399</span>  boost::format(<span class="stringliteral">"%1%: Quantization dimension for per-axis quantization expected to be 0 on tensor %2%, "</span></div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="stringliteral">"but got: %3%"</span>) % descName % tensorName % quantizationDim.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>()));</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  }</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span> }</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span> </div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span> <span class="keywordtype">void</span> ValidatePerAxisQuantizationOffset(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& tensorInfo,</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="keyword">const</span> std::string& descName,</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="keyword">const</span> std::string& tensorName)</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>  int32_t quantizationOffset = tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>();</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <span class="keywordflow">if</span> (quantizationOffset != 0)</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  {</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(boost::str(</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  boost::format(<span class="stringliteral">"%1%: Quantization offset for per-axis quantization expected to be 0 on tensor %2%, "</span></div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <span class="stringliteral">"but got: %3%"</span>) % descName % tensorName % quantizationOffset));</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  }</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span> }</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span> </div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span> <span class="keywordtype">void</span> ValidatePerAxisQuantization(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo,</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputInfo,</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& weightInfo,</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<TensorInfo></a>& optionalBiasInfo,</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  <span class="keyword">const</span> std::string& descName)</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>  <span class="keywordflow">if</span> (weightInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#ab85cd8cc10c96a7c99c14042c251fc48">HasPerAxisQuantization</a>())</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  {</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> inputDataType = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>();</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> outputDataType = outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>();</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span> </div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> canHavePerAxisQuantization = (<a class="code" href="namespacearmnn.xhtml#ad91bc7bfe29186f5d78c28386c6c5309">IsQuantized8BitType</a>(inputDataType)) && inputDataType == outputDataType;</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span> </div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <span class="keywordflow">if</span> (!canHavePerAxisQuantization)</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  {</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(boost::str(</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  boost::format(<span class="stringliteral">"%1%: Per-axis quantization parameters set on tensor %2%, "</span></div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  <span class="stringliteral">"but data type does not support per-axis quantization."</span>) % descName % <span class="stringliteral">"weight"</span>));</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  }</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>  ValidPerAxisQuantizedDataType(weightInfo, descName, <span class="stringliteral">"weight"</span>);</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  ValidatePerAxisQuantizationDimension(weightInfo, descName, <span class="stringliteral">"weight"</span>);</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  ValidatePerAxisQuantizationOffset(weightInfo, descName, <span class="stringliteral">"weight"</span>);</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span> </div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <span class="keywordflow">if</span> (optionalBiasInfo.<a class="code" href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">has_value</a>())</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>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& biasInfo = optionalBiasInfo.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>();</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  <span class="keywordflow">if</span> (!biasInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#ab85cd8cc10c96a7c99c14042c251fc48">HasPerAxisQuantization</a>())</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>  <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="l00448"></a><span class="lineno"> 448</span>  boost::format(<span class="stringliteral">"%1%: Per-axis quantization parameters not set on bias tensor, despite being set on "</span></div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="stringliteral">"weight tensor."</span>) % descName));</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  }</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span> </div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  ValidateTensorDataType(biasInfo, DataType::Signed32, descName, <span class="stringliteral">"bias"</span>);</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  ValidatePerAxisQuantizationDimension(biasInfo, descName, <span class="stringliteral">"bias"</span>);</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  ValidatePerAxisQuantizationOffset(biasInfo, descName, <span class="stringliteral">"bias"</span>);</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  }</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  }</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span> }</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span> </div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span> } <span class="comment">// anonymous namespace</span></div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span> </div><div class="line"><a name="l00461"></a><span class="lineno"><a class="line" href="structarmnn_1_1_queue_descriptor.xhtml#a765d2cee4ccce5b9467e0c2b6d25b84a"> 461</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a765d2cee4ccce5b9467e0c2b6d25b84a">QueueDescriptor::ValidateInputsOutputs</a>(<span class="keyword">const</span> std::string& descName,</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numExpectedIn, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numExpectedOut)<span class="keyword"> const</span></div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span> <span class="keyword"></span>{</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  ValidateTensors(<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>, numExpectedIn, descName, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  ValidateTensors(<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>, numExpectedOut, descName, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span> }</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span> </div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00469"></a><span class="lineno"><a class="line" href="structarmnn_1_1_mem_copy_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 469</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_mem_copy_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">MemCopyQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span> <span class="keyword"></span>{</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"MemCopyQueueDescriptor"</span>};</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span> </div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  ValidateNumOutputs(workloadInfo, descriptorName , 1);</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="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span> </div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  ValidateTensorNumElementsMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span> </div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <span class="keywordflow">if</span> (<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size() != <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>.size())</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_invalid_argument_exception.xhtml">InvalidArgumentException</a>(boost::str(</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  boost::format(<span class="stringliteral">"%1%: Number of inputs (%2%) does not match the number of outputs (%3%)."</span>) %</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  descriptorName % <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size() % <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>.size()));</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  }</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span> </div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size(); ++i)</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  {</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <span class="keywordflow">if</span> (!<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[i])</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  {</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(boost::str(boost::format(<span class="stringliteral">"%1%: Invalid NULL input %2%."</span>) %</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  descriptorName % i));</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> </div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  <span class="keywordflow">if</span> (!<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[i])</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  {</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  <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">"%1%: Invalid NULL output %2%"</span>) %</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  descriptorName % i));</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>  }</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span> }</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span> </div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00506"></a><span class="lineno"><a class="line" href="structarmnn_1_1_mem_import_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 506</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_mem_import_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">MemImportQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span> <span class="keyword"></span>{</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  ValidateNumInputs(workloadInfo, <span class="stringliteral">"MemImportQueueDescriptor"</span>, 1);</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  ValidateNumOutputs(workloadInfo, <span class="stringliteral">"MemImportQueueDescriptor"</span> , 1);</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span> </div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size() != 1)</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  {</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  <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="l00514"></a><span class="lineno"> 514</span>  boost::format(<span class="stringliteral">"Number of input infos (%1%) is not 1."</span>)</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  % workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size()));</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span> </div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  }</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span> </div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size() != workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size())</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  {</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(boost::str(</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  boost::format(<span class="stringliteral">"Number of input infos (%1%) does not match the number of output infos (%2%)"</span>)</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  % workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size() % workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size()));</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>  <span class="keywordflow">for</span> (std::size_t i = 0; i < workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size(); ++i)</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  {</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[i].GetNumElements() !=</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[i].GetNumElements())</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">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(boost::str(</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  boost::format(<span class="stringliteral">"Number of elements for tensor input and output %1% does not match"</span>)</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  % i ));</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  }</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="keywordflow">if</span> (<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size() != 1)</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  {</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(boost::str(</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  boost::format(<span class="stringliteral">"Number of inputs (%1%) is not 1."</span>)</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  % <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size()));</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  }</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span> </div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <span class="keywordflow">if</span> (<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size() != <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>.size())</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">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(boost::str(</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  boost::format(<span class="stringliteral">"Number of inputs (%1%) does not match the number of outputs (%2%)"</span>)</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  % <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size() % <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>.size()));</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  }</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span> </div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size(); ++i)</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">if</span> (!<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[i])</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(boost::str(boost::format(<span class="stringliteral">"Invalid null input %1%"</span>) % i));</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  }</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span> </div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <span class="keywordflow">if</span> (!<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[i])</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  {</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(boost::str(boost::format(<span class="stringliteral">"Invalid null output %1%"</span>) % i));</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  }</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  }</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span> }</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span> </div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00566"></a><span class="lineno"><a class="line" href="structarmnn_1_1_mem_sync_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 566</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_mem_sync_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">MemSyncQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span> <span class="keyword"></span>{</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  ValidateNumInputs(workloadInfo, <span class="stringliteral">"MemSyncQueueDescriptor"</span>, 1);</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  ValidateNumOutputs(workloadInfo, <span class="stringliteral">"MemSyncQueueDescriptor"</span> , 1);</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="keywordflow">if</span> (<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size() != 1)</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  {</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <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="l00574"></a><span class="lineno"> 574</span>  boost::format(<span class="stringliteral">"Number of inputs (%1%) is not 1."</span>)</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  % <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size()));</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  }</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span> </div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  <span class="keywordflow">if</span> (<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>.size() != 0)</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  {</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</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="l00581"></a><span class="lineno"> 581</span>  boost::format(<span class="stringliteral">"Number of outputs (%1%) is not 0."</span>)</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  % <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size() % <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>.size()));</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  }</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span> </div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  <span class="keywordflow">if</span> (!<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0])</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  {</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(boost::str(boost::format(<span class="stringliteral">"Invalid null input 0"</span>)));</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  }</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span> }</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> <span class="comment">//---------------------------------------------------------------</span></div><div class="line"><a name="l00592"></a><span class="lineno"><a class="line" href="structarmnn_1_1_activation_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 592</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_activation_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ActivationQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span> <span class="keyword"></span>{</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"ActivationQueueDescriptor"</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>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</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="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span> </div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  std::vector<DataType> supportedTypes =</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>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  };</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span> </div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span> }</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span> </div><div class="line"><a name="l00617"></a><span class="lineno"><a class="line" href="structarmnn_1_1_arg_min_max_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 617</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_arg_min_max_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ArgMinMaxQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span> <span class="keyword"></span>{</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"ArgMinMaxQueueDescriptor"</span>};</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span> </div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</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>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span> </div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  <span class="keywordflow">if</span> (outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>)</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  {</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Output of ArgMinMax layer must be Int32."</span>);</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> </div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  std::vector<DataType> supportedInputTypes =</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>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>,</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a></div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  };</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span> </div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  ValidateDataTypes(inputTensorInfo, supportedInputTypes, descriptorName);</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span> </div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  <span class="keyword">auto</span> inputShape = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  <span class="keyword">auto</span> outputShape = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span> </div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  <span class="keyword">auto</span> inputNumDimensions = inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  <span class="keyword">auto</span> unsignedAxis = <a class="code" href="namespacearmnn_utils.xhtml#ac93cb1365b4bcb67df2a3164606096c5">armnnUtils::GetUnsignedAxis</a>(inputNumDimensions, m_Parameters.m_Axis);</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span> </div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  <span class="keyword">const</span> std::string outputShapeError{<span class="stringliteral">": Output tensor shape does not match shape inferred from input tensor."</span>};</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span> </div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  <span class="comment">// 1D input shape results in scalar output shape</span></div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  <span class="keywordflow">if</span> (inputShape.GetNumDimensions() == 1)</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  {</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  <span class="keywordflow">if</span> (outputShape.GetNumDimensions() != 1 && outputShape[0] != 1)</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  {</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + outputShapeError);</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>  }</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  {</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < unsignedAxis; ++i)</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  {</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  <span class="keywordflow">if</span> (outputShape[i] != inputShape[i])</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  {</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + outputShapeError);</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="keywordflow">for</span> (<span class="keyword">auto</span> i = unsignedAxis + 1; i < inputNumDimensions; ++i)</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  {</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  <span class="keywordflow">if</span> (outputShape[i - 1] != inputShape[i])</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  {</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + outputShapeError);</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  }</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>  }</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"><a class="line" href="structarmnn_1_1_softmax_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 680</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_softmax_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">SoftmaxQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span> <span class="keyword"></span>{</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"SoftmaxQueueDescriptor"</span>};</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span> </div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span> </div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span> </div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  std::vector<DataType> supportedTypes =</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>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  };</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span> </div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span> }</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span> </div><div class="line"><a name="l00705"></a><span class="lineno"><a class="line" href="structarmnn_1_1_splitter_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 705</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_splitter_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">SplitterQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span> <span class="keyword"></span>{</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"SplitterQueueDescriptor"</span>};</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span> </div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</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="comment">// Check the supported data types</span></div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  {</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">DataType::Boolean</a>,</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>,</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  };</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span> </div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">long</span> i = 0ul; i < workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size(); ++i)</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>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[i];</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  ValidateDataTypes(outputTensorInfo, supportedTypes, descriptorName);</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="keyword">const</span> std::string outputName = <span class="stringliteral">"output_"</span> + std::to_string(i);</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, outputName);</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  }</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span> </div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size() <= 0)</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  {</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": At least one output needs to be provided."</span>);</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> </div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size() != m_ViewOrigins.size())</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  descriptorName + <span class="stringliteral">": Number of split windows "</span></div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  <span class="stringliteral">"has to match number of workloadInfo.m_OutputTensorInfos. "</span></div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  <span class="stringliteral">"Number of windows: "</span> +</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  to_string(m_ViewOrigins.size()) +</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  <span class="stringliteral">". Number of workloadInfo.m_OutputTensorInfos: "</span> + to_string(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size()));</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  }</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span> </div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  <span class="comment">//The dimensionality of all the windows has to match the dimensionality (not shape) of the input.</span></div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  std::size_t inputDims = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0].GetNumDimensions();</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> w = 0; w < m_ViewOrigins.size(); ++w )</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  {</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  <span class="comment">//Checks that the dimensionality of input is same as the split windows.</span></div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  <a class="code" href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml">ViewOrigin</a> <span class="keyword">const</span>& e = m_ViewOrigins[w];</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  <span class="keywordflow">if</span> (e.<a class="code" href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml#af2f0a8c9eb32861711c0ce30b7986c44">m_Origin</a>.size() != inputDims)</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  {</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Window origin have to "</span></div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  <span class="stringliteral">"have the same dimensionality as the input tensor. "</span></div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  <span class="stringliteral">"Window origin (index: "</span> +</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  to_string(w) + <span class="stringliteral">") has "</span> + to_string(e.<a class="code" href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml#af2f0a8c9eb32861711c0ce30b7986c44">m_Origin</a>.size()) +</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  <span class="stringliteral">" dimensions, the input "</span></div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  <span class="stringliteral">"tensor has "</span> +</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  to_string(inputDims) + <span class="stringliteral">" dimensions."</span>);</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  }</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < e.<a class="code" href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml#af2f0a8c9eb32861711c0ce30b7986c44">m_Origin</a>.size(); ++i)</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">if</span> (e.<a class="code" href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml#af2f0a8c9eb32861711c0ce30b7986c44">m_Origin</a>[i] + workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[w].GetShape()[i] ></div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0].GetShape()[i])</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  {</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Window extent coordinates have to "</span></div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  <span class="stringliteral">"be smaller or equal than the size of the input in that coord."</span>);</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  }</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  }</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  }</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span> }</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span> </div><div class="line"><a name="l00776"></a><span class="lineno"><a class="line" href="structarmnn_1_1_concat_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 776</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ConcatQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span> <span class="keyword"></span>{</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"ConcatQueueDescriptor"</span>};</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span> </div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span> </div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  <span class="keywordflow">if</span> (<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>.size() <= 0)</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": At least one input needs to be provided."</span>);</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  }</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  <span class="keywordflow">if</span> (<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>.size() <= 0)</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": At least one output needs to be provided."</span>);</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  }</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span> </div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size() <= 0)</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  {</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": At least one TensorInfo input needs to be provided."</span>);</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  }</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size() <= 0)</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  {</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": At least one TensorInfo output needs to be provided."</span>);</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  }</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span> </div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  <span class="keywordflow">if</span>(m_Parameters.GetConcatAxis() > workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0].GetShape().GetNumDimensions())</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  {</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Invalid concatenation axis provided."</span>);</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="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0].GetShape().GetNumDimensions() - m_Parameters.GetConcatAxis() == 1)</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  {</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  }</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span> </div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size() != m_ViewOrigins.size())</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  {</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  descriptorName + <span class="stringliteral">": Number of split windows "</span></div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  <span class="stringliteral">"has to match number of workloadInfo.m_InputTensorInfos. "</span></div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  <span class="stringliteral">"Number of windows: "</span> +</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  to_string(m_ViewOrigins.size()) +</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  <span class="stringliteral">". Number of workloadInfo.m_InputTensorInfos: "</span> + to_string(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size()));</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</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>  <span class="comment">//The dimensionality of all the windows has to match the dimensionality (not shape) of the output.</span></div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  std::size_t outputDims = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0].GetNumDimensions();</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> w = 0; w < m_ViewOrigins.size(); ++w )</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  {</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  <span class="comment">//Checks that the dimensionality of output is same as the split windows.</span></div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ViewOrigin</a> <span class="keyword">const</span>& e = m_ViewOrigins[w];</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  <span class="keywordflow">if</span> (e.<a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml#af2f0a8c9eb32861711c0ce30b7986c44">m_Origin</a>.size() != outputDims)</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  {</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Window origin have to "</span></div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  <span class="stringliteral">"have the same dimensionality as the output tensor. "</span></div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  <span class="stringliteral">"Window origin (index: "</span> +</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  to_string(w) + <span class="stringliteral">") has "</span> + to_string(e.<a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml#af2f0a8c9eb32861711c0ce30b7986c44">m_Origin</a>.size()) +</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  <span class="stringliteral">" dimensions, the output "</span></div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  <span class="stringliteral">"tensor has "</span> +</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  to_string(outputDims) + <span class="stringliteral">" dimensions."</span>);</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="comment">//Checks that the merge windows are within the output tensor.</span></div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < e.<a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml#af2f0a8c9eb32861711c0ce30b7986c44">m_Origin</a>.size(); ++i)</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  {</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  <span class="keywordflow">if</span> (e.<a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml#af2f0a8c9eb32861711c0ce30b7986c44">m_Origin</a>[i] + workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[w].GetShape()[i]</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  > workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0].GetShape()[i])</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  {</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Window extent coordinates have to "</span></div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  <span class="stringliteral">"be smaller or equal than the size of the output in that coord."</span>);</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  }</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  }</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  }</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span> </div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span>  <span class="comment">// Check the supported data types</span></div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  {</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">DataType::Boolean</a>,</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>,</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  };</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span> </div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">long</span> i = 0ul; i < workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size(); ++i)</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  {</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[i];</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span> </div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  <span class="keyword">const</span> std::string inputName = <span class="stringliteral">"input_"</span> + std::to_string(i);</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, inputName, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  }</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span> }</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span> </div><div class="line"><a name="l00871"></a><span class="lineno"><a class="line" href="structarmnn_1_1_stack_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 871</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_stack_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">StackQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span> <span class="keyword"></span>{</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"StackQueueDescriptor"</span>};</div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span> </div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span> </div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  <span class="keywordflow">if</span> (m_Parameters.m_NumInputs != workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size())</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  {</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Must have the defined number of input tensors."</span>);</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  }</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span> </div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  <span class="comment">// All inputs must have the same shape, which is defined in parameters</span></div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape = m_Parameters.m_InputShape;</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size(); ++i)</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span>  {</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span>  <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[i].GetShape() != inputShape)</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  {</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": All input tensor shapes must match the defined shape."</span>);</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  }</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span>  }</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span> </div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  <span class="keywordflow">if</span> (inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() > 4)</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>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Input tensor may have up to 4 dimensions."</span>);</div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  }</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span> </div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>  <span class="comment">// m_Axis is 0-based and may take values from 0 to the number of input dimensions (inclusive),</span></div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span>  <span class="comment">// since the output tensor has an additional dimension.</span></div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  <span class="keywordflow">if</span> (m_Parameters.m_Axis > inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>())</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  {</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Axis may not be greater "</span></div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  <span class="stringliteral">"than the number of input dimensions."</span>);</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  }</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span> </div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span>  <span class="comment">// Output shape must be as inferred from the input shape</span></div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0].GetShape();</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < m_Parameters.m_Axis; ++i)</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  {</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  <span class="keywordflow">if</span> (outputShape[i] != inputShape[i])</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span>  {</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Output tensor must "</span></div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span>  <span class="stringliteral">"match shape inferred from input tensor."</span>);</div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span>  }</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="keywordflow">if</span> (outputShape[m_Parameters.m_Axis] != m_Parameters.m_NumInputs)</div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  {</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Output tensor must "</span></div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  <span class="stringliteral">"match shape inferred from input tensor."</span>);</div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  }</div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span> </div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = m_Parameters.m_Axis + 1; i < inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() + 1; ++i)</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="keywordflow">if</span> (outputShape[i] != inputShape[i-1])</div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  {</div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Output tensor must "</span></div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  <span class="stringliteral">"match shape inferred from input tensor."</span>);</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>  }</div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span> </div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  <span class="keywordflow">if</span> (outputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() > 5)</div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span>  {</div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Output tensor may have up to 5 dimensions."</span>);</div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  }</div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span> </div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  <span class="comment">// Check the supported data types</span></div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  {</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">DataType::Boolean</a>,</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>,</div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></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>  ValidateDataTypes(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0], supportedTypes, descriptorName);</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span> </div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 1ul; i < workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size(); ++i)</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  {</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  ValidateTensorDataTypesMatch(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0],</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span>  workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[i],</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  descriptorName,</div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  <span class="stringliteral">"input_0"</span>,</div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  <span class="stringliteral">"input_"</span> + std::to_string(i));</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> </div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span>  ValidateTensorDataTypesMatch(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0],</div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0],</div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  descriptorName,</div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  <span class="stringliteral">"input_0"</span>,</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span> }</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span> </div><div class="line"><a name="l00966"></a><span class="lineno"><a class="line" href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 966</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">FullyConnectedQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span> <span class="keyword"></span>{</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"FullyConnectedQueueDescriptor"</span>};</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span> </div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span> </div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span> </div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 2, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span> </div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span>  <span class="keywordflow">if</span> (!(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() == 2 || inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() == 4))</div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  {</div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Input tensor must have 2 or 4 dimensions."</span>);</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> </div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  ValidatePointer(m_Weight, descriptorName, <span class="stringliteral">"weight"</span>);</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span> </div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& weightTensorInfo = m_Weight->GetTensorInfo();</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  ValidateTensorNumDimensions(weightTensorInfo, descriptorName, 2, <span class="stringliteral">"weight"</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="keywordflow">if</span> (m_Parameters.m_BiasEnabled)</div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  {</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span>  ValidatePointer(m_Bias, descriptorName, <span class="stringliteral">"bias"</span>);</div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span> </div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  <span class="comment">// Validates type and quantization values.</span></div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& biasTensorInfo = m_Bias->GetTensorInfo();</div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span>  ValidateBiasTensorQuantization(biasTensorInfo, inputTensorInfo, weightTensorInfo, descriptorName);</div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span> </div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span>  ValidateTensorDataType(biasTensorInfo, <a class="code" href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">GetBiasDataType</a>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()), descriptorName, <span class="stringliteral">"bias"</span>);</div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span>  ValidateTensorNumDimensions(biasTensorInfo, descriptorName, 1, <span class="stringliteral">"bias"</span>);</div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span>  }</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span> </div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  <span class="comment">// Check the supported data types</span></div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  {</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  };</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span> </div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span> }</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span> </div><div class="line"><a name="l01015"></a><span class="lineno"><a class="line" href="structarmnn_1_1_normalization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1015</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_normalization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">NormalizationQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span> <span class="keyword"></span>{</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"NormalizationQueueDescriptor"</span>};</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>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</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>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span> </div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span>  <span class="comment">// Check the supported data types</span></div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  std::vector<DataType> supportedTypes =</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="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span>  };</div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span> </div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span> </div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span> </div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span>  ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</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"><a class="line" href="structarmnn_1_1_addition_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1042</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_addition_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">AdditionQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span> <span class="keyword"></span>{</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"AdditionQueueDescriptor"</span>};</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>  ValidateNumInputs(workloadInfo, descriptorName, 2);</div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span> </div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span> </div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>  {</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></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> </div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>  ValidateDataTypes(inputTensorInfo0, supportedTypes, descriptorName);</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  ValidateDataTypes(inputTensorInfo1, supportedTypes, descriptorName);</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  ValidateDataTypes(outputTensorInfo, supportedTypes, descriptorName);</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>  ValidateTensorDataTypesMatch(inputTensorInfo0, inputTensorInfo1, descriptorName, <span class="stringliteral">"input_0"</span>, <span class="stringliteral">"input_1"</span>);</div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>  ValidateTensorDataTypesMatch(inputTensorInfo1, outputTensorInfo, descriptorName, <span class="stringliteral">"input_1"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span> </div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>  ValidateBroadcastTensorShapesMatch(inputTensorInfo0,</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>  inputTensorInfo1,</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>  outputTensorInfo,</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>  descriptorName,</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>  <span class="stringliteral">"input_0"</span>,</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>  <span class="stringliteral">"input_1"</span>);</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span> }</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span> </div><div class="line"><a name="l01078"></a><span class="lineno"><a class="line" href="structarmnn_1_1_multiplication_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1078</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_multiplication_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">MultiplicationQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span> <span class="keyword"></span>{</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"MultiplicationQueueDescriptor"</span>};</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>  ValidateNumInputs(workloadInfo, descriptorName, 2);</div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</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="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span> </div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>  {</div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>,</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a></div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>  };</div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span> </div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>  ValidateDataTypes(inputTensorInfo0, supportedTypes, descriptorName);</div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>  ValidateDataTypes(inputTensorInfo1, supportedTypes, descriptorName);</div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>  ValidateDataTypes(outputTensorInfo, supportedTypes, descriptorName);</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>  ValidateTensorDataTypesMatch(inputTensorInfo0, inputTensorInfo1, descriptorName, <span class="stringliteral">"input_0"</span>, <span class="stringliteral">"input_1"</span>);</div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>  ValidateTensorDataTypesMatch(inputTensorInfo1, outputTensorInfo, descriptorName, <span class="stringliteral">"input_1"</span>, <span class="stringliteral">"output"</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>  ValidateBroadcastTensorShapesMatch(inputTensorInfo0,</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>  inputTensorInfo1,</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>  outputTensorInfo,</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>  descriptorName,</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>  <span class="stringliteral">"input_0"</span>,</div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span>  <span class="stringliteral">"input_1"</span>);</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span> }</div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span> </div><div class="line"><a name="l01114"></a><span class="lineno"><a class="line" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1114</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">BatchNormalizationQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span> <span class="keyword"></span>{</div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"BatchNormalizationQueueDescriptor"</span>};</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span> </div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span> </div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span> </div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span>  {</div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  };</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span> </div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>  ValidateDataTypes(outputTensorInfo, supportedTypes, descriptorName);</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>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>  ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span> </div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span>  ValidatePointer(m_Mean, descriptorName, <span class="stringliteral">"mean"</span>);</div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>  ValidatePointer(m_Variance, descriptorName, <span class="stringliteral">"variance"</span>);</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>  ValidatePointer(m_Beta, descriptorName, <span class="stringliteral">"beta"</span>);</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>  ValidatePointer(m_Gamma, descriptorName, <span class="stringliteral">"gamma"</span>);</div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span> </div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& mean = m_Mean->GetTensorInfo();</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& variance = m_Variance->GetTensorInfo();</div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& beta = m_Beta->GetTensorInfo();</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& gamma = m_Gamma->GetTensorInfo();</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span> </div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>  ValidateTensorNumDimensions(mean, descriptorName, 1, <span class="stringliteral">"mean"</span>);</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>  ValidateTensorNumDimensions(variance, descriptorName, 1, <span class="stringliteral">"variance"</span>);</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>  ValidateTensorNumDimensions(beta, descriptorName, 1, <span class="stringliteral">"beta"</span>);</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>  ValidateTensorNumDimensions(gamma, descriptorName, 1, <span class="stringliteral">"gamma"</span>);</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span> </div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  ValidateTensorShapesMatch(mean, variance, descriptorName, <span class="stringliteral">"mean"</span>, <span class="stringliteral">"variance"</span>);</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  ValidateTensorShapesMatch(mean, beta, descriptorName, <span class="stringliteral">"mean"</span>, <span class="stringliteral">"beta"</span>);</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  ValidateTensorShapesMatch(mean, gamma, descriptorName, <span class="stringliteral">"mean"</span>, <span class="stringliteral">"gamma"</span>);</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span> }</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span> </div><div class="line"><a name="l01159"></a><span class="lineno"><a class="line" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1159</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">Convolution2dQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span> <span class="keyword"></span>{</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"Convolution2dQueueDescriptor"</span>};</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span> </div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span> </div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span> </div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  ValidateTensorNumDimensions(inputTensorInfo, descriptorName, 4, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>  ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 4, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span> </div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>  ValidatePointer(m_Weight, descriptorName, <span class="stringliteral">"weight"</span>);</div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span> </div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& weightTensorInfo = m_Weight->GetTensorInfo();</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  ValidateTensorNumDimensions(weightTensorInfo, descriptorName, 4, <span class="stringliteral">"weight"</span>);</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span> </div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>  ValidateWeightDataType(inputTensorInfo, weightTensorInfo, descriptorName);</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span> </div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<TensorInfo></a> optionalBiasTensorInfo;</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>  <span class="keywordflow">if</span> (m_Parameters.m_BiasEnabled)</div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  {</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>  ValidatePointer(m_Bias, descriptorName, <span class="stringliteral">"bias"</span>);</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span> </div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  optionalBiasTensorInfo = MakeOptional<TensorInfo>(m_Bias->GetTensorInfo());</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& biasTensorInfo = optionalBiasTensorInfo.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>();</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span> </div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>  ValidateTensorDataType(biasTensorInfo, <a class="code" href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">GetBiasDataType</a>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()), descriptorName, <span class="stringliteral">"bias"</span>);</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>  ValidateBiasTensorQuantization(biasTensorInfo, inputTensorInfo, weightTensorInfo, descriptorName);</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> </div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>  ValidatePerAxisQuantization(inputTensorInfo,</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>  outputTensorInfo,</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>  weightTensorInfo,</div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>  optionalBiasTensorInfo,</div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>  descriptorName);</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>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  {</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>,</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>,</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a></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> </div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</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> </div><div class="line"><a name="l01212"></a><span class="lineno"><a class="line" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1212</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">DepthwiseConvolution2dQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span> <span class="keyword"></span>{</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"DepthwiseConvolution2dQueueDescriptor"</span>};</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>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span> </div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span> </div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span>  ValidateTensorNumDimensions(inputTensorInfo, descriptorName, 4, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>  ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 4, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span> </div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>  ValidatePointer(m_Weight, descriptorName, <span class="stringliteral">"weight"</span>);</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>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& weightTensorInfo = m_Weight->GetTensorInfo();</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>  ValidateTensorNumDimensions(weightTensorInfo, descriptorName, 4, <span class="stringliteral">"weight"</span>);</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">if</span> (m_Parameters.m_DilationX < 1 || m_Parameters.m_DilationY < 1 )</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>  {</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>  boost::str(boost::format(<span class="stringliteral">"%1%: dilationX (provided %2%) and dilationY (provided %3%) "</span></div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>  <span class="stringliteral">"cannot be smaller than 1."</span>) % descriptorName %</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>  m_Parameters.m_DilationX % m_Parameters.m_DilationX));</div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>  }</div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span> </div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channelIndex = (m_Parameters.m_DataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>) ? 1 : 3;</div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span> </div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  <span class="comment">// Expected weight shape: [ M, I, H, W ] - This shape does NOT depend on the data layout</span></div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>  <span class="comment">// inputChannels * channelMultiplier should be equal to outputChannels.</span></div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numWeightChannelMultiplier = weightTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0];</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numWeightInputChannels = weightTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[1];</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numWeightOutputChannels = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[channelIndex];</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>  <span class="keywordflow">if</span> (numWeightChannelMultiplier * numWeightInputChannels != numWeightOutputChannels)</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>  {</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span>  boost::str(boost::format(<span class="stringliteral">"%1%: output_channels (provided %2%) should be "</span></div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>  <span class="stringliteral">"equal to input_channels (provided %3%) multiplied by channel_multiplier "</span></div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>  <span class="stringliteral">"(provided %4%)."</span>) % descriptorName % numWeightOutputChannels %</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>  numWeightInputChannels % numWeightChannelMultiplier));</div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>  }</div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span> </div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  ValidateWeightDataType(inputTensorInfo, weightTensorInfo, descriptorName);</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span> </div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<TensorInfo></a> optionalBiasTensorInfo;</div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>  <span class="keywordflow">if</span> (m_Parameters.m_BiasEnabled)</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>  ValidatePointer(m_Bias, descriptorName, <span class="stringliteral">"bias"</span>);</div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span> </div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span>  optionalBiasTensorInfo = MakeOptional<TensorInfo>(m_Bias->GetTensorInfo());</div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& biasTensorInfo = optionalBiasTensorInfo.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>();</div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span> </div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>  ValidateBiasTensorQuantization(biasTensorInfo, inputTensorInfo, weightTensorInfo, descriptorName);</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>  ValidateTensorDataType(biasTensorInfo, <a class="code" href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">GetBiasDataType</a>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()), descriptorName, <span class="stringliteral">"bias"</span>);</div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span>  }</div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>  ValidatePerAxisQuantization(inputTensorInfo,</div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span>  outputTensorInfo,</div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span>  weightTensorInfo,</div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>  optionalBiasTensorInfo,</div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span>  descriptorName);</div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span> </div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span>  {</div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>,</div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a></div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span>  };</div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span> </div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</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> </div><div class="line"><a name="l01287"></a><span class="lineno"><a class="line" href="structarmnn_1_1_permute_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1287</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_permute_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">PermuteQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span> <span class="keyword"></span>{</div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"PermuteQueueDescriptor"</span>};</div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span> </div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</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="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a>& mapping = m_Parameters.m_DimMappings;</div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span> </div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span> </div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>  ValidateTensorNumDimensions(inputTensorInfo, descriptorName, mapping.<a class="code" href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>(), <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span>  ValidateTensorNumDimensions(outputTensorInfo, descriptorName, mapping.<a class="code" href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>(), <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span> </div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0u; i < mapping.<a class="code" href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>(); ++i)</div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span>  {</div><div class="line"><a name="l01304"></a><span class="lineno"> 1304</span>  <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i] != outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[mapping[i]])</div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span>  {</div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": src dimension "</span> + to_string(i) +</div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>  <span class="stringliteral">" (="</span> + to_string(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i]) + <span class="stringliteral">") "</span> +</div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</span>  <span class="stringliteral">"must match dst dimension "</span> + to_string(mapping[i]) +</div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span>  <span class="stringliteral">" (="</span> + to_string(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[mapping[i]]) + <span class="stringliteral">")"</span>);</div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>  }</div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span>  }</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>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</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> </div><div class="line"><a name="l01316"></a><span class="lineno"><a class="line" href="structarmnn_1_1_pooling2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1316</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_pooling2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">Pooling2dQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span> <span class="keyword"></span>{</div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"Pooling2dQueueDescriptor"</span>};</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span> </div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span> </div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span> </div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>  ValidateTensorNumDimensions(inputTensorInfo, descriptorName, 4, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>  ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 4, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span> </div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>  {</div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>  };</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span> </div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span> }</div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span> </div><div class="line"><a name="l01343"></a><span class="lineno"><a class="line" href="structarmnn_1_1_resize_bilinear_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1343</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_resize_bilinear_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ResizeBilinearQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span> <span class="keyword"></span>{</div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"ResizeBilinearQueueDescriptor"</span>};</div><div class="line"><a name="l01346"></a><span class="lineno"> 1346</span> </div><div class="line"><a name="l01347"></a><span class="lineno"> 1347</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span> </div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</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>  ValidateTensorNumDimensions(inputTensorInfo, descriptorName, 4, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span>  ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 4, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span> </div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>  {</div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span>  };</div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span> </div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</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>  <span class="comment">// ResizeBilinear only changes width and height: batch and channel count must match.</span></div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatchSize = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0];</div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputBatchSize = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0];</div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span>  <span class="keywordflow">if</span> (inputBatchSize != outputBatchSize)</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">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</span>  boost::str(boost::format(<span class="stringliteral">"%1%: Input batch size (%2%) "</span></div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span>  <span class="stringliteral">"does not match output batch size (%3%)"</span>) %</div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span>  descriptorName % inputBatchSize % outputBatchSize));</div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span>  }</div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span> </div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>  <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">DataLayoutIndexed</a> dimensionIndices(m_Parameters.m_DataLayout);</div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannelCount = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">GetChannelsIndex</a>()];</div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannelCount = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">GetChannelsIndex</a>()];</div><div class="line"><a name="l01382"></a><span class="lineno"> 1382</span>  <span class="keywordflow">if</span> (inputChannelCount != outputChannelCount)</div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span>  {</div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span>  boost::str(boost::format(<span class="stringliteral">"%1%: Input channel count (%2%) "</span></div><div class="line"><a name="l01386"></a><span class="lineno"> 1386</span>  <span class="stringliteral">"does not match output channel count (%3%)"</span>) %</div><div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>  descriptorName % inputChannelCount % outputChannelCount));</div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</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> </div><div class="line"><a name="l01391"></a><span class="lineno"><a class="line" href="structarmnn_1_1_resize_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1391</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_resize_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ResizeQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span> <span class="keyword"></span>{</div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"ResizeQueueDescriptor"</span>};</div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span> </div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span> </div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01399"></a><span class="lineno"> 1399</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</span> </div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span>  ValidateTensorNumDimensions(inputTensorInfo, descriptorName, 4, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>  ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 4, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span> </div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>  {</div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01409"></a><span class="lineno"> 1409</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l01410"></a><span class="lineno"> 1410</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span>  };</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>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span> </div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>  <span class="comment">// Resize only changes width and height: batch and channel count must match.</span></div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatchSize = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0];</div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputBatchSize = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[0];</div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>  <span class="keywordflow">if</span> (inputBatchSize != outputBatchSize)</div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>  {</div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span>  boost::str(boost::format(<span class="stringliteral">"%1%: Input batch size (%2%) "</span></div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>  <span class="stringliteral">"does not match output batch size (%3%)"</span>) %</div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>  descriptorName % inputBatchSize % outputBatchSize));</div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>  }</div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span> </div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</span>  <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">DataLayoutIndexed</a> dimensionIndices(m_Parameters.m_DataLayout);</div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannelCount = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">GetChannelsIndex</a>()];</div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannelCount = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">GetChannelsIndex</a>()];</div><div class="line"><a name="l01431"></a><span class="lineno"> 1431</span>  <span class="keywordflow">if</span> (inputChannelCount != outputChannelCount)</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="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(</div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>  boost::str(boost::format(<span class="stringliteral">"%1%: Input channel count (%2%) "</span></div><div class="line"><a name="l01435"></a><span class="lineno"> 1435</span>  <span class="stringliteral">"does not match output channel count (%3%)"</span>) %</div><div class="line"><a name="l01436"></a><span class="lineno"> 1436</span>  descriptorName % inputChannelCount % outputChannelCount));</div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</span>  }</div><div class="line"><a name="l01438"></a><span class="lineno"> 1438</span> }</div><div class="line"><a name="l01439"></a><span class="lineno"> 1439</span> </div><div class="line"><a name="l01440"></a><span class="lineno"><a class="line" href="structarmnn_1_1_fake_quantization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1440</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_fake_quantization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">FakeQuantizationQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</span> <span class="keyword"></span>{</div><div class="line"><a name="l01442"></a><span class="lineno"> 1442</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"FakeQuantizationQueueDescriptor"</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>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</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>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01448"></a><span class="lineno"> 1448</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01449"></a><span class="lineno"> 1449</span> </div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span>  ValidateTensorNumDimensions(inputTensorInfo, descriptorName, 2, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span>  ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 2, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span> </div><div class="line"><a name="l01453"></a><span class="lineno"> 1453</span>  ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01454"></a><span class="lineno"> 1454</span> </div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span>  <span class="keywordflow">if</span> (m_Parameters.m_Min > m_Parameters.m_Max)</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">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": min cannot be greater than max"</span>);</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> }</div><div class="line"><a name="l01460"></a><span class="lineno"> 1460</span> </div><div class="line"><a name="l01461"></a><span class="lineno"><a class="line" href="structarmnn_1_1_instance_normalization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1461</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_instance_normalization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">InstanceNormalizationQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01462"></a><span class="lineno"> 1462</span> <span class="keyword"></span>{</div><div class="line"><a name="l01463"></a><span class="lineno"> 1463</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"InstanceNormalizationQueueDescriptor"</span>};</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>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01466"></a><span class="lineno"> 1466</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01467"></a><span class="lineno"> 1467</span> </div><div class="line"><a name="l01468"></a><span class="lineno"> 1468</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</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>  <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() > 4)</div><div class="line"><a name="l01472"></a><span class="lineno"> 1472</span>  {</div><div class="line"><a name="l01473"></a><span class="lineno"> 1473</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Input tensors with rank greater than 4 are not supported."</span>);</div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</span>  }</div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span> </div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span>  ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01477"></a><span class="lineno"> 1477</span> </div><div class="line"><a name="l01478"></a><span class="lineno"> 1478</span>  <span class="comment">// Check the supported data types</span></div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span>  {</div><div class="line"><a name="l01481"></a><span class="lineno"> 1481</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01482"></a><span class="lineno"> 1482</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01483"></a><span class="lineno"> 1483</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a></div><div class="line"><a name="l01484"></a><span class="lineno"> 1484</span>  };</div><div class="line"><a name="l01485"></a><span class="lineno"> 1485</span> </div><div class="line"><a name="l01486"></a><span class="lineno"> 1486</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01487"></a><span class="lineno"> 1487</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01488"></a><span class="lineno"> 1488</span> }</div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span> </div><div class="line"><a name="l01490"></a><span class="lineno"><a class="line" href="structarmnn_1_1_l2_normalization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1490</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_l2_normalization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">L2NormalizationQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span> <span class="keyword"></span>{</div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"L2NormalizationQueueDescriptor"</span>};</div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span> </div><div class="line"><a name="l01494"></a><span class="lineno"> 1494</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01495"></a><span class="lineno"> 1495</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span> </div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01499"></a><span class="lineno"> 1499</span> </div><div class="line"><a name="l01500"></a><span class="lineno"> 1500</span>  <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() > 4)</div><div class="line"><a name="l01501"></a><span class="lineno"> 1501</span>  {</div><div class="line"><a name="l01502"></a><span class="lineno"> 1502</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Input tensors with rank greater than 4 are not supported."</span>);</div><div class="line"><a name="l01503"></a><span class="lineno"> 1503</span>  }</div><div class="line"><a name="l01504"></a><span class="lineno"> 1504</span> </div><div class="line"><a name="l01505"></a><span class="lineno"> 1505</span>  ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</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>  <span class="comment">// Check the supported data types</span></div><div class="line"><a name="l01508"></a><span class="lineno"> 1508</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l01509"></a><span class="lineno"> 1509</span>  {</div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01511"></a><span class="lineno"> 1511</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01513"></a><span class="lineno"> 1513</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01514"></a><span class="lineno"> 1514</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l01515"></a><span class="lineno"> 1515</span>  };</div><div class="line"><a name="l01516"></a><span class="lineno"> 1516</span> </div><div class="line"><a name="l01517"></a><span class="lineno"> 1517</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01518"></a><span class="lineno"> 1518</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01519"></a><span class="lineno"> 1519</span> }</div><div class="line"><a name="l01520"></a><span class="lineno"> 1520</span> </div><div class="line"><a name="l01521"></a><span class="lineno"><a class="line" href="structarmnn_1_1_log_softmax_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1521</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_log_softmax_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">LogSoftmaxQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01522"></a><span class="lineno"> 1522</span> <span class="keyword"></span>{</div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"LogSoftmaxQueueDescriptor"</span>};</div><div class="line"><a name="l01524"></a><span class="lineno"> 1524</span> </div><div class="line"><a name="l01525"></a><span class="lineno"> 1525</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01527"></a><span class="lineno"> 1527</span> </div><div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span> </div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span>  ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01532"></a><span class="lineno"> 1532</span> </div><div class="line"><a name="l01533"></a><span class="lineno"> 1533</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l01534"></a><span class="lineno"> 1534</span>  {</div><div class="line"><a name="l01535"></a><span class="lineno"> 1535</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01536"></a><span class="lineno"> 1536</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01537"></a><span class="lineno"> 1537</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span>  };</div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span> </div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01542"></a><span class="lineno"> 1542</span> }</div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span> </div><div class="line"><a name="l01544"></a><span class="lineno"><a class="line" href="structarmnn_1_1_constant_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1544</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_constant_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ConstantQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span> <span class="keyword"></span>{</div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"ConstantQueueDescriptor"</span>};</div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span> </div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span>  ValidateNumInputs(workloadInfo, descriptorName, 0);</div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</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="keywordflow">if</span> (!m_LayerOutput)</div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>  {</div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": No const input specified."</span>);</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> </div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>  ValidateTensorShapesMatch(m_LayerOutput->GetTensorInfo(), outputTensorInfo, descriptorName, <span class="stringliteral">"constant"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01558"></a><span class="lineno"> 1558</span> </div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span>  <span class="comment">// Check the supported data types</span></div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>  {</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>,</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>,</div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></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> </div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span>  ValidateDataTypes(outputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span> }</div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span> </div><div class="line"><a name="l01575"></a><span class="lineno"><a class="line" href="structarmnn_1_1_reshape_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1575</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_reshape_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ReshapeQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span> <span class="keyword"></span>{</div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"ReshapeQueueDescriptor"</span>};</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>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01580"></a><span class="lineno"> 1580</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</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">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01583"></a><span class="lineno"> 1583</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</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>  ValidateTensorNumElementsMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01586"></a><span class="lineno"> 1586</span> </div><div class="line"><a name="l01587"></a><span class="lineno"> 1587</span>  <span class="comment">// Check the supported data types</span></div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</span>  std::vector<DataType> supportedTypes =</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>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>,</div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>,</div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l01596"></a><span class="lineno"> 1596</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a></div><div class="line"><a name="l01597"></a><span class="lineno"> 1597</span>  };</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>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01600"></a><span class="lineno"> 1600</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</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"><a class="line" href="structarmnn_1_1_space_to_batch_nd_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1603</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_space_to_batch_nd_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">SpaceToBatchNdQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01604"></a><span class="lineno"> 1604</span> <span class="keyword"></span>{</div><div class="line"><a name="l01605"></a><span class="lineno"> 1605</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"SpaceToBatchNdQueueDescriptor"</span>};</div><div class="line"><a name="l01606"></a><span class="lineno"> 1606</span> </div><div class="line"><a name="l01607"></a><span class="lineno"> 1607</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01608"></a><span class="lineno"> 1608</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01609"></a><span class="lineno"> 1609</span> </div><div class="line"><a name="l01610"></a><span class="lineno"> 1610</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01611"></a><span class="lineno"> 1611</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</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>  ValidateTensorNumDimensions(inputTensorInfo, descriptorName, 4, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l01614"></a><span class="lineno"> 1614</span>  ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 4, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01615"></a><span class="lineno"> 1615</span> </div><div class="line"><a name="l01616"></a><span class="lineno"> 1616</span>  <span class="keywordflow">if</span> (m_Parameters.m_BlockShape.size() != 2)</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_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Block Shape must contain 2 spatial dimensions."</span>);</div><div class="line"><a name="l01619"></a><span class="lineno"> 1619</span>  }</div><div class="line"><a name="l01620"></a><span class="lineno"> 1620</span> </div><div class="line"><a name="l01621"></a><span class="lineno"> 1621</span>  <span class="keywordflow">if</span> (m_Parameters.m_BlockShape.size() != m_Parameters.m_PadList.size())</div><div class="line"><a name="l01622"></a><span class="lineno"> 1622</span>  {</div><div class="line"><a name="l01623"></a><span class="lineno"> 1623</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Pad List must contain the same number of "</span></div><div class="line"><a name="l01624"></a><span class="lineno"> 1624</span>  <span class="stringliteral">"dimensions as Block Shape."</span>);</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> </div><div class="line"><a name="l01627"></a><span class="lineno"> 1627</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</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>  std::pair<unsigned int, unsigned int> heightPad = m_Parameters.m_PadList[0];</div><div class="line"><a name="l01630"></a><span class="lineno"> 1630</span>  std::pair<unsigned int, unsigned int> widthPad = m_Parameters.m_PadList[1];</div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span> </div><div class="line"><a name="l01632"></a><span class="lineno"> 1632</span>  <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">DataLayoutIndexed</a> dimensionIndices(m_Parameters.m_DataLayout);</div><div class="line"><a name="l01633"></a><span class="lineno"> 1633</span> </div><div class="line"><a name="l01634"></a><span class="lineno"> 1634</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = inputShape[dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>()] +</div><div class="line"><a name="l01635"></a><span class="lineno"> 1635</span>  widthPad.first + widthPad.second;</div><div class="line"><a name="l01636"></a><span class="lineno"> 1636</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = inputShape[dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>()] +</div><div class="line"><a name="l01637"></a><span class="lineno"> 1637</span>  heightPad.first + heightPad.second;</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>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numInputElements = inputShape[0] * inputHeight * inputWidth *</div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span>  inputShape[dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">GetChannelsIndex</a>()];</div><div class="line"><a name="l01641"></a><span class="lineno"> 1641</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numOutputElements = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>();</div><div class="line"><a name="l01642"></a><span class="lineno"> 1642</span> </div><div class="line"><a name="l01643"></a><span class="lineno"> 1643</span>  <span class="keywordflow">if</span> (numOutputElements != numInputElements)</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_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Input tensor has "</span> +</div><div class="line"><a name="l01646"></a><span class="lineno"> 1646</span>  to_string(numInputElements) + <span class="stringliteral">" after padding but output tensor has "</span> +</div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span>  to_string(numOutputElements) + <span class="stringliteral">" elements."</span>);</div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span>  }</div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span> </div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span>  <span class="keywordflow">if</span> (inputHeight % m_Parameters.m_BlockShape[0] != 0 || inputWidth % m_Parameters.m_BlockShape[1] != 0)</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">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Input shape after padding must be "</span></div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span>  <span class="stringliteral">"divisible by Block Shape in all spatial dimensions"</span>);</div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span>  }</div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span> </div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l01657"></a><span class="lineno"> 1657</span>  {</div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01660"></a><span class="lineno"> 1660</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01662"></a><span class="lineno"> 1662</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></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>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01667"></a><span class="lineno"> 1667</span> }</div><div class="line"><a name="l01668"></a><span class="lineno"> 1668</span> </div><div class="line"><a name="l01669"></a><span class="lineno"><a class="line" href="structarmnn_1_1_space_to_depth_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1669</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_space_to_depth_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">SpaceToDepthQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01670"></a><span class="lineno"> 1670</span> <span class="keyword"></span>{</div><div class="line"><a name="l01671"></a><span class="lineno"> 1671</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"SpaceToDepthQueueDescriptor"</span>};</div><div class="line"><a name="l01672"></a><span class="lineno"> 1672</span> </div><div class="line"><a name="l01673"></a><span class="lineno"> 1673</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01674"></a><span class="lineno"> 1674</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</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>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01677"></a><span class="lineno"> 1677</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</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>  ValidateTensorNumDimensions(inputTensorInfo, descriptorName, 4, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l01680"></a><span class="lineno"> 1680</span>  ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 4, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01681"></a><span class="lineno"> 1681</span> </div><div class="line"><a name="l01682"></a><span class="lineno"> 1682</span>  std::vector<DataType> supportedTypes =</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>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01687"></a><span class="lineno"> 1687</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l01688"></a><span class="lineno"> 1688</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></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> </div><div class="line"><a name="l01691"></a><span class="lineno"> 1691</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span>  ValidateDataTypes(outputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span> </div><div class="line"><a name="l01694"></a><span class="lineno"> 1694</span>  ValidateTensorNumElementsMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01695"></a><span class="lineno"> 1695</span> </div><div class="line"><a name="l01696"></a><span class="lineno"> 1696</span>  <span class="keywordflow">if</span> (m_Parameters.m_BlockSize == 0)</div><div class="line"><a name="l01697"></a><span class="lineno"> 1697</span>  {</div><div class="line"><a name="l01698"></a><span class="lineno"> 1698</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Block size cannot be 0."</span>);</div><div class="line"><a name="l01699"></a><span class="lineno"> 1699</span>  }</div><div class="line"><a name="l01700"></a><span class="lineno"> 1700</span> </div><div class="line"><a name="l01701"></a><span class="lineno"> 1701</span>  <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">DataLayoutIndexed</a> dimensionIndices(m_Parameters.m_DataLayout);</div><div class="line"><a name="l01702"></a><span class="lineno"> 1702</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> wIndex = dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>();</div><div class="line"><a name="l01703"></a><span class="lineno"> 1703</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> hIndex = dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>();</div><div class="line"><a name="l01704"></a><span class="lineno"> 1704</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> cIndex = dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">GetChannelsIndex</a>();</div><div class="line"><a name="l01705"></a><span class="lineno"> 1705</span> </div><div class="line"><a name="l01706"></a><span class="lineno"> 1706</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l01707"></a><span class="lineno"> 1707</span>  <span class="keywordflow">if</span> (inputShape[hIndex] % m_Parameters.m_BlockSize != 0 || inputShape[wIndex] % m_Parameters.m_BlockSize != 0)</div><div class="line"><a name="l01708"></a><span class="lineno"> 1708</span>  {</div><div class="line"><a name="l01709"></a><span class="lineno"> 1709</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Input shape must be divisible "</span></div><div class="line"><a name="l01710"></a><span class="lineno"> 1710</span>  <span class="stringliteral">"by block size in all spatial dimensions"</span>);</div><div class="line"><a name="l01711"></a><span class="lineno"> 1711</span>  }</div><div class="line"><a name="l01712"></a><span class="lineno"> 1712</span> </div><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l01714"></a><span class="lineno"> 1714</span>  <span class="keywordflow">if</span> (outputShape[cIndex] % (m_Parameters.m_BlockSize * m_Parameters.m_BlockSize) != 0)</div><div class="line"><a name="l01715"></a><span class="lineno"> 1715</span>  {</div><div class="line"><a name="l01716"></a><span class="lineno"> 1716</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": The depth of the output tensor"</span></div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span>  <span class="stringliteral">"must be divisible by the square of block size."</span> );</div><div class="line"><a name="l01718"></a><span class="lineno"> 1718</span>  }</div><div class="line"><a name="l01719"></a><span class="lineno"> 1719</span> }</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span> </div><div class="line"><a name="l01721"></a><span class="lineno"><a class="line" href="structarmnn_1_1_floor_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1721</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_floor_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">FloorQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span> <span class="keyword"></span>{</div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"FloorQueueDescriptor"</span>};</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span> </div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01726"></a><span class="lineno"> 1726</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l01727"></a><span class="lineno"> 1727</span> </div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l01730"></a><span class="lineno"> 1730</span> </div><div class="line"><a name="l01731"></a><span class="lineno"> 1731</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l01732"></a><span class="lineno"> 1732</span>  {</div><div class="line"><a name="l01733"></a><span class="lineno"> 1733</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01734"></a><span class="lineno"> 1734</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01735"></a><span class="lineno"> 1735</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01736"></a><span class="lineno"> 1736</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l01737"></a><span class="lineno"> 1737</span>  };</div><div class="line"><a name="l01738"></a><span class="lineno"> 1738</span> </div><div class="line"><a name="l01739"></a><span class="lineno"> 1739</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l01740"></a><span class="lineno"> 1740</span> </div><div class="line"><a name="l01741"></a><span class="lineno"> 1741</span>  <span class="keywordflow">if</span> (inputTensorInfo != outputTensorInfo)</div><div class="line"><a name="l01742"></a><span class="lineno"> 1742</span>  {</div><div class="line"><a name="l01743"></a><span class="lineno"> 1743</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Input and output tensor infos do not match."</span>);</div><div class="line"><a name="l01744"></a><span class="lineno"> 1744</span>  }</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> </div><div class="line"><a name="l01747"></a><span class="lineno"><a class="line" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 1747</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">LstmQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l01748"></a><span class="lineno"> 1748</span> <span class="keyword"></span>{</div><div class="line"><a name="l01749"></a><span class="lineno"> 1749</span>  <span class="comment">// ported from android/ml/nn/common/operations/LSTM.cpp CheckInputTensorDimensions()</span></div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span> </div><div class="line"><a name="l01751"></a><span class="lineno"> 1751</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"LstmQueueDescriptor"</span>};</div><div class="line"><a name="l01752"></a><span class="lineno"> 1752</span> </div><div class="line"><a name="l01753"></a><span class="lineno"> 1753</span>  <span class="comment">// check dimensions of all inputs and outputs</span></div><div class="line"><a name="l01754"></a><span class="lineno"> 1754</span>  <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size() != 3)</div><div class="line"><a name="l01755"></a><span class="lineno"> 1755</span>  {</div><div class="line"><a name="l01756"></a><span class="lineno"> 1756</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Invalid number of inputs."</span>);</div><div class="line"><a name="l01757"></a><span class="lineno"> 1757</span>  }</div><div class="line"><a name="l01758"></a><span class="lineno"> 1758</span>  <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size() != 4)</div><div class="line"><a name="l01759"></a><span class="lineno"> 1759</span>  {</div><div class="line"><a name="l01760"></a><span class="lineno"> 1760</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Invalid number of outputs."</span>);</div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span>  }</div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span> </div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span>  {</div><div class="line"><a name="l01765"></a><span class="lineno"> 1765</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l01766"></a><span class="lineno"> 1766</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l01767"></a><span class="lineno"> 1767</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l01768"></a><span class="lineno"> 1768</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l01769"></a><span class="lineno"> 1769</span>  };</div><div class="line"><a name="l01770"></a><span class="lineno"> 1770</span> </div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span>  <span class="comment">// check for supported type of one input and match them with all the other input and output</span></div><div class="line"><a name="l01772"></a><span class="lineno"> 1772</span>  ValidateDataTypes(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0], supportedTypes, descriptorName);</div><div class="line"><a name="l01773"></a><span class="lineno"> 1773</span> </div><div class="line"><a name="l01774"></a><span class="lineno"> 1774</span>  <span class="comment">// type matches all other inputs</span></div><div class="line"><a name="l01775"></a><span class="lineno"> 1775</span>  <span class="keywordflow">for</span> (uint32_t i = 1u; i < workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>.size(); ++i)</div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>  {</div><div class="line"><a name="l01777"></a><span class="lineno"> 1777</span>  ValidateTensorDataTypesMatch(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0],</div><div class="line"><a name="l01778"></a><span class="lineno"> 1778</span>  workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[i],</div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span>  descriptorName,</div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span>  <span class="stringliteral">"input_0"</span>,</div><div class="line"><a name="l01781"></a><span class="lineno"> 1781</span>  <span class="stringliteral">"input_"</span> + std::to_string(i));</div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span>  }</div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span>  <span class="comment">// type matches all other outputs</span></div><div class="line"><a name="l01784"></a><span class="lineno"> 1784</span>  <span class="keywordflow">for</span> (uint32_t i = 0u; i < workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size(); ++i)</div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span>  {</div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span>  ValidateTensorDataTypesMatch(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0],</div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span>  workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[i],</div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span>  <span class="stringliteral">"LstmQueueDescriptor"</span>,</div><div class="line"><a name="l01789"></a><span class="lineno"> 1789</span>  <span class="stringliteral">"input_0"</span>,</div><div class="line"><a name="l01790"></a><span class="lineno"> 1790</span>  <span class="stringliteral">"output_"</span> + std::to_string(i));</div><div class="line"><a name="l01791"></a><span class="lineno"> 1791</span>  }</div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span> </div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span>  <span class="comment">// Making sure clipping parameters have valid values.</span></div><div class="line"><a name="l01794"></a><span class="lineno"> 1794</span>  <span class="comment">// == 0 means no clipping</span></div><div class="line"><a name="l01795"></a><span class="lineno"> 1795</span>  <span class="comment">// > 0 means clipping</span></div><div class="line"><a name="l01796"></a><span class="lineno"> 1796</span>  <span class="keywordflow">if</span> (m_Parameters.m_ClippingThresCell < 0.0f)</div><div class="line"><a name="l01797"></a><span class="lineno"> 1797</span>  {</div><div class="line"><a name="l01798"></a><span class="lineno"> 1798</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": negative cell clipping threshold is invalid"</span>);</div><div class="line"><a name="l01799"></a><span class="lineno"> 1799</span>  }</div><div class="line"><a name="l01800"></a><span class="lineno"> 1800</span>  <span class="keywordflow">if</span> (m_Parameters.m_ClippingThresProj < 0.0f)</div><div class="line"><a name="l01801"></a><span class="lineno"> 1801</span>  {</div><div class="line"><a name="l01802"></a><span class="lineno"> 1802</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": negative projection clipping threshold is invalid"</span>);</div><div class="line"><a name="l01803"></a><span class="lineno"> 1803</span>  }</div><div class="line"><a name="l01804"></a><span class="lineno"> 1804</span> </div><div class="line"><a name="l01805"></a><span class="lineno"> 1805</span> </div><div class="line"><a name="l01806"></a><span class="lineno"> 1806</span>  <span class="comment">// Inferring batch size, number of outputs and number of cells from the inputs.</span></div><div class="line"><a name="l01807"></a><span class="lineno"> 1807</span>  <span class="keyword">const</span> uint32_t n_input = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0].GetShape()[1];</div><div class="line"><a name="l01808"></a><span class="lineno"> 1808</span>  <span class="keyword">const</span> uint32_t n_batch = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0].GetShape()[0];</div><div class="line"><a name="l01809"></a><span class="lineno"> 1809</span>  ValidatePointer(m_InputToOutputWeights, <span class="stringliteral">"Null pointer check"</span>, <span class="stringliteral">"InputToOutputWeights"</span>);</div><div class="line"><a name="l01810"></a><span class="lineno"> 1810</span>  <span class="keyword">const</span> uint32_t n_cell = m_InputToOutputWeights->GetShape()[0];</div><div class="line"><a name="l01811"></a><span class="lineno"> 1811</span>  ValidatePointer(m_RecurrentToOutputWeights, <span class="stringliteral">"Null pointer check"</span>, <span class="stringliteral">"RecurrentToOutputWeights"</span>);</div><div class="line"><a name="l01812"></a><span class="lineno"> 1812</span>  <span class="keyword">const</span> uint32_t n_output = m_RecurrentToOutputWeights->GetShape()[1];</div><div class="line"><a name="l01813"></a><span class="lineno"> 1813</span> </div><div class="line"><a name="l01814"></a><span class="lineno"> 1814</span>  <span class="comment">// input tensor</span></div><div class="line"><a name="l01815"></a><span class="lineno"> 1815</span>  ValidateTensorNumDimNumElem(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0], 2, (n_batch * n_input),</div><div class="line"><a name="l01816"></a><span class="lineno"> 1816</span>  descriptorName + <span class="stringliteral">" input_0"</span>);</div><div class="line"><a name="l01817"></a><span class="lineno"> 1817</span>  <span class="comment">// outputStateInTensor</span></div><div class="line"><a name="l01818"></a><span class="lineno"> 1818</span>  ValidateTensorNumDimNumElem(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1], 2, (n_batch * n_output),</div><div class="line"><a name="l01819"></a><span class="lineno"> 1819</span>  descriptorName + <span class="stringliteral">" input_1"</span>);</div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span>  <span class="comment">// outputStateInTensor</span></div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span>  ValidateTensorNumDimNumElem(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[2], 2, (n_batch * n_cell),</div><div class="line"><a name="l01822"></a><span class="lineno"> 1822</span>  descriptorName + <span class="stringliteral">" input_2"</span>);</div><div class="line"><a name="l01823"></a><span class="lineno"> 1823</span>  <span class="comment">// scratchBufferTensor</span></div><div class="line"><a name="l01824"></a><span class="lineno"> 1824</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> scratchBufferSize = m_Parameters.m_CifgEnabled ? n_cell * 3 : n_cell * 4;</div><div class="line"><a name="l01825"></a><span class="lineno"> 1825</span>  ValidateTensorNumDimNumElem(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0], 2, (n_batch * scratchBufferSize),</div><div class="line"><a name="l01826"></a><span class="lineno"> 1826</span>  descriptorName + <span class="stringliteral">" output_0"</span>);</div><div class="line"><a name="l01827"></a><span class="lineno"> 1827</span>  <span class="comment">// outputStateOutTensor</span></div><div class="line"><a name="l01828"></a><span class="lineno"> 1828</span>  ValidateTensorNumDimNumElem(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[1], 2, (n_batch * n_output),</div><div class="line"><a name="l01829"></a><span class="lineno"> 1829</span>  descriptorName + <span class="stringliteral">" output_1"</span>);</div><div class="line"><a name="l01830"></a><span class="lineno"> 1830</span>  <span class="comment">// cellStateOutTensor</span></div><div class="line"><a name="l01831"></a><span class="lineno"> 1831</span>  ValidateTensorNumDimNumElem(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[2], 2, (n_batch * n_cell),</div><div class="line"><a name="l01832"></a><span class="lineno"> 1832</span>  descriptorName + <span class="stringliteral">" output_2"</span>);</div><div class="line"><a name="l01833"></a><span class="lineno"> 1833</span>  <span class="comment">// outputTensor</span></div><div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>  ValidateTensorNumDimNumElem(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[3], 2, (n_batch * n_output),</div><div class="line"><a name="l01835"></a><span class="lineno"> 1835</span>  descriptorName + <span class="stringliteral">" output_3"</span>);</div><div class="line"><a name="l01836"></a><span class="lineno"> 1836</span> </div><div class="line"><a name="l01837"></a><span class="lineno"> 1837</span> </div><div class="line"><a name="l01838"></a><span class="lineno"> 1838</span>  <span class="comment">// check that dimensions of inputs/outputs and QueueDescriptor data match with each other</span></div><div class="line"><a name="l01839"></a><span class="lineno"> 1839</span>  <span class="keywordflow">if</span> ( m_InputToInputWeights )</div><div class="line"><a name="l01840"></a><span class="lineno"> 1840</span>  {</div><div class="line"><a name="l01841"></a><span class="lineno"> 1841</span>  ValidateTensorNumDimNumElem(m_InputToInputWeights->GetTensorInfo(), 2,</div><div class="line"><a name="l01842"></a><span class="lineno"> 1842</span>  (n_cell * n_input), <span class="stringliteral">"InputLayerNormWeights"</span>);</div><div class="line"><a name="l01843"></a><span class="lineno"> 1843</span>  }</div><div class="line"><a name="l01844"></a><span class="lineno"> 1844</span> </div><div class="line"><a name="l01845"></a><span class="lineno"> 1845</span>  ValidatePointer(m_InputToForgetWeights, <span class="stringliteral">"Null pointer check"</span>, <span class="stringliteral">"InputToForgetWeights"</span>);</div><div class="line"><a name="l01846"></a><span class="lineno"> 1846</span>  ValidateTensorNumDimNumElem(m_InputToForgetWeights->GetTensorInfo(), 2,</div><div class="line"><a name="l01847"></a><span class="lineno"> 1847</span>  (n_cell * n_input), <span class="stringliteral">"InputToForgetWeights"</span>);</div><div class="line"><a name="l01848"></a><span class="lineno"> 1848</span> </div><div class="line"><a name="l01849"></a><span class="lineno"> 1849</span>  ValidatePointer(m_InputToCellWeights, <span class="stringliteral">"Null pointer check"</span>, <span class="stringliteral">"InputToCellWeights"</span>);</div><div class="line"><a name="l01850"></a><span class="lineno"> 1850</span>  ValidateTensorNumDimNumElem(m_InputToCellWeights->GetTensorInfo(), 2,</div><div class="line"><a name="l01851"></a><span class="lineno"> 1851</span>  (n_cell * n_input), <span class="stringliteral">"InputToCellWeights"</span>);</div><div class="line"><a name="l01852"></a><span class="lineno"> 1852</span> </div><div class="line"><a name="l01853"></a><span class="lineno"> 1853</span>  <span class="keywordflow">if</span> ( m_RecurrentToInputWeights )</div><div class="line"><a name="l01854"></a><span class="lineno"> 1854</span>  {</div><div class="line"><a name="l01855"></a><span class="lineno"> 1855</span>  ValidateTensorNumDimNumElem(m_RecurrentToInputWeights->GetTensorInfo(), 2,</div><div class="line"><a name="l01856"></a><span class="lineno"> 1856</span>  (n_cell * n_output), <span class="stringliteral">"RecurrentToInputWeights"</span>);</div><div class="line"><a name="l01857"></a><span class="lineno"> 1857</span>  }</div><div class="line"><a name="l01858"></a><span class="lineno"> 1858</span> </div><div class="line"><a name="l01859"></a><span class="lineno"> 1859</span>  ValidatePointer(m_RecurrentToForgetWeights, <span class="stringliteral">"Null pointer check"</span>, <span class="stringliteral">"RecurrentToForgetWeights"</span>);</div><div class="line"><a name="l01860"></a><span class="lineno"> 1860</span>  ValidateTensorNumDimNumElem(m_RecurrentToForgetWeights->GetTensorInfo(), 2,</div><div class="line"><a name="l01861"></a><span class="lineno"> 1861</span>  (n_cell * n_output), <span class="stringliteral">"RecurrentToForgetWeights"</span>);</div><div class="line"><a name="l01862"></a><span class="lineno"> 1862</span> </div><div class="line"><a name="l01863"></a><span class="lineno"> 1863</span>  ValidatePointer(m_RecurrentToCellWeights, <span class="stringliteral">"Null pointer check"</span>, <span class="stringliteral">"RecurrentToCellWeights"</span>);</div><div class="line"><a name="l01864"></a><span class="lineno"> 1864</span>  ValidateTensorNumDimNumElem(m_RecurrentToCellWeights->GetTensorInfo(), 2,</div><div class="line"><a name="l01865"></a><span class="lineno"> 1865</span>  (n_cell * n_output), <span class="stringliteral">"RecurrentToCellWeights"</span>);</div><div class="line"><a name="l01866"></a><span class="lineno"> 1866</span> </div><div class="line"><a name="l01867"></a><span class="lineno"> 1867</span>  <span class="comment">// Make sure the input-gate's parameters are either both present (regular</span></div><div class="line"><a name="l01868"></a><span class="lineno"> 1868</span>  <span class="comment">// LSTM) or not at all (CIFG-LSTM). And CifgEnable is set accordingly.</span></div><div class="line"><a name="l01869"></a><span class="lineno"> 1869</span>  <span class="keywordtype">bool</span> cifg_weights_all_or_none = ((m_InputToInputWeights && m_RecurrentToInputWeights &&</div><div class="line"><a name="l01870"></a><span class="lineno"> 1870</span>  !m_Parameters.m_CifgEnabled) ||</div><div class="line"><a name="l01871"></a><span class="lineno"> 1871</span>  (!m_InputToInputWeights && !m_RecurrentToInputWeights &&</div><div class="line"><a name="l01872"></a><span class="lineno"> 1872</span>  m_Parameters.m_CifgEnabled));</div><div class="line"><a name="l01873"></a><span class="lineno"> 1873</span>  <span class="keywordflow">if</span> (!cifg_weights_all_or_none)</div><div class="line"><a name="l01874"></a><span class="lineno"> 1874</span>  {</div><div class="line"><a name="l01875"></a><span class="lineno"> 1875</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Input-Gate's parameters InputToInputWeights and "</span></div><div class="line"><a name="l01876"></a><span class="lineno"> 1876</span>  <span class="stringliteral">"RecurrentToInputWeights must either both be present (regular LSTM) "</span></div><div class="line"><a name="l01877"></a><span class="lineno"> 1877</span>  <span class="stringliteral">"or both not present (CIFG-LSTM). In addition CifgEnable must be set "</span></div><div class="line"><a name="l01878"></a><span class="lineno"> 1878</span>  <span class="stringliteral">"accordingly."</span>);</div><div class="line"><a name="l01879"></a><span class="lineno"> 1879</span>  }</div><div class="line"><a name="l01880"></a><span class="lineno"> 1880</span> </div><div class="line"><a name="l01881"></a><span class="lineno"> 1881</span>  <span class="keywordflow">if</span> ( m_CellToInputWeights )</div><div class="line"><a name="l01882"></a><span class="lineno"> 1882</span>  {</div><div class="line"><a name="l01883"></a><span class="lineno"> 1883</span>  ValidateTensorNumDimNumElem(m_CellToInputWeights->GetTensorInfo(), 1,</div><div class="line"><a name="l01884"></a><span class="lineno"> 1884</span>  n_cell, <span class="stringliteral">"CellToInputWeights"</span>);</div><div class="line"><a name="l01885"></a><span class="lineno"> 1885</span>  }</div><div class="line"><a name="l01886"></a><span class="lineno"> 1886</span>  <span class="keywordflow">if</span> ( m_CellToForgetWeights )</div><div class="line"><a name="l01887"></a><span class="lineno"> 1887</span>  {</div><div class="line"><a name="l01888"></a><span class="lineno"> 1888</span>  ValidateTensorNumDimNumElem(m_CellToForgetWeights->GetTensorInfo(), 1,</div><div class="line"><a name="l01889"></a><span class="lineno"> 1889</span>  n_cell, <span class="stringliteral">"CellToForgetWeights"</span>);</div><div class="line"><a name="l01890"></a><span class="lineno"> 1890</span>  }</div><div class="line"><a name="l01891"></a><span class="lineno"> 1891</span>  <span class="keywordflow">if</span> ( m_CellToOutputWeights )</div><div class="line"><a name="l01892"></a><span class="lineno"> 1892</span>  {</div><div class="line"><a name="l01893"></a><span class="lineno"> 1893</span>  ValidateTensorNumDimNumElem(m_CellToOutputWeights->GetTensorInfo(), 1,</div><div class="line"><a name="l01894"></a><span class="lineno"> 1894</span>  n_cell, <span class="stringliteral">"CellToOutputWeights"</span>);</div><div class="line"><a name="l01895"></a><span class="lineno"> 1895</span>  }</div><div class="line"><a name="l01896"></a><span class="lineno"> 1896</span> </div><div class="line"><a name="l01897"></a><span class="lineno"> 1897</span>  <span class="comment">// Making sure the peephole weights are there all or none. And PeepholeEnable is set accordingly.</span></div><div class="line"><a name="l01898"></a><span class="lineno"> 1898</span>  <span class="keywordtype">bool</span> peephole_weights_all_or_none =</div><div class="line"><a name="l01899"></a><span class="lineno"> 1899</span>  (((m_CellToInputWeights || m_Parameters.m_CifgEnabled) && m_CellToForgetWeights</div><div class="line"><a name="l01900"></a><span class="lineno"> 1900</span>  && m_CellToOutputWeights && m_Parameters.m_PeepholeEnabled)</div><div class="line"><a name="l01901"></a><span class="lineno"> 1901</span>  || ( !m_CellToInputWeights && !m_CellToForgetWeights</div><div class="line"><a name="l01902"></a><span class="lineno"> 1902</span>  && !m_CellToOutputWeights && !m_Parameters.m_PeepholeEnabled));</div><div class="line"><a name="l01903"></a><span class="lineno"> 1903</span>  <span class="keywordflow">if</span> (!peephole_weights_all_or_none)</div><div class="line"><a name="l01904"></a><span class="lineno"> 1904</span>  {</div><div class="line"><a name="l01905"></a><span class="lineno"> 1905</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Invalid combination of peephole parameters."</span>);</div><div class="line"><a name="l01906"></a><span class="lineno"> 1906</span>  }</div><div class="line"><a name="l01907"></a><span class="lineno"> 1907</span> </div><div class="line"><a name="l01908"></a><span class="lineno"> 1908</span>  <span class="comment">// Make sure the input gate bias is present only when not a CIFG-LSTM.</span></div><div class="line"><a name="l01909"></a><span class="lineno"> 1909</span>  <span class="keywordflow">if</span> (m_Parameters.m_CifgEnabled)</div><div class="line"><a name="l01910"></a><span class="lineno"> 1910</span>  {</div><div class="line"><a name="l01911"></a><span class="lineno"> 1911</span>  <span class="keywordflow">if</span> (m_InputGateBias)</div><div class="line"><a name="l01912"></a><span class="lineno"> 1912</span>  {</div><div class="line"><a name="l01913"></a><span class="lineno"> 1913</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": InputGateBias is present and CIFG-LSTM is enabled."</span>);</div><div class="line"><a name="l01914"></a><span class="lineno"> 1914</span>  }</div><div class="line"><a name="l01915"></a><span class="lineno"> 1915</span>  }</div><div class="line"><a name="l01916"></a><span class="lineno"> 1916</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01917"></a><span class="lineno"> 1917</span>  {</div><div class="line"><a name="l01918"></a><span class="lineno"> 1918</span>  <span class="keywordflow">if</span> (!m_InputGateBias)</div><div class="line"><a name="l01919"></a><span class="lineno"> 1919</span>  {</div><div class="line"><a name="l01920"></a><span class="lineno"> 1920</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": If CIFG-LSTM is disabled InputGateBias "</span></div><div class="line"><a name="l01921"></a><span class="lineno"> 1921</span>  <span class="stringliteral">"must be present."</span>);</div><div class="line"><a name="l01922"></a><span class="lineno"> 1922</span>  }</div><div class="line"><a name="l01923"></a><span class="lineno"> 1923</span>  ValidateTensorNumDimNumElem(m_InputGateBias->GetTensorInfo(), 1,</div><div class="line"><a name="l01924"></a><span class="lineno"> 1924</span>  n_cell, <span class="stringliteral">"InputGateBias"</span>);</div><div class="line"><a name="l01925"></a><span class="lineno"> 1925</span>  }</div><div class="line"><a name="l01926"></a><span class="lineno"> 1926</span> </div><div class="line"><a name="l01927"></a><span class="lineno"> 1927</span>  ValidatePointer(m_ForgetGateBias, <span class="stringliteral">"Null pointer check"</span>, <span class="stringliteral">"ForgetGateBias"</span>);</div><div class="line"><a name="l01928"></a><span class="lineno"> 1928</span>  ValidateTensorNumDimNumElem(m_ForgetGateBias->GetTensorInfo(), 1, n_cell, <span class="stringliteral">"ForgetGateBias"</span>);</div><div class="line"><a name="l01929"></a><span class="lineno"> 1929</span> </div><div class="line"><a name="l01930"></a><span class="lineno"> 1930</span>  ValidatePointer(m_CellBias, <span class="stringliteral">"Null pointer check"</span>, <span class="stringliteral">"CellBias"</span>);</div><div class="line"><a name="l01931"></a><span class="lineno"> 1931</span>  ValidateTensorNumDimNumElem(m_CellBias->GetTensorInfo(), 1, n_cell, <span class="stringliteral">"CellBias"</span>);</div><div class="line"><a name="l01932"></a><span class="lineno"> 1932</span> </div><div class="line"><a name="l01933"></a><span class="lineno"> 1933</span>  ValidatePointer(m_OutputGateBias, <span class="stringliteral">"Null pointer check"</span>, <span class="stringliteral">"OutputGateBias"</span>);</div><div class="line"><a name="l01934"></a><span class="lineno"> 1934</span>  ValidateTensorNumDimNumElem(m_OutputGateBias->GetTensorInfo(), 1, n_cell, <span class="stringliteral">"OutputGateBias"</span>);</div><div class="line"><a name="l01935"></a><span class="lineno"> 1935</span> </div><div class="line"><a name="l01936"></a><span class="lineno"> 1936</span>  <span class="keywordflow">if</span> (m_ProjectionWeights)</div><div class="line"><a name="l01937"></a><span class="lineno"> 1937</span>  {</div><div class="line"><a name="l01938"></a><span class="lineno"> 1938</span>  ValidateTensorNumDimNumElem(m_ProjectionWeights->GetTensorInfo(), 2,</div><div class="line"><a name="l01939"></a><span class="lineno"> 1939</span>  (n_cell * n_output), <span class="stringliteral">"ProjectionWeights"</span>);</div><div class="line"><a name="l01940"></a><span class="lineno"> 1940</span>  }</div><div class="line"><a name="l01941"></a><span class="lineno"> 1941</span>  <span class="keywordflow">if</span> (m_ProjectionBias)</div><div class="line"><a name="l01942"></a><span class="lineno"> 1942</span>  {</div><div class="line"><a name="l01943"></a><span class="lineno"> 1943</span>  ValidateTensorNumDimNumElem(m_ProjectionBias->GetTensorInfo(), 1, n_output, <span class="stringliteral">"ProjectionBias"</span>);</div><div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>  }</div><div class="line"><a name="l01945"></a><span class="lineno"> 1945</span> </div><div class="line"><a name="l01946"></a><span class="lineno"> 1946</span>  <span class="comment">// Making sure the projection tensors are consistent:</span></div><div class="line"><a name="l01947"></a><span class="lineno"> 1947</span>  <span class="comment">// 1) If projection weight is not present, then projection bias should not be</span></div><div class="line"><a name="l01948"></a><span class="lineno"> 1948</span>  <span class="comment">// present.</span></div><div class="line"><a name="l01949"></a><span class="lineno"> 1949</span>  <span class="comment">// 2) If projection weight is present, then projection bias is optional.</span></div><div class="line"><a name="l01950"></a><span class="lineno"> 1950</span>  <span class="keywordtype">bool</span> projecton_tensors_consistent = ((!m_ProjectionWeights && !m_ProjectionBias &&</div><div class="line"><a name="l01951"></a><span class="lineno"> 1951</span>  !m_Parameters.m_ProjectionEnabled)</div><div class="line"><a name="l01952"></a><span class="lineno"> 1952</span>  || (m_ProjectionWeights && !m_ProjectionBias &&</div><div class="line"><a name="l01953"></a><span class="lineno"> 1953</span>  m_Parameters.m_ProjectionEnabled)</div><div class="line"><a name="l01954"></a><span class="lineno"> 1954</span>  || (m_ProjectionWeights && m_ProjectionBias &&</div><div class="line"><a name="l01955"></a><span class="lineno"> 1955</span>  m_Parameters.m_ProjectionEnabled));</div><div class="line"><a name="l01956"></a><span class="lineno"> 1956</span>  <span class="keywordflow">if</span> (!projecton_tensors_consistent)</div><div class="line"><a name="l01957"></a><span class="lineno"> 1957</span>  {</div><div class="line"><a name="l01958"></a><span class="lineno"> 1958</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Projection tensors are inconsistent."</span>);</div><div class="line"><a name="l01959"></a><span class="lineno"> 1959</span>  }</div><div class="line"><a name="l01960"></a><span class="lineno"> 1960</span> </div><div class="line"><a name="l01961"></a><span class="lineno"> 1961</span>  <span class="comment">// The four layer normalization weights either all have values or none of them have values. Additionally, if</span></div><div class="line"><a name="l01962"></a><span class="lineno"> 1962</span>  <span class="comment">// CIFG is used, input layer normalization weights tensor is omitted and the other layer normalization weights</span></div><div class="line"><a name="l01963"></a><span class="lineno"> 1963</span>  <span class="comment">// either all have values or none of them have values. Layer normalization is used when the values of all the</span></div><div class="line"><a name="l01964"></a><span class="lineno"> 1964</span>  <span class="comment">// layer normalization weights are present</span></div><div class="line"><a name="l01965"></a><span class="lineno"> 1965</span>  <span class="keywordflow">if</span> (m_InputLayerNormWeights)</div><div class="line"><a name="l01966"></a><span class="lineno"> 1966</span>  {</div><div class="line"><a name="l01967"></a><span class="lineno"> 1967</span>  ValidateTensorNumDimNumElem(m_InputLayerNormWeights->GetTensorInfo(), 1, n_cell, <span class="stringliteral">"InputLayerNormWeights"</span>);</div><div class="line"><a name="l01968"></a><span class="lineno"> 1968</span>  }</div><div class="line"><a name="l01969"></a><span class="lineno"> 1969</span>  <span class="keywordflow">if</span> (m_ForgetLayerNormWeights)</div><div class="line"><a name="l01970"></a><span class="lineno"> 1970</span>  {</div><div class="line"><a name="l01971"></a><span class="lineno"> 1971</span>  ValidateTensorNumDimNumElem(m_ForgetLayerNormWeights->GetTensorInfo(), 1, n_cell, <span class="stringliteral">"ForgetLayerNormWeights"</span>);</div><div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>  }</div><div class="line"><a name="l01973"></a><span class="lineno"> 1973</span>  <span class="keywordflow">if</span> (m_CellLayerNormWeights)</div><div class="line"><a name="l01974"></a><span class="lineno"> 1974</span>  {</div><div class="line"><a name="l01975"></a><span class="lineno"> 1975</span>  ValidateTensorNumDimNumElem(m_CellLayerNormWeights->GetTensorInfo(), 1, n_cell, <span class="stringliteral">"CellLayerNormWeights"</span>);</div><div class="line"><a name="l01976"></a><span class="lineno"> 1976</span>  }</div><div class="line"><a name="l01977"></a><span class="lineno"> 1977</span>  <span class="keywordflow">if</span> (m_OutputLayerNormWeights)</div><div class="line"><a name="l01978"></a><span class="lineno"> 1978</span>  {</div><div class="line"><a name="l01979"></a><span class="lineno"> 1979</span>  ValidateTensorNumDimNumElem(m_OutputLayerNormWeights->GetTensorInfo(), 1, n_cell, <span class="stringliteral">"OutputLayerNormWeights"</span>);</div><div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>  }</div><div class="line"><a name="l01981"></a><span class="lineno"> 1981</span> </div><div class="line"><a name="l01982"></a><span class="lineno"> 1982</span>  <span class="keywordflow">if</span> (m_Parameters.m_LayerNormEnabled)</div><div class="line"><a name="l01983"></a><span class="lineno"> 1983</span>  {</div><div class="line"><a name="l01984"></a><span class="lineno"> 1984</span>  <span class="keywordflow">if</span> (!m_Parameters.m_CifgEnabled)</div><div class="line"><a name="l01985"></a><span class="lineno"> 1985</span>  {</div><div class="line"><a name="l01986"></a><span class="lineno"> 1986</span>  <span class="keywordflow">if</span> (!m_InputLayerNormWeights)</div><div class="line"><a name="l01987"></a><span class="lineno"> 1987</span>  {</div><div class="line"><a name="l01988"></a><span class="lineno"> 1988</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Layer normalisation is enabled and CIFG-LSTM is "</span></div><div class="line"><a name="l01989"></a><span class="lineno"> 1989</span>  <span class="stringliteral">"disabled but InputLayerNormWeights are not present"</span>);</div><div class="line"><a name="l01990"></a><span class="lineno"> 1990</span>  }</div><div class="line"><a name="l01991"></a><span class="lineno"> 1991</span>  ValidateTensorNumDimNumElem(m_InputLayerNormWeights->GetTensorInfo(),</div><div class="line"><a name="l01992"></a><span class="lineno"> 1992</span>  1, n_cell, <span class="stringliteral">"InputLayerNormWeights"</span>);</div><div class="line"><a name="l01993"></a><span class="lineno"> 1993</span>  }</div><div class="line"><a name="l01994"></a><span class="lineno"> 1994</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (m_InputLayerNormWeights)</div><div class="line"><a name="l01995"></a><span class="lineno"> 1995</span>  {</div><div class="line"><a name="l01996"></a><span class="lineno"> 1996</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">":InputLayerNormWeights are present while CIFG is "</span></div><div class="line"><a name="l01997"></a><span class="lineno"> 1997</span>  <span class="stringliteral">"enabled"</span>);</div><div class="line"><a name="l01998"></a><span class="lineno"> 1998</span>  }</div><div class="line"><a name="l01999"></a><span class="lineno"> 1999</span> </div><div class="line"><a name="l02000"></a><span class="lineno"> 2000</span>  ValidatePointer(m_ForgetLayerNormWeights, <span class="stringliteral">"Null pointer check layer normalisation enabled"</span>,</div><div class="line"><a name="l02001"></a><span class="lineno"> 2001</span>  <span class="stringliteral">"ForgetLayerNormWeights"</span>);</div><div class="line"><a name="l02002"></a><span class="lineno"> 2002</span>  ValidateTensorNumDimNumElem(m_ForgetLayerNormWeights->GetTensorInfo(), 1, n_cell, <span class="stringliteral">"ForgetLayerNormWeights"</span>);</div><div class="line"><a name="l02003"></a><span class="lineno"> 2003</span> </div><div class="line"><a name="l02004"></a><span class="lineno"> 2004</span>  ValidatePointer(m_OutputLayerNormWeights, <span class="stringliteral">"Null pointer check layer normalisation enabled"</span>,</div><div class="line"><a name="l02005"></a><span class="lineno"> 2005</span>  <span class="stringliteral">"OutputLayerNormWeights"</span>);</div><div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>  ValidateTensorNumDimNumElem(m_OutputLayerNormWeights->GetTensorInfo(), 1, n_cell, <span class="stringliteral">"OutputLayerNormWeights"</span>);</div><div class="line"><a name="l02007"></a><span class="lineno"> 2007</span> </div><div class="line"><a name="l02008"></a><span class="lineno"> 2008</span>  ValidatePointer(m_CellLayerNormWeights, <span class="stringliteral">"Null pointer check layer normalisation enabled"</span>,</div><div class="line"><a name="l02009"></a><span class="lineno"> 2009</span>  <span class="stringliteral">"CellLayerNormWeights"</span>);</div><div class="line"><a name="l02010"></a><span class="lineno"> 2010</span>  ValidateTensorNumDimNumElem(m_CellLayerNormWeights->GetTensorInfo(), 1, n_cell, <span class="stringliteral">"CellLayerNormWeights"</span>);</div><div class="line"><a name="l02011"></a><span class="lineno"> 2011</span>  }</div><div class="line"><a name="l02012"></a><span class="lineno"> 2012</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (m_InputLayerNormWeights || m_ForgetLayerNormWeights || m_OutputLayerNormWeights || m_CellLayerNormWeights)</div><div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>  {</div><div class="line"><a name="l02014"></a><span class="lineno"> 2014</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Layer normalisation is disabled but one or more layer "</span></div><div class="line"><a name="l02015"></a><span class="lineno"> 2015</span>  <span class="stringliteral">"normalisation weights are present."</span>);</div><div class="line"><a name="l02016"></a><span class="lineno"> 2016</span>  }</div><div class="line"><a name="l02017"></a><span class="lineno"> 2017</span> }</div><div class="line"><a name="l02018"></a><span class="lineno"> 2018</span> </div><div class="line"><a name="l02019"></a><span class="lineno"><a class="line" href="structarmnn_1_1_convert_fp32_to_fp16_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2019</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_convert_fp32_to_fp16_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ConvertFp32ToFp16QueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02020"></a><span class="lineno"> 2020</span> <span class="keyword"></span>{</div><div class="line"><a name="l02021"></a><span class="lineno"> 2021</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"ConvertFp32ToFp16QueueDescriptor"</span>};</div><div class="line"><a name="l02022"></a><span class="lineno"> 2022</span> </div><div class="line"><a name="l02023"></a><span class="lineno"> 2023</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02024"></a><span class="lineno"> 2024</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02025"></a><span class="lineno"> 2025</span> </div><div class="line"><a name="l02026"></a><span class="lineno"> 2026</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02027"></a><span class="lineno"> 2027</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02028"></a><span class="lineno"> 2028</span> </div><div class="line"><a name="l02029"></a><span class="lineno"> 2029</span>  <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>)</div><div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>  {</div><div class="line"><a name="l02031"></a><span class="lineno"> 2031</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Input tensor type must be Float32."</span>);</div><div class="line"><a name="l02032"></a><span class="lineno"> 2032</span>  }</div><div class="line"><a name="l02033"></a><span class="lineno"> 2033</span> </div><div class="line"><a name="l02034"></a><span class="lineno"> 2034</span>  <span class="keywordflow">if</span> (outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>)</div><div class="line"><a name="l02035"></a><span class="lineno"> 2035</span>  {</div><div class="line"><a name="l02036"></a><span class="lineno"> 2036</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Output tensor type must be Float16."</span>);</div><div class="line"><a name="l02037"></a><span class="lineno"> 2037</span>  }</div><div class="line"><a name="l02038"></a><span class="lineno"> 2038</span> </div><div class="line"><a name="l02039"></a><span class="lineno"> 2039</span>  ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02040"></a><span class="lineno"> 2040</span> }</div><div class="line"><a name="l02041"></a><span class="lineno"> 2041</span> </div><div class="line"><a name="l02042"></a><span class="lineno"><a class="line" href="structarmnn_1_1_convert_fp16_to_fp32_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2042</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_convert_fp16_to_fp32_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ConvertFp16ToFp32QueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02043"></a><span class="lineno"> 2043</span> <span class="keyword"></span>{</div><div class="line"><a name="l02044"></a><span class="lineno"> 2044</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"ConvertFp16ToFp32QueueDescriptor"</span>};</div><div class="line"><a name="l02045"></a><span class="lineno"> 2045</span> </div><div class="line"><a name="l02046"></a><span class="lineno"> 2046</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02047"></a><span class="lineno"> 2047</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02048"></a><span class="lineno"> 2048</span> </div><div class="line"><a name="l02049"></a><span class="lineno"> 2049</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02050"></a><span class="lineno"> 2050</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02051"></a><span class="lineno"> 2051</span> </div><div class="line"><a name="l02052"></a><span class="lineno"> 2052</span>  <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>)</div><div class="line"><a name="l02053"></a><span class="lineno"> 2053</span>  {</div><div class="line"><a name="l02054"></a><span class="lineno"> 2054</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Input tensor type must be Float16."</span>);</div><div class="line"><a name="l02055"></a><span class="lineno"> 2055</span>  }</div><div class="line"><a name="l02056"></a><span class="lineno"> 2056</span> </div><div class="line"><a name="l02057"></a><span class="lineno"> 2057</span>  <span class="keywordflow">if</span> (outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>)</div><div class="line"><a name="l02058"></a><span class="lineno"> 2058</span>  {</div><div class="line"><a name="l02059"></a><span class="lineno"> 2059</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Output tensor type must be Float32."</span>);</div><div class="line"><a name="l02060"></a><span class="lineno"> 2060</span>  }</div><div class="line"><a name="l02061"></a><span class="lineno"> 2061</span> </div><div class="line"><a name="l02062"></a><span class="lineno"> 2062</span>  ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02063"></a><span class="lineno"> 2063</span> }</div><div class="line"><a name="l02064"></a><span class="lineno"> 2064</span> </div><div class="line"><a name="l02065"></a><span class="lineno"><a class="line" href="structarmnn_1_1_division_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2065</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_division_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">DivisionQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02066"></a><span class="lineno"> 2066</span> <span class="keyword"></span>{</div><div class="line"><a name="l02067"></a><span class="lineno"> 2067</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"DivisionQueueDescriptor"</span>};</div><div class="line"><a name="l02068"></a><span class="lineno"> 2068</span> </div><div class="line"><a name="l02069"></a><span class="lineno"> 2069</span>  ValidateNumInputs(workloadInfo, descriptorName, 2);</div><div class="line"><a name="l02070"></a><span class="lineno"> 2070</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02071"></a><span class="lineno"> 2071</span> </div><div class="line"><a name="l02072"></a><span class="lineno"> 2072</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02073"></a><span class="lineno"> 2073</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02074"></a><span class="lineno"> 2074</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02075"></a><span class="lineno"> 2075</span> </div><div class="line"><a name="l02076"></a><span class="lineno"> 2076</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l02077"></a><span class="lineno"> 2077</span>  {</div><div class="line"><a name="l02078"></a><span class="lineno"> 2078</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02079"></a><span class="lineno"> 2079</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02080"></a><span class="lineno"> 2080</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>,</div><div class="line"><a name="l02081"></a><span class="lineno"> 2081</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02082"></a><span class="lineno"> 2082</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a></div><div class="line"><a name="l02083"></a><span class="lineno"> 2083</span>  };</div><div class="line"><a name="l02084"></a><span class="lineno"> 2084</span> </div><div class="line"><a name="l02085"></a><span class="lineno"> 2085</span>  ValidateDataTypes(inputTensorInfo0, supportedTypes, descriptorName);</div><div class="line"><a name="l02086"></a><span class="lineno"> 2086</span>  ValidateDataTypes(inputTensorInfo1, supportedTypes, descriptorName);</div><div class="line"><a name="l02087"></a><span class="lineno"> 2087</span>  ValidateDataTypes(outputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02088"></a><span class="lineno"> 2088</span> </div><div class="line"><a name="l02089"></a><span class="lineno"> 2089</span>  ValidateBroadcastTensorShapesMatch(inputTensorInfo0,</div><div class="line"><a name="l02090"></a><span class="lineno"> 2090</span>  inputTensorInfo1,</div><div class="line"><a name="l02091"></a><span class="lineno"> 2091</span>  outputTensorInfo,</div><div class="line"><a name="l02092"></a><span class="lineno"> 2092</span>  descriptorName,</div><div class="line"><a name="l02093"></a><span class="lineno"> 2093</span>  <span class="stringliteral">"input_0"</span>,</div><div class="line"><a name="l02094"></a><span class="lineno"> 2094</span>  <span class="stringliteral">"input_1"</span>);</div><div class="line"><a name="l02095"></a><span class="lineno"> 2095</span> }</div><div class="line"><a name="l02096"></a><span class="lineno"> 2096</span> </div><div class="line"><a name="l02097"></a><span class="lineno"><a class="line" href="structarmnn_1_1_subtraction_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2097</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_subtraction_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">SubtractionQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02098"></a><span class="lineno"> 2098</span> <span class="keyword"></span>{</div><div class="line"><a name="l02099"></a><span class="lineno"> 2099</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"SubtractionQueueDescriptor"</span>};</div><div class="line"><a name="l02100"></a><span class="lineno"> 2100</span> </div><div class="line"><a name="l02101"></a><span class="lineno"> 2101</span>  ValidateNumInputs(workloadInfo, descriptorName, 2);</div><div class="line"><a name="l02102"></a><span class="lineno"> 2102</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02103"></a><span class="lineno"> 2103</span> </div><div class="line"><a name="l02104"></a><span class="lineno"> 2104</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02105"></a><span class="lineno"> 2105</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02106"></a><span class="lineno"> 2106</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02107"></a><span class="lineno"> 2107</span> </div><div class="line"><a name="l02108"></a><span class="lineno"> 2108</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l02109"></a><span class="lineno"> 2109</span>  {</div><div class="line"><a name="l02110"></a><span class="lineno"> 2110</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02111"></a><span class="lineno"> 2111</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02112"></a><span class="lineno"> 2112</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>,</div><div class="line"><a name="l02113"></a><span class="lineno"> 2113</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02114"></a><span class="lineno"> 2114</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a></div><div class="line"><a name="l02115"></a><span class="lineno"> 2115</span>  };</div><div class="line"><a name="l02116"></a><span class="lineno"> 2116</span> </div><div class="line"><a name="l02117"></a><span class="lineno"> 2117</span>  ValidateDataTypes(inputTensorInfo0, supportedTypes, descriptorName);</div><div class="line"><a name="l02118"></a><span class="lineno"> 2118</span>  ValidateDataTypes(inputTensorInfo1, supportedTypes, descriptorName);</div><div class="line"><a name="l02119"></a><span class="lineno"> 2119</span>  ValidateDataTypes(outputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02120"></a><span class="lineno"> 2120</span> </div><div class="line"><a name="l02121"></a><span class="lineno"> 2121</span>  ValidateBroadcastTensorShapesMatch(inputTensorInfo0,</div><div class="line"><a name="l02122"></a><span class="lineno"> 2122</span>  inputTensorInfo1,</div><div class="line"><a name="l02123"></a><span class="lineno"> 2123</span>  outputTensorInfo,</div><div class="line"><a name="l02124"></a><span class="lineno"> 2124</span>  descriptorName,</div><div class="line"><a name="l02125"></a><span class="lineno"> 2125</span>  <span class="stringliteral">"input_0"</span>,</div><div class="line"><a name="l02126"></a><span class="lineno"> 2126</span>  <span class="stringliteral">"input_1"</span>);</div><div class="line"><a name="l02127"></a><span class="lineno"> 2127</span> }</div><div class="line"><a name="l02128"></a><span class="lineno"> 2128</span> </div><div class="line"><a name="l02129"></a><span class="lineno"><a class="line" href="structarmnn_1_1_maximum_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2129</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_maximum_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">MaximumQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02130"></a><span class="lineno"> 2130</span> <span class="keyword"></span>{</div><div class="line"><a name="l02131"></a><span class="lineno"> 2131</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"MaximumQueueDescriptor"</span>};</div><div class="line"><a name="l02132"></a><span class="lineno"> 2132</span> </div><div class="line"><a name="l02133"></a><span class="lineno"> 2133</span>  ValidateNumInputs(workloadInfo, descriptorName, 2);</div><div class="line"><a name="l02134"></a><span class="lineno"> 2134</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02135"></a><span class="lineno"> 2135</span> </div><div class="line"><a name="l02136"></a><span class="lineno"> 2136</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02137"></a><span class="lineno"> 2137</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02138"></a><span class="lineno"> 2138</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02139"></a><span class="lineno"> 2139</span> </div><div class="line"><a name="l02140"></a><span class="lineno"> 2140</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l02141"></a><span class="lineno"> 2141</span>  {</div><div class="line"><a name="l02142"></a><span class="lineno"> 2142</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02143"></a><span class="lineno"> 2143</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02144"></a><span class="lineno"> 2144</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02145"></a><span class="lineno"> 2145</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>,</div><div class="line"><a name="l02146"></a><span class="lineno"> 2146</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l02147"></a><span class="lineno"> 2147</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02148"></a><span class="lineno"> 2148</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02149"></a><span class="lineno"> 2149</span>  };</div><div class="line"><a name="l02150"></a><span class="lineno"> 2150</span> </div><div class="line"><a name="l02151"></a><span class="lineno"> 2151</span>  ValidateDataTypes(inputTensorInfo0, supportedTypes, descriptorName);</div><div class="line"><a name="l02152"></a><span class="lineno"> 2152</span>  ValidateDataTypes(inputTensorInfo1, supportedTypes, descriptorName);</div><div class="line"><a name="l02153"></a><span class="lineno"> 2153</span>  ValidateDataTypes(outputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02154"></a><span class="lineno"> 2154</span> </div><div class="line"><a name="l02155"></a><span class="lineno"> 2155</span>  ValidateBroadcastTensorShapesMatch(inputTensorInfo0,</div><div class="line"><a name="l02156"></a><span class="lineno"> 2156</span>  inputTensorInfo1,</div><div class="line"><a name="l02157"></a><span class="lineno"> 2157</span>  outputTensorInfo,</div><div class="line"><a name="l02158"></a><span class="lineno"> 2158</span>  descriptorName,</div><div class="line"><a name="l02159"></a><span class="lineno"> 2159</span>  <span class="stringliteral">"input_0"</span>,</div><div class="line"><a name="l02160"></a><span class="lineno"> 2160</span>  <span class="stringliteral">"input_1"</span>);</div><div class="line"><a name="l02161"></a><span class="lineno"> 2161</span> }</div><div class="line"><a name="l02162"></a><span class="lineno"> 2162</span> </div><div class="line"><a name="l02163"></a><span class="lineno"><a class="line" href="structarmnn_1_1_mean_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2163</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_mean_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">MeanQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02164"></a><span class="lineno"> 2164</span> <span class="keyword"></span>{</div><div class="line"><a name="l02165"></a><span class="lineno"> 2165</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"MeanQueueDescriptor"</span>};</div><div class="line"><a name="l02166"></a><span class="lineno"> 2166</span> </div><div class="line"><a name="l02167"></a><span class="lineno"> 2167</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02168"></a><span class="lineno"> 2168</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02169"></a><span class="lineno"> 2169</span> </div><div class="line"><a name="l02170"></a><span class="lineno"> 2170</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02171"></a><span class="lineno"> 2171</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02172"></a><span class="lineno"> 2172</span> </div><div class="line"><a name="l02173"></a><span class="lineno"> 2173</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l02174"></a><span class="lineno"> 2174</span>  {</div><div class="line"><a name="l02175"></a><span class="lineno"> 2175</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02176"></a><span class="lineno"> 2176</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02177"></a><span class="lineno"> 2177</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02178"></a><span class="lineno"> 2178</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02179"></a><span class="lineno"> 2179</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02180"></a><span class="lineno"> 2180</span>  };</div><div class="line"><a name="l02181"></a><span class="lineno"> 2181</span> </div><div class="line"><a name="l02182"></a><span class="lineno"> 2182</span>  <span class="comment">// First check if input tensor data type is supported, then</span></div><div class="line"><a name="l02183"></a><span class="lineno"> 2183</span>  <span class="comment">// check if this data type matches the output tensor data type</span></div><div class="line"><a name="l02184"></a><span class="lineno"> 2184</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02185"></a><span class="lineno"> 2185</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02186"></a><span class="lineno"> 2186</span> </div><div class="line"><a name="l02187"></a><span class="lineno"> 2187</span>  <span class="keywordflow">if</span> (m_Parameters.m_KeepDims)</div><div class="line"><a name="l02188"></a><span class="lineno"> 2188</span>  {</div><div class="line"><a name="l02189"></a><span class="lineno"> 2189</span>  ValidateTensorNumDimensions(outputTensorInfo, descriptorName, inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(), <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02190"></a><span class="lineno"> 2190</span>  }</div><div class="line"><a name="l02191"></a><span class="lineno"> 2191</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (m_Parameters.m_Axis.empty())</div><div class="line"><a name="l02192"></a><span class="lineno"> 2192</span>  {</div><div class="line"><a name="l02193"></a><span class="lineno"> 2193</span>  ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 1, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02194"></a><span class="lineno"> 2194</span>  }</div><div class="line"><a name="l02195"></a><span class="lineno"> 2195</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l02196"></a><span class="lineno"> 2196</span>  {</div><div class="line"><a name="l02197"></a><span class="lineno"> 2197</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputDim =</div><div class="line"><a name="l02198"></a><span class="lineno"> 2198</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(m_Parameters.m_Axis.size());</div><div class="line"><a name="l02199"></a><span class="lineno"> 2199</span>  ValidateTensorNumDimensions(outputTensorInfo,</div><div class="line"><a name="l02200"></a><span class="lineno"> 2200</span>  descriptorName,</div><div class="line"><a name="l02201"></a><span class="lineno"> 2201</span>  outputDim > 0 ? outputDim : 1,</div><div class="line"><a name="l02202"></a><span class="lineno"> 2202</span>  <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02203"></a><span class="lineno"> 2203</span>  }</div><div class="line"><a name="l02204"></a><span class="lineno"> 2204</span> }</div><div class="line"><a name="l02205"></a><span class="lineno"> 2205</span> </div><div class="line"><a name="l02206"></a><span class="lineno"><a class="line" href="structarmnn_1_1_pad_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2206</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_pad_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">PadQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02207"></a><span class="lineno"> 2207</span> <span class="keyword"></span>{</div><div class="line"><a name="l02208"></a><span class="lineno"> 2208</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"PadQueueDescriptor"</span>};</div><div class="line"><a name="l02209"></a><span class="lineno"> 2209</span> </div><div class="line"><a name="l02210"></a><span class="lineno"> 2210</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02211"></a><span class="lineno"> 2211</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02212"></a><span class="lineno"> 2212</span> </div><div class="line"><a name="l02213"></a><span class="lineno"> 2213</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02214"></a><span class="lineno"> 2214</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02215"></a><span class="lineno"> 2215</span> </div><div class="line"><a name="l02216"></a><span class="lineno"> 2216</span>  <span class="comment">// input and output should have the same number of dimensions</span></div><div class="line"><a name="l02217"></a><span class="lineno"> 2217</span>  ValidateTensorNumDimensions(outputTensorInfo, descriptorName, inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>(), <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02218"></a><span class="lineno"> 2218</span> </div><div class="line"><a name="l02219"></a><span class="lineno"> 2219</span>  <span class="comment">// there should be entry in the pad list for each dimension in the input tensor</span></div><div class="line"><a name="l02220"></a><span class="lineno"> 2220</span>  <span class="keywordflow">if</span> (m_Parameters.m_PadList.size() != inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()) {</div><div class="line"><a name="l02221"></a><span class="lineno"> 2221</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">":Pad List should contain the same number of entries "</span></div><div class="line"><a name="l02222"></a><span class="lineno"> 2222</span>  <span class="stringliteral">"as there are dimensions in the input tensor that is "</span> +</div><div class="line"><a name="l02223"></a><span class="lineno"> 2223</span>  std::to_string(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>()) + <span class="stringliteral">" entries "</span> +</div><div class="line"><a name="l02224"></a><span class="lineno"> 2224</span>  <span class="stringliteral">" not "</span> + std::to_string(m_Parameters.m_PadList.size()) + <span class="stringliteral">" entries."</span>);</div><div class="line"><a name="l02225"></a><span class="lineno"> 2225</span>  }</div><div class="line"><a name="l02226"></a><span class="lineno"> 2226</span> }</div><div class="line"><a name="l02227"></a><span class="lineno"> 2227</span> </div><div class="line"><a name="l02228"></a><span class="lineno"><a class="line" href="structarmnn_1_1_quantize_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2228</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_quantize_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">QuantizeQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02229"></a><span class="lineno"> 2229</span> <span class="keyword"></span>{</div><div class="line"><a name="l02230"></a><span class="lineno"> 2230</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"QuantizeQueueDescriptor"</span>};</div><div class="line"><a name="l02231"></a><span class="lineno"> 2231</span> </div><div class="line"><a name="l02232"></a><span class="lineno"> 2232</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02233"></a><span class="lineno"> 2233</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02234"></a><span class="lineno"> 2234</span> </div><div class="line"><a name="l02235"></a><span class="lineno"> 2235</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02236"></a><span class="lineno"> 2236</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02237"></a><span class="lineno"> 2237</span> </div><div class="line"><a name="l02238"></a><span class="lineno"> 2238</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l02239"></a><span class="lineno"> 2239</span>  {</div><div class="line"><a name="l02240"></a><span class="lineno"> 2240</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02241"></a><span class="lineno"> 2241</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02242"></a><span class="lineno"> 2242</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02243"></a><span class="lineno"> 2243</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>,</div><div class="line"><a name="l02244"></a><span class="lineno"> 2244</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">DataType::QAsymmS8</a>,</div><div class="line"><a name="l02245"></a><span class="lineno"> 2245</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02246"></a><span class="lineno"> 2246</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02247"></a><span class="lineno"> 2247</span>  };</div><div class="line"><a name="l02248"></a><span class="lineno"> 2248</span> </div><div class="line"><a name="l02249"></a><span class="lineno"> 2249</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02250"></a><span class="lineno"> 2250</span> </div><div class="line"><a name="l02251"></a><span class="lineno"> 2251</span>  <span class="keywordflow">if</span> (!<a class="code" href="namespacearmnn.xhtml#ad44c007f21af2d0375e3ef9400a1b275">IsQuantizedType</a>(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()))</div><div class="line"><a name="l02252"></a><span class="lineno"> 2252</span>  {</div><div class="line"><a name="l02253"></a><span class="lineno"> 2253</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Output of quantized layer must be quantized type."</span>);</div><div class="line"><a name="l02254"></a><span class="lineno"> 2254</span>  }</div><div class="line"><a name="l02255"></a><span class="lineno"> 2255</span> }</div><div class="line"><a name="l02256"></a><span class="lineno"> 2256</span> </div><div class="line"><a name="l02257"></a><span class="lineno"><a class="line" href="structarmnn_1_1_batch_to_space_nd_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2257</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_batch_to_space_nd_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">BatchToSpaceNdQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02258"></a><span class="lineno"> 2258</span> <span class="keyword"></span>{</div><div class="line"><a name="l02259"></a><span class="lineno"> 2259</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"BatchToSpaceNdQueueDescriptor"</span>};</div><div class="line"><a name="l02260"></a><span class="lineno"> 2260</span> </div><div class="line"><a name="l02261"></a><span class="lineno"> 2261</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02262"></a><span class="lineno"> 2262</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02263"></a><span class="lineno"> 2263</span> </div><div class="line"><a name="l02264"></a><span class="lineno"> 2264</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02265"></a><span class="lineno"> 2265</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02266"></a><span class="lineno"> 2266</span> </div><div class="line"><a name="l02267"></a><span class="lineno"> 2267</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l02268"></a><span class="lineno"> 2268</span>  {</div><div class="line"><a name="l02269"></a><span class="lineno"> 2269</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02270"></a><span class="lineno"> 2270</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02271"></a><span class="lineno"> 2271</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02272"></a><span class="lineno"> 2272</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02273"></a><span class="lineno"> 2273</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02274"></a><span class="lineno"> 2274</span>  };</div><div class="line"><a name="l02275"></a><span class="lineno"> 2275</span> </div><div class="line"><a name="l02276"></a><span class="lineno"> 2276</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02277"></a><span class="lineno"> 2277</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02278"></a><span class="lineno"> 2278</span> }</div><div class="line"><a name="l02279"></a><span class="lineno"> 2279</span> </div><div class="line"><a name="l02280"></a><span class="lineno"><a class="line" href="structarmnn_1_1_strided_slice_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2280</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_strided_slice_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">StridedSliceQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02281"></a><span class="lineno"> 2281</span> <span class="keyword"></span>{</div><div class="line"><a name="l02282"></a><span class="lineno"> 2282</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"StridedSliceQueueDescriptor"</span>};</div><div class="line"><a name="l02283"></a><span class="lineno"> 2283</span> </div><div class="line"><a name="l02284"></a><span class="lineno"> 2284</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02285"></a><span class="lineno"> 2285</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02286"></a><span class="lineno"> 2286</span> </div><div class="line"><a name="l02287"></a><span class="lineno"> 2287</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02288"></a><span class="lineno"> 2288</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02289"></a><span class="lineno"> 2289</span> </div><div class="line"><a name="l02290"></a><span class="lineno"> 2290</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l02291"></a><span class="lineno"> 2291</span>  {</div><div class="line"><a name="l02292"></a><span class="lineno"> 2292</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02293"></a><span class="lineno"> 2293</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02294"></a><span class="lineno"> 2294</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02295"></a><span class="lineno"> 2295</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02296"></a><span class="lineno"> 2296</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02297"></a><span class="lineno"> 2297</span>  };</div><div class="line"><a name="l02298"></a><span class="lineno"> 2298</span> </div><div class="line"><a name="l02299"></a><span class="lineno"> 2299</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02300"></a><span class="lineno"> 2300</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02301"></a><span class="lineno"> 2301</span> </div><div class="line"><a name="l02302"></a><span class="lineno"> 2302</span>  ValidateTensorQuantizationSpace(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02303"></a><span class="lineno"> 2303</span> </div><div class="line"><a name="l02304"></a><span class="lineno"> 2304</span>  <span class="keyword">const</span> uint32_t rank = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l02305"></a><span class="lineno"> 2305</span>  <span class="keywordflow">if</span> (rank > 4)</div><div class="line"><a name="l02306"></a><span class="lineno"> 2306</span>  {</div><div class="line"><a name="l02307"></a><span class="lineno"> 2307</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Input tensors with rank greater than 4 are not supported."</span>);</div><div class="line"><a name="l02308"></a><span class="lineno"> 2308</span>  }</div><div class="line"><a name="l02309"></a><span class="lineno"> 2309</span> </div><div class="line"><a name="l02310"></a><span class="lineno"> 2310</span>  <span class="comment">// Begin, End & Stride length must be of rank(input0)</span></div><div class="line"><a name="l02311"></a><span class="lineno"> 2311</span>  <span class="keywordflow">if</span> (m_Parameters.m_Begin.size() != rank)</div><div class="line"><a name="l02312"></a><span class="lineno"> 2312</span>  {</div><div class="line"><a name="l02313"></a><span class="lineno"> 2313</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Begin length must be of rank "</span> + std::to_string(rank));</div><div class="line"><a name="l02314"></a><span class="lineno"> 2314</span>  }</div><div class="line"><a name="l02315"></a><span class="lineno"> 2315</span> </div><div class="line"><a name="l02316"></a><span class="lineno"> 2316</span>  <span class="keywordflow">if</span> (m_Parameters.m_End.size() != rank)</div><div class="line"><a name="l02317"></a><span class="lineno"> 2317</span>  {</div><div class="line"><a name="l02318"></a><span class="lineno"> 2318</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": End length must be of rank "</span> + std::to_string(rank));</div><div class="line"><a name="l02319"></a><span class="lineno"> 2319</span>  }</div><div class="line"><a name="l02320"></a><span class="lineno"> 2320</span> </div><div class="line"><a name="l02321"></a><span class="lineno"> 2321</span>  <span class="keywordflow">if</span> (m_Parameters.m_Stride.size() != rank)</div><div class="line"><a name="l02322"></a><span class="lineno"> 2322</span>  {</div><div class="line"><a name="l02323"></a><span class="lineno"> 2323</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Stride length must be of rank "</span> + std::to_string(rank));</div><div class="line"><a name="l02324"></a><span class="lineno"> 2324</span>  }</div><div class="line"><a name="l02325"></a><span class="lineno"> 2325</span> </div><div class="line"><a name="l02326"></a><span class="lineno"> 2326</span>  <span class="comment">// Stride entries must be non-zero</span></div><div class="line"><a name="l02327"></a><span class="lineno"> 2327</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>& stride : m_Parameters.m_Stride)</div><div class="line"><a name="l02328"></a><span class="lineno"> 2328</span>  {</div><div class="line"><a name="l02329"></a><span class="lineno"> 2329</span>  <span class="keywordflow">if</span> (stride == 0)</div><div class="line"><a name="l02330"></a><span class="lineno"> 2330</span>  {</div><div class="line"><a name="l02331"></a><span class="lineno"> 2331</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Stride entries must be non-zero."</span>);</div><div class="line"><a name="l02332"></a><span class="lineno"> 2332</span>  }</div><div class="line"><a name="l02333"></a><span class="lineno"> 2333</span>  }</div><div class="line"><a name="l02334"></a><span class="lineno"> 2334</span> }</div><div class="line"><a name="l02335"></a><span class="lineno"> 2335</span> </div><div class="line"><a name="l02336"></a><span class="lineno"><a class="line" href="structarmnn_1_1_minimum_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2336</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_minimum_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">MinimumQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02337"></a><span class="lineno"> 2337</span> <span class="keyword"></span>{</div><div class="line"><a name="l02338"></a><span class="lineno"> 2338</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"MinimumQueueDescriptor"</span>};</div><div class="line"><a name="l02339"></a><span class="lineno"> 2339</span> </div><div class="line"><a name="l02340"></a><span class="lineno"> 2340</span>  ValidateNumInputs(workloadInfo, descriptorName, 2);</div><div class="line"><a name="l02341"></a><span class="lineno"> 2341</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02342"></a><span class="lineno"> 2342</span> </div><div class="line"><a name="l02343"></a><span class="lineno"> 2343</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02344"></a><span class="lineno"> 2344</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02345"></a><span class="lineno"> 2345</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02346"></a><span class="lineno"> 2346</span> </div><div class="line"><a name="l02347"></a><span class="lineno"> 2347</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l02348"></a><span class="lineno"> 2348</span>  {</div><div class="line"><a name="l02349"></a><span class="lineno"> 2349</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02350"></a><span class="lineno"> 2350</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02351"></a><span class="lineno"> 2351</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02352"></a><span class="lineno"> 2352</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>,</div><div class="line"><a name="l02353"></a><span class="lineno"> 2353</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02354"></a><span class="lineno"> 2354</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02355"></a><span class="lineno"> 2355</span>  };</div><div class="line"><a name="l02356"></a><span class="lineno"> 2356</span> </div><div class="line"><a name="l02357"></a><span class="lineno"> 2357</span>  ValidateDataTypes(inputTensorInfo0, supportedTypes, descriptorName);</div><div class="line"><a name="l02358"></a><span class="lineno"> 2358</span>  ValidateDataTypes(inputTensorInfo1, supportedTypes, descriptorName);</div><div class="line"><a name="l02359"></a><span class="lineno"> 2359</span>  ValidateDataTypes(outputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02360"></a><span class="lineno"> 2360</span> </div><div class="line"><a name="l02361"></a><span class="lineno"> 2361</span>  ValidateBroadcastTensorShapesMatch(inputTensorInfo0,</div><div class="line"><a name="l02362"></a><span class="lineno"> 2362</span>  inputTensorInfo1,</div><div class="line"><a name="l02363"></a><span class="lineno"> 2363</span>  outputTensorInfo,</div><div class="line"><a name="l02364"></a><span class="lineno"> 2364</span>  descriptorName,</div><div class="line"><a name="l02365"></a><span class="lineno"> 2365</span>  <span class="stringliteral">"input_0"</span>,</div><div class="line"><a name="l02366"></a><span class="lineno"> 2366</span>  <span class="stringliteral">"input_1"</span>);</div><div class="line"><a name="l02367"></a><span class="lineno"> 2367</span> }</div><div class="line"><a name="l02368"></a><span class="lineno"> 2368</span> </div><div class="line"><a name="l02369"></a><span class="lineno"><a class="line" href="structarmnn_1_1_debug_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2369</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_debug_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">DebugQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02370"></a><span class="lineno"> 2370</span> <span class="keyword"></span>{</div><div class="line"><a name="l02371"></a><span class="lineno"> 2371</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"DebugQueueDescriptor"</span>};</div><div class="line"><a name="l02372"></a><span class="lineno"> 2372</span> </div><div class="line"><a name="l02373"></a><span class="lineno"> 2373</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02374"></a><span class="lineno"> 2374</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02375"></a><span class="lineno"> 2375</span> }</div><div class="line"><a name="l02376"></a><span class="lineno"> 2376</span> </div><div class="line"><a name="l02377"></a><span class="lineno"><a class="line" href="structarmnn_1_1_equal_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2377</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_equal_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">EqualQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02378"></a><span class="lineno"> 2378</span> <span class="keyword"></span>{</div><div class="line"><a name="l02379"></a><span class="lineno"> 2379</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"EqualQueueDescriptor"</span>};</div><div class="line"><a name="l02380"></a><span class="lineno"> 2380</span> </div><div class="line"><a name="l02381"></a><span class="lineno"> 2381</span>  ValidateNumInputs(workloadInfo, descriptorName, 2);</div><div class="line"><a name="l02382"></a><span class="lineno"> 2382</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02383"></a><span class="lineno"> 2383</span> </div><div class="line"><a name="l02384"></a><span class="lineno"> 2384</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02385"></a><span class="lineno"> 2385</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02386"></a><span class="lineno"> 2386</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02387"></a><span class="lineno"> 2387</span> </div><div class="line"><a name="l02388"></a><span class="lineno"> 2388</span>  ValidateBroadcastTensorShapesMatch(inputTensorInfo0,</div><div class="line"><a name="l02389"></a><span class="lineno"> 2389</span>  inputTensorInfo1,</div><div class="line"><a name="l02390"></a><span class="lineno"> 2390</span>  outputTensorInfo,</div><div class="line"><a name="l02391"></a><span class="lineno"> 2391</span>  descriptorName,</div><div class="line"><a name="l02392"></a><span class="lineno"> 2392</span>  <span class="stringliteral">"input_0"</span>,</div><div class="line"><a name="l02393"></a><span class="lineno"> 2393</span>  <span class="stringliteral">"input_1"</span>);</div><div class="line"><a name="l02394"></a><span class="lineno"> 2394</span> </div><div class="line"><a name="l02395"></a><span class="lineno"> 2395</span>  <span class="keywordflow">if</span> (outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">DataType::Boolean</a>)</div><div class="line"><a name="l02396"></a><span class="lineno"> 2396</span>  {</div><div class="line"><a name="l02397"></a><span class="lineno"> 2397</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Output tensor type must be Boolean."</span>);</div><div class="line"><a name="l02398"></a><span class="lineno"> 2398</span>  }</div><div class="line"><a name="l02399"></a><span class="lineno"> 2399</span> }</div><div class="line"><a name="l02400"></a><span class="lineno"> 2400</span> </div><div class="line"><a name="l02401"></a><span class="lineno"><a class="line" href="structarmnn_1_1_greater_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2401</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_greater_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">GreaterQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02402"></a><span class="lineno"> 2402</span> <span class="keyword"></span>{</div><div class="line"><a name="l02403"></a><span class="lineno"> 2403</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"GreaterQueueDescriptor"</span>};</div><div class="line"><a name="l02404"></a><span class="lineno"> 2404</span> </div><div class="line"><a name="l02405"></a><span class="lineno"> 2405</span>  ValidateNumInputs(workloadInfo, descriptorName, 2);</div><div class="line"><a name="l02406"></a><span class="lineno"> 2406</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02407"></a><span class="lineno"> 2407</span> </div><div class="line"><a name="l02408"></a><span class="lineno"> 2408</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02409"></a><span class="lineno"> 2409</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02410"></a><span class="lineno"> 2410</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02411"></a><span class="lineno"> 2411</span> </div><div class="line"><a name="l02412"></a><span class="lineno"> 2412</span>  ValidateBroadcastTensorShapesMatch(inputTensorInfo0,</div><div class="line"><a name="l02413"></a><span class="lineno"> 2413</span>  inputTensorInfo1,</div><div class="line"><a name="l02414"></a><span class="lineno"> 2414</span>  outputTensorInfo,</div><div class="line"><a name="l02415"></a><span class="lineno"> 2415</span>  descriptorName,</div><div class="line"><a name="l02416"></a><span class="lineno"> 2416</span>  <span class="stringliteral">"input_0"</span>,</div><div class="line"><a name="l02417"></a><span class="lineno"> 2417</span>  <span class="stringliteral">"input_1"</span>);</div><div class="line"><a name="l02418"></a><span class="lineno"> 2418</span> </div><div class="line"><a name="l02419"></a><span class="lineno"> 2419</span>  <span class="keywordflow">if</span> (outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">DataType::Boolean</a>)</div><div class="line"><a name="l02420"></a><span class="lineno"> 2420</span>  {</div><div class="line"><a name="l02421"></a><span class="lineno"> 2421</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Output tensor type must be Boolean."</span>);</div><div class="line"><a name="l02422"></a><span class="lineno"> 2422</span>  }</div><div class="line"><a name="l02423"></a><span class="lineno"> 2423</span> }</div><div class="line"><a name="l02424"></a><span class="lineno"> 2424</span> </div><div class="line"><a name="l02425"></a><span class="lineno"><a class="line" href="structarmnn_1_1_rsqrt_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2425</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_rsqrt_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">RsqrtQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02426"></a><span class="lineno"> 2426</span> <span class="keyword"></span>{</div><div class="line"><a name="l02427"></a><span class="lineno"> 2427</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"RsqrtQueueDescriptor"</span>};</div><div class="line"><a name="l02428"></a><span class="lineno"> 2428</span> </div><div class="line"><a name="l02429"></a><span class="lineno"> 2429</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02430"></a><span class="lineno"> 2430</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02431"></a><span class="lineno"> 2431</span> </div><div class="line"><a name="l02432"></a><span class="lineno"> 2432</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02433"></a><span class="lineno"> 2433</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02434"></a><span class="lineno"> 2434</span> </div><div class="line"><a name="l02435"></a><span class="lineno"> 2435</span>  ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02436"></a><span class="lineno"> 2436</span> </div><div class="line"><a name="l02437"></a><span class="lineno"> 2437</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l02438"></a><span class="lineno"> 2438</span>  {</div><div class="line"><a name="l02439"></a><span class="lineno"> 2439</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02440"></a><span class="lineno"> 2440</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02441"></a><span class="lineno"> 2441</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02442"></a><span class="lineno"> 2442</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02443"></a><span class="lineno"> 2443</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02444"></a><span class="lineno"> 2444</span>  };</div><div class="line"><a name="l02445"></a><span class="lineno"> 2445</span> </div><div class="line"><a name="l02446"></a><span class="lineno"> 2446</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02447"></a><span class="lineno"> 2447</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02448"></a><span class="lineno"> 2448</span> }</div><div class="line"><a name="l02449"></a><span class="lineno"> 2449</span> </div><div class="line"><a name="l02450"></a><span class="lineno"><a class="line" href="structarmnn_1_1_gather_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2450</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_gather_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">GatherQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02451"></a><span class="lineno"> 2451</span> <span class="keyword"></span>{</div><div class="line"><a name="l02452"></a><span class="lineno"> 2452</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"GatherQueueDescriptor"</span>};</div><div class="line"><a name="l02453"></a><span class="lineno"> 2453</span> </div><div class="line"><a name="l02454"></a><span class="lineno"> 2454</span>  ValidateNumInputs(workloadInfo, descriptorName, 2);</div><div class="line"><a name="l02455"></a><span class="lineno"> 2455</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02456"></a><span class="lineno"> 2456</span> </div><div class="line"><a name="l02457"></a><span class="lineno"> 2457</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& indicesTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02458"></a><span class="lineno"> 2458</span>  <span class="keywordflow">if</span> (indicesTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>)</div><div class="line"><a name="l02459"></a><span class="lineno"> 2459</span>  {</div><div class="line"><a name="l02460"></a><span class="lineno"> 2460</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Indices tensor type must be Int32."</span>);</div><div class="line"><a name="l02461"></a><span class="lineno"> 2461</span>  }</div><div class="line"><a name="l02462"></a><span class="lineno"> 2462</span> </div><div class="line"><a name="l02463"></a><span class="lineno"> 2463</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02464"></a><span class="lineno"> 2464</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02465"></a><span class="lineno"> 2465</span> </div><div class="line"><a name="l02466"></a><span class="lineno"> 2466</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l02467"></a><span class="lineno"> 2467</span>  {</div><div class="line"><a name="l02468"></a><span class="lineno"> 2468</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02469"></a><span class="lineno"> 2469</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02470"></a><span class="lineno"> 2470</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02471"></a><span class="lineno"> 2471</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02472"></a><span class="lineno"> 2472</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02473"></a><span class="lineno"> 2473</span>  };</div><div class="line"><a name="l02474"></a><span class="lineno"> 2474</span> </div><div class="line"><a name="l02475"></a><span class="lineno"> 2475</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02476"></a><span class="lineno"> 2476</span> </div><div class="line"><a name="l02477"></a><span class="lineno"> 2477</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02478"></a><span class="lineno"> 2478</span> </div><div class="line"><a name="l02479"></a><span class="lineno"> 2479</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputDim = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() + indicesTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>() - 1;</div><div class="line"><a name="l02480"></a><span class="lineno"> 2480</span>  ValidateTensorNumDimensions(outputTensorInfo, descriptorName, outputDim, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02481"></a><span class="lineno"> 2481</span> }</div><div class="line"><a name="l02482"></a><span class="lineno"> 2482</span> </div><div class="line"><a name="l02483"></a><span class="lineno"><a class="line" href="structarmnn_1_1_detection_post_process_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2483</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_detection_post_process_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">DetectionPostProcessQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02484"></a><span class="lineno"> 2484</span> <span class="keyword"></span>{</div><div class="line"><a name="l02485"></a><span class="lineno"> 2485</span>  <span class="keyword">const</span> std::string& descriptorName{<span class="stringliteral">"DetectionPostProcessQueueDescriptor"</span>};</div><div class="line"><a name="l02486"></a><span class="lineno"> 2486</span> </div><div class="line"><a name="l02487"></a><span class="lineno"> 2487</span>  ValidateNumInputs(workloadInfo, descriptorName, 2);</div><div class="line"><a name="l02488"></a><span class="lineno"> 2488</span> </div><div class="line"><a name="l02489"></a><span class="lineno"> 2489</span>  <span class="keywordflow">if</span> (workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size() != 4)</div><div class="line"><a name="l02490"></a><span class="lineno"> 2490</span>  {</div><div class="line"><a name="l02491"></a><span class="lineno"> 2491</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Requires exactly four outputs. "</span> +</div><div class="line"><a name="l02492"></a><span class="lineno"> 2492</span>  to_string(workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>.size()) + <span class="stringliteral">" has been provided."</span>);</div><div class="line"><a name="l02493"></a><span class="lineno"> 2493</span>  }</div><div class="line"><a name="l02494"></a><span class="lineno"> 2494</span> </div><div class="line"><a name="l02495"></a><span class="lineno"> 2495</span>  <span class="keywordflow">if</span> (m_Anchors == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l02496"></a><span class="lineno"> 2496</span>  {</div><div class="line"><a name="l02497"></a><span class="lineno"> 2497</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Anchors tensor descriptor is missing."</span>);</div><div class="line"><a name="l02498"></a><span class="lineno"> 2498</span>  }</div><div class="line"><a name="l02499"></a><span class="lineno"> 2499</span> </div><div class="line"><a name="l02500"></a><span class="lineno"> 2500</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& boxEncodingsInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02501"></a><span class="lineno"> 2501</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& <a class="code" href="_neon_end_to_end_tests_8cpp.xhtml#a64c1dd1b6dd60be9f4a16db9c8f427a5">scoresInfo</a> = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02502"></a><span class="lineno"> 2502</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& <a class="code" href="_neon_end_to_end_tests_8cpp.xhtml#a358cb7cd3c0647b25be049fd734b8c22">anchorsInfo</a> = m_Anchors->GetTensorInfo();</div><div class="line"><a name="l02503"></a><span class="lineno"> 2503</span> </div><div class="line"><a name="l02504"></a><span class="lineno"> 2504</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& detectionBoxesInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02505"></a><span class="lineno"> 2505</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& detectionClassesInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[1];</div><div class="line"><a name="l02506"></a><span class="lineno"> 2506</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& detectionScoresInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[2];</div><div class="line"><a name="l02507"></a><span class="lineno"> 2507</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& numDetectionsInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[3];</div><div class="line"><a name="l02508"></a><span class="lineno"> 2508</span> </div><div class="line"><a name="l02509"></a><span class="lineno"> 2509</span>  ValidateTensorNumDimensions(boxEncodingsInfo, descriptorName, 3, <span class="stringliteral">"box encodings"</span>);</div><div class="line"><a name="l02510"></a><span class="lineno"> 2510</span>  ValidateTensorNumDimensions(scoresInfo, descriptorName, 3, <span class="stringliteral">"scores"</span>);</div><div class="line"><a name="l02511"></a><span class="lineno"> 2511</span>  ValidateTensorNumDimensions(anchorsInfo, descriptorName, 2, <span class="stringliteral">"anchors"</span>);</div><div class="line"><a name="l02512"></a><span class="lineno"> 2512</span> </div><div class="line"><a name="l02513"></a><span class="lineno"> 2513</span>  <span class="keyword">const</span> std::vector<DataType> supportedInputTypes =</div><div class="line"><a name="l02514"></a><span class="lineno"> 2514</span>  {</div><div class="line"><a name="l02515"></a><span class="lineno"> 2515</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02516"></a><span class="lineno"> 2516</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02517"></a><span class="lineno"> 2517</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02518"></a><span class="lineno"> 2518</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02519"></a><span class="lineno"> 2519</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02520"></a><span class="lineno"> 2520</span>  };</div><div class="line"><a name="l02521"></a><span class="lineno"> 2521</span> </div><div class="line"><a name="l02522"></a><span class="lineno"> 2522</span>  ValidateDataTypes(boxEncodingsInfo, supportedInputTypes, descriptorName);</div><div class="line"><a name="l02523"></a><span class="lineno"> 2523</span>  ValidateDataTypes(scoresInfo, supportedInputTypes, descriptorName);</div><div class="line"><a name="l02524"></a><span class="lineno"> 2524</span>  ValidateDataTypes(anchorsInfo, supportedInputTypes, descriptorName);</div><div class="line"><a name="l02525"></a><span class="lineno"> 2525</span> </div><div class="line"><a name="l02526"></a><span class="lineno"> 2526</span>  ValidateTensorNumDimensions(detectionBoxesInfo, descriptorName, 3, <span class="stringliteral">"detection boxes"</span>);</div><div class="line"><a name="l02527"></a><span class="lineno"> 2527</span>  ValidateTensorNumDimensions(detectionScoresInfo, descriptorName, 2, <span class="stringliteral">"detection scores"</span>);</div><div class="line"><a name="l02528"></a><span class="lineno"> 2528</span>  ValidateTensorNumDimensions(detectionClassesInfo, descriptorName, 2, <span class="stringliteral">"detection classes"</span>);</div><div class="line"><a name="l02529"></a><span class="lineno"> 2529</span>  ValidateTensorNumDimensions(numDetectionsInfo, descriptorName, 1, <span class="stringliteral">"num detections"</span>);</div><div class="line"><a name="l02530"></a><span class="lineno"> 2530</span> </div><div class="line"><a name="l02531"></a><span class="lineno"> 2531</span>  <span class="comment">// NOTE: Output is always Float32 regardless of input type</span></div><div class="line"><a name="l02532"></a><span class="lineno"> 2532</span>  ValidateTensorDataType(detectionBoxesInfo, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>, descriptorName, <span class="stringliteral">"detection boxes"</span>);</div><div class="line"><a name="l02533"></a><span class="lineno"> 2533</span>  ValidateTensorDataType(detectionScoresInfo, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>, descriptorName, <span class="stringliteral">"detection scores"</span>);</div><div class="line"><a name="l02534"></a><span class="lineno"> 2534</span>  ValidateTensorDataType(detectionClassesInfo, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>, descriptorName, <span class="stringliteral">"detection classes"</span>);</div><div class="line"><a name="l02535"></a><span class="lineno"> 2535</span>  ValidateTensorDataType(numDetectionsInfo, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>, descriptorName, <span class="stringliteral">"num detections"</span>);</div><div class="line"><a name="l02536"></a><span class="lineno"> 2536</span> </div><div class="line"><a name="l02537"></a><span class="lineno"> 2537</span>  <span class="keywordflow">if</span> (m_Parameters.m_NmsIouThreshold <= 0.0f || m_Parameters.m_NmsIouThreshold > 1.0f)</div><div class="line"><a name="l02538"></a><span class="lineno"> 2538</span>  {</div><div class="line"><a name="l02539"></a><span class="lineno"> 2539</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Intersection over union threshold "</span></div><div class="line"><a name="l02540"></a><span class="lineno"> 2540</span>  <span class="stringliteral">"must be positive and less than or equal to 1."</span>);</div><div class="line"><a name="l02541"></a><span class="lineno"> 2541</span>  }</div><div class="line"><a name="l02542"></a><span class="lineno"> 2542</span> </div><div class="line"><a name="l02543"></a><span class="lineno"> 2543</span>  <span class="keywordflow">if</span> (scoresInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[2] != m_Parameters.m_NumClasses + 1)</div><div class="line"><a name="l02544"></a><span class="lineno"> 2544</span>  {</div><div class="line"><a name="l02545"></a><span class="lineno"> 2545</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Number of classes with background "</span></div><div class="line"><a name="l02546"></a><span class="lineno"> 2546</span>  <span class="stringliteral">"should be equal to number of classes + 1."</span>);</div><div class="line"><a name="l02547"></a><span class="lineno"> 2547</span>  }</div><div class="line"><a name="l02548"></a><span class="lineno"> 2548</span> }</div><div class="line"><a name="l02549"></a><span class="lineno"> 2549</span> </div><div class="line"><a name="l02550"></a><span class="lineno"><a class="line" href="structarmnn_1_1_dequantize_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2550</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_dequantize_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">DequantizeQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02551"></a><span class="lineno"> 2551</span> <span class="keyword"></span>{</div><div class="line"><a name="l02552"></a><span class="lineno"> 2552</span>  <span class="keyword">const</span> std::string& descriptorName{<span class="stringliteral">"DequantizeQueueDescriptor"</span>};</div><div class="line"><a name="l02553"></a><span class="lineno"> 2553</span> </div><div class="line"><a name="l02554"></a><span class="lineno"> 2554</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02555"></a><span class="lineno"> 2555</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02556"></a><span class="lineno"> 2556</span> </div><div class="line"><a name="l02557"></a><span class="lineno"> 2557</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02558"></a><span class="lineno"> 2558</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02559"></a><span class="lineno"> 2559</span> </div><div class="line"><a name="l02560"></a><span class="lineno"> 2560</span>  <span class="keywordflow">if</span> (!<a class="code" href="namespacearmnn.xhtml#ad44c007f21af2d0375e3ef9400a1b275">IsQuantizedType</a>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()))</div><div class="line"><a name="l02561"></a><span class="lineno"> 2561</span>  {</div><div class="line"><a name="l02562"></a><span class="lineno"> 2562</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Input to dequantize layer must be quantized type."</span>);</div><div class="line"><a name="l02563"></a><span class="lineno"> 2563</span>  }</div><div class="line"><a name="l02564"></a><span class="lineno"> 2564</span> </div><div class="line"><a name="l02565"></a><span class="lineno"> 2565</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l02566"></a><span class="lineno"> 2566</span>  {</div><div class="line"><a name="l02567"></a><span class="lineno"> 2567</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02568"></a><span class="lineno"> 2568</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02569"></a><span class="lineno"> 2569</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a></div><div class="line"><a name="l02570"></a><span class="lineno"> 2570</span>  };</div><div class="line"><a name="l02571"></a><span class="lineno"> 2571</span> </div><div class="line"><a name="l02572"></a><span class="lineno"> 2572</span>  ValidateDataTypes(outputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02573"></a><span class="lineno"> 2573</span> }</div><div class="line"><a name="l02574"></a><span class="lineno"> 2574</span> </div><div class="line"><a name="l02575"></a><span class="lineno"><a class="line" href="structarmnn_1_1_merge_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2575</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_merge_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">MergeQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02576"></a><span class="lineno"> 2576</span> <span class="keyword"></span>{</div><div class="line"><a name="l02577"></a><span class="lineno"> 2577</span>  <span class="keyword">const</span> std::string& descriptorName{<span class="stringliteral">"MergeQueueDescriptor"</span>};</div><div class="line"><a name="l02578"></a><span class="lineno"> 2578</span> </div><div class="line"><a name="l02579"></a><span class="lineno"> 2579</span>  ValidateNumInputs(workloadInfo, descriptorName, 2);</div><div class="line"><a name="l02580"></a><span class="lineno"> 2580</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02581"></a><span class="lineno"> 2581</span> </div><div class="line"><a name="l02582"></a><span class="lineno"> 2582</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02583"></a><span class="lineno"> 2583</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02584"></a><span class="lineno"> 2584</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02585"></a><span class="lineno"> 2585</span> </div><div class="line"><a name="l02586"></a><span class="lineno"> 2586</span>  ValidateTensorShapesMatch(inputTensorInfo0, inputTensorInfo1, descriptorName, <span class="stringliteral">"input_0"</span>, <span class="stringliteral">"input_1"</span>);</div><div class="line"><a name="l02587"></a><span class="lineno"> 2587</span>  ValidateTensorShapesMatch(inputTensorInfo0, outputTensorInfo, descriptorName, <span class="stringliteral">"input_0"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02588"></a><span class="lineno"> 2588</span> </div><div class="line"><a name="l02589"></a><span class="lineno"> 2589</span>  ValidateTensorDataTypesMatch(inputTensorInfo0, inputTensorInfo1, descriptorName, <span class="stringliteral">"input_0"</span>, <span class="stringliteral">"input_1"</span>);</div><div class="line"><a name="l02590"></a><span class="lineno"> 2590</span>  ValidateTensorDataTypesMatch(inputTensorInfo0, outputTensorInfo, descriptorName, <span class="stringliteral">"input_0"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02591"></a><span class="lineno"> 2591</span> }</div><div class="line"><a name="l02592"></a><span class="lineno"> 2592</span> </div><div class="line"><a name="l02593"></a><span class="lineno"><a class="line" href="structarmnn_1_1_switch_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2593</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_switch_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">SwitchQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02594"></a><span class="lineno"> 2594</span> <span class="keyword"></span>{</div><div class="line"><a name="l02595"></a><span class="lineno"> 2595</span>  <span class="keyword">const</span> std::string& descriptorName{<span class="stringliteral">"SwitchQueueDescriptor"</span>};</div><div class="line"><a name="l02596"></a><span class="lineno"> 2596</span> </div><div class="line"><a name="l02597"></a><span class="lineno"> 2597</span>  ValidateNumInputs(workloadInfo, descriptorName, 2);</div><div class="line"><a name="l02598"></a><span class="lineno"> 2598</span>  ValidateNumOutputs(workloadInfo, descriptorName, 2);</div><div class="line"><a name="l02599"></a><span class="lineno"> 2599</span> </div><div class="line"><a name="l02600"></a><span class="lineno"> 2600</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02601"></a><span class="lineno"> 2601</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02602"></a><span class="lineno"> 2602</span> </div><div class="line"><a name="l02603"></a><span class="lineno"> 2603</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02604"></a><span class="lineno"> 2604</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[1];</div><div class="line"><a name="l02605"></a><span class="lineno"> 2605</span> </div><div class="line"><a name="l02606"></a><span class="lineno"> 2606</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l02607"></a><span class="lineno"> 2607</span>  {</div><div class="line"><a name="l02608"></a><span class="lineno"> 2608</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02609"></a><span class="lineno"> 2609</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02610"></a><span class="lineno"> 2610</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02611"></a><span class="lineno"> 2611</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02612"></a><span class="lineno"> 2612</span>  };</div><div class="line"><a name="l02613"></a><span class="lineno"> 2613</span> </div><div class="line"><a name="l02614"></a><span class="lineno"> 2614</span>  ValidateDataTypes(inputTensorInfo0, supportedTypes, descriptorName);</div><div class="line"><a name="l02615"></a><span class="lineno"> 2615</span>  ValidateDataTypes(inputTensorInfo1, supportedTypes, descriptorName);</div><div class="line"><a name="l02616"></a><span class="lineno"> 2616</span> </div><div class="line"><a name="l02617"></a><span class="lineno"> 2617</span>  ValidateDataTypes(outputTensorInfo0, supportedTypes, descriptorName);</div><div class="line"><a name="l02618"></a><span class="lineno"> 2618</span>  ValidateDataTypes(outputTensorInfo1, supportedTypes, descriptorName);</div><div class="line"><a name="l02619"></a><span class="lineno"> 2619</span> </div><div class="line"><a name="l02620"></a><span class="lineno"> 2620</span>  ValidateTensorShapesMatch(inputTensorInfo0,</div><div class="line"><a name="l02621"></a><span class="lineno"> 2621</span>  outputTensorInfo0,</div><div class="line"><a name="l02622"></a><span class="lineno"> 2622</span>  descriptorName,</div><div class="line"><a name="l02623"></a><span class="lineno"> 2623</span>  <span class="stringliteral">"input_0"</span>,</div><div class="line"><a name="l02624"></a><span class="lineno"> 2624</span>  <span class="stringliteral">"output_0"</span>);</div><div class="line"><a name="l02625"></a><span class="lineno"> 2625</span> </div><div class="line"><a name="l02626"></a><span class="lineno"> 2626</span>  ValidateTensorShapesMatch(inputTensorInfo0,</div><div class="line"><a name="l02627"></a><span class="lineno"> 2627</span>  outputTensorInfo1,</div><div class="line"><a name="l02628"></a><span class="lineno"> 2628</span>  descriptorName,</div><div class="line"><a name="l02629"></a><span class="lineno"> 2629</span>  <span class="stringliteral">"input_0"</span>,</div><div class="line"><a name="l02630"></a><span class="lineno"> 2630</span>  <span class="stringliteral">"output_1"</span>);</div><div class="line"><a name="l02631"></a><span class="lineno"> 2631</span> }</div><div class="line"><a name="l02632"></a><span class="lineno"> 2632</span> </div><div class="line"><a name="l02633"></a><span class="lineno"><a class="line" href="structarmnn_1_1_pre_compiled_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2633</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_pre_compiled_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">PreCompiledQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <span class="comment">/*workloadInfo*/</span>)<span class="keyword"> const</span></div><div class="line"><a name="l02634"></a><span class="lineno"> 2634</span> <span class="keyword"></span>{</div><div class="line"><a name="l02635"></a><span class="lineno"> 2635</span>  <span class="comment">// This is internally generated so it should not need validation.</span></div><div class="line"><a name="l02636"></a><span class="lineno"> 2636</span> }</div><div class="line"><a name="l02637"></a><span class="lineno"> 2637</span> </div><div class="line"><a name="l02638"></a><span class="lineno"><a class="line" href="structarmnn_1_1_prelu_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2638</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_prelu_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">PreluQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02639"></a><span class="lineno"> 2639</span> <span class="keyword"></span>{</div><div class="line"><a name="l02640"></a><span class="lineno"> 2640</span>  <span class="keyword">const</span> std::string& descriptorName{<span class="stringliteral">"PreluQueueDescriptor"</span>};</div><div class="line"><a name="l02641"></a><span class="lineno"> 2641</span> </div><div class="line"><a name="l02642"></a><span class="lineno"> 2642</span>  ValidateNumInputs(workloadInfo, descriptorName, 2);</div><div class="line"><a name="l02643"></a><span class="lineno"> 2643</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02644"></a><span class="lineno"> 2644</span> </div><div class="line"><a name="l02645"></a><span class="lineno"> 2645</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02646"></a><span class="lineno"> 2646</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& alphaTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02647"></a><span class="lineno"> 2647</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02648"></a><span class="lineno"> 2648</span> </div><div class="line"><a name="l02649"></a><span class="lineno"> 2649</span>  std::vector<DataType> supportedTypes</div><div class="line"><a name="l02650"></a><span class="lineno"> 2650</span>  {</div><div class="line"><a name="l02651"></a><span class="lineno"> 2651</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02652"></a><span class="lineno"> 2652</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02653"></a><span class="lineno"> 2653</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02654"></a><span class="lineno"> 2654</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02655"></a><span class="lineno"> 2655</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02656"></a><span class="lineno"> 2656</span>  };</div><div class="line"><a name="l02657"></a><span class="lineno"> 2657</span> </div><div class="line"><a name="l02658"></a><span class="lineno"> 2658</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02659"></a><span class="lineno"> 2659</span>  ValidateDataTypes(alphaTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02660"></a><span class="lineno"> 2660</span> </div><div class="line"><a name="l02661"></a><span class="lineno"> 2661</span>  ValidateDataTypes(outputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02662"></a><span class="lineno"> 2662</span> </div><div class="line"><a name="l02663"></a><span class="lineno"> 2663</span>  ValidateTensorDataTypesMatch(inputTensorInfo, alphaTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"alpha"</span>);</div><div class="line"><a name="l02664"></a><span class="lineno"> 2664</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"ouptut"</span>);</div><div class="line"><a name="l02665"></a><span class="lineno"> 2665</span> </div><div class="line"><a name="l02666"></a><span class="lineno"> 2666</span>  ValidateBroadcastTensorShapesMatch(inputTensorInfo,</div><div class="line"><a name="l02667"></a><span class="lineno"> 2667</span>  alphaTensorInfo,</div><div class="line"><a name="l02668"></a><span class="lineno"> 2668</span>  outputTensorInfo,</div><div class="line"><a name="l02669"></a><span class="lineno"> 2669</span>  descriptorName,</div><div class="line"><a name="l02670"></a><span class="lineno"> 2670</span>  <span class="stringliteral">"input"</span>,</div><div class="line"><a name="l02671"></a><span class="lineno"> 2671</span>  <span class="stringliteral">"alpha"</span>);</div><div class="line"><a name="l02672"></a><span class="lineno"> 2672</span> }</div><div class="line"><a name="l02673"></a><span class="lineno"> 2673</span> </div><div class="line"><a name="l02674"></a><span class="lineno"><a class="line" href="structarmnn_1_1_transpose_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2674</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_transpose_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">TransposeConvolution2dQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02675"></a><span class="lineno"> 2675</span> <span class="keyword"></span>{</div><div class="line"><a name="l02676"></a><span class="lineno"> 2676</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"TransposeConvolution2dQueueDescriptor"</span>};</div><div class="line"><a name="l02677"></a><span class="lineno"> 2677</span> </div><div class="line"><a name="l02678"></a><span class="lineno"> 2678</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02679"></a><span class="lineno"> 2679</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02680"></a><span class="lineno"> 2680</span> </div><div class="line"><a name="l02681"></a><span class="lineno"> 2681</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02682"></a><span class="lineno"> 2682</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02683"></a><span class="lineno"> 2683</span> </div><div class="line"><a name="l02684"></a><span class="lineno"> 2684</span>  ValidateTensorNumDimensions(inputTensorInfo, descriptorName, 4, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l02685"></a><span class="lineno"> 2685</span>  ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 4, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02686"></a><span class="lineno"> 2686</span> </div><div class="line"><a name="l02687"></a><span class="lineno"> 2687</span>  ValidatePointer(m_Weight, descriptorName, <span class="stringliteral">"weight"</span>);</div><div class="line"><a name="l02688"></a><span class="lineno"> 2688</span> </div><div class="line"><a name="l02689"></a><span class="lineno"> 2689</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& weightTensorInfo = m_Weight->GetTensorInfo();</div><div class="line"><a name="l02690"></a><span class="lineno"> 2690</span>  ValidateTensorNumDimensions(weightTensorInfo, descriptorName, 4, <span class="stringliteral">"weight"</span>);</div><div class="line"><a name="l02691"></a><span class="lineno"> 2691</span> </div><div class="line"><a name="l02692"></a><span class="lineno"> 2692</span>  ValidateWeightDataType(inputTensorInfo, weightTensorInfo, descriptorName);</div><div class="line"><a name="l02693"></a><span class="lineno"> 2693</span> </div><div class="line"><a name="l02694"></a><span class="lineno"> 2694</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<TensorInfo></a> optionalBiasTensorInfo;</div><div class="line"><a name="l02695"></a><span class="lineno"> 2695</span>  <span class="keywordflow">if</span> (m_Parameters.m_BiasEnabled)</div><div class="line"><a name="l02696"></a><span class="lineno"> 2696</span>  {</div><div class="line"><a name="l02697"></a><span class="lineno"> 2697</span>  ValidatePointer(m_Bias, descriptorName, <span class="stringliteral">"bias"</span>);</div><div class="line"><a name="l02698"></a><span class="lineno"> 2698</span> </div><div class="line"><a name="l02699"></a><span class="lineno"> 2699</span>  optionalBiasTensorInfo = MakeOptional<TensorInfo>(m_Bias->GetTensorInfo());</div><div class="line"><a name="l02700"></a><span class="lineno"> 2700</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& biasTensorInfo = optionalBiasTensorInfo.<a class="code" href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">value</a>();</div><div class="line"><a name="l02701"></a><span class="lineno"> 2701</span> </div><div class="line"><a name="l02702"></a><span class="lineno"> 2702</span>  ValidateTensorDataType(biasTensorInfo, <a class="code" href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">GetBiasDataType</a>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>()), descriptorName, <span class="stringliteral">"bias"</span>);</div><div class="line"><a name="l02703"></a><span class="lineno"> 2703</span>  ValidateBiasTensorQuantization(biasTensorInfo, inputTensorInfo, weightTensorInfo, descriptorName);</div><div class="line"><a name="l02704"></a><span class="lineno"> 2704</span>  }</div><div class="line"><a name="l02705"></a><span class="lineno"> 2705</span> </div><div class="line"><a name="l02706"></a><span class="lineno"> 2706</span>  ValidatePerAxisQuantization(inputTensorInfo,</div><div class="line"><a name="l02707"></a><span class="lineno"> 2707</span>  outputTensorInfo,</div><div class="line"><a name="l02708"></a><span class="lineno"> 2708</span>  weightTensorInfo,</div><div class="line"><a name="l02709"></a><span class="lineno"> 2709</span>  optionalBiasTensorInfo,</div><div class="line"><a name="l02710"></a><span class="lineno"> 2710</span>  descriptorName);</div><div class="line"><a name="l02711"></a><span class="lineno"> 2711</span> </div><div class="line"><a name="l02712"></a><span class="lineno"> 2712</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l02713"></a><span class="lineno"> 2713</span>  {</div><div class="line"><a name="l02714"></a><span class="lineno"> 2714</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02715"></a><span class="lineno"> 2715</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02716"></a><span class="lineno"> 2716</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02717"></a><span class="lineno"> 2717</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02718"></a><span class="lineno"> 2718</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02719"></a><span class="lineno"> 2719</span>  };</div><div class="line"><a name="l02720"></a><span class="lineno"> 2720</span> </div><div class="line"><a name="l02721"></a><span class="lineno"> 2721</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02722"></a><span class="lineno"> 2722</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02723"></a><span class="lineno"> 2723</span> }</div><div class="line"><a name="l02724"></a><span class="lineno"> 2724</span> </div><div class="line"><a name="l02725"></a><span class="lineno"><a class="line" href="structarmnn_1_1_transpose_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2725</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_transpose_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">TransposeQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02726"></a><span class="lineno"> 2726</span> <span class="keyword"></span>{</div><div class="line"><a name="l02727"></a><span class="lineno"> 2727</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"TransposeQueueDescriptor"</span>};</div><div class="line"><a name="l02728"></a><span class="lineno"> 2728</span> </div><div class="line"><a name="l02729"></a><span class="lineno"> 2729</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02730"></a><span class="lineno"> 2730</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02731"></a><span class="lineno"> 2731</span> </div><div class="line"><a name="l02732"></a><span class="lineno"> 2732</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a>& mapping = m_Parameters.m_DimMappings;</div><div class="line"><a name="l02733"></a><span class="lineno"> 2733</span> </div><div class="line"><a name="l02734"></a><span class="lineno"> 2734</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02735"></a><span class="lineno"> 2735</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02736"></a><span class="lineno"> 2736</span> </div><div class="line"><a name="l02737"></a><span class="lineno"> 2737</span>  ValidateTensorNumDimensions(inputTensorInfo, descriptorName, mapping.<a class="code" href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>(), <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l02738"></a><span class="lineno"> 2738</span>  ValidateTensorNumDimensions(outputTensorInfo, descriptorName, mapping.<a class="code" href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>(), <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02739"></a><span class="lineno"> 2739</span> </div><div class="line"><a name="l02740"></a><span class="lineno"> 2740</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0u; i < mapping.<a class="code" href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">GetSize</a>(); ++i)</div><div class="line"><a name="l02741"></a><span class="lineno"> 2741</span>  {</div><div class="line"><a name="l02742"></a><span class="lineno"> 2742</span>  <span class="keywordflow">if</span> (inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[mapping[i]] != outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i])</div><div class="line"><a name="l02743"></a><span class="lineno"> 2743</span>  {</div><div class="line"><a name="l02744"></a><span class="lineno"> 2744</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": src dimension "</span> + to_string(mapping[i]) +</div><div class="line"><a name="l02745"></a><span class="lineno"> 2745</span>  <span class="stringliteral">" (="</span> + to_string(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[mapping[i]]) + <span class="stringliteral">") "</span> +</div><div class="line"><a name="l02746"></a><span class="lineno"> 2746</span>  <span class="stringliteral">"must match dst dimension "</span> + to_string(i) +</div><div class="line"><a name="l02747"></a><span class="lineno"> 2747</span>  <span class="stringliteral">" (="</span> + to_string(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[i]) + <span class="stringliteral">")"</span>);</div><div class="line"><a name="l02748"></a><span class="lineno"> 2748</span>  }</div><div class="line"><a name="l02749"></a><span class="lineno"> 2749</span>  }</div><div class="line"><a name="l02750"></a><span class="lineno"> 2750</span> </div><div class="line"><a name="l02751"></a><span class="lineno"> 2751</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02752"></a><span class="lineno"> 2752</span> }</div><div class="line"><a name="l02753"></a><span class="lineno"> 2753</span> </div><div class="line"><a name="l02754"></a><span class="lineno"><a class="line" href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2754</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">QuantizedLstmQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02755"></a><span class="lineno"> 2755</span> <span class="keyword"></span>{</div><div class="line"><a name="l02756"></a><span class="lineno"> 2756</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"QuantizedLstmQueueDescriptor"</span>};</div><div class="line"><a name="l02757"></a><span class="lineno"> 2757</span> </div><div class="line"><a name="l02758"></a><span class="lineno"> 2758</span>  <span class="comment">// Validate number of inputs/outputs</span></div><div class="line"><a name="l02759"></a><span class="lineno"> 2759</span>  ValidateNumInputs(workloadInfo, descriptorName, 3);</div><div class="line"><a name="l02760"></a><span class="lineno"> 2760</span>  ValidateNumOutputs(workloadInfo, descriptorName, 2);</div><div class="line"><a name="l02761"></a><span class="lineno"> 2761</span> </div><div class="line"><a name="l02762"></a><span class="lineno"> 2762</span>  <span class="comment">// Input/output tensor infos</span></div><div class="line"><a name="l02763"></a><span class="lineno"> 2763</span>  <span class="keyword">auto</span> inputInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02764"></a><span class="lineno"> 2764</span>  <span class="keyword">auto</span> cellStateInInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l02765"></a><span class="lineno"> 2765</span>  <span class="keyword">auto</span> outputStateInInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[2];</div><div class="line"><a name="l02766"></a><span class="lineno"> 2766</span> </div><div class="line"><a name="l02767"></a><span class="lineno"> 2767</span>  <span class="keyword">auto</span> cellStateOutInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02768"></a><span class="lineno"> 2768</span>  <span class="keyword">auto</span> outputStateOutInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[1];</div><div class="line"><a name="l02769"></a><span class="lineno"> 2769</span> </div><div class="line"><a name="l02770"></a><span class="lineno"> 2770</span>  std::vector<DataType> inputOutputSupportedTypes =</div><div class="line"><a name="l02771"></a><span class="lineno"> 2771</span>  {</div><div class="line"><a name="l02772"></a><span class="lineno"> 2772</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a></div><div class="line"><a name="l02773"></a><span class="lineno"> 2773</span>  };</div><div class="line"><a name="l02774"></a><span class="lineno"> 2774</span> </div><div class="line"><a name="l02775"></a><span class="lineno"> 2775</span>  std::vector<DataType> cellStateSupportedTypes =</div><div class="line"><a name="l02776"></a><span class="lineno"> 2776</span>  {</div><div class="line"><a name="l02777"></a><span class="lineno"> 2777</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02778"></a><span class="lineno"> 2778</span>  };</div><div class="line"><a name="l02779"></a><span class="lineno"> 2779</span> </div><div class="line"><a name="l02780"></a><span class="lineno"> 2780</span>  std::vector<DataType> weightsSupportedTypes =</div><div class="line"><a name="l02781"></a><span class="lineno"> 2781</span>  {</div><div class="line"><a name="l02782"></a><span class="lineno"> 2782</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a></div><div class="line"><a name="l02783"></a><span class="lineno"> 2783</span>  };</div><div class="line"><a name="l02784"></a><span class="lineno"> 2784</span> </div><div class="line"><a name="l02785"></a><span class="lineno"> 2785</span>  std::vector<DataType> biasSupportedTypes =</div><div class="line"><a name="l02786"></a><span class="lineno"> 2786</span>  {</div><div class="line"><a name="l02787"></a><span class="lineno"> 2787</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a></div><div class="line"><a name="l02788"></a><span class="lineno"> 2788</span>  };</div><div class="line"><a name="l02789"></a><span class="lineno"> 2789</span> </div><div class="line"><a name="l02790"></a><span class="lineno"> 2790</span>  <span class="comment">// Validate types of input/output tensors</span></div><div class="line"><a name="l02791"></a><span class="lineno"> 2791</span>  ValidateDataTypes(inputInfo, inputOutputSupportedTypes, descriptorName);</div><div class="line"><a name="l02792"></a><span class="lineno"> 2792</span>  ValidateDataTypes(cellStateInInfo, cellStateSupportedTypes, descriptorName);</div><div class="line"><a name="l02793"></a><span class="lineno"> 2793</span>  ValidateDataTypes(outputStateInInfo, inputOutputSupportedTypes, descriptorName);</div><div class="line"><a name="l02794"></a><span class="lineno"> 2794</span> </div><div class="line"><a name="l02795"></a><span class="lineno"> 2795</span>  ValidateDataTypes(cellStateOutInfo, cellStateSupportedTypes, descriptorName);</div><div class="line"><a name="l02796"></a><span class="lineno"> 2796</span>  ValidateDataTypes(outputStateOutInfo, inputOutputSupportedTypes, descriptorName);</div><div class="line"><a name="l02797"></a><span class="lineno"> 2797</span> </div><div class="line"><a name="l02798"></a><span class="lineno"> 2798</span>  <span class="comment">// Validate matching types of input/output tensors</span></div><div class="line"><a name="l02799"></a><span class="lineno"> 2799</span>  ValidateTensorDataTypesMatch(inputInfo, outputStateInInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"outputStateIn"</span>);</div><div class="line"><a name="l02800"></a><span class="lineno"> 2800</span>  ValidateTensorDataTypesMatch(outputStateInInfo, outputStateOutInfo, descriptorName,</div><div class="line"><a name="l02801"></a><span class="lineno"> 2801</span>  <span class="stringliteral">"outputStateIn"</span>, <span class="stringliteral">"outputStateOut"</span>);</div><div class="line"><a name="l02802"></a><span class="lineno"> 2802</span>  ValidateTensorDataTypesMatch(cellStateInInfo, cellStateOutInfo, descriptorName, <span class="stringliteral">"cellStateIn"</span>, <span class="stringliteral">"cellStateOut"</span>);</div><div class="line"><a name="l02803"></a><span class="lineno"> 2803</span> </div><div class="line"><a name="l02804"></a><span class="lineno"> 2804</span>  <span class="comment">// Validate matching quantization info for input/output tensors</span></div><div class="line"><a name="l02805"></a><span class="lineno"> 2805</span>  ValidateTensorQuantizationSpace(inputInfo, outputStateInInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"outputStateIn"</span>);</div><div class="line"><a name="l02806"></a><span class="lineno"> 2806</span>  ValidateTensorQuantizationSpace(inputInfo, outputStateOutInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"outputStateOut"</span>);</div><div class="line"><a name="l02807"></a><span class="lineno"> 2807</span>  ValidateTensorQuantizationSpace(cellStateInInfo, cellStateOutInfo, descriptorName, <span class="stringliteral">"cellStateIn"</span>, <span class="stringliteral">"cellStateOut"</span>);</div><div class="line"><a name="l02808"></a><span class="lineno"> 2808</span> </div><div class="line"><a name="l02809"></a><span class="lineno"> 2809</span>  <span class="comment">// Infer number of batches, input size and output size from tensor dimensions</span></div><div class="line"><a name="l02810"></a><span class="lineno"> 2810</span>  <span class="keyword">const</span> uint32_t numBatches = inputInfo.GetShape()[0];</div><div class="line"><a name="l02811"></a><span class="lineno"> 2811</span>  <span class="keyword">const</span> uint32_t inputSize = inputInfo.GetShape()[1];</div><div class="line"><a name="l02812"></a><span class="lineno"> 2812</span>  <span class="keyword">const</span> uint32_t outputSize = cellStateInInfo.GetShape()[1];</div><div class="line"><a name="l02813"></a><span class="lineno"> 2813</span> </div><div class="line"><a name="l02814"></a><span class="lineno"> 2814</span>  <span class="comment">// Validate number of dimensions and number of elements for input/output tensors</span></div><div class="line"><a name="l02815"></a><span class="lineno"> 2815</span>  ValidateTensorNumDimNumElem(inputInfo, 2, (numBatches * inputSize), descriptorName + <span class="stringliteral">" input"</span>);</div><div class="line"><a name="l02816"></a><span class="lineno"> 2816</span>  ValidateTensorNumDimNumElem(cellStateInInfo, 2, (numBatches * outputSize), descriptorName + <span class="stringliteral">" cellStateIn"</span>);</div><div class="line"><a name="l02817"></a><span class="lineno"> 2817</span>  ValidateTensorNumDimNumElem(outputStateInInfo, 2, (numBatches * outputSize), descriptorName + <span class="stringliteral">" outputStateIn"</span>);</div><div class="line"><a name="l02818"></a><span class="lineno"> 2818</span>  ValidateTensorNumDimNumElem(cellStateOutInfo, 2, (numBatches * outputSize), descriptorName + <span class="stringliteral">" cellStateOut"</span>);</div><div class="line"><a name="l02819"></a><span class="lineno"> 2819</span>  ValidateTensorNumDimNumElem(outputStateOutInfo, 2, (numBatches * outputSize), descriptorName + <span class="stringliteral">" outputStateOut"</span>);</div><div class="line"><a name="l02820"></a><span class="lineno"> 2820</span> </div><div class="line"><a name="l02821"></a><span class="lineno"> 2821</span>  <span class="comment">// Validate number of dimensions and number of elements for weights tensors</span></div><div class="line"><a name="l02822"></a><span class="lineno"> 2822</span>  ValidatePointer(m_InputToInputWeights, descriptorName, <span class="stringliteral">"InputToInputWeights"</span>);</div><div class="line"><a name="l02823"></a><span class="lineno"> 2823</span>  <span class="keyword">auto</span> inputToInputWeightsInfo = m_InputToInputWeights->GetTensorInfo();</div><div class="line"><a name="l02824"></a><span class="lineno"> 2824</span>  ValidateTensorNumDimNumElem(inputToInputWeightsInfo, 2, (outputSize * inputSize), <span class="stringliteral">" InputToInputWeights"</span>);</div><div class="line"><a name="l02825"></a><span class="lineno"> 2825</span> </div><div class="line"><a name="l02826"></a><span class="lineno"> 2826</span>  ValidatePointer(m_InputToForgetWeights, descriptorName, <span class="stringliteral">"InputToForgetWeights"</span>);</div><div class="line"><a name="l02827"></a><span class="lineno"> 2827</span>  <span class="keyword">auto</span> inputToForgetWeightsInfo = m_InputToForgetWeights->GetTensorInfo();</div><div class="line"><a name="l02828"></a><span class="lineno"> 2828</span>  ValidateTensorNumDimNumElem(inputToForgetWeightsInfo, 2, (outputSize * inputSize), <span class="stringliteral">" InputToForgetWeights"</span>);</div><div class="line"><a name="l02829"></a><span class="lineno"> 2829</span> </div><div class="line"><a name="l02830"></a><span class="lineno"> 2830</span>  ValidatePointer(m_InputToCellWeights, descriptorName, <span class="stringliteral">"InputToCellWeights"</span>);</div><div class="line"><a name="l02831"></a><span class="lineno"> 2831</span>  <span class="keyword">auto</span> inputToCellWeightsInfo = m_InputToCellWeights->GetTensorInfo();</div><div class="line"><a name="l02832"></a><span class="lineno"> 2832</span>  ValidateTensorNumDimNumElem(inputToCellWeightsInfo, 2, (outputSize * inputSize), <span class="stringliteral">" InputToCellWeights"</span>);</div><div class="line"><a name="l02833"></a><span class="lineno"> 2833</span> </div><div class="line"><a name="l02834"></a><span class="lineno"> 2834</span>  ValidatePointer(m_InputToOutputWeights, descriptorName, <span class="stringliteral">"InputToOutputWeights"</span>);</div><div class="line"><a name="l02835"></a><span class="lineno"> 2835</span>  <span class="keyword">auto</span> inputToOutputWeightsInfo = m_InputToOutputWeights->GetTensorInfo();</div><div class="line"><a name="l02836"></a><span class="lineno"> 2836</span>  ValidateTensorNumDimNumElem(inputToOutputWeightsInfo, 2, (outputSize * inputSize), <span class="stringliteral">" InputToOutputWeights"</span>);</div><div class="line"><a name="l02837"></a><span class="lineno"> 2837</span> </div><div class="line"><a name="l02838"></a><span class="lineno"> 2838</span>  ValidatePointer(m_RecurrentToInputWeights, descriptorName, <span class="stringliteral">"RecurrentToInputWeights"</span>);</div><div class="line"><a name="l02839"></a><span class="lineno"> 2839</span>  <span class="keyword">auto</span> recurrentToInputWeightsInfo = m_RecurrentToInputWeights->GetTensorInfo();</div><div class="line"><a name="l02840"></a><span class="lineno"> 2840</span>  ValidateTensorNumDimNumElem(recurrentToInputWeightsInfo, 2, (outputSize * outputSize), <span class="stringliteral">" RecurrentToInputWeights"</span>);</div><div class="line"><a name="l02841"></a><span class="lineno"> 2841</span> </div><div class="line"><a name="l02842"></a><span class="lineno"> 2842</span>  ValidatePointer(m_RecurrentToForgetWeights, descriptorName, <span class="stringliteral">"RecurrentToForgetWeights"</span>);</div><div class="line"><a name="l02843"></a><span class="lineno"> 2843</span>  <span class="keyword">auto</span> recurrentToForgetWeightsInfo = m_RecurrentToForgetWeights->GetTensorInfo();</div><div class="line"><a name="l02844"></a><span class="lineno"> 2844</span>  ValidateTensorNumDimNumElem(recurrentToForgetWeightsInfo, 2, (outputSize * outputSize),</div><div class="line"><a name="l02845"></a><span class="lineno"> 2845</span>  <span class="stringliteral">" RecurrentToForgetWeights"</span>);</div><div class="line"><a name="l02846"></a><span class="lineno"> 2846</span> </div><div class="line"><a name="l02847"></a><span class="lineno"> 2847</span>  ValidatePointer(m_RecurrentToCellWeights, descriptorName, <span class="stringliteral">"RecurrentToCellWeights"</span>);</div><div class="line"><a name="l02848"></a><span class="lineno"> 2848</span>  <span class="keyword">auto</span> recurrentToCellWeightsInfo = m_RecurrentToCellWeights->GetTensorInfo();</div><div class="line"><a name="l02849"></a><span class="lineno"> 2849</span>  ValidateTensorNumDimNumElem(recurrentToCellWeightsInfo, 2, (outputSize * outputSize), <span class="stringliteral">" RecurrentToCellWeights"</span>);</div><div class="line"><a name="l02850"></a><span class="lineno"> 2850</span> </div><div class="line"><a name="l02851"></a><span class="lineno"> 2851</span>  ValidatePointer(m_RecurrentToOutputWeights, descriptorName, <span class="stringliteral">"RecurrentToOutputWeights"</span>);</div><div class="line"><a name="l02852"></a><span class="lineno"> 2852</span>  <span class="keyword">auto</span> recurrentToOutputWeightsInfo = m_RecurrentToOutputWeights->GetTensorInfo();</div><div class="line"><a name="l02853"></a><span class="lineno"> 2853</span>  ValidateTensorNumDimNumElem(recurrentToOutputWeightsInfo, 2, (outputSize * outputSize), <span class="stringliteral">" RecurrentToCellWeights"</span>);</div><div class="line"><a name="l02854"></a><span class="lineno"> 2854</span> </div><div class="line"><a name="l02855"></a><span class="lineno"> 2855</span>  <span class="comment">// Validate data types for weights tensors (all should match each other)</span></div><div class="line"><a name="l02856"></a><span class="lineno"> 2856</span>  ValidateDataTypes(inputToInputWeightsInfo, weightsSupportedTypes, descriptorName);</div><div class="line"><a name="l02857"></a><span class="lineno"> 2857</span> </div><div class="line"><a name="l02858"></a><span class="lineno"> 2858</span>  ValidateTensorDataTypesMatch(inputToInputWeightsInfo, inputToForgetWeightsInfo, descriptorName,</div><div class="line"><a name="l02859"></a><span class="lineno"> 2859</span>  <span class="stringliteral">"inputToInputWeights"</span>, <span class="stringliteral">"inputToForgetWeights"</span>);</div><div class="line"><a name="l02860"></a><span class="lineno"> 2860</span>  ValidateTensorDataTypesMatch(inputToInputWeightsInfo, inputToCellWeightsInfo, descriptorName,</div><div class="line"><a name="l02861"></a><span class="lineno"> 2861</span>  <span class="stringliteral">"inputToInputWeights"</span>, <span class="stringliteral">"inputToCellWeights"</span>);</div><div class="line"><a name="l02862"></a><span class="lineno"> 2862</span>  ValidateTensorDataTypesMatch(inputToInputWeightsInfo, inputToOutputWeightsInfo, descriptorName,</div><div class="line"><a name="l02863"></a><span class="lineno"> 2863</span>  <span class="stringliteral">"inputToInputWeights"</span>, <span class="stringliteral">"inputToOutputWeights"</span>);</div><div class="line"><a name="l02864"></a><span class="lineno"> 2864</span> </div><div class="line"><a name="l02865"></a><span class="lineno"> 2865</span>  ValidateTensorDataTypesMatch(inputToInputWeightsInfo, recurrentToInputWeightsInfo, descriptorName,</div><div class="line"><a name="l02866"></a><span class="lineno"> 2866</span>  <span class="stringliteral">"inputToInputWeights"</span>, <span class="stringliteral">"recurrentToInputWeights"</span>);</div><div class="line"><a name="l02867"></a><span class="lineno"> 2867</span>  ValidateTensorDataTypesMatch(inputToInputWeightsInfo, recurrentToForgetWeightsInfo, descriptorName,</div><div class="line"><a name="l02868"></a><span class="lineno"> 2868</span>  <span class="stringliteral">"inputToInputWeights"</span>, <span class="stringliteral">"recurrentToForgeteights"</span>);</div><div class="line"><a name="l02869"></a><span class="lineno"> 2869</span>  ValidateTensorDataTypesMatch(inputToInputWeightsInfo, recurrentToCellWeightsInfo, descriptorName,</div><div class="line"><a name="l02870"></a><span class="lineno"> 2870</span>  <span class="stringliteral">"inputToInputWeights"</span>, <span class="stringliteral">"recurrentToCellWeights"</span>);</div><div class="line"><a name="l02871"></a><span class="lineno"> 2871</span>  ValidateTensorDataTypesMatch(inputToInputWeightsInfo, recurrentToOutputWeightsInfo, descriptorName,</div><div class="line"><a name="l02872"></a><span class="lineno"> 2872</span>  <span class="stringliteral">"inputToInputWeights"</span>, <span class="stringliteral">"recurrentToOutputWeights"</span>);</div><div class="line"><a name="l02873"></a><span class="lineno"> 2873</span> </div><div class="line"><a name="l02874"></a><span class="lineno"> 2874</span>  <span class="comment">// Validate matching quantization info for weight tensors (all should match each other)</span></div><div class="line"><a name="l02875"></a><span class="lineno"> 2875</span>  ValidateTensorQuantizationSpace(inputToInputWeightsInfo, inputToForgetWeightsInfo,</div><div class="line"><a name="l02876"></a><span class="lineno"> 2876</span>  descriptorName, <span class="stringliteral">"inputToInputWeights"</span>, <span class="stringliteral">"inputToForgetWeights"</span>);</div><div class="line"><a name="l02877"></a><span class="lineno"> 2877</span>  ValidateTensorQuantizationSpace(inputToInputWeightsInfo, inputToCellWeightsInfo,</div><div class="line"><a name="l02878"></a><span class="lineno"> 2878</span>  descriptorName, <span class="stringliteral">"inputToInputWeights"</span>, <span class="stringliteral">"inputToCellWeights"</span>);</div><div class="line"><a name="l02879"></a><span class="lineno"> 2879</span>  ValidateTensorQuantizationSpace(inputToInputWeightsInfo, inputToOutputWeightsInfo,</div><div class="line"><a name="l02880"></a><span class="lineno"> 2880</span>  descriptorName, <span class="stringliteral">"inputToInputWeights"</span>, <span class="stringliteral">"inputToOutputWeights"</span>);</div><div class="line"><a name="l02881"></a><span class="lineno"> 2881</span> </div><div class="line"><a name="l02882"></a><span class="lineno"> 2882</span>  ValidateTensorQuantizationSpace(inputToInputWeightsInfo, recurrentToInputWeightsInfo,</div><div class="line"><a name="l02883"></a><span class="lineno"> 2883</span>  descriptorName, <span class="stringliteral">"inputToInputWeights"</span>, <span class="stringliteral">"recurrentToInputWeights"</span>);</div><div class="line"><a name="l02884"></a><span class="lineno"> 2884</span>  ValidateTensorQuantizationSpace(inputToInputWeightsInfo, recurrentToForgetWeightsInfo,</div><div class="line"><a name="l02885"></a><span class="lineno"> 2885</span>  descriptorName, <span class="stringliteral">"inputToInputWeights"</span>, <span class="stringliteral">"recurrentToForgetWeights"</span>);</div><div class="line"><a name="l02886"></a><span class="lineno"> 2886</span>  ValidateTensorQuantizationSpace(inputToInputWeightsInfo, recurrentToCellWeightsInfo,</div><div class="line"><a name="l02887"></a><span class="lineno"> 2887</span>  descriptorName, <span class="stringliteral">"inputToInputWeights"</span>, <span class="stringliteral">"recurrentToCellWeights"</span>);</div><div class="line"><a name="l02888"></a><span class="lineno"> 2888</span>  ValidateTensorQuantizationSpace(inputToInputWeightsInfo, recurrentToOutputWeightsInfo,</div><div class="line"><a name="l02889"></a><span class="lineno"> 2889</span>  descriptorName, <span class="stringliteral">"inputToInputWeights"</span>, <span class="stringliteral">"recurrentToOutputWeights"</span>);</div><div class="line"><a name="l02890"></a><span class="lineno"> 2890</span> </div><div class="line"><a name="l02891"></a><span class="lineno"> 2891</span>  <span class="comment">// Validate number of dimensions and number of elements in bias tensors</span></div><div class="line"><a name="l02892"></a><span class="lineno"> 2892</span>  ValidatePointer(m_InputGateBias, descriptorName, <span class="stringliteral">"InputGateBias"</span>);</div><div class="line"><a name="l02893"></a><span class="lineno"> 2893</span>  <span class="keyword">auto</span> inputGateBiasInfo = m_InputGateBias->GetTensorInfo();</div><div class="line"><a name="l02894"></a><span class="lineno"> 2894</span>  ValidateTensorNumDimNumElem(inputGateBiasInfo, 1, outputSize, <span class="stringliteral">" InputGateBias"</span>);</div><div class="line"><a name="l02895"></a><span class="lineno"> 2895</span> </div><div class="line"><a name="l02896"></a><span class="lineno"> 2896</span>  ValidatePointer(m_ForgetGateBias, descriptorName, <span class="stringliteral">"ForgetGateBias"</span>);</div><div class="line"><a name="l02897"></a><span class="lineno"> 2897</span>  <span class="keyword">auto</span> forgetGateBiasInfo = m_ForgetGateBias->GetTensorInfo();</div><div class="line"><a name="l02898"></a><span class="lineno"> 2898</span>  ValidateTensorNumDimNumElem(forgetGateBiasInfo, 1, outputSize, <span class="stringliteral">" ForgetGateBias"</span>);</div><div class="line"><a name="l02899"></a><span class="lineno"> 2899</span> </div><div class="line"><a name="l02900"></a><span class="lineno"> 2900</span>  ValidatePointer(m_CellBias, descriptorName, <span class="stringliteral">"CellBias"</span>);</div><div class="line"><a name="l02901"></a><span class="lineno"> 2901</span>  <span class="keyword">auto</span> cellBiasInfo = m_CellBias->GetTensorInfo();</div><div class="line"><a name="l02902"></a><span class="lineno"> 2902</span>  ValidateTensorNumDimNumElem(cellBiasInfo, 1, outputSize, <span class="stringliteral">" CellBias"</span>);</div><div class="line"><a name="l02903"></a><span class="lineno"> 2903</span> </div><div class="line"><a name="l02904"></a><span class="lineno"> 2904</span>  ValidatePointer(m_OutputGateBias, descriptorName, <span class="stringliteral">"OutputGateBias"</span>);</div><div class="line"><a name="l02905"></a><span class="lineno"> 2905</span>  <span class="keyword">auto</span> outputGateBiasInfo = m_OutputGateBias->GetTensorInfo();</div><div class="line"><a name="l02906"></a><span class="lineno"> 2906</span>  ValidateTensorNumDimNumElem(outputGateBiasInfo, 1, outputSize, <span class="stringliteral">" OutputGateBias"</span>);</div><div class="line"><a name="l02907"></a><span class="lineno"> 2907</span> </div><div class="line"><a name="l02908"></a><span class="lineno"> 2908</span>  <span class="comment">// Validate data types for bias tensors (all should match each other)</span></div><div class="line"><a name="l02909"></a><span class="lineno"> 2909</span>  ValidateDataTypes(inputGateBiasInfo, biasSupportedTypes, descriptorName);</div><div class="line"><a name="l02910"></a><span class="lineno"> 2910</span> </div><div class="line"><a name="l02911"></a><span class="lineno"> 2911</span>  ValidateTensorDataTypesMatch(inputGateBiasInfo, forgetGateBiasInfo, descriptorName,</div><div class="line"><a name="l02912"></a><span class="lineno"> 2912</span>  <span class="stringliteral">"inputGateBias"</span>, <span class="stringliteral">"forgetGateBias"</span>);</div><div class="line"><a name="l02913"></a><span class="lineno"> 2913</span>  ValidateTensorDataTypesMatch(inputGateBiasInfo, cellBiasInfo, descriptorName,</div><div class="line"><a name="l02914"></a><span class="lineno"> 2914</span>  <span class="stringliteral">"inputGateBias"</span>, <span class="stringliteral">"cellBias"</span>);</div><div class="line"><a name="l02915"></a><span class="lineno"> 2915</span>  ValidateTensorDataTypesMatch(inputGateBiasInfo, outputGateBiasInfo, descriptorName,</div><div class="line"><a name="l02916"></a><span class="lineno"> 2916</span>  <span class="stringliteral">"inputGateBias"</span>, <span class="stringliteral">"outputGateBias"</span>);</div><div class="line"><a name="l02917"></a><span class="lineno"> 2917</span> </div><div class="line"><a name="l02918"></a><span class="lineno"> 2918</span>  <span class="comment">// Validate bias tensor quantization info</span></div><div class="line"><a name="l02919"></a><span class="lineno"> 2919</span>  ValidateBiasTensorQuantization(inputGateBiasInfo, inputInfo, inputToInputWeightsInfo, descriptorName);</div><div class="line"><a name="l02920"></a><span class="lineno"> 2920</span>  ValidateBiasTensorQuantization(forgetGateBiasInfo, inputInfo, inputToInputWeightsInfo, descriptorName);</div><div class="line"><a name="l02921"></a><span class="lineno"> 2921</span>  ValidateBiasTensorQuantization(cellBiasInfo, inputInfo, inputToInputWeightsInfo, descriptorName);</div><div class="line"><a name="l02922"></a><span class="lineno"> 2922</span>  ValidateBiasTensorQuantization(outputGateBiasInfo, inputInfo, inputToInputWeightsInfo, descriptorName);</div><div class="line"><a name="l02923"></a><span class="lineno"> 2923</span> }</div><div class="line"><a name="l02924"></a><span class="lineno"> 2924</span> </div><div class="line"><a name="l02925"></a><span class="lineno"><a class="line" href="structarmnn_1_1_abs_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2925</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_abs_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">AbsQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02926"></a><span class="lineno"> 2926</span> <span class="keyword"></span>{</div><div class="line"><a name="l02927"></a><span class="lineno"> 2927</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"AbsQueueDescriptor"</span>};</div><div class="line"><a name="l02928"></a><span class="lineno"> 2928</span> </div><div class="line"><a name="l02929"></a><span class="lineno"> 2929</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02930"></a><span class="lineno"> 2930</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02931"></a><span class="lineno"> 2931</span> </div><div class="line"><a name="l02932"></a><span class="lineno"> 2932</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02933"></a><span class="lineno"> 2933</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02934"></a><span class="lineno"> 2934</span> </div><div class="line"><a name="l02935"></a><span class="lineno"> 2935</span>  ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02936"></a><span class="lineno"> 2936</span> </div><div class="line"><a name="l02937"></a><span class="lineno"> 2937</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l02938"></a><span class="lineno"> 2938</span>  {</div><div class="line"><a name="l02939"></a><span class="lineno"> 2939</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l02940"></a><span class="lineno"> 2940</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l02941"></a><span class="lineno"> 2941</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l02942"></a><span class="lineno"> 2942</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l02943"></a><span class="lineno"> 2943</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l02944"></a><span class="lineno"> 2944</span>  };</div><div class="line"><a name="l02945"></a><span class="lineno"> 2945</span> </div><div class="line"><a name="l02946"></a><span class="lineno"> 2946</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l02947"></a><span class="lineno"> 2947</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02948"></a><span class="lineno"> 2948</span> }</div><div class="line"><a name="l02949"></a><span class="lineno"> 2949</span> </div><div class="line"><a name="l02950"></a><span class="lineno"><a class="line" href="structarmnn_1_1_slice_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 2950</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_slice_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">SliceQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l02951"></a><span class="lineno"> 2951</span> <span class="keyword"></span>{</div><div class="line"><a name="l02952"></a><span class="lineno"> 2952</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"SliceQueueDescriptor"</span>};</div><div class="line"><a name="l02953"></a><span class="lineno"> 2953</span> </div><div class="line"><a name="l02954"></a><span class="lineno"> 2954</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02955"></a><span class="lineno"> 2955</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l02956"></a><span class="lineno"> 2956</span> </div><div class="line"><a name="l02957"></a><span class="lineno"> 2957</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l02958"></a><span class="lineno"> 2958</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l02959"></a><span class="lineno"> 2959</span> </div><div class="line"><a name="l02960"></a><span class="lineno"> 2960</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02961"></a><span class="lineno"> 2961</span> </div><div class="line"><a name="l02962"></a><span class="lineno"> 2962</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> rank = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l02963"></a><span class="lineno"> 2963</span>  <span class="keywordflow">if</span> (rank > 4)</div><div class="line"><a name="l02964"></a><span class="lineno"> 2964</span>  {</div><div class="line"><a name="l02965"></a><span class="lineno"> 2965</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Input tensors with rank greater than 4 are not supported."</span>);</div><div class="line"><a name="l02966"></a><span class="lineno"> 2966</span>  }</div><div class="line"><a name="l02967"></a><span class="lineno"> 2967</span> </div><div class="line"><a name="l02968"></a><span class="lineno"> 2968</span>  ValidateTensorNumDimensions(outputTensorInfo, descriptorName, rank, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l02969"></a><span class="lineno"> 2969</span> </div><div class="line"><a name="l02970"></a><span class="lineno"> 2970</span>  <span class="comment">// Check if m_Begin and m_Size have the expected length</span></div><div class="line"><a name="l02971"></a><span class="lineno"> 2971</span>  <span class="keywordflow">if</span> (m_Parameters.m_Begin.size() != rank)</div><div class="line"><a name="l02972"></a><span class="lineno"> 2972</span>  {</div><div class="line"><a name="l02973"></a><span class="lineno"> 2973</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName +</div><div class="line"><a name="l02974"></a><span class="lineno"> 2974</span>  <span class="stringliteral">": Length of begin offset descriptor must equal rank "</span> + std::to_string(rank));</div><div class="line"><a name="l02975"></a><span class="lineno"> 2975</span>  }</div><div class="line"><a name="l02976"></a><span class="lineno"> 2976</span>  <span class="keywordflow">if</span> (m_Parameters.m_Size.size() != rank)</div><div class="line"><a name="l02977"></a><span class="lineno"> 2977</span>  {</div><div class="line"><a name="l02978"></a><span class="lineno"> 2978</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName +</div><div class="line"><a name="l02979"></a><span class="lineno"> 2979</span>  <span class="stringliteral">": Length of size descriptor must equal rank "</span> + std::to_string(rank));</div><div class="line"><a name="l02980"></a><span class="lineno"> 2980</span>  }</div><div class="line"><a name="l02981"></a><span class="lineno"> 2981</span> </div><div class="line"><a name="l02982"></a><span class="lineno"> 2982</span>  <span class="comment">// Check if the shape of the output tensor matches m_Size</span></div><div class="line"><a name="l02983"></a><span class="lineno"> 2983</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape = outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l02984"></a><span class="lineno"> 2984</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0u; i < rank; ++i)</div><div class="line"><a name="l02985"></a><span class="lineno"> 2985</span>  {</div><div class="line"><a name="l02986"></a><span class="lineno"> 2986</span>  <span class="keywordflow">if</span> (m_Parameters.m_Size[i] != outputShape[i])</div><div class="line"><a name="l02987"></a><span class="lineno"> 2987</span>  {</div><div class="line"><a name="l02988"></a><span class="lineno"> 2988</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Size descriptor does not match output tensor."</span>);</div><div class="line"><a name="l02989"></a><span class="lineno"> 2989</span>  }</div><div class="line"><a name="l02990"></a><span class="lineno"> 2990</span>  }</div><div class="line"><a name="l02991"></a><span class="lineno"> 2991</span> </div><div class="line"><a name="l02992"></a><span class="lineno"> 2992</span>  <span class="comment">// Check if the sum of begin offset and size in a given dimension</span></div><div class="line"><a name="l02993"></a><span class="lineno"> 2993</span>  <span class="comment">// does not exceed the size of corresponding input</span></div><div class="line"><a name="l02994"></a><span class="lineno"> 2994</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape = inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l02995"></a><span class="lineno"> 2995</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0u; i < rank; ++i)</div><div class="line"><a name="l02996"></a><span class="lineno"> 2996</span>  {</div><div class="line"><a name="l02997"></a><span class="lineno"> 2997</span>  <span class="keywordflow">if</span> (m_Parameters.m_Begin[i] + m_Parameters.m_Size[i] > inputShape[i])</div><div class="line"><a name="l02998"></a><span class="lineno"> 2998</span>  {</div><div class="line"><a name="l02999"></a><span class="lineno"> 2999</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Sum of begin offset and size for dimension "</span> +</div><div class="line"><a name="l03000"></a><span class="lineno"> 3000</span>  std::to_string(i) + <span class="stringliteral">" exceeds input size."</span>);</div><div class="line"><a name="l03001"></a><span class="lineno"> 3001</span>  }</div><div class="line"><a name="l03002"></a><span class="lineno"> 3002</span>  }</div><div class="line"><a name="l03003"></a><span class="lineno"> 3003</span> }</div><div class="line"><a name="l03004"></a><span class="lineno"> 3004</span> </div><div class="line"><a name="l03005"></a><span class="lineno"><a class="line" href="structarmnn_1_1_depth_to_space_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 3005</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_depth_to_space_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">DepthToSpaceQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l03006"></a><span class="lineno"> 3006</span> <span class="keyword"></span>{</div><div class="line"><a name="l03007"></a><span class="lineno"> 3007</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"DepthToSpaceQueueDescriptor"</span>};</div><div class="line"><a name="l03008"></a><span class="lineno"> 3008</span> </div><div class="line"><a name="l03009"></a><span class="lineno"> 3009</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l03010"></a><span class="lineno"> 3010</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l03011"></a><span class="lineno"> 3011</span> </div><div class="line"><a name="l03012"></a><span class="lineno"> 3012</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l03013"></a><span class="lineno"> 3013</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l03014"></a><span class="lineno"> 3014</span> </div><div class="line"><a name="l03015"></a><span class="lineno"> 3015</span>  ValidateTensorNumDimensions(inputInfo, descriptorName, 4, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l03016"></a><span class="lineno"> 3016</span>  ValidateTensorNumDimensions(outputInfo, descriptorName, 4, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l03017"></a><span class="lineno"> 3017</span> </div><div class="line"><a name="l03018"></a><span class="lineno"> 3018</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l03019"></a><span class="lineno"> 3019</span>  {</div><div class="line"><a name="l03020"></a><span class="lineno"> 3020</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l03021"></a><span class="lineno"> 3021</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l03022"></a><span class="lineno"> 3022</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l03023"></a><span class="lineno"> 3023</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l03024"></a><span class="lineno"> 3024</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l03025"></a><span class="lineno"> 3025</span>  };</div><div class="line"><a name="l03026"></a><span class="lineno"> 3026</span> </div><div class="line"><a name="l03027"></a><span class="lineno"> 3027</span>  ValidateDataTypes(inputInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l03028"></a><span class="lineno"> 3028</span>  ValidateDataTypes(outputInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l03029"></a><span class="lineno"> 3029</span> </div><div class="line"><a name="l03030"></a><span class="lineno"> 3030</span>  ValidateTensorNumElementsMatch(inputInfo, outputInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l03031"></a><span class="lineno"> 3031</span> </div><div class="line"><a name="l03032"></a><span class="lineno"> 3032</span>  <span class="keywordflow">if</span> (m_Parameters.m_BlockSize == 0)</div><div class="line"><a name="l03033"></a><span class="lineno"> 3033</span>  {</div><div class="line"><a name="l03034"></a><span class="lineno"> 3034</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Block size cannot be 0."</span>);</div><div class="line"><a name="l03035"></a><span class="lineno"> 3035</span>  }</div><div class="line"><a name="l03036"></a><span class="lineno"> 3036</span> </div><div class="line"><a name="l03037"></a><span class="lineno"> 3037</span>  <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">DataLayoutIndexed</a> dimensionIndices(m_Parameters.m_DataLayout);</div><div class="line"><a name="l03038"></a><span class="lineno"> 3038</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> wIndex = dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>();</div><div class="line"><a name="l03039"></a><span class="lineno"> 3039</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> hIndex = dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>();</div><div class="line"><a name="l03040"></a><span class="lineno"> 3040</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> cIndex = dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">GetChannelsIndex</a>();</div><div class="line"><a name="l03041"></a><span class="lineno"> 3041</span> </div><div class="line"><a name="l03042"></a><span class="lineno"> 3042</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& outputShape = outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l03043"></a><span class="lineno"> 3043</span>  <span class="keywordflow">if</span> (outputShape[hIndex] % m_Parameters.m_BlockSize != 0 || outputShape[wIndex] % m_Parameters.m_BlockSize != 0)</div><div class="line"><a name="l03044"></a><span class="lineno"> 3044</span>  {</div><div class="line"><a name="l03045"></a><span class="lineno"> 3045</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Output width and height shape"</span></div><div class="line"><a name="l03046"></a><span class="lineno"> 3046</span>  <span class="stringliteral">"must be divisible by block size."</span>);</div><div class="line"><a name="l03047"></a><span class="lineno"> 3047</span>  }</div><div class="line"><a name="l03048"></a><span class="lineno"> 3048</span> </div><div class="line"><a name="l03049"></a><span class="lineno"> 3049</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape = inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>();</div><div class="line"><a name="l03050"></a><span class="lineno"> 3050</span>  <span class="keywordflow">if</span> (inputShape[cIndex] % (m_Parameters.m_BlockSize * m_Parameters.m_BlockSize) != 0)</div><div class="line"><a name="l03051"></a><span class="lineno"> 3051</span>  {</div><div class="line"><a name="l03052"></a><span class="lineno"> 3052</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": The depth of the input tensor"</span></div><div class="line"><a name="l03053"></a><span class="lineno"> 3053</span>  <span class="stringliteral">"must be divisible by the square of block size."</span> );</div><div class="line"><a name="l03054"></a><span class="lineno"> 3054</span>  }</div><div class="line"><a name="l03055"></a><span class="lineno"> 3055</span> }</div><div class="line"><a name="l03056"></a><span class="lineno"> 3056</span> </div><div class="line"><a name="l03057"></a><span class="lineno"><a class="line" href="structarmnn_1_1_comparison_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 3057</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_comparison_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ComparisonQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l03058"></a><span class="lineno"> 3058</span> <span class="keyword"></span>{</div><div class="line"><a name="l03059"></a><span class="lineno"> 3059</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"ComparisonQueueDescriptor"</span>};</div><div class="line"><a name="l03060"></a><span class="lineno"> 3060</span> </div><div class="line"><a name="l03061"></a><span class="lineno"> 3061</span>  ValidateNumInputs(workloadInfo, descriptorName, 2);</div><div class="line"><a name="l03062"></a><span class="lineno"> 3062</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l03063"></a><span class="lineno"> 3063</span> </div><div class="line"><a name="l03064"></a><span class="lineno"> 3064</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo0 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l03065"></a><span class="lineno"> 3065</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo1 = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[1];</div><div class="line"><a name="l03066"></a><span class="lineno"> 3066</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l03067"></a><span class="lineno"> 3067</span> </div><div class="line"><a name="l03068"></a><span class="lineno"> 3068</span>  ValidateBroadcastTensorShapesMatch(inputTensorInfo0,</div><div class="line"><a name="l03069"></a><span class="lineno"> 3069</span>  inputTensorInfo1,</div><div class="line"><a name="l03070"></a><span class="lineno"> 3070</span>  outputTensorInfo,</div><div class="line"><a name="l03071"></a><span class="lineno"> 3071</span>  descriptorName,</div><div class="line"><a name="l03072"></a><span class="lineno"> 3072</span>  <span class="stringliteral">"input_0"</span>,</div><div class="line"><a name="l03073"></a><span class="lineno"> 3073</span>  <span class="stringliteral">"input_1"</span>);</div><div class="line"><a name="l03074"></a><span class="lineno"> 3074</span> </div><div class="line"><a name="l03075"></a><span class="lineno"> 3075</span>  <span class="keywordflow">if</span> (outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>() != <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">DataType::Boolean</a>)</div><div class="line"><a name="l03076"></a><span class="lineno"> 3076</span>  {</div><div class="line"><a name="l03077"></a><span class="lineno"> 3077</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>(descriptorName + <span class="stringliteral">": Output tensor type must be Boolean."</span>);</div><div class="line"><a name="l03078"></a><span class="lineno"> 3078</span>  }</div><div class="line"><a name="l03079"></a><span class="lineno"> 3079</span> }</div><div class="line"><a name="l03080"></a><span class="lineno"> 3080</span> </div><div class="line"><a name="l03081"></a><span class="lineno"><a class="line" href="structarmnn_1_1_elementwise_unary_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf"> 3081</a></span> <span class="keywordtype">void</span> <a class="code" href="structarmnn_1_1_elementwise_unary_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">ElementwiseUnaryQueueDescriptor::Validate</a>(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& workloadInfo)<span class="keyword"> const</span></div><div class="line"><a name="l03082"></a><span class="lineno"> 3082</span> <span class="keyword"></span>{</div><div class="line"><a name="l03083"></a><span class="lineno"> 3083</span>  <span class="keyword">const</span> std::string descriptorName{<span class="stringliteral">"ElementwiseUnaryQueueDescriptor"</span>};</div><div class="line"><a name="l03084"></a><span class="lineno"> 3084</span> </div><div class="line"><a name="l03085"></a><span class="lineno"> 3085</span>  ValidateNumInputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l03086"></a><span class="lineno"> 3086</span>  ValidateNumOutputs(workloadInfo, descriptorName, 1);</div><div class="line"><a name="l03087"></a><span class="lineno"> 3087</span> </div><div class="line"><a name="l03088"></a><span class="lineno"> 3088</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a>[0];</div><div class="line"><a name="l03089"></a><span class="lineno"> 3089</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo = workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a>[0];</div><div class="line"><a name="l03090"></a><span class="lineno"> 3090</span> </div><div class="line"><a name="l03091"></a><span class="lineno"> 3091</span>  ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l03092"></a><span class="lineno"> 3092</span> </div><div class="line"><a name="l03093"></a><span class="lineno"> 3093</span>  std::vector<DataType> supportedTypes =</div><div class="line"><a name="l03094"></a><span class="lineno"> 3094</span>  {</div><div class="line"><a name="l03095"></a><span class="lineno"> 3095</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">DataType::BFloat16</a>,</div><div class="line"><a name="l03096"></a><span class="lineno"> 3096</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">DataType::Float16</a>,</div><div class="line"><a name="l03097"></a><span class="lineno"> 3097</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>,</div><div class="line"><a name="l03098"></a><span class="lineno"> 3098</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>,</div><div class="line"><a name="l03099"></a><span class="lineno"> 3099</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a></div><div class="line"><a name="l03100"></a><span class="lineno"> 3100</span>  };</div><div class="line"><a name="l03101"></a><span class="lineno"> 3101</span> </div><div class="line"><a name="l03102"></a><span class="lineno"> 3102</span>  ValidateDataTypes(inputTensorInfo, supportedTypes, descriptorName);</div><div class="line"><a name="l03103"></a><span class="lineno"> 3103</span>  ValidateTensorDataTypesMatch(inputTensorInfo, outputTensorInfo, descriptorName, <span class="stringliteral">"input"</span>, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l03104"></a><span class="lineno"> 3104</span> }</div><div class="line"><a name="l03105"></a><span class="lineno"> 3105</span> </div><div class="line"><a name="l03106"></a><span class="lineno"> 3106</span> } <span class="comment">// namespace armnn</span></div><div class="ttc" id="structarmnn_1_1_permute_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_permute_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::PermuteQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01287">WorkloadData.cpp:1287</a></div></div> +<div class="ttc" id="structarmnn_1_1_division_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_division_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::DivisionQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02065">WorkloadData.cpp:2065</a></div></div> +<div class="ttc" id="structarmnn_1_1_merge_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_merge_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::MergeQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02575">WorkloadData.cpp:2575</a></div></div> +<div class="ttc" id="_workload_data_8hpp_xhtml"><div class="ttname"><a href="_workload_data_8hpp.xhtml">WorkloadData.hpp</a></div></div> +<div class="ttc" id="_data_layout_indexed_8hpp_xhtml"><div class="ttname"><a href="_data_layout_indexed_8hpp.xhtml">DataLayoutIndexed.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_subtraction_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_subtraction_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::SubtractionQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02097">WorkloadData.cpp:2097</a></div></div> +<div class="ttc" id="structarmnn_1_1_log_softmax_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_log_softmax_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::LogSoftmaxQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01521">WorkloadData.cpp:1521</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a22f377fc4e10dc1773a3f979061e85f1"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a22f377fc4e10dc1773a3f979061e85f1">armnn::TensorInfo::IsTypeSpaceMatch</a></div><div class="ttdeci">bool IsTypeSpaceMatch(const TensorInfo &other) const</div><div class="ttdoc">Check that the types are the same and, if quantize, that the quantization parameters are the same...</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00218">Tensor.cpp:218</a></div></div> +<div class="ttc" id="structarmnn_1_1_instance_normalization_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_instance_normalization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::InstanceNormalizationQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01461">WorkloadData.cpp:1461</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a27226c864bac7454a8504f8edb15d95b">armnn::DataType::Boolean</a></div></div> +<div class="ttc" id="structarmnn_1_1_mem_sync_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_mem_sync_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::MemSyncQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00566">WorkloadData.cpp:566</a></div></div> +<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml_a414e6f95548e6f7a01d5028b55ad3941"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">armnnUtils::DataLayoutIndexed::GetWidthIndex</a></div><div class="ttdeci">unsigned int GetWidthIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00025">DataLayoutIndexed.hpp:25</a></div></div> +<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin_xhtml_af2f0a8c9eb32861711c0ce30b7986c44"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml#af2f0a8c9eb32861711c0ce30b7986c44">armnn::ConcatQueueDescriptor::ViewOrigin::m_Origin</a></div><div class="ttdeci">std::vector< unsigned int > m_Origin</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00110">WorkloadData.hpp:110</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00088">Tensor.hpp:88</a></div></div> +<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin_xhtml"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">armnn::ConcatQueueDescriptor::ViewOrigin</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00104">WorkloadData.hpp:104</a></div></div> +<div class="ttc" id="classarmnn_1_1_optional_xhtml"><div class="ttname"><a href="classarmnn_1_1_optional.xhtml">armnn::Optional< unsigned int ></a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad44c007f21af2d0375e3ef9400a1b275"><div class="ttname"><a href="namespacearmnn.xhtml#ad44c007f21af2d0375e3ef9400a1b275">armnn::IsQuantizedType</a></div><div class="ttdeci">constexpr bool IsQuantizedType()</div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8hpp_source.xhtml#l00236">TypesUtils.hpp:236</a></div></div> +<div class="ttc" id="_deprecated_8hpp_xhtml_ab66a241a0ed3ee89c866e777b035d0ed"><div class="ttname"><a href="_deprecated_8hpp.xhtml#ab66a241a0ed3ee89c866e777b035d0ed">ARMNN_NO_DEPRECATE_WARN_BEGIN</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_BEGIN</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.xhtml#l00033">Deprecated.hpp:33</a></div></div> +<div class="ttc" id="structarmnn_1_1_mean_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_mean_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::MeanQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02163">WorkloadData.cpp:2163</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_abs_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_abs_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::AbsQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02925">WorkloadData.cpp:2925</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_normalization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::NormalizationQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01015">WorkloadData.cpp:1015</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_transpose_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::TransposeQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02725">WorkloadData.cpp:2725</a></div></div> +<div class="ttc" id="structarmnn_1_1_space_to_depth_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_space_to_depth_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::SpaceToDepthQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01669">WorkloadData.cpp:1669</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_ab85cd8cc10c96a7c99c14042c251fc48"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#ab85cd8cc10c96a7c99c14042c251fc48">armnn::TensorInfo::HasPerAxisQuantization</a></div><div class="ttdeci">bool HasPerAxisQuantization() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00232">Tensor.cpp:232</a></div></div> +<div class="ttc" id="structarmnn_1_1_minimum_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_minimum_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::MinimumQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02336">WorkloadData.cpp:2336</a></div></div> +<div class="ttc" id="structarmnn_1_1_floor_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_floor_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::FloorQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01721">WorkloadData.cpp:1721</a></div></div> +<div class="ttc" id="structarmnn_1_1_gather_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_gather_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::GatherQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02450">WorkloadData.cpp:2450</a></div></div> +<div class="ttc" id="structarmnn_1_1_fake_quantization_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_fake_quantization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::FakeQuantizationQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01440">WorkloadData.cpp:1440</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div> +<div class="ttc" id="structarmnn_1_1_space_to_batch_nd_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_space_to_batch_nd_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::SpaceToBatchNdQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01603">WorkloadData.cpp:1603</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::BatchNormalizationQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01114">WorkloadData.cpp:1114</a></div></div> +<div class="ttc" id="structarmnn_1_1_detection_post_process_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_detection_post_process_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::DetectionPostProcessQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02483">WorkloadData.cpp:2483</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b8fc85ce966c035d789cf22db5088a1"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b8fc85ce966c035d789cf22db5088a1">armnn::TensorInfo::GetQuantizationDim</a></div><div class="ttdeci">Optional< unsigned int > GetQuantizationDim() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00280">Tensor.cpp:280</a></div></div> +<div class="ttc" id="structarmnn_1_1_maximum_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_maximum_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::MaximumQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02129">WorkloadData.cpp:2129</a></div></div> +<div class="ttc" id="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin_xhtml"><div class="ttname"><a href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml">armnn::SplitterQueueDescriptor::ViewOrigin</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00085">WorkloadData.hpp:85</a></div></div> +<div class="ttc" id="structarmnn_1_1_equal_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_equal_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::EqualQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02377">WorkloadData.cpp:2377</a></div></div> +<div class="ttc" id="structarmnn_1_1_mem_import_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_mem_import_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::MemImportQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00506">WorkloadData.cpp:506</a></div></div> +<div class="ttc" id="_neon_end_to_end_tests_8cpp_xhtml_a358cb7cd3c0647b25be049fd734b8c22"><div class="ttname"><a href="_neon_end_to_end_tests_8cpp.xhtml#a358cb7cd3c0647b25be049fd734b8c22">anchorsInfo</a></div><div class="ttdeci">armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::Float32)</div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9d02ea768c081d4bdb2b7cab0b3f510d">armnn::DataType::QAsymmS8</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div></div> +<div class="ttc" id="structarmnn_1_1_queue_descriptor_xhtml_a765d2cee4ccce5b9467e0c2b6d25b84a"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml#a765d2cee4ccce5b9467e0c2b6d25b84a">armnn::QueueDescriptor::ValidateInputsOutputs</a></div><div class="ttdeci">void ValidateInputsOutputs(const std::string &descName, unsigned int numExpectedIn, unsigned int numExpectedOut) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00461">WorkloadData.cpp:461</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="structarmnn_1_1_strided_slice_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_strided_slice_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::StridedSliceQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02280">WorkloadData.cpp:2280</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div> +<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml_a490ec6b59006d1fe1ec2ea30e69fb97c"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml#a490ec6b59006d1fe1ec2ea30e69fb97c">armnn::PermutationVector::GetSize</a></div><div class="ttdeci">SizeType GetSize() const</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00202">Types.hpp:202</a></div></div> +<div class="ttc" id="classarmnn_1_1_optional_reference_switch_xhtml_a77c7d528ac063d870b8c8426ec81c1c3"><div class="ttname"><a href="classarmnn_1_1_optional_reference_switch.xhtml#a77c7d528ac063d870b8c8426ec81c1c3">armnn::OptionalReferenceSwitch::value</a></div><div class="ttdeci">const T & value() const</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00146">Optional.hpp:146</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8bc11f1fa23ef42532f9fdd04d355270"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8bc11f1fa23ef42532f9fdd04d355270">armnn::TensorInfo::GetQuantizationScales</a></div><div class="ttdeci">std::vector< float > GetQuantizationScales() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00237">Tensor.cpp:237</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_af672d1c9e2a120a18926cb645981fbb7"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#af672d1c9e2a120a18926cb645981fbb7">armnn::TensorInfo::HasMultipleQuantizationScales</a></div><div class="ttdeci">bool HasMultipleQuantizationScales() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00098">Tensor.hpp:98</a></div></div> +<div class="ttc" id="structarmnn_1_1_debug_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_debug_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::DebugQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02369">WorkloadData.cpp:2369</a></div></div> +<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml_a61c00316c443adc233c24e85c6c5b740"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">armnnUtils::DataLayoutIndexed::GetHeightIndex</a></div><div class="ttdeci">unsigned int GetHeightIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00024">DataLayoutIndexed.hpp:24</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::LstmQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01747">WorkloadData.cpp:1747</a></div></div> +<div class="ttc" id="structarmnn_1_1_splitter_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_splitter_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::SplitterQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00705">WorkloadData.cpp:705</a></div></div> +<div class="ttc" id="structarmnn_1_1_convert_fp16_to_fp32_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_convert_fp16_to_fp32_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ConvertFp16ToFp32QueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02042">WorkloadData.cpp:2042</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a81b5ff8545adad19a1c9d4ca076d552c"><div class="ttname"><a href="namespacearmnn.xhtml#a81b5ff8545adad19a1c9d4ca076d552c">armnn::GetDataTypeName</a></div><div class="ttdeci">constexpr const char * GetDataTypeName(DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8hpp_source.xhtml#l00168">TypesUtils.hpp:168</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad91bc7bfe29186f5d78c28386c6c5309"><div class="ttname"><a href="namespacearmnn.xhtml#ad91bc7bfe29186f5d78c28386c6c5309">armnn::IsQuantized8BitType</a></div><div class="ttdeci">constexpr bool IsQuantized8BitType(DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_types_utils_8hpp_source.xhtml#l00241">TypesUtils.hpp:241</a></div></div> +<div class="ttc" id="structarmnn_1_1_activation_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_activation_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ActivationQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00592">WorkloadData.cpp:592</a></div></div> +<div class="ttc" id="structarmnn_1_1_workload_info_xhtml_ac97905bfa0daab357b91df1347600309"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">armnn::WorkloadInfo::m_InputTensorInfos</a></div><div class="ttdeci">std::vector< TensorInfo > m_InputTensorInfos</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00018">WorkloadInfo.hpp:18</a></div></div> +<div class="ttc" id="structarmnn_1_1_transpose_convolution2d_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_transpose_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::TransposeConvolution2dQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02674">WorkloadData.cpp:2674</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="_deprecated_8hpp_xhtml_ad762b11b48e5c1d1c1743f529485728a"><div class="ttname"><a href="_deprecated_8hpp.xhtml#ad762b11b48e5c1d1c1743f529485728a">ARMNN_NO_DEPRECATE_WARN_END</a></div><div class="ttdeci">#define ARMNN_NO_DEPRECATE_WARN_END</div><div class="ttdef"><b>Definition:</b> <a href="_deprecated_8hpp_source.xhtml#l00034">Deprecated.hpp:34</a></div></div> +<div class="ttc" id="structarmnn_1_1_slice_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_slice_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::SliceQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02950">WorkloadData.cpp:2950</a></div></div> +<div class="ttc" id="structarmnn_1_1_multiplication_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_multiplication_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::MultiplicationQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01078">WorkloadData.cpp:1078</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a770b51078da02f44a819e9f95d8058b5"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">armnn::TensorInfo::GetQuantizationOffset</a></div><div class="ttdeci">int32_t GetQuantizationOffset() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00264">Tensor.cpp:264</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a047ca888c43bd7fb5702853bf72410d0"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">armnn::TensorInfo::GetQuantizationScale</a></div><div class="ttdeci">float GetQuantizationScale() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00247">Tensor.cpp:247</a></div></div> +<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml">armnnUtils::DataLayoutIndexed</a></div><div class="ttdoc">Provides access to the appropriate indexes for Channels, Height and Width based on DataLayout...</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00017">DataLayoutIndexed.hpp:17</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#aea909c7327109228ef618d459015def3">armnn::TensorInfo::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00095">Tensor.hpp:95</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div> +<div class="ttc" id="classarmnn_1_1_optional_base_xhtml_a86b749ce2c4bc627fa8a1fcfaf0e314f"><div class="ttname"><a href="classarmnn_1_1_optional_base.xhtml#a86b749ce2c4bc627fa8a1fcfaf0e314f">armnn::OptionalBase::has_value</a></div><div class="ttdeci">bool has_value() const noexcept</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00053">Optional.hpp:53</a></div></div> +<div class="ttc" id="structarmnn_1_1_rsqrt_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_rsqrt_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::RsqrtQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02425">WorkloadData.cpp:2425</a></div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml_ac93cb1365b4bcb67df2a3164606096c5"><div class="ttname"><a href="namespacearmnn_utils.xhtml#ac93cb1365b4bcb67df2a3164606096c5">armnnUtils::GetUnsignedAxis</a></div><div class="ttdeci">unsigned int GetUnsignedAxis(const unsigned int inputDimension, const int axis)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_utils_8cpp_source.xhtml#l00127">TensorUtils.cpp:127</a></div></div> +<div class="ttc" id="structarmnn_1_1_l2_normalization_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_l2_normalization_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::L2NormalizationQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01490">WorkloadData.cpp:1490</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::BatchToSpaceNdQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02257">WorkloadData.cpp:2257</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a></div></div> +<div class="ttc" id="structarmnn_1_1_addition_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_addition_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::AdditionQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01042">WorkloadData.cpp:1042</a></div></div> +<div class="ttc" id="structarmnn_1_1_workload_info_xhtml_a67b178f8a836bc1e52b8de109760adfd"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">armnn::WorkloadInfo::m_OutputTensorInfos</a></div><div class="ttdeci">std::vector< TensorInfo > m_OutputTensorInfos</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00019">WorkloadInfo.hpp:19</a></div></div> +<div class="ttc" id="structarmnn_1_1_mem_copy_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_mem_copy_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::MemCopyQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00469">WorkloadData.cpp:469</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::Convolution2dQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01159">WorkloadData.cpp:1159</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_elementwise_unary_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_elementwise_unary_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ElementwiseUnaryQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l03081">WorkloadData.cpp:3081</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="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6acdb56b2d2f73c26480207524f2dbe0af">armnn::DataType::BFloat16</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_dequantize_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_dequantize_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::DequantizeQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02550">WorkloadData.cpp:2550</a></div></div> +<div class="ttc" id="structarmnn_1_1_stack_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_stack_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::StackQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00871">WorkloadData.cpp:871</a></div></div> +<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00173">Types.hpp:173</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantize_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_quantize_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::QuantizeQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02228">WorkloadData.cpp:2228</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a872803f5667392efc3c8e5607bd453ad"><div class="ttname"><a href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">armnn::GetBiasDataType</a></div><div class="ttdeci">DataType GetBiasDataType(DataType inputDataType)</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00025">WorkloadData.cpp:25</a></div></div> +<div class="ttc" id="structarmnn_1_1_comparison_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_comparison_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ComparisonQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l03057">WorkloadData.cpp:3057</a></div></div> +<div class="ttc" id="_cpu_tensor_handle_8hpp_xhtml"><div class="ttname"><a href="_cpu_tensor_handle_8hpp.xhtml">CpuTensorHandle.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_constant_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_constant_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ConstantQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01544">WorkloadData.cpp:1544</a></div></div> +<div class="ttc" id="structarmnn_1_1_convert_fp32_to_fp16_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_convert_fp32_to_fp16_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ConvertFp32ToFp16QueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02019">WorkloadData.cpp:2019</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::DepthwiseConvolution2dQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01212">WorkloadData.cpp:1212</a></div></div> +<div class="ttc" id="structarmnn_1_1_pad_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_pad_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::PadQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02206">WorkloadData.cpp:2206</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::QuantizedLstmQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02754">WorkloadData.cpp:2754</a></div></div> +<div class="ttc" id="structarmnn_1_1_pre_compiled_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_pre_compiled_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::PreCompiledQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02633">WorkloadData.cpp:2633</a></div></div> +<div class="ttc" id="_tensor_utils_8hpp_xhtml"><div class="ttname"><a href="_tensor_utils_8hpp.xhtml">TensorUtils.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ConcatQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00776">WorkloadData.cpp:776</a></div></div> +<div class="ttc" id="structarmnn_1_1_queue_descriptor_xhtml_a6abd491bb99ffe88bd472c1ae5a1ed1a"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">armnn::QueueDescriptor::m_Outputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Outputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00031">WorkloadData.hpp:31</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_pooling2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::Pooling2dQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01316">WorkloadData.cpp:1316</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="structarmnn_1_1_reshape_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_reshape_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ReshapeQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01575">WorkloadData.cpp:1575</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_switch_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_switch_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::SwitchQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02593">WorkloadData.cpp:2593</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::FullyConnectedQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00966">WorkloadData.cpp:966</a></div></div> +<div class="ttc" id="structarmnn_1_1_greater_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_greater_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::GreaterQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02401">WorkloadData.cpp:2401</a></div></div> +<div class="ttc" id="_neon_end_to_end_tests_8cpp_xhtml_a64c1dd1b6dd60be9f4a16db9c8f427a5"><div class="ttname"><a href="_neon_end_to_end_tests_8cpp.xhtml#a64c1dd1b6dd60be9f4a16db9c8f427a5">scoresInfo</a></div><div class="ttdeci">armnn::TensorInfo scoresInfo({ 1, 6, 3 }, armnn::DataType::Float32)</div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml"><div class="ttname"><a href="namespacearmnn_utils.xhtml">armnnUtils</a></div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00013">DataLayoutIndexed.hpp:13</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div> +<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div> +<div class="ttc" id="structarmnn_1_1_arg_min_max_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_arg_min_max_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ArgMinMaxQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00617">WorkloadData.cpp:617</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a></div></div> +<div class="ttc" id="structarmnn_1_1_queue_descriptor_xhtml_a4b50e46a6810018f3edecfb68b2a76b3"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">armnn::QueueDescriptor::m_Inputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Inputs</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00030">WorkloadData.hpp:30</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></div></div> +<div class="ttc" id="structarmnn_1_1_prelu_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_prelu_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::PreluQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l02638">WorkloadData.cpp:2638</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_bilinear_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_resize_bilinear_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ResizeBilinearQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01343">WorkloadData.cpp:1343</a></div></div> +<div class="ttc" id="structarmnn_1_1_depth_to_space_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_depth_to_space_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::DepthToSpaceQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l03005">WorkloadData.cpp:3005</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorInfo::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00092">Tensor.hpp:92</a></div></div> +<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml_a861b2621ee46e4b63379988b360b8cd9"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">armnnUtils::DataLayoutIndexed::GetChannelsIndex</a></div><div class="ttdeci">unsigned int GetChannelsIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00023">DataLayoutIndexed.hpp:23</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a7c00efeb540198b33b8558c76e5cc2dd"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a7c00efeb540198b33b8558c76e5cc2dd">armnn::TensorInfo::IsQuantized</a></div><div class="ttdeci">bool IsQuantized() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00290">Tensor.cpp:290</a></div></div> +<div class="ttc" id="structarmnn_1_1_softmax_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_softmax_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::SoftmaxQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00680">WorkloadData.cpp:680</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="structarmnn_1_1_resize_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_resize_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::ResizeQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01391">WorkloadData.cpp:1391</a></div></div> +<div class="ttc" id="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin_xhtml_af2f0a8c9eb32861711c0ce30b7986c44"><div class="ttname"><a href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml#af2f0a8c9eb32861711c0ce30b7986c44">armnn::SplitterQueueDescriptor::ViewOrigin::m_Origin</a></div><div class="ttdeci">std::vector< unsigned int > m_Origin</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00091">WorkloadData.hpp:91</a></div></div> +</div><!-- fragment --></div><!-- contents --> +</div><!-- doc-content --> +<!-- start footer part --> +<div id="nav-path" class="navpath"><!-- id is needed for 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