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diff --git a/20.02/_conv2d_test_impl_8cpp_source.xhtml b/20.02/_conv2d_test_impl_8cpp_source.xhtml new file mode 100644 index 0000000000..d8c2823675 --- /dev/null +++ b/20.02/_conv2d_test_impl_8cpp_source.xhtml @@ -0,0 +1,254 @@ +<!-- 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/test/layerTests/Conv2dTestImpl.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('_conv2d_test_impl_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">Conv2dTestImpl.cpp</div> </div> +</div><!--header--> +<div class="contents"> +<a href="_conv2d_test_impl_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="_conv2d_test_impl_8hpp.xhtml">Conv2dTestImpl.hpp</a>"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <<a class="code" href="_quantize_helper_8hpp.xhtml">QuantizeHelper.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 <<a class="code" href="_ignore_unused_8hpp.xhtml">armnn/utility/IgnoreUnused.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <<a class="code" href="_data_layout_indexed_8hpp.xhtml">armnnUtils/DataLayoutIndexed.hpp</a>></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <<a class="code" href="_permute_8hpp.xhtml">armnnUtils/Permute.hpp</a>></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <<a class="code" href="_cpu_tensor_handle_8hpp.xhtml">backendsCommon/CpuTensorHandle.hpp</a>></span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <<a class="code" href="_data_layout_utils_8hpp.xhtml">backendsCommon/test/DataLayoutUtils.hpp</a>></span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include <<a class="code" href="_tensor_copy_utils_8hpp.xhtml">backendsCommon/test/TensorCopyUtils.hpp</a>></span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include <<a class="code" href="_workload_test_utils_8hpp.xhtml">backendsCommon/test/WorkloadTestUtils.hpp</a>></span></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="preprocessor">#include <<a class="code" href="_tensor_helpers_8hpp.xhtml">test/TensorHelpers.hpp</a>></span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> </div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="preprocessor">#include <boost/numeric/conversion/cast.hpp></span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> </div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#include <string></span></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="comment">//</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="comment">// Static data</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="comment">//</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="comment">// 2-channel bias used by a number of Conv2d tests.</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="keyword">static</span> std::vector<float> Bias2({0, 2});</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="keyword">static</span> std::vector<float> Bias4({1, 2, 3, 4});</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> </div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="keyword">static</span> std::vector<float> Bias8({1, 2, 3, 4, 1, 2, 3, 4});</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="comment">// 3-channel 16x8 image used as common input data for a number of Conv2d tests.</span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="keyword">static</span> std::vector<float> ConvInput3x8x16({</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1</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> </div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn_utils.xhtml">armnnUtils</a>;</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> </div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> <span class="comment">//</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> <span class="comment">// Helper templates</span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> <span class="comment">//</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">// Helper template that returns either Bias2 or an empty vector depending on whether bias is enabled.</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00074"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#ad80bc46727797692d35f94d5935469cb"> 74</a></span> boost::multi_array<T, 1> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#ad80bc46727797692d35f94d5935469cb">GetBias2</a>(<span class="keywordtype">bool</span> biasEnabled, <span class="keywordtype">float</span> qScale)</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> {</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">if</span>(biasEnabled)</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  {</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc({<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(Bias2.size())}, ArmnnType);</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(Bias2, qScale, 0.0f));</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="keywordflow">return</span> bias;</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  }</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keywordflow">else</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>  <span class="keywordflow">return</span> boost::multi_array<T, 1>();</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> }</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> </div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> <span class="comment">// Helper template that returns either Bias4 or an empty vector depending on whether bias is enabled.</span></div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00090"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#aa794621b8665d1df93a1c9aa95d5a90d"> 90</a></span> boost::multi_array<T, 1> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#aa794621b8665d1df93a1c9aa95d5a90d">GetBias4</a>(<span class="keywordtype">bool</span> biasEnabled, <span class="keywordtype">float</span> qScale)</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> {</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keywordflow">if</span>(biasEnabled)</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  {</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc({<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(Bias4.size())}, ArmnnType);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(Bias4, qScale, 0.0f));</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keywordflow">return</span> bias;</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  }</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keywordflow">else</span></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">return</span> boost::multi_array<T, 1>();</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> }</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> </div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> <span class="comment">// Helper template that returns either Bias8 or an empty vector depending on whether bias is enabled.</span></div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00106"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#ae04bff4e44deed6908feae29e57ffe0c"> 106</a></span> boost::multi_array<T, 1> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#ae04bff4e44deed6908feae29e57ffe0c">GetBias8</a>(<span class="keywordtype">bool</span> biasEnabled, <span class="keywordtype">float</span> qScale)</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="keywordflow">if</span>(biasEnabled)</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  {</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc({<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(Bias4.size())}, ArmnnType);</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(Bias8, qScale, 0.0f));</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keywordflow">return</span> bias;</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">else</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  {</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keywordflow">return</span> boost::multi_array<T, 1>();</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  }</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> }</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> </div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> <span class="comment">// Helper template that returns either Bias4 or an empty vector depending on whether bias is enabled.</span></div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00122"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#a3481304dfd3e941b809c64979b940ad5"> 122</a></span> boost::multi_array<T, 1> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a3481304dfd3e941b809c64979b940ad5">GetBias</a>(<span class="keywordtype">bool</span> biasEnabled, <span class="keywordtype">float</span> qScale, <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo, <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> {</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">armnnUtils::DataLayoutIndexed</a> dataLayoutIndexed(layout);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channelsIndex = dataLayoutIndexed.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a861b2621ee46e4b63379988b360b8cd9">GetChannelsIndex</a>();</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = outputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()[channelsIndex];</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">switch</span> (outputChannels)</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">case</span> 2:</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  {</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <span class="keywordflow">return</span> GetBias2<ArmnnType>(biasEnabled, qScale);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  }</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keywordflow">case</span> 4:</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  {</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keywordflow">return</span> GetBias4<ArmnnType>(biasEnabled, qScale);</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  }</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keywordflow">case</span> 8:</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  {</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keywordflow">return</span> GetBias8<ArmnnType>(biasEnabled, qScale);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  }</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  }</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> }</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> </div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> <span class="comment">//</span></div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> <span class="comment">// Implementation templates</span></div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> <span class="comment">//</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> </div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> <span class="comment">// Mapping from input type to bias type for fully connected layers.</span></div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> <span class="comment">// float => float, uint8_t => int32_t</span></div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> <span class="keyword">template</span><<span class="keyword">typename</span> T></div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> <span class="keyword">struct </span>FullyConnectedBiasTypeForInputType;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> </div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> <span class="keyword">template</span><></div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> <span class="keyword">struct </span>FullyConnectedBiasTypeForInputType<float></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="keyword">using</span> Type = float;</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> };</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> </div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> <span class="keyword">template</span><></div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> <span class="keyword">struct </span>FullyConnectedBiasTypeForInputType<uint8_t></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="keyword">using</span> Type = int32_t;</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> };</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> </div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> <span class="comment">// Modifies a std::vector in-place using a specified bias.</span></div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> <span class="keyword">template</span><<span class="keyword">typename</span> T, <span class="keyword">typename</span> B></div><div class="line"><a name="l00169"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346"> 169</a></span> <span class="keywordtype">void</span> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a>(std::vector<T>& v, <span class="keywordtype">float</span> vScale, int32_t vOffset,</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keyword">const</span> std::vector<B>& bias, <span class="keywordtype">float</span> bScale, int32_t bOffset, uint32_t w, uint32_t h)</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span> {</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  BOOST_ASSERT_MSG((armnn::IsQuantizedType<T>() && vScale != 0.0f) || (!armnn::IsQuantizedType<T>()),</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="stringliteral">"Invalid type and parameter combination."</span>);</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  BOOST_ASSERT_MSG((armnn::IsQuantizedType<B>() && bScale != 0.0f) || (!armnn::IsQuantizedType<B>()),</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="stringliteral">"Invalid type and parameter combination."</span>);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> </div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="comment">// Note we need to dequantize and re-quantize the image value and the bias.</span></div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordflow">for</span> (uint32_t i = 0; i < bias.size(); ++i)</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  {</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keywordtype">float</span> dBias = <a class="code" href="namespacearmnn_utils.xhtml#a5135dc1ce7a8aeb97623c1a92c5a3543">SelectiveDequantize</a>(bias[i], bScale, bOffset);</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="keywordflow">for</span> (uint32_t y = 0; y < h; ++y)</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  {</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keywordflow">for</span> (uint32_t x = 0; x < w; ++x)</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>  uint32_t offset = (i * h + y) * w + x;</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  BOOST_ASSERT(offset < v.size());</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  T& outRef = v[offset];</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keywordtype">float</span> dOutput = <a class="code" href="namespacearmnn_utils.xhtml#a5135dc1ce7a8aeb97623c1a92c5a3543">SelectiveDequantize</a>(outRef, vScale, vOffset);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  outRef = SelectiveQuantize<T>(dOutput + dBias, vScale, vOffset);</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  }</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  }</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  }</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> }</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> </div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> <span class="comment">//</span></div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> <span class="comment">// Convolution2d implementations</span></div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> <span class="comment">//</span></div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> </div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> <span class="keyword">template</span><<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnType, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnBType,</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keyword">typename</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<ArmnnType></a>, <span class="keyword">typename</span> <a class="code" href="_inference_test_image_8hpp.xhtml#a65983f8cb907d873f2328bb8307c296aa9d5ed678fe57bcca610140957afab571">B</a> = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<ArmnnBType></a>></div><div class="line"><a name="l00201"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#a7bd1547ceefdc1acedbb1fa6445b2968"> 201</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a7bd1547ceefdc1acedbb1fa6445b2968">SimpleConvolution2dTestImpl</a>(</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keyword">const</span> boost::multi_array<T, 4>& originalInput,</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keyword">const</span> boost::multi_array<T, 4>& originalKernel,</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keyword">const</span> boost::multi_array<B, 1>& bias,</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="keyword">const</span> boost::multi_array<T, 4>& originalOutputExpected,</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  int32_t qOffset,</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>,</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  uint32_t padLeft = 0,</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  uint32_t padTop = 0,</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  uint32_t padRight = 0,</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  uint32_t padBottom = 0,</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  uint32_t strideX = 1,</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  uint32_t strideY = 1,</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  uint32_t dilationX = 1,</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  uint32_t dilationY = 1)</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span> {</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalInput.shape()[2]);</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalInput.shape()[3]);</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalInput.shape()[1]);</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalInput.shape()[0]);</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span> </div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalOutputExpected.shape()[2]);</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalOutputExpected.shape()[3]);</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalOutputExpected.shape()[1]);</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalOutputExpected.shape()[0]);</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span> </div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalKernel.shape()[2]);</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalKernel.shape()[3]);</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalKernel.shape()[1]);</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelDepthMul = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalKernel.shape()[0]);</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="keywordtype">bool</span> biasEnabled = bias.size() > 0;</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> </div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="comment">// This function currently assumes 1 batch of input/output (and duplicates this into 2 batches).</span></div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  BOOST_ASSERT(inputNum == 1);</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  BOOST_ASSERT(outputNum == 1);</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> </div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="comment">// If a bias is used, its size must equal the number of output channels.</span></div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels);</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span> </div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span> </div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="comment">// Note these tensors will use two (identical) batches.</span></div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo =</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(2*inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo =</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(2*outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc =</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(kernelDepthMul, kernelChannels, kernelHeight, kernelWidth, layout, ArmnnType);</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc({<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(bias.size())}, ArmnnBType);</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span> </div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  {</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  inputTensorInfo.SetQuantizationScale(qScale);</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  inputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  kernelDesc.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  kernelDesc.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  biasDesc.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale*qScale);</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  biasDesc.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(0);</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  }</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> </div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> ret(outputTensorInfo);</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="comment">// Construct input data - two batches of the same input image.</span></div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  std::vector<T> inputImage;</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  inputImage.assign(originalInput.data(), originalInput.data() + 1*inputChannels*inputHeight*inputWidth);</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  std::vector<T> inputData;</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  inputData.insert(inputData.end(), inputImage.begin(), inputImage.end());</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  inputData.insert(inputData.end(), inputImage.begin(), inputImage.end());</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="comment">// at this point if we require it permute the input data</span></div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a> NCHWToNHWC = { 0, 3, 1, 2 };</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</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>  std::vector<T> tmp(inputData.size());</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  inputData = tmp;</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  }</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> </div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="keyword">auto</span> batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData);</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span> </div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  std::vector<T> outputImage;</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  outputImage.assign(originalOutputExpected.data(),</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  originalOutputExpected.data() + outputChannels*outputHeight*outputWidth);</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span> </div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="comment">// Apply bias to output image if it is enabled.</span></div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <span class="keywordflow">if</span>(biasEnabled)</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  {</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  std::vector<T> biasV;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  biasV.assign(bias.data(), bias.data() + outputChannels);</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <a class="code" href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a>(outputImage, outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(), outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>(),</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  biasV, biasDesc.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(), biasDesc.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>(),</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  outputWidth, outputHeight);</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  }</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span> </div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="comment">// Construct expected output data - two identical images.</span></div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  std::vector<T> outputData;</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  outputData.insert(outputData.end(), outputImage.begin(), outputImage.end());</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  outputData.insert(outputData.end(), outputImage.begin(), outputImage.end());</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span> </div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <span class="comment">// at this point if we require it permute the expected output</span></div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</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>  std::vector<T> tmp(outputData.size());</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC, outputData.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  outputData = tmp;</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  }</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  ret.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<T, 4>(outputTensorInfo, outputData);</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span> </div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</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>  <a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">armnn::Convolution2dQueueDescriptor</a> data;</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> weightsTensor(kernelDesc);</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> biasTensor(biasDesc);</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <span class="comment">// Permute the kernel if necessary</span></div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  boost::multi_array<T, 4> kernel = boost::multi_array<T, 4>(originalKernel);</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</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>  <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(kernelDesc.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC, originalKernel.data(), kernel.data(), <span class="keyword">sizeof</span>(T));</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>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&weightsTensor, &kernel[0][0][0][0]);</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="keywordflow">if</span>(biasEnabled)</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  {</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&biasTensor, &bias[0]);</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  }</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span> </div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span> </div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  data.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &weightsTensor;</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  data.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &biasTensor; <span class="comment">// Still set this whether or not bias is enabled - can be a source of bugs.</span></div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strideX;</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strideY;</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = padLeft;</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = padRight;</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = padTop;</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = padBottom;</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = biasEnabled;</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = layout;</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = dilationX;</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = dilationY;</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>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a2184995027cd2c9f9980206de9658855">CreateConvolution2d</a>(data, info);</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  inputHandle->Allocate();</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  outputHandle->Allocate();</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span> </div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &batchedInput[0][0][0][0]);</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>  ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span> </div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.<a class="code" href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a>[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span> </div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span> }</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span> </div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span> <span class="keyword">template</span><<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnType, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnBType,</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <span class="keyword">typename</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<ArmnnType></a>, <span class="keyword">typename</span> B = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<ArmnnBType></a>></div><div class="line"><a name="l00367"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#ac79e75b3bcb6cb8c34f0bd4e3e35f73e"> 367</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#ac79e75b3bcb6cb8c34f0bd4e3e35f73e">SimpleConvolution2dNhwcTestImpl</a>(</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="keyword">const</span> boost::multi_array<T, 4>& input,</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="keyword">const</span> boost::multi_array<T, 4>& kernel,</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="keyword">const</span> boost::multi_array<B, 1>& bias,</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  <span class="keyword">const</span> boost::multi_array<T, 4>& outputExpected,</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout,</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  int32_t qOffset,</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  uint32_t padLeft = 1,</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  uint32_t padTop = 1,</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  uint32_t padRight = 1,</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  uint32_t padBottom = 1,</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  uint32_t strideX = 1,</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  uint32_t strideY = 1)</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span> {</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(qScale, qOffset);</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(input.shape()[0]);</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(input.shape()[3]);</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(input.shape()[1]);</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(input.shape()[2]);</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span> </div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelChanMul = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(kernel.shape()[0]);</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(kernel.shape()[3]);</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(kernel.shape()[1]);</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(kernel.shape()[2]);</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>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputExpected.shape()[0]);</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputExpected.shape()[3]);</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputExpected.shape()[1]);</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputExpected.shape()[2]);</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span> </div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keywordtype">bool</span> biasEnabled = bias.size() > 0;</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span> </div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="comment">// Creates the tensors.</span></div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({inputNum, inputHeight, inputWidth, inputChannels}, ArmnnType);</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({outputNum, outputHeight, outputWidth, outputChannels},</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  ArmnnType);</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({kernelChanMul, kernelHeight, kernelWidth, kernelChannels}, ArmnnType);</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc({<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(bias.size())}, ArmnnBType);</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span> </div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <span class="comment">// Construct the input data.</span></div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  std::vector<T> inputData;</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  inputData.assign(input.data(), input.data() + inputHeight*inputWidth*inputChannels);</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <span class="keyword">auto</span> batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData);</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span> </div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <span class="comment">// Construct the output data, with bias applied, as appropriate.</span></div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  std::vector<T> outputData;</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  outputData.assign(outputExpected.data(), outputExpected.data() + outputHeight*outputWidth*outputChannels);</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span> </div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> ret(outputTensorInfo);</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  ret.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<T, 4>(outputTensorInfo, outputData);</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span> </div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span> </div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> weightsTensor(kernelDesc);</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&weightsTensor, &kernel[0][0][0][0]);</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span> </div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> biasTensor(biasDesc);</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span> </div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">armnn::Convolution2dQueueDescriptor</a> data;</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span> </div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  data.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &weightsTensor;</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  data.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &biasTensor; <span class="comment">// Still set this whether or not bias is enabled - can be a source of bugs.</span></div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strideX;</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strideY;</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = padLeft;</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = padRight;</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = padTop;</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = padBottom;</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = biasEnabled;</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</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>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span> </div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a2184995027cd2c9f9980206de9658855">CreateConvolution2d</a>(data, info);</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  inputHandle->Allocate();</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  outputHandle->Allocate();</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span> </div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &batchedInput[0][0][0][0]);</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>  ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span> </div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.<a class="code" href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a>[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span> </div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <span class="keywordflow">return</span> ret;</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="keyword">template</span><armnn::DataType ArmnnType, armnn::DataType ArmnnBType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00460"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#af541f19e3d1ad345cc9208fc2d2e7b19"> 460</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T,4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#af541f19e3d1ad345cc9208fc2d2e7b19">Convolution1dTestImpl</a>(</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  int32_t qOffset,</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <span class="keywordtype">bool</span> biasEnabled)</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>  <span class="keyword">using</span> B = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<ArmnnBType></a>;</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <span class="comment">// Until we have a specialist 1D convolution layer, we can fake one using</span></div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <span class="comment">// 2D convolution with the final dimension set to 1.</span></div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <span class="comment">// I don't anticipate this being particularly slow, given that convolution is implemented</span></div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <span class="comment">// as a matrix multiplication, at which point dimension doesn't matter.</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>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 1;</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 2;</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 3;</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 5; <span class="comment">// The 1D size (could view as 'width' or 'height').</span></div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelSize = 3;</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padSize = 2;</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> stride = 1;</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 7; <span class="comment">// (inputSize + 2 * padSize - kernelSize + 1) / stride.</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({batchSize, inputChannels, inputSize, 1}, ArmnnType);</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputInfo({batchSize, outputChannels, outputSize, 1}, ArmnnType);</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelInfo({outputChannels, inputChannels, kernelSize, 1}, ArmnnType);</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasInfo({outputChannels}, ArmnnBType);</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span> </div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  {</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  inputInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  inputInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  outputInfo.SetQuantizationScale(qScale);</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  outputInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  kernelInfo.SetQuantizationScale(qScale);</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  kernelInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  biasInfo.SetQuantizationScale(inputInfo.GetQuantizationScale()*kernelInfo.GetQuantizationScale());</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  biasInfo.SetQuantizationOffset(0);</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  }</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span> </div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  std::vector<T> inputData = QuantizedVector<T>(</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>  5.0f, -2.0f, 2.5f, 0.0f, 1.0f,</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  -3.0f, 3.2f, 5.0f, 2.0f, 3.0f,</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>  inputInfo.GetQuantizationScale(),</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  inputInfo.GetQuantizationOffset());</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span> </div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  std::vector<T> kernelData = QuantizedVector<T>(</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  {</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  1.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  0.0f, 2.0f, -1.5f,</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>  0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  0.2f, 0.2f, 0.2f,</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span> </div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  0.5f, 0.0f, 0.5f,</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  0.0f, -1.0f, 0.0f</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>  kernelInfo.GetQuantizationScale(),</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  kernelInfo.GetQuantizationOffset());</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span> </div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  std::vector<B> biasData =</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  QuantizedVector<B>({ 1.0f, 0.0f, 0.0f }, biasInfo.GetQuantizationScale(), biasInfo.GetQuantizationOffset());</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>  std::vector<T> outputData = QuantizedVector<T>(</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  {</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  4.5f, -10.8f, 5.0f + 6.4f - 7.5f, -2.0f + 10.0f -3.0f, 2.5f + 4.0f - 4.5f, 6.0f, 1.0f,</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  -0.6f, -0.6f + 0.64f, -0.6f + 0.64f + 1.0f, 0.64f + 1.0f + 0.4f, 1.0f + 0.4f + 0.6f, 0.4f + 0.6f, 0.6f,</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  2.5f, -1.0f + 3.0f, 1.25f - 3.2f + 2.5f, -1.0f - 5.0f, 1.25f + 0.5f - 2.0f, -3.0f, 0.5f</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>  outputInfo.GetQuantizationScale(),</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  outputInfo.GetQuantizationOffset());</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span> </div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <span class="comment">// Optionally apply bias to output image.</span></div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <span class="keywordflow">if</span>(biasEnabled)</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>  <a class="code" href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a>(outputData, outputInfo.GetQuantizationScale(), outputInfo.GetQuantizationOffset(),</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  biasData, biasInfo.GetQuantizationScale(), biasInfo.GetQuantizationOffset(),</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  1, outputSize);</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  }</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span> </div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputInfo);</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputInfo);</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span> </div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">armnn::Convolution2dQueueDescriptor</a> data;</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> weightsTensor(kernelInfo);</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> biasTensor(biasInfo);</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>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&weightsTensor, kernelData.data());</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&biasTensor, biasData.data());</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>  AddInputToWorkload(data, info, inputInfo, inputHandle.get());</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  AddOutputToWorkload(data, info, outputInfo, outputHandle.get());</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span> </div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  data.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &weightsTensor;</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  data.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &biasTensor;</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = stride;</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 0;</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 0;</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = padSize;</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = padSize;</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = biasEnabled;</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span> </div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a2184995027cd2c9f9980206de9658855">CreateConvolution2d</a>(data, info);</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  inputHandle->Allocate();</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  outputHandle->Allocate();</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span> </div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), inputData.data());</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span> </div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span> </div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  <span class="comment">// Output</span></div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T,4></a> ret(outputInfo);</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.<a class="code" href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a>[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  ret.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<T, 4>(outputInfo, outputData);</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  <span class="keywordflow">return</span> ret;</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> </div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00582"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#a8225effadfc56a5d831ae0f7f686a6cf"> 582</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a8225effadfc56a5d831ae0f7f686a6cf">SimpleConvolution2d3x3NhwcTestCommon</a>(</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  int32_t qOffset,</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> dataLayout)</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span> {</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(biasEnabled);</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  <span class="comment">// Use common single-batch 5x5 image.</span></div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span> </div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({1, 3, 4, 1}, ArmnnType);</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc,</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>  1, 5, 2, 3,</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  8, 7, 3, 6,</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  3, 3, 9, 1</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  });</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span> </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>  <span class="comment">// Use a 2-element batch of 3-channel 3x3 kernels.</span></div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({1, 3, 3, 1}, ArmnnType);</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, {</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  4, 5, 6,</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  0, 0, 0,</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  3, 2, 1</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  });</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span> </div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  <span class="comment">// Expected output is 1 batch of a 5x5 image.</span></div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({1, 3, 4, 1}, ArmnnType);</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span> </div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  <span class="keyword">const</span> std::vector<float> outputData =</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  {</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  23, 41, 33, 21,</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  44, 65, 76, 52,</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  82, 85, 79, 42</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  };</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span> </div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, outputData);</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span> </div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  <span class="keywordflow">return</span> SimpleConvolution2dNhwcTestImpl<ArmnnType, ArmnnType>(</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  workloadFactory,</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  memoryManager,</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  input,</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  kernel,</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  boost::multi_array<T, 1>(),</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  expectedOutput,</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  dataLayout,</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  qScale,</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  qOffset);</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span> }</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span> </div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00635"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#aafa5b575d2bc27ec7229f1d87ab8efdb"> 635</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#aafa5b575d2bc27ec7229f1d87ab8efdb">SimpleConvolution2d3x3Stride2x2TestCommon</a>(</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  int32_t qOffset,</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>& dataLayout)</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span> {</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a>(biasEnabled);</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span> </div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  <span class="comment">// Input is a single-batch, 1 channel, 5x5 image.</span></div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({1, 5, 5, 1}, ArmnnType);</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc,</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  {</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  1, 5, 2, 3, 5,</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  8, 7, 3, 6, 3,</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  3, 3, 9, 1, 9,</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  4, 1, 8, 1, 3,</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  6, 8, 1, 9, 2</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> </div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  <span class="comment">// Use a 3x3 kernel.</span></div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({1, 3, 3, 1}, ArmnnType);</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc,</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>  4, 5, 6,</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  0, 0, 0,</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  3, 2, 1</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> </div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  <span class="comment">// Expected output is a single-batch, 1 channel, 3x3 image.</span></div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({1, 3, 3, 1}, ArmnnType);</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>  <span class="keyword">const</span> std::vector<T> outputData =</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>  23, 33, 24,</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  91, 99, 48,</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  26, 50, 19</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> </div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, outputData);</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>  uint32_t padLeft = 1;</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  uint32_t padTop = 1;</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  uint32_t padRight = 1;</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  uint32_t padBottom = 1;</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  uint32_t strideX = 2;</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  uint32_t strideY = 2;</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>  <span class="keywordflow">return</span> SimpleConvolution2dNhwcTestImpl<ArmnnType, ArmnnType>(</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  workloadFactory,</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  memoryManager,</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  input,</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  kernel,</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  boost::multi_array<T, 1>(),</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  expectedOutput,</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  dataLayout,</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  qScale,</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  qOffset,</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  padLeft,</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  padTop,</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  padRight,</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  padBottom,</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  strideX,</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  strideY);</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span> }</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span> </div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span> <span class="keyword">template</span><armnn::DataType ArmnnType, armnn::DataType ArmnnBType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00703"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#a3660079f1e20e5b1618402dfc5214441"> 703</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a3660079f1e20e5b1618402dfc5214441">SimpleConvolution2d3x5TestCommon</a>(</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  int32_t qOffset,</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</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">// Use common single-batch 3-channel 16x8 image.</span></div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({1, 3, 8, 16}, ArmnnType);</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, QuantizedVector<T>(ConvInput3x8x16, qScale, qOffset));</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span> </div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  <span class="comment">// Use a 2-element batch with 3-channel 3x5 kernels.</span></div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({2, 3, 5, 3}, ArmnnType);</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  QuantizedVector<T>({</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  1, 1, 1,</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  1, -1, 1,</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  1, 1, 1,</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  1, 1, 1,</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  1, 1, 1,</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span> </div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  0, 0, 0,</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  0, 0, 0,</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  0, 0, 0,</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  0, 0, 0,</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  0, 0, 0,</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span> </div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  2, 2, 2,</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  2, 2, 2,</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  2, 2, 2,</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  2, 2, 2,</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  2, 2, 2,</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>  0, 0, 0,</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  0, 0, 0,</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  0, 0, 0,</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  0, 0, 0,</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  0, 0, 0,</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span> </div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  1, 1, 1,</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  1, 1, 1,</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  1, 1, 1,</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  1, 1, 1,</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  1, 1, 1,</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span> </div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  0, 0, 0,</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  0, 0, 0,</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  0, 0, 0,</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  0, 0, 0,</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  0, 0, 0</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>  qScale, qOffset)));</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span> </div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  <span class="comment">// Expected output is 2 batch elements of a 1-channel 14x4 image.</span></div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({1, 2, 4, 14}, ArmnnType);</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>(</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  QuantizedVector<T>({</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24,</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25,</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f,</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  -23.5f, -23.5f, -23.5f,</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f,</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  -23.5f, -23.5f, -23.5f,</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>  5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0</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>  qScale, qOffset)));</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span> </div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  <span class="keywordflow">return</span> SimpleConvolution2dTestImpl<ArmnnType, ArmnnBType>(</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  workloadFactory,</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  memoryManager,</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  input,</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  kernel,</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  GetBias2<ArmnnBType>(biasEnabled, qScale * qScale),</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  expectedOutput,</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  qScale,</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  qOffset,</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>  layout);</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span> }</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="keyword">template</span><<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnType, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnBType,</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  <span class="keyword">typename</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<ArmnnType></a>></div><div class="line"><a name="l00790"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#a5070a9bac7ba582ed116a8b2323ed2a5"> 790</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a5070a9bac7ba582ed116a8b2323ed2a5">SimpleConvolution2d3x3TestCommon</a>(</div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  int32_t qOffset,</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span> {</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  <span class="comment">// Use a 3x3 kernel, which exercises ArmCompute's direct convolution path.</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="comment">// Use common single-batch 3-channel 16x8 image.</span></div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({1, 3, 8, 16}, ArmnnType);</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, QuantizedVector<T>(ConvInput3x8x16, qScale, qOffset));</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>  <span class="comment">// Use a 2-element batch of 3-channel 3x3 kernels.</span></div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({2, 3, 3, 3}, ArmnnType);</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  QuantizedVector<T>({</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  1, 1, 1,</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  1, -1, 1,</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  1, 1, 1,</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>  0, 0, 0,</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  0, 0, 0,</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  0, 0, 0,</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span> </div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  2, 2, 2,</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  2, 2, 2,</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  2, 2, 2,</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> </div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  0, 0, 0,</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  0, 0, 0,</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  0, 0, 0,</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span> </div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  1, 1, 1,</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  1, 1, 1,</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  1, 1, 1,</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span> </div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  0, 0, 0,</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  0, 0, 0,</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  0, 0, 0</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  },</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  qScale, qOffset)));</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span> </div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  <span class="comment">// Expected output is 1 batch of a 2-channel 14x6 image.</span></div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({1, 2, 6, 14}, ArmnnType);</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>(</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  QuantizedVector<T>({</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15,</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16,</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,</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>  3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>  3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span>  3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  },</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  qScale, qOffset)));</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span> </div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  <span class="keywordflow">return</span> SimpleConvolution2dTestImpl<ArmnnType, ArmnnBType>(</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  workloadFactory,</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  memoryManager,</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  input,</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  kernel,</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  GetBias2<ArmnnBType>(biasEnabled, qScale * qScale),</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  expectedOutput,</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  qScale,</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  qOffset,</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  layout);</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> </div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span> <span class="keyword">template</span><<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnType, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnBType,</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  <span class="keyword">typename</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<ArmnnType></a>></div><div class="line"><a name="l00869"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#a35ad1225c524b4594b461e613695ee4a"> 869</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a35ad1225c524b4594b461e613695ee4a">Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon</a>(</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout,</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  int32_t qOffset)</div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span> {</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  <span class="comment">// Use a single-batch 1-channel 3x3 image as input.</span></div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({1, 1, 3, 3}, ArmnnType);</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, std::vector<T>(</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  QuantizedVector<T>({</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  11,21,31,</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  12,22,32,</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  13,23,33</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>  },</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  qScale, qOffset)));</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span> </div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span>  <span class="comment">// Use 1 batch of a 1-channel 2x2 kernel.</span></div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({1, 1, 2, 2}, ArmnnType);</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span>  boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  QuantizedVector<T>({</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span>  -11,-21,</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span>  -12,-22,</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  },</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  qScale, qOffset)));</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span> </div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span> <span class="comment">// Expected output is 1 batch of a 1-channel 6x8 image.</span></div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span> <span class="comment">// Manually calculated like this:</span></div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span> <span class="comment">//[-11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ..]</span></div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span> <span class="comment">//[-11*0 -21*0 -12*0 -22*11 ; -11*0 -21*0 -12*11 -22*21 ; -11*0 -21*0 -12*21 -22*31 ; -11*0 -21*0 -12*31 -22*0 ..]</span></div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span> <span class="comment">//[-11*0 -21*11 -12*0 -22*12 ; -11*11 -21*21 -12*12 -22*22 ; -11*21 -21*31 -12*22 -22*32 ; -11*31 -21*0 -12*32 -22*0 ..]</span></div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span> <span class="comment">//[-11*0 -21*12 -12*0 -22*13 ; -11*12 -21*22 -12*13 -22*23 ; -11*22 -21*32 -12*23 -22*33 ; -11*32 -21*0 -12*33 -22*0 ..]</span></div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span> <span class="comment">//[-11*0 -21*13 -12*0 -22*0 ; -11*13 -21*23 -12*0 -22*0 ; -11*23 -21*33 -12*0 -22*0 ; -11*33 -21*0 -12*0 -22*0 ..]</span></div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span> <span class="comment">//[-11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ..]</span></div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span> <span class="comment">//[..... ..... ..... ..... ; ..... ..... ..... ..... ; ..... ..... ..... ..... ; ..... ..... ..... ..... ..]</span></div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({1, 1, 8, 6}, ArmnnType);</div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span>  boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>(</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  QuantizedVector<T>({</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  -242, -594, -934, -372, 0, 0,</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  -495, -1190, -1850, -725, 0, 0,</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span>  -538, -1256, -1916, -748, 0, 0,</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  -273, -626, -946, -363, 0, 0,</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span>  0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span>  0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span>  0, 0, 0, 0, 0, 0</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>  qScale, qOffset)));</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">return</span> SimpleConvolution2dTestImpl<ArmnnType, ArmnnBType>(</div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  workloadFactory,</div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  memoryManager,</div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>  input,</div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  kernel,</div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  GetBias2<ArmnnBType>(<span class="keyword">false</span>, qScale * qScale),</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>  expectedOutput,</div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  qScale,</div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  qOffset,</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  layout,</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span>  1, <span class="comment">// Padding left.</span></div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  2, <span class="comment">// Padding top.</span></div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span>  3, <span class="comment">// Padding right.</span></div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  4); <span class="comment">// Padding bottom.</span></div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span> }</div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span> </div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span> <span class="keyword">template</span><<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnType, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnBType,</div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  <span class="keyword">typename</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<ArmnnType></a>></div><div class="line"><a name="l00936"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#af32b0642214e3129d8e93fa45a12e704"> 936</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#af32b0642214e3129d8e93fa45a12e704">SimpleConvolution2dAsymmetricPaddingTestCommon</a>(</div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout,</div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  int32_t qOffset)</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span> {</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  <span class="comment">// Use a single-batch 1-channel 5x5 image as input.</span></div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputDesc({ 1, 1, 5, 5 }, ArmnnType);</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, std::vector<T>(</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  QuantizedVector<T>({</div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  11,21,31,41,51,</div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span>  12,22,32,42,52,</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  13,23,33,43,53,</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  14,24,34,44,54,</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  15,25,35,45,55,</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  }, qScale, qOffset)));</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span> </div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  <span class="comment">// Use 1 batch of a 1-channel 4x4 kernel.</span></div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({ 1, 1, 4, 4 }, ArmnnType);</div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span>  QuantizedVector<T>({</div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  -11,-21,-31,-41,</div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span>  -12,-22,-32,-42,</div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  -13,-23,-33,-43,</div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  -14,-24,-34,-44,</div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  },</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  qScale, qOffset)));</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>  <span class="comment">// Expected output is 1 batch of a 1-channel 5x5 image.</span></div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputDesc({ 1, 1, 5, 5 }, ArmnnType);</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  std::vector<T> myVec(outputDesc.GetNumElements(), 0);</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>(</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span>  QuantizedVector<T>({</div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  -7140, -10580, -13940, -9300, -5230,</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  -9590, -14120, -18520, -12290, -6860,</div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span>  -9980, -14560, -18960, -12560, -7000,</div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span>  -7518, -10904, -14144, -9318, -5152,</div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span>  -5032, -7256, -9376, -6142, -3368,</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>  qScale, qOffset)));</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">return</span> SimpleConvolution2dTestImpl<ArmnnType, ArmnnBType>(</div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  workloadFactory,</div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  memoryManager,</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  input,</div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  kernel,</div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  GetBias2<ArmnnBType>(<span class="keyword">false</span>, qScale * qScale),</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  expectedOutput,</div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  qScale,</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  qOffset,</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span>  layout,</div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span>  1, <span class="comment">// Padding left.</span></div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  1, <span class="comment">// Padding top.</span></div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span>  2, <span class="comment">// Padding right.</span></div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  2); <span class="comment">// Padding bottom.</span></div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span> }</div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span> </div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span> <span class="keyword">template</span><armnn::DataType ArmnnType, armnn::DataType ArmnnBType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00995"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#ad12c52b6d41931219bdfec5fbf5990bd"> 995</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#ad12c52b6d41931219bdfec5fbf5990bd">Convolution2d3x3DilationTestCommon</a>(</div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span>  <span class="keyword">const</span> std::vector<float>& inputNoQuantizedValues,</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& inputTensorInfo,</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  <span class="keyword">const</span> std::vector<float>& kernelNoQuantizedValues,</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& kernelTensorInfo,</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  <span class="keyword">const</span> std::vector<float>& outputExpectedNoQuantizedValues,</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& outputTensorInfo,</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>  uint32_t dilationX,</div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  uint32_t dilationY,</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>,</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>  uint32_t padLeft = 0,</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  uint32_t padTop = 0,</div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  uint32_t padRight = 0,</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>  uint32_t padBottom = 0,</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>  uint32_t strideX = 1,</div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  uint32_t strideY = 1,</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  <span class="keywordtype">bool</span> biasEnabled = <span class="keyword">false</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"> 1015</span> {</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>  <span class="keywordtype">float</span> qScale;</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>  int32_t qOffset;</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>  <span class="keywordflow">switch</span> (ArmnnType)</div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>  {</div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>:</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>  qScale = 0.1f;</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>  qOffset = 128;</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span>  }</div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>:</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>  qScale = 0.1f;</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  qOffset = 0;</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>  }</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>:</div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span>  <span class="keywordflow">default</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>  qScale = 0.f;</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>  qOffset = 0;</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>  <span class="keywordflow">break</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>  }</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>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  kernelTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>  kernelTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span> </div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo,</div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>  std::vector<T>(QuantizedVector<T>(inputNoQuantizedValues,</div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>  inputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>  inputTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>  <span class="keyword">auto</span> kernel = MakeTensor<T, 4>(kernelTensorInfo,</div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>  std::vector<T>(QuantizedVector<T>(kernelNoQuantizedValues,</div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>  kernelTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>  kernelTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>  <span class="keyword">auto</span> expectedOutput =</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>  MakeTensor<T, 4>(outputTensorInfo,</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>  std::vector<T>(QuantizedVector<T>(outputExpectedNoQuantizedValues,</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>  outputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>  outputTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span> </div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span>  <span class="keywordflow">return</span> SimpleConvolution2dTestImpl<ArmnnType, ArmnnBType>(</div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>  workloadFactory,</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  memoryManager,</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  input,</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span>  kernel,</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>  GetBias2<ArmnnBType>(biasEnabled, qScale * qScale),</div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>  expectedOutput,</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>  qScale,</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>  qOffset,</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>  layout,</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>  padLeft,</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>  padTop,</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>  padRight,</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>  padBottom,</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>  strideX,</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>  strideY,</div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>  dilationX,</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>  dilationY);</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</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> <span class="keyword">template</span><armnn::DataType ArmnnType, armnn::DataType ArmnnBType, <span class="keyword">typename</span> T></div><div class="line"><a name="l01083"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#a90abce368d7f16012bef5ee461329484"> 1083</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a90abce368d7f16012bef5ee461329484">Convolution2d3x3Dilation3x3Test</a>(</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</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_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span> {</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({1, 1, 10, 10}, ArmnnType);</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span>  std::vector<float> inputNoQuantizedValues =</div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>  {</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>  0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>  0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>  0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0</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> </div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 1, 1, 3, 3}, ArmnnType);</div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>  std::vector<float> kernelNoQuantizedValues =</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>  {</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>  1, 2, 3,</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>  4, 5, 6,</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>  7, 8, 9</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>  };</div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span> </div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>  <span class="comment">// Since the dilation rate is 3 this will dilate the kernel to be like 7x7,</span></div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>  <span class="comment">// therefore the output will be 4x4: (I−K+2P)/S +1 => (10-7 +0)/1 +1</span></div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 4, 4}, ArmnnType);</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>  std::vector<float> outputExpectedNoQuantizedValues =</div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>  {</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>  6., 5., 5., 5.,</div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>  6., 5., 5., 5.,</div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>  6., 5., 5., 5.,</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span>  3., 2., 2., 2.</div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>  };</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span> </div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span>  <span class="keywordflow">return</span> Convolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(</div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>  workloadFactory,</div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span>  memoryManager,</div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>  inputNoQuantizedValues,</div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>  inputTensorInfo,</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>  kernelNoQuantizedValues,</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span>  kernelTensorInfo,</div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>  outputExpectedNoQuantizedValues,</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  outputTensorInfo,</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>  3,</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>  3,</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>  layout,</div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>  biasEnabled);</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span> }</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span> </div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span> <span class="keyword">template</span><armnn::DataType ArmnnType, armnn::DataType ArmnnBType, <span class="keyword">typename</span> T></div><div class="line"><a name="l01139"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#a99ef3f48cbd057e0169bc80dc77331ef"> 1139</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a99ef3f48cbd057e0169bc80dc77331ef">Convolution2d2x3x3Dilation3x3Test</a>(</div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span> {</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({1, 2, 10, 10}, ArmnnType);</div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>  std::vector<float> inputNoQuantizedValues =</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>  {</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>  0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>  0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>  0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span> </div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>  0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span>  0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>  0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  };</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span> </div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 1, 2, 3, 3}, ArmnnType);</div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>  std::vector<float> kernelNoQuantizedValues =</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>  1, 2, 3,</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  4, 5, 6,</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>  7, 8, 9,</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span> </div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  1, 2, 3,</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>  4, 5, 6,</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>  7, 8, 9</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> </div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  <span class="comment">// Since the dilation rate is 3 this will dilate the kernel to be like 7x7,</span></div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  <span class="comment">// therefore the output will be 4x4: (I−K+2P)/S +1 => (10-7 +0)/1 +1</span></div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 4, 4}, ArmnnType);</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>  std::vector<float> outputExpectedNoQuantizedValues =</div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>  {</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>  12., 10., 10., 10.,</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>  12., 10., 10., 10.,</div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  12., 10., 10., 10.,</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>  6., 4., 4., 4.</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>  };</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span> </div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>  <span class="keywordflow">return</span> Convolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(</div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>  workloadFactory,</div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span>  memoryManager,</div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  inputNoQuantizedValues,</div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  inputTensorInfo,</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>  kernelNoQuantizedValues,</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>  kernelTensorInfo,</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>  outputExpectedNoQuantizedValues,</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>  outputTensorInfo,</div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  3,</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  3,</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>  layout,</div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>  biasEnabled);</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> </div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span> <span class="keyword">template</span><armnn::DataType ArmnnType, armnn::DataType ArmnnBType, <span class="keyword">typename</span> T></div><div class="line"><a name="l01210"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#acf553288e3b5060768fb91e064993678"> 1210</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#acf553288e3b5060768fb91e064993678">Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test</a>(</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &workloadFactory,</div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a> &memoryManager,</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span> {</div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({1, 1, 10, 10}, ArmnnType);</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span>  std::vector<float> inputNoQuantizedValues =</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>  1, 1, 1, 1, 1, 1, 1, 1, 1, 1,</div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span>  1, 1, 1, 1, 1, 1, 1, 1, 1, 1,</div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span>  1, 1, 1, 1, 1, 1, 1, 1, 1, 1,</div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span>  1, 1, 1, 1, 1, 1, 1, 1, 1, 1,</div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>  1, 1, 1, 1, 1, 1, 1, 1, 1, 1,</div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>  1, 1, 1, 1, 1, 1, 1, 1, 1, 1,</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>  1, 1, 1, 1, 1, 1, 1, 1, 1, 1,</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>  1, 1, 1, 1, 1, 1, 1, 1, 1, 1,</div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>  1, 1, 1, 1, 1, 1, 1, 1, 1, 1,</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>  1, 1, 1, 1, 1, 1, 1, 1, 1, 1</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> </div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 1, 1, 2, 2}, ArmnnType);</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>  std::vector<float> kernelNoQuantizedValues =</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>  {</div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>  1, 2,</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>  3, 4</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="comment">// Since the dilation rate is 2 this will dilate the kernel to be like 3x3: d(K-1)+1 --> 2 x (2-1) + 1 = 3,</span></div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>  <span class="comment">// therefore the output will be 4x4: (I − K + 2P)/S +1 => trunc ( (10 - 3 + 2x2 ) / 3 + 1 )</span></div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  <span class="comment">// where, dilation size = d = 2; kernel size = K = 2; input size = I = 10; padding size = P = 2; stride = S = 3</span></div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 4, 4}, ArmnnType);</div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>  std::vector<float> outputExpectedNoQuantizedValues =</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  {</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  4, 7, 7, 3,</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>  6, 10, 10, 4,</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>  6, 10, 10, 4,</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>  2, 3, 3, 1</div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span>  };</div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>  uint32_t padLeft = 1;</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>  uint32_t padTop = 1;</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>  uint32_t padRight = 1;</div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>  uint32_t padBottom = 1;</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>  <span class="keywordflow">return</span> Convolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>  workloadFactory,</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>  memoryManager,</div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>  inputNoQuantizedValues,</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>  inputTensorInfo,</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>  kernelNoQuantizedValues,</div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>  kernelTensorInfo,</div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span>  outputExpectedNoQuantizedValues,</div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>  outputTensorInfo,</div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>  2,</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>  2,</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>  layout,</div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span>  padLeft,</div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>  padTop,</div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span>  padRight,</div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span>  padBottom,</div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>  3,</div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span>  3,</div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>  biasEnabled</div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>  );</div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span> }</div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span> </div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l01277"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#a638295d292bfdcf71899b57396703c80"> 1277</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T,4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a638295d292bfdcf71899b57396703c80">CompareConvolution2dTestImpl</a>(</div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& refWorkloadFactory)</div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span> {</div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 8;</div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 16;</div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 3;</div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 5;</div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span> </div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelHeight = 3;</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelWidth = 3;</div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span> </div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideX = 2;</div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideY = 3;</div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padX = 1;</div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padY = 1;</div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span> </div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = inputNum;</div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 2;</div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = (inputHeight + 2 * padY - kernelHeight + strideY) / strideY;</div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = (inputWidth + 2 * padX - kernelWidth + strideX) / strideX;</div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span> </div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc;</div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc;</div><div class="line"><a name="l01304"></a><span class="lineno"> 1304</span> </div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth};</div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};</div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelShape[] = {outputChannels, inputChannels, kernelHeight, kernelWidth};</div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasShape[] = {outputChannels};</div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span> </div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>  inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, inputShape, ArmnnType);</div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape, ArmnnType);</div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>  kernelDesc = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, kernelShape, ArmnnType);</div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>  biasDesc = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(1, biasShape, ArmnnType);</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>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T,4></a> ret(outputTensorInfo);</div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span> </div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>  <span class="keyword">auto</span> input = MakeRandomTensor<T, 4>(inputTensorInfo, 124908);</div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>  <span class="keyword">auto</span> kernel = MakeRandomTensor<T, 4>(kernelDesc, 891234);</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>  <span class="keyword">auto</span> bias = MakeRandomTensor<T, 1>(biasDesc, 1028);</div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span> </div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span> </div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>  <a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">armnn::Convolution2dQueueDescriptor</a> data;</div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> weightsTensor(kernelDesc);</div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> biasTensor(biasDesc);</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span> </div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&weightsTensor, &kernel[0][0][0][0]);</div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&biasTensor, &bias[0]);</div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span> </div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span>  data.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &weightsTensor;</div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>  data.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &biasTensor;</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strideX;</div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strideY;</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = padX;</div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = padX;</div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = padY;</div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = padY;</div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span> </div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span>  std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span>  std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</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>  <a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">armnn::Convolution2dQueueDescriptor</a> refData = data;</div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> refInfo = info;</div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span>  SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());</div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>  SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());</div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span> </div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a2184995027cd2c9f9980206de9658855">CreateConvolution2d</a>(data, info);</div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span>  std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a2184995027cd2c9f9980206de9658855">CreateConvolution2d</a>(refData, refInfo);</div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span> </div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>  outputHandleRef->Allocate();</div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>  inputHandleRef->Allocate();</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>  inputHandle->Allocate();</div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span>  outputHandle->Allocate();</div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span> </div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &input[0][0][0][0]);</div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandleRef.get(), &input[0][0][0][0]);</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>  ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span> </div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span>  workloadRef->PostAllocationConfigure();</div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>  workloadRef->Execute();</div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span> </div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.<a class="code" href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a>[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a>[0][0][0][0], outputHandleRef.get());</div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span> </div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l01373"></a><span class="lineno"> 1373</span> }</div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</span> </div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span> <span class="comment">//</span></div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span> <span class="comment">// DepthwiseConvolution2d implementations</span></div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span> <span class="comment">//</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> <span class="keyword">template</span><<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnType, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnBType,</div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>  <span class="keyword">typename</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<ArmnnType></a>, <span class="keyword">typename</span> B = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<ArmnnBType></a>></div><div class="line"><a name="l01381"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#aa405363108e52032fb1e23c3f5a03a57"> 1381</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#aa405363108e52032fb1e23c3f5a03a57">DepthwiseConvolution2dAsymmetricTestImpl</a>(</div><div class="line"><a name="l01382"></a><span class="lineno"> 1382</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</span>  <span class="keyword">const</span> boost::multi_array<T, 4>& input,</div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span>  <span class="keyword">const</span> boost::multi_array<T, 4>& kernel,</div><div class="line"><a name="l01386"></a><span class="lineno"> 1386</span>  <span class="keyword">const</span> boost::multi_array<B, 1>& bias,</div><div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>  <span class="keyword">const</span> boost::multi_array<T, 4>& outputExpected,</div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l01389"></a><span class="lineno"> 1389</span>  int32_t qOffset,</div><div class="line"><a name="l01390"></a><span class="lineno"> 1390</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout,</div><div class="line"><a name="l01391"></a><span class="lineno"> 1391</span>  uint32_t padLeft = 0,</div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span>  uint32_t padTop = 0,</div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>  uint32_t padRight = 0,</div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span>  uint32_t padBottom = 0,</div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>  uint32_t strideX = 1,</div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>  uint32_t strideY = 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="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(input.shape()[0]);</div><div class="line"><a name="l01399"></a><span class="lineno"> 1399</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(input.shape()[1]);</div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(input.shape()[2]);</div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(input.shape()[3]);</div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelChanMul = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(kernel.shape()[0]);</div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(kernel.shape()[1]);</div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(kernel.shape()[2]);</div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(kernel.shape()[3]);</div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputExpected.shape()[0]);</div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputExpected.shape()[1]);</div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputExpected.shape()[2]);</div><div class="line"><a name="l01409"></a><span class="lineno"> 1409</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(outputExpected.shape()[3]);</div><div class="line"><a name="l01410"></a><span class="lineno"> 1410</span> </div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>  <span class="comment">// If a bias is used, its size must equal the number of output channels.</span></div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span>  <span class="keywordtype">bool</span> biasEnabled = bias.size() > 0;</div><div class="line"><a name="l01413"></a><span class="lineno"> 1413</span>  BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels);</div><div class="line"><a name="l01414"></a><span class="lineno"> 1414</span> </div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span>  <span class="comment">// Creates the tensors.</span></div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo =</div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>  <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);</div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo =</div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>  <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);</div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({kernelChanMul, kernelChannels, kernelHeight, kernelWidth}, ArmnnType);</div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc({<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(bias.size())}, ArmnnBType);</div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span> </div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>  <span class="keywordflow">if</span> (armnn::IsQuantizedType<T>())</div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>  {</div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>  inputTensorInfo.SetQuantizationScale(qScale);</div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span>  inputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span>  kernelDesc.SetQuantizationScale(qScale);</div><div class="line"><a name="l01431"></a><span class="lineno"> 1431</span>  kernelDesc.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l01432"></a><span class="lineno"> 1432</span>  biasDesc.SetQuantizationScale(qScale*qScale);</div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</span>  biasDesc.SetQuantizationOffset(0);</div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>  }</div><div class="line"><a name="l01435"></a><span class="lineno"> 1435</span> </div><div class="line"><a name="l01436"></a><span class="lineno"> 1436</span>  <span class="comment">// Construct the input data.</span></div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</span>  std::vector<T> inputData;</div><div class="line"><a name="l01438"></a><span class="lineno"> 1438</span>  inputData.assign(input.data(), input.data() + inputChannels*inputHeight*inputWidth);</div><div class="line"><a name="l01439"></a><span class="lineno"> 1439</span> </div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span>  <span class="comment">// At this point if we require it permute the input data</span></div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a> NCHWToNHWC = { 0, 3, 1, 2 };</div><div class="line"><a name="l01442"></a><span class="lineno"> 1442</span>  <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</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>  std::vector<T> tmp(inputData.size());</div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span>  <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l01446"></a><span class="lineno"> 1446</span>  inputData = tmp;</div><div class="line"><a name="l01447"></a><span class="lineno"> 1447</span>  }</div><div class="line"><a name="l01448"></a><span class="lineno"> 1448</span> </div><div class="line"><a name="l01449"></a><span class="lineno"> 1449</span>  <span class="keyword">auto</span> batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData);</div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span> </div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span>  <span class="comment">// Construct the output data, with bias applied, as appropriate.</span></div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>  std::vector<T> outputData;</div><div class="line"><a name="l01453"></a><span class="lineno"> 1453</span>  outputData.assign(outputExpected.data(), outputExpected.data() + outputChannels*outputHeight*outputWidth);</div><div class="line"><a name="l01454"></a><span class="lineno"> 1454</span>  <span class="keywordflow">if</span> (biasEnabled)</div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span>  {</div><div class="line"><a name="l01456"></a><span class="lineno"> 1456</span>  std::vector<T> biasV;</div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span>  biasV.assign(bias.data(), bias.data() + outputChannels);</div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span>  <a class="code" href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a>(outputData, outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(), outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>(),</div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span>  biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),</div><div class="line"><a name="l01460"></a><span class="lineno"> 1460</span>  outputWidth, outputHeight);</div><div class="line"><a name="l01461"></a><span class="lineno"> 1461</span>  }</div><div class="line"><a name="l01462"></a><span class="lineno"> 1462</span> </div><div class="line"><a name="l01463"></a><span class="lineno"> 1463</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> ret(outputTensorInfo);</div><div class="line"><a name="l01464"></a><span class="lineno"> 1464</span> </div><div class="line"><a name="l01465"></a><span class="lineno"> 1465</span>  <span class="comment">// At this point if we require it permute the expected output</span></div><div class="line"><a name="l01466"></a><span class="lineno"> 1466</span>  <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</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>  std::vector<T> tmp(outputData.size());</div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span>  <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC, outputData.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l01470"></a><span class="lineno"> 1470</span>  outputData = tmp;</div><div class="line"><a name="l01471"></a><span class="lineno"> 1471</span>  }</div><div class="line"><a name="l01472"></a><span class="lineno"> 1472</span> </div><div class="line"><a name="l01473"></a><span class="lineno"> 1473</span>  ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData);</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>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</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>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> weightsTensor(kernelDesc);</div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span> </div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&weightsTensor, &kernel[0][0][0][0]);</div><div class="line"><a name="l01481"></a><span class="lineno"> 1481</span> </div><div class="line"><a name="l01482"></a><span class="lineno"> 1482</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> biasTensor(biasDesc);</div><div class="line"><a name="l01483"></a><span class="lineno"> 1483</span>  <span class="keywordflow">if</span> (biasEnabled)</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>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&biasTensor, &bias[0]);</div><div class="line"><a name="l01486"></a><span class="lineno"> 1486</span>  }</div><div class="line"><a name="l01487"></a><span class="lineno"> 1487</span> </div><div class="line"><a name="l01488"></a><span class="lineno"> 1488</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">armnn::DepthwiseConvolution2dQueueDescriptor</a> data;</div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span>  data.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &weightsTensor;</div><div class="line"><a name="l01490"></a><span class="lineno"> 1490</span>  data.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &biasTensor; <span class="comment">// Still set this whether or not bias is enabled - it can be a source of bugs.</span></div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strideX;</div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strideY;</div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = padLeft;</div><div class="line"><a name="l01494"></a><span class="lineno"> 1494</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = padRight;</div><div class="line"><a name="l01495"></a><span class="lineno"> 1495</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = padTop;</div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = padBottom;</div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = biasEnabled;</div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = layout;</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>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l01501"></a><span class="lineno"> 1501</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l01502"></a><span class="lineno"> 1502</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</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>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#accb9759dfd2880efe0f8d2705ddee448">CreateDepthwiseConvolution2d</a>(data, info);</div><div class="line"><a name="l01505"></a><span class="lineno"> 1505</span>  inputHandle->Allocate();</div><div class="line"><a name="l01506"></a><span class="lineno"> 1506</span>  outputHandle->Allocate();</div><div class="line"><a name="l01507"></a><span class="lineno"> 1507</span> </div><div class="line"><a name="l01508"></a><span class="lineno"> 1508</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &batchedInput[0][0][0][0]);</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>  ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l01511"></a><span class="lineno"> 1511</span> </div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l01513"></a><span class="lineno"> 1513</span> </div><div class="line"><a name="l01514"></a><span class="lineno"> 1514</span>  <span class="keywordflow">return</span> ret;</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> <span class="keyword">template</span><armnn::DataType ArmnnType, armnn::DataType ArmnnBType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l01518"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#a01eae690cbfa5359968f4b8ee13b8814"> 1518</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a01eae690cbfa5359968f4b8ee13b8814">DepthwiseConvolution2dDepthMul1TestImpl</a>(</div><div class="line"><a name="l01519"></a><span class="lineno"> 1519</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01520"></a><span class="lineno"> 1520</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01521"></a><span class="lineno"> 1521</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l01522"></a><span class="lineno"> 1522</span>  int32_t qOffset,</div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l01524"></a><span class="lineno"> 1524</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l01525"></a><span class="lineno"> 1525</span> {</div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span>  <span class="keyword">using</span> B = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<ArmnnBType></a>;</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 3;</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 3;</div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 2;</div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 1;</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>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelHeight = 3;</div><div class="line"><a name="l01534"></a><span class="lineno"> 1534</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelWidth = 3;</div><div class="line"><a name="l01535"></a><span class="lineno"> 1535</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelChannels = inputChannels;</div><div class="line"><a name="l01536"></a><span class="lineno"> 1536</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelDepthMultiplier = 1;</div><div class="line"><a name="l01537"></a><span class="lineno"> 1537</span> </div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = 1;</div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = 1;</div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = kernelChannels;</div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = inputNum;</div><div class="line"><a name="l01542"></a><span class="lineno"> 1542</span> </div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo =</div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>  <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo =</div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span>  <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);</div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({kernelDepthMultiplier, kernelChannels, kernelHeight, kernelWidth},</div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span>  ArmnnType);</div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc({ outputChannels }, ArmnnBType);</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="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>  {</div><div class="line"><a name="l01554"></a><span class="lineno"> 1554</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01558"></a><span class="lineno"> 1558</span>  kernelDesc.SetQuantizationScale(qScale);</div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span>  kernelDesc.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>  biasDesc.SetQuantizationScale(qScale*qScale);</div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>  biasDesc.SetQuantizationOffset(0);</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span>  }</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span>  std::vector<T> inputData = std::vector<T>(</div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span>  QuantizedVector<T>({</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span>  1.f, 2.f, 1.f,</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span>  2.f, 1.f, 2.f,</div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span>  1.f, 2.f, 1.f,</div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span> </div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span>  1.f, 2.f, 1.f,</div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span>  2.f, 1.f, 2.f,</div><div class="line"><a name="l01571"></a><span class="lineno"> 1571</span>  1.f, 2.f, 1.f,</div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span>  },</div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>()));</div><div class="line"><a name="l01575"></a><span class="lineno"> 1575</span> </div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span>  <span class="comment">// at this point if we require it permute the input data</span></div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a> NCHWToNHWC = { 0, 3, 1, 2 };</div><div class="line"><a name="l01578"></a><span class="lineno"> 1578</span>  <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div><div class="line"><a name="l01579"></a><span class="lineno"> 1579</span>  {</div><div class="line"><a name="l01580"></a><span class="lineno"> 1580</span>  std::vector<T> tmp(inputData.size());</div><div class="line"><a name="l01581"></a><span class="lineno"> 1581</span>  <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC, inputData.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l01582"></a><span class="lineno"> 1582</span>  inputData = tmp;</div><div class="line"><a name="l01583"></a><span class="lineno"> 1583</span>  }</div><div class="line"><a name="l01584"></a><span class="lineno"> 1584</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo, inputData);</div><div class="line"><a name="l01585"></a><span class="lineno"> 1585</span> </div><div class="line"><a name="l01586"></a><span class="lineno"> 1586</span>  std::vector<B> biasV(QuantizedVector<B>({ 0, 2 },</div><div class="line"><a name="l01587"></a><span class="lineno"> 1587</span>  biasDesc.GetQuantizationScale(),</div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</span>  biasDesc.GetQuantizationOffset()));</div><div class="line"><a name="l01589"></a><span class="lineno"> 1589</span> </div><div class="line"><a name="l01590"></a><span class="lineno"> 1590</span>  <span class="keyword">auto</span> bias = MakeTensor<B, 1>(biasDesc, biasV);</div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span> </div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span>  std::vector<T> kernelData = std::vector<T>(</div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span>  QuantizedVector<T>({</div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>  1.f, 0.f, 1.f,</div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</span>  0.f, 0.f, 0.f,</div><div class="line"><a name="l01596"></a><span class="lineno"> 1596</span>  -1.f, 0.f, -1.f,</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>  1.f, 0.f, 1.f,</div><div class="line"><a name="l01599"></a><span class="lineno"> 1599</span>  0.f, 0.f, 0.f,</div><div class="line"><a name="l01600"></a><span class="lineno"> 1600</span>  -1.f, 0.f, -1.f,</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>  kernelDesc.GetQuantizationScale(),</div><div class="line"><a name="l01603"></a><span class="lineno"> 1603</span>  kernelDesc.GetQuantizationOffset()));</div><div class="line"><a name="l01604"></a><span class="lineno"> 1604</span> </div><div class="line"><a name="l01605"></a><span class="lineno"> 1605</span>  <span class="keyword">auto</span> kernel = MakeTensor<T, 4>(kernelDesc, kernelData);</div><div class="line"><a name="l01606"></a><span class="lineno"> 1606</span> </div><div class="line"><a name="l01607"></a><span class="lineno"> 1607</span>  <span class="comment">// Manually calculated.</span></div><div class="line"><a name="l01608"></a><span class="lineno"> 1608</span>  std::vector<T> outputImage(</div><div class="line"><a name="l01609"></a><span class="lineno"> 1609</span>  QuantizedVector<T>({ 0.f, 0.f },</div><div class="line"><a name="l01610"></a><span class="lineno"> 1610</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l01611"></a><span class="lineno"> 1611</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>())</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> </div><div class="line"><a name="l01614"></a><span class="lineno"> 1614</span>  <span class="comment">// Optionally apply bias to output image.</span></div><div class="line"><a name="l01615"></a><span class="lineno"> 1615</span>  <span class="keywordflow">if</span>(biasEnabled)</div><div class="line"><a name="l01616"></a><span class="lineno"> 1616</span>  {</div><div class="line"><a name="l01617"></a><span class="lineno"> 1617</span>  <a class="code" href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a>(outputImage, outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(), outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>(),</div><div class="line"><a name="l01618"></a><span class="lineno"> 1618</span>  biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),</div><div class="line"><a name="l01619"></a><span class="lineno"> 1619</span>  outputWidth, outputHeight);</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> </div><div class="line"><a name="l01622"></a><span class="lineno"> 1622</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> ret(outputTensorInfo);</div><div class="line"><a name="l01623"></a><span class="lineno"> 1623</span>  <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div><div class="line"><a name="l01624"></a><span class="lineno"> 1624</span>  {</div><div class="line"><a name="l01625"></a><span class="lineno"> 1625</span>  std::vector<T> tmp(outputImage.size());</div><div class="line"><a name="l01626"></a><span class="lineno"> 1626</span>  <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC, outputImage.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l01627"></a><span class="lineno"> 1627</span>  outputImage = tmp;</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> </div><div class="line"><a name="l01630"></a><span class="lineno"> 1630</span>  ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputImage);</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>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l01633"></a><span class="lineno"> 1633</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l01634"></a><span class="lineno"> 1634</span> </div><div class="line"><a name="l01635"></a><span class="lineno"> 1635</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">armnn::DepthwiseConvolution2dQueueDescriptor</a> data;</div><div class="line"><a name="l01636"></a><span class="lineno"> 1636</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l01637"></a><span class="lineno"> 1637</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> weightsTensor(kernelDesc);</div><div class="line"><a name="l01638"></a><span class="lineno"> 1638</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> biasTensor(biasDesc);</div><div class="line"><a name="l01639"></a><span class="lineno"> 1639</span> </div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&weightsTensor, &kernel[0][0][0][0]);</div><div class="line"><a name="l01641"></a><span class="lineno"> 1641</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&biasTensor, &bias[0]);</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>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l01644"></a><span class="lineno"> 1644</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l01645"></a><span class="lineno"> 1645</span> </div><div class="line"><a name="l01646"></a><span class="lineno"> 1646</span>  data.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &weightsTensor;</div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span>  data.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &biasTensor; <span class="comment">// Still set this whether or not bias is enabled.</span></div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 0;</div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 0;</div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 0;</div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 0;</div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = biasEnabled;</div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = layout;</div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</span> </div><div class="line"><a name="l01657"></a><span class="lineno"> 1657</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#accb9759dfd2880efe0f8d2705ddee448">CreateDepthwiseConvolution2d</a>(data, info);</div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</span>  inputHandle->Allocate();</div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span>  outputHandle->Allocate();</div><div class="line"><a name="l01660"></a><span class="lineno"> 1660</span> </div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &input[0][0][0][0]);</div><div class="line"><a name="l01662"></a><span class="lineno"> 1662</span> </div><div class="line"><a name="l01663"></a><span class="lineno"> 1663</span>  ExecuteWorkload(*workload, memoryManager);</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>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span> </div><div class="line"><a name="l01667"></a><span class="lineno"> 1667</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l01668"></a><span class="lineno"> 1668</span> }</div><div class="line"><a name="l01669"></a><span class="lineno"> 1669</span> </div><div class="line"><a name="l01670"></a><span class="lineno"> 1670</span> <span class="keyword">template</span><armnn::DataType ArmnnType, armnn::DataType ArmnnBType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l01671"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#ae3cc54b77789d10caeb5a438a0821ba0"> 1671</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#ae3cc54b77789d10caeb5a438a0821ba0">DepthwiseConvolution2dTestImpl</a>(</div><div class="line"><a name="l01672"></a><span class="lineno"> 1672</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01673"></a><span class="lineno"> 1673</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01674"></a><span class="lineno"> 1674</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l01675"></a><span class="lineno"> 1675</span>  int32_t qOffset,</div><div class="line"><a name="l01676"></a><span class="lineno"> 1676</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l01677"></a><span class="lineno"> 1677</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l01678"></a><span class="lineno"> 1678</span> {</div><div class="line"><a name="l01679"></a><span class="lineno"> 1679</span>  <span class="keyword">using</span> B = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<ArmnnBType></a>;</div><div class="line"><a name="l01680"></a><span class="lineno"> 1680</span> </div><div class="line"><a name="l01681"></a><span class="lineno"> 1681</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depthMultiplier = 2;</div><div class="line"><a name="l01682"></a><span class="lineno"> 1682</span> </div><div class="line"><a name="l01683"></a><span class="lineno"> 1683</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 8;</div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 16;</div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 2;</div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatchSize = 1;</div><div class="line"><a name="l01687"></a><span class="lineno"> 1687</span> </div><div class="line"><a name="l01688"></a><span class="lineno"> 1688</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelHeight = 5;</div><div class="line"><a name="l01689"></a><span class="lineno"> 1689</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelWidth = 3;</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>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = inputHeight - kernelHeight + 1 + 2;</div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = (inputWidth - kernelWidth + 1)/2;</div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = inputChannels * depthMultiplier;</div><div class="line"><a name="l01694"></a><span class="lineno"> 1694</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputBatchSize = inputBatchSize;</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(</div><div class="line"><a name="l01697"></a><span class="lineno"> 1697</span>  inputBatchSize, inputChannels, inputHeight, inputWidth, layout, ArmnnType);</div><div class="line"><a name="l01698"></a><span class="lineno"> 1698</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(</div><div class="line"><a name="l01699"></a><span class="lineno"> 1699</span>  outputBatchSize, outputChannels, outputHeight, outputWidth, layout, ArmnnType);</div><div class="line"><a name="l01700"></a><span class="lineno"> 1700</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({depthMultiplier, inputChannels, kernelHeight, kernelWidth},</div><div class="line"><a name="l01701"></a><span class="lineno"> 1701</span>  ArmnnType);</div><div class="line"><a name="l01702"></a><span class="lineno"> 1702</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc({outputChannels}, ArmnnBType);</div><div class="line"><a name="l01703"></a><span class="lineno"> 1703</span> </div><div class="line"><a name="l01704"></a><span class="lineno"> 1704</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l01705"></a><span class="lineno"> 1705</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l01706"></a><span class="lineno"> 1706</span>  {</div><div class="line"><a name="l01707"></a><span class="lineno"> 1707</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01708"></a><span class="lineno"> 1708</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01709"></a><span class="lineno"> 1709</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01710"></a><span class="lineno"> 1710</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01711"></a><span class="lineno"> 1711</span>  kernelDesc.SetQuantizationScale(qScale);</div><div class="line"><a name="l01712"></a><span class="lineno"> 1712</span>  kernelDesc.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>  biasDesc.SetQuantizationScale(qScale*qScale);</div><div class="line"><a name="l01714"></a><span class="lineno"> 1714</span>  biasDesc.SetQuantizationOffset(0);</div><div class="line"><a name="l01715"></a><span class="lineno"> 1715</span>  }</div><div class="line"><a name="l01716"></a><span class="lineno"> 1716</span> </div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span>  <span class="comment">// NOTE: originalInputData is in NCHW format</span></div><div class="line"><a name="l01718"></a><span class="lineno"> 1718</span>  std::vector<T> originalInputData = std::vector<T>(</div><div class="line"><a name="l01719"></a><span class="lineno"> 1719</span>  QuantizedVector<T>({</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>  0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span>  0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span>  0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>  0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span>  0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span>  0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l01726"></a><span class="lineno"> 1726</span>  0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l01727"></a><span class="lineno"> 1727</span>  0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,</div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span>  0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span>  0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01730"></a><span class="lineno"> 1730</span>  0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01731"></a><span class="lineno"> 1731</span>  0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01732"></a><span class="lineno"> 1732</span>  0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01733"></a><span class="lineno"> 1733</span>  0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01734"></a><span class="lineno"> 1734</span>  0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01735"></a><span class="lineno"> 1735</span>  0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f</div><div class="line"><a name="l01736"></a><span class="lineno"> 1736</span>  },</div><div class="line"><a name="l01737"></a><span class="lineno"> 1737</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l01738"></a><span class="lineno"> 1738</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>()));</div><div class="line"><a name="l01739"></a><span class="lineno"> 1739</span> </div><div class="line"><a name="l01740"></a><span class="lineno"> 1740</span>  std::vector<T> inputData = originalInputData;</div><div class="line"><a name="l01741"></a><span class="lineno"> 1741</span>  <span class="comment">// at this point if we require it permute the input data</span></div><div class="line"><a name="l01742"></a><span class="lineno"> 1742</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a> NCHWToNHWC = { 0, 3, 1, 2 };</div><div class="line"><a name="l01743"></a><span class="lineno"> 1743</span>  <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</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>  <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC,</div><div class="line"><a name="l01746"></a><span class="lineno"> 1746</span>  originalInputData.data(), inputData.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l01747"></a><span class="lineno"> 1747</span>  }</div><div class="line"><a name="l01748"></a><span class="lineno"> 1748</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo, inputData);</div><div class="line"><a name="l01749"></a><span class="lineno"> 1749</span> </div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span>  std::vector<B> biasV = QuantizedVector<B>({ 0, 2, 1, -1 },</div><div class="line"><a name="l01751"></a><span class="lineno"> 1751</span>  biasDesc.GetQuantizationScale(),</div><div class="line"><a name="l01752"></a><span class="lineno"> 1752</span>  biasDesc.GetQuantizationOffset());</div><div class="line"><a name="l01753"></a><span class="lineno"> 1753</span> </div><div class="line"><a name="l01754"></a><span class="lineno"> 1754</span>  <span class="keyword">auto</span> bias = MakeTensor<B, 1>(biasDesc, biasV);</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>  std::vector<T> kernelData = std::vector<T>(</div><div class="line"><a name="l01757"></a><span class="lineno"> 1757</span>  QuantizedVector<T>({</div><div class="line"><a name="l01758"></a><span class="lineno"> 1758</span>  1, 1, 1,</div><div class="line"><a name="l01759"></a><span class="lineno"> 1759</span>  1, -1, 1,</div><div class="line"><a name="l01760"></a><span class="lineno"> 1760</span>  1, 1, 1,</div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span>  1, 1, 1,</div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span>  1, 1, 1,</div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span> </div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span>  2, 2, 2,</div><div class="line"><a name="l01765"></a><span class="lineno"> 1765</span>  2, 2, 2,</div><div class="line"><a name="l01766"></a><span class="lineno"> 1766</span>  2, 2, 2,</div><div class="line"><a name="l01767"></a><span class="lineno"> 1767</span>  2, 2, 2,</div><div class="line"><a name="l01768"></a><span class="lineno"> 1768</span>  2, 2, 2,</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>  0, 0, 0,</div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span>  0, -1, 0,</div><div class="line"><a name="l01772"></a><span class="lineno"> 1772</span>  0, 0, 0,</div><div class="line"><a name="l01773"></a><span class="lineno"> 1773</span>  0, 0, 0,</div><div class="line"><a name="l01774"></a><span class="lineno"> 1774</span>  0, 0, 0,</div><div class="line"><a name="l01775"></a><span class="lineno"> 1775</span> </div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>  0, 0, 0,</div><div class="line"><a name="l01777"></a><span class="lineno"> 1777</span>  0, 0, 0,</div><div class="line"><a name="l01778"></a><span class="lineno"> 1778</span>  0, 1, 0,</div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span>  0, 0, 0,</div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span>  0, 0, 0</div><div class="line"><a name="l01781"></a><span class="lineno"> 1781</span>  },</div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span>  kernelDesc.GetQuantizationScale(),</div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span>  kernelDesc.GetQuantizationOffset()));</div><div class="line"><a name="l01784"></a><span class="lineno"> 1784</span> </div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span>  <span class="keyword">auto</span> kernel = MakeTensor<T, 4>(kernelDesc, kernelData);</div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span> </div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span>  <span class="comment">// Manually calculated.</span></div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span>  std::vector<T> originalOutputImage = std::vector<T>(</div><div class="line"><a name="l01789"></a><span class="lineno"> 1789</span>  QuantizedVector<T>({</div><div class="line"><a name="l01790"></a><span class="lineno"> 1790</span>  3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f,</div><div class="line"><a name="l01791"></a><span class="lineno"> 1791</span>  6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f,</div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span>  5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f,</div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span>  6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f,</div><div class="line"><a name="l01794"></a><span class="lineno"> 1794</span>  6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f, 6.5f,</div><div class="line"><a name="l01795"></a><span class="lineno"> 1795</span>  5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f, 5.0f,</div><div class="line"><a name="l01796"></a><span class="lineno"> 1796</span> </div><div class="line"><a name="l01797"></a><span class="lineno"> 1797</span>  -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,</div><div class="line"><a name="l01798"></a><span class="lineno"> 1798</span>  0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01799"></a><span class="lineno"> 1799</span>  -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,</div><div class="line"><a name="l01800"></a><span class="lineno"> 1800</span>  -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,</div><div class="line"><a name="l01801"></a><span class="lineno"> 1801</span>  -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,</div><div class="line"><a name="l01802"></a><span class="lineno"> 1802</span>  -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,</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>  8.0f, 8.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01805"></a><span class="lineno"> 1805</span>  10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01806"></a><span class="lineno"> 1806</span>  10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01807"></a><span class="lineno"> 1807</span>  10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01808"></a><span class="lineno"> 1808</span>  10.0f, 10.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01809"></a><span class="lineno"> 1809</span>  8.0f, 8.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01810"></a><span class="lineno"> 1810</span> </div><div class="line"><a name="l01811"></a><span class="lineno"> 1811</span>  0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01812"></a><span class="lineno"> 1812</span>  0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01813"></a><span class="lineno"> 1813</span>  0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01814"></a><span class="lineno"> 1814</span>  0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01815"></a><span class="lineno"> 1815</span>  0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,</div><div class="line"><a name="l01816"></a><span class="lineno"> 1816</span>  0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f</div><div class="line"><a name="l01817"></a><span class="lineno"> 1817</span>  },</div><div class="line"><a name="l01818"></a><span class="lineno"> 1818</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l01819"></a><span class="lineno"> 1819</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>()));</div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span> </div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span>  <span class="comment">// Optionally apply bias to output image.</span></div><div class="line"><a name="l01822"></a><span class="lineno"> 1822</span>  <span class="keywordflow">if</span>(biasEnabled)</div><div class="line"><a name="l01823"></a><span class="lineno"> 1823</span>  {</div><div class="line"><a name="l01824"></a><span class="lineno"> 1824</span>  <a class="code" href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a>(originalOutputImage,</div><div class="line"><a name="l01825"></a><span class="lineno"> 1825</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(),</div><div class="line"><a name="l01826"></a><span class="lineno"> 1826</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>(),</div><div class="line"><a name="l01827"></a><span class="lineno"> 1827</span>  biasV,</div><div class="line"><a name="l01828"></a><span class="lineno"> 1828</span>  biasDesc.GetQuantizationScale(),</div><div class="line"><a name="l01829"></a><span class="lineno"> 1829</span>  biasDesc.GetQuantizationOffset(),</div><div class="line"><a name="l01830"></a><span class="lineno"> 1830</span>  outputWidth,</div><div class="line"><a name="l01831"></a><span class="lineno"> 1831</span>  outputHeight);</div><div class="line"><a name="l01832"></a><span class="lineno"> 1832</span>  }</div><div class="line"><a name="l01833"></a><span class="lineno"> 1833</span> </div><div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> ret(outputTensorInfo);</div><div class="line"><a name="l01835"></a><span class="lineno"> 1835</span>  std::vector<T> outputImage = originalOutputImage;</div><div class="line"><a name="l01836"></a><span class="lineno"> 1836</span>  <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</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>  <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC,</div><div class="line"><a name="l01839"></a><span class="lineno"> 1839</span>  originalOutputImage.data(), outputImage.data(), <span class="keyword">sizeof</span>(T));</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> </div><div class="line"><a name="l01842"></a><span class="lineno"> 1842</span>  ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputImage);</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>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l01845"></a><span class="lineno"> 1845</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l01846"></a><span class="lineno"> 1846</span> </div><div class="line"><a name="l01847"></a><span class="lineno"> 1847</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">armnn::DepthwiseConvolution2dQueueDescriptor</a> data;</div><div class="line"><a name="l01848"></a><span class="lineno"> 1848</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l01849"></a><span class="lineno"> 1849</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> weightsTensor(kernelDesc);</div><div class="line"><a name="l01850"></a><span class="lineno"> 1850</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> biasTensor(biasDesc);</div><div class="line"><a name="l01851"></a><span class="lineno"> 1851</span> </div><div class="line"><a name="l01852"></a><span class="lineno"> 1852</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&weightsTensor, &kernel[0][0][0][0]);</div><div class="line"><a name="l01853"></a><span class="lineno"> 1853</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&biasTensor, &bias[0]);</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>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l01856"></a><span class="lineno"> 1856</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</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>  data.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &weightsTensor;</div><div class="line"><a name="l01859"></a><span class="lineno"> 1859</span>  data.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &biasTensor; <span class="comment">// Still set this whether or not bias is enabled.</span></div><div class="line"><a name="l01860"></a><span class="lineno"> 1860</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 2;</div><div class="line"><a name="l01861"></a><span class="lineno"> 1861</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l01862"></a><span class="lineno"> 1862</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 0;</div><div class="line"><a name="l01863"></a><span class="lineno"> 1863</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 0;</div><div class="line"><a name="l01864"></a><span class="lineno"> 1864</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l01865"></a><span class="lineno"> 1865</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l01866"></a><span class="lineno"> 1866</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = biasEnabled;</div><div class="line"><a name="l01867"></a><span class="lineno"> 1867</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = layout;</div><div class="line"><a name="l01868"></a><span class="lineno"> 1868</span> </div><div class="line"><a name="l01869"></a><span class="lineno"> 1869</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#accb9759dfd2880efe0f8d2705ddee448">CreateDepthwiseConvolution2d</a>(data, info);</div><div class="line"><a name="l01870"></a><span class="lineno"> 1870</span>  inputHandle->Allocate();</div><div class="line"><a name="l01871"></a><span class="lineno"> 1871</span>  outputHandle->Allocate();</div><div class="line"><a name="l01872"></a><span class="lineno"> 1872</span> </div><div class="line"><a name="l01873"></a><span class="lineno"> 1873</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &input[0][0][0][0]);</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>  ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l01876"></a><span class="lineno"> 1876</span> </div><div class="line"><a name="l01877"></a><span class="lineno"> 1877</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l01878"></a><span class="lineno"> 1878</span> </div><div class="line"><a name="l01879"></a><span class="lineno"> 1879</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l01880"></a><span class="lineno"> 1880</span> }</div><div class="line"><a name="l01881"></a><span class="lineno"> 1881</span> </div><div class="line"><a name="l01882"></a><span class="lineno"> 1882</span> <span class="keyword">template</span><<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnType, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnBType,</div><div class="line"><a name="l01883"></a><span class="lineno"> 1883</span>  <span class="keyword">typename</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<ArmnnType></a>, <span class="keyword">typename</span> B = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<ArmnnBType></a>></div><div class="line"><a name="l01884"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#a46e9706106f1b08c964d953154c66ad6"> 1884</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#ae3cc54b77789d10caeb5a438a0821ba0">DepthwiseConvolution2dTestImpl</a>(</div><div class="line"><a name="l01885"></a><span class="lineno"> 1885</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01886"></a><span class="lineno"> 1886</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01887"></a><span class="lineno"> 1887</span>  <span class="keyword">const</span> boost::multi_array<T, 4>& originalInput,</div><div class="line"><a name="l01888"></a><span class="lineno"> 1888</span>  <span class="keyword">const</span> boost::multi_array<T, 4>& originalKernel,</div><div class="line"><a name="l01889"></a><span class="lineno"> 1889</span>  <span class="keyword">const</span> boost::multi_array<B, 1>& bias,</div><div class="line"><a name="l01890"></a><span class="lineno"> 1890</span>  <span class="keyword">const</span> boost::multi_array<T, 4>& originalOutputExpected,</div><div class="line"><a name="l01891"></a><span class="lineno"> 1891</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l01892"></a><span class="lineno"> 1892</span>  int32_t qOffset,</div><div class="line"><a name="l01893"></a><span class="lineno"> 1893</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>,</div><div class="line"><a name="l01894"></a><span class="lineno"> 1894</span>  uint32_t padLeft = 0,</div><div class="line"><a name="l01895"></a><span class="lineno"> 1895</span>  uint32_t padTop = 0,</div><div class="line"><a name="l01896"></a><span class="lineno"> 1896</span>  uint32_t padRight = 0,</div><div class="line"><a name="l01897"></a><span class="lineno"> 1897</span>  uint32_t padBottom = 0,</div><div class="line"><a name="l01898"></a><span class="lineno"> 1898</span>  uint32_t strideX = 1,</div><div class="line"><a name="l01899"></a><span class="lineno"> 1899</span>  uint32_t strideY = 1,</div><div class="line"><a name="l01900"></a><span class="lineno"> 1900</span>  uint32_t dilationX = 1,</div><div class="line"><a name="l01901"></a><span class="lineno"> 1901</span>  uint32_t dilationY = 1)</div><div class="line"><a name="l01902"></a><span class="lineno"> 1902</span> {</div><div class="line"><a name="l01903"></a><span class="lineno"> 1903</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalInput.shape()[2]);</div><div class="line"><a name="l01904"></a><span class="lineno"> 1904</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalInput.shape()[3]);</div><div class="line"><a name="l01905"></a><span class="lineno"> 1905</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalInput.shape()[1]);</div><div class="line"><a name="l01906"></a><span class="lineno"> 1906</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalInput.shape()[0]);</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalOutputExpected.shape()[2]);</div><div class="line"><a name="l01909"></a><span class="lineno"> 1909</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalOutputExpected.shape()[3]);</div><div class="line"><a name="l01910"></a><span class="lineno"> 1910</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalOutputExpected.shape()[1]);</div><div class="line"><a name="l01911"></a><span class="lineno"> 1911</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalOutputExpected.shape()[0]);</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelHeight = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalKernel.shape()[2]);</div><div class="line"><a name="l01914"></a><span class="lineno"> 1914</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelWidth = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalKernel.shape()[3]);</div><div class="line"><a name="l01915"></a><span class="lineno"> 1915</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelChannels = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalKernel.shape()[1]);</div><div class="line"><a name="l01916"></a><span class="lineno"> 1916</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelDepthMul = <a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>>(originalKernel.shape()[0]);</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="keywordtype">bool</span> biasEnabled = bias.size() > 0;</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="comment">// This function currently assumes 1 batch of input/output (and duplicates this into 2 batches).</span></div><div class="line"><a name="l01921"></a><span class="lineno"> 1921</span>  BOOST_ASSERT(inputNum == 1);</div><div class="line"><a name="l01922"></a><span class="lineno"> 1922</span>  BOOST_ASSERT(outputNum == 1);</div><div class="line"><a name="l01923"></a><span class="lineno"> 1923</span> </div><div class="line"><a name="l01924"></a><span class="lineno"> 1924</span>  <span class="comment">// If a bias is used, its size must equal the number of output channels.</span></div><div class="line"><a name="l01925"></a><span class="lineno"> 1925</span>  BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels);</div><div class="line"><a name="l01926"></a><span class="lineno"> 1926</span> </div><div class="line"><a name="l01927"></a><span class="lineno"> 1927</span> </div><div class="line"><a name="l01928"></a><span class="lineno"> 1928</span>  <span class="comment">// Note these tensors will use two (identical) batches.</span></div><div class="line"><a name="l01929"></a><span class="lineno"> 1929</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo =</div><div class="line"><a name="l01930"></a><span class="lineno"> 1930</span>  <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(2*inputNum, inputChannels, inputHeight, inputWidth, layout, ArmnnType);</div><div class="line"><a name="l01931"></a><span class="lineno"> 1931</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo =</div><div class="line"><a name="l01932"></a><span class="lineno"> 1932</span>  <a class="code" href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a>(2*outputNum, outputChannels, outputHeight, outputWidth, layout, ArmnnType);</div><div class="line"><a name="l01933"></a><span class="lineno"> 1933</span> </div><div class="line"><a name="l01934"></a><span class="lineno"> 1934</span>  <span class="comment">// Kernel must be NCHW layout always, independently of the layout of the input and output for depthwise convolution.</span></div><div class="line"><a name="l01935"></a><span class="lineno"> 1935</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc({kernelDepthMul, kernelChannels, kernelHeight, kernelWidth}, ArmnnType);</div><div class="line"><a name="l01936"></a><span class="lineno"> 1936</span> </div><div class="line"><a name="l01937"></a><span class="lineno"> 1937</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc({<span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">></span>(bias.size())}, ArmnnBType);</div><div class="line"><a name="l01938"></a><span class="lineno"> 1938</span> </div><div class="line"><a name="l01939"></a><span class="lineno"> 1939</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l01940"></a><span class="lineno"> 1940</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l01941"></a><span class="lineno"> 1941</span>  {</div><div class="line"><a name="l01942"></a><span class="lineno"> 1942</span>  inputTensorInfo.SetQuantizationScale(qScale);</div><div class="line"><a name="l01943"></a><span class="lineno"> 1943</span>  inputTensorInfo.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01945"></a><span class="lineno"> 1945</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01946"></a><span class="lineno"> 1946</span>  kernelDesc.SetQuantizationScale(qScale);</div><div class="line"><a name="l01947"></a><span class="lineno"> 1947</span>  kernelDesc.SetQuantizationOffset(qOffset);</div><div class="line"><a name="l01948"></a><span class="lineno"> 1948</span>  biasDesc.SetQuantizationScale(qScale*qScale);</div><div class="line"><a name="l01949"></a><span class="lineno"> 1949</span>  biasDesc.SetQuantizationOffset(0);</div><div class="line"><a name="l01950"></a><span class="lineno"> 1950</span>  }</div><div class="line"><a name="l01951"></a><span class="lineno"> 1951</span> </div><div class="line"><a name="l01952"></a><span class="lineno"> 1952</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> ret(outputTensorInfo);</div><div class="line"><a name="l01953"></a><span class="lineno"> 1953</span> </div><div class="line"><a name="l01954"></a><span class="lineno"> 1954</span>  <span class="comment">// Construct input data</span></div><div class="line"><a name="l01955"></a><span class="lineno"> 1955</span>  std::vector<T> input;</div><div class="line"><a name="l01956"></a><span class="lineno"> 1956</span>  input.assign(originalInput.data(), originalInput.data() + 1*inputChannels*inputHeight*inputWidth);</div><div class="line"><a name="l01957"></a><span class="lineno"> 1957</span>  std::vector<T> inputData;</div><div class="line"><a name="l01958"></a><span class="lineno"> 1958</span>  inputData.insert(inputData.end(), input.begin(), input.end());</div><div class="line"><a name="l01959"></a><span class="lineno"> 1959</span>  inputData.insert(inputData.end(), input.begin(), input.end());</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">// at this point if we require it permute the input data</span></div><div class="line"><a name="l01962"></a><span class="lineno"> 1962</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a> NCHWToNHWC = { 0, 3, 1, 2 };</div><div class="line"><a name="l01963"></a><span class="lineno"> 1963</span>  <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</div><div class="line"><a name="l01964"></a><span class="lineno"> 1964</span>  {</div><div class="line"><a name="l01965"></a><span class="lineno"> 1965</span>  std::vector<T> tmp(inputData.size());</div><div class="line"><a name="l01966"></a><span class="lineno"> 1966</span>  <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l01967"></a><span class="lineno"> 1967</span>  inputData = tmp;</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> </div><div class="line"><a name="l01970"></a><span class="lineno"> 1970</span>  <span class="keyword">auto</span> batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData);</div><div class="line"><a name="l01971"></a><span class="lineno"> 1971</span> </div><div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>  std::vector<T> output;</div><div class="line"><a name="l01973"></a><span class="lineno"> 1973</span>  output.assign(originalOutputExpected.data(),</div><div class="line"><a name="l01974"></a><span class="lineno"> 1974</span>  originalOutputExpected.data() + outputChannels*outputHeight*outputWidth);</div><div class="line"><a name="l01975"></a><span class="lineno"> 1975</span> </div><div class="line"><a name="l01976"></a><span class="lineno"> 1976</span>  <span class="comment">// Apply bias to output data if it is enabled.</span></div><div class="line"><a name="l01977"></a><span class="lineno"> 1977</span>  <span class="keywordflow">if</span>(biasEnabled)</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>  std::vector<T> biasV;</div><div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>  biasV.assign(bias.data(), bias.data() + outputChannels);</div><div class="line"><a name="l01981"></a><span class="lineno"> 1981</span>  <a class="code" href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a>(output, outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a047ca888c43bd7fb5702853bf72410d0">GetQuantizationScale</a>(), outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a770b51078da02f44a819e9f95d8058b5">GetQuantizationOffset</a>(),</div><div class="line"><a name="l01982"></a><span class="lineno"> 1982</span>  biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),</div><div class="line"><a name="l01983"></a><span class="lineno"> 1983</span>  outputWidth, outputHeight);</div><div class="line"><a name="l01984"></a><span class="lineno"> 1984</span>  }</div><div class="line"><a name="l01985"></a><span class="lineno"> 1985</span> </div><div class="line"><a name="l01986"></a><span class="lineno"> 1986</span>  <span class="comment">// Construct expected output data</span></div><div class="line"><a name="l01987"></a><span class="lineno"> 1987</span>  std::vector<T> outputData;</div><div class="line"><a name="l01988"></a><span class="lineno"> 1988</span>  outputData.insert(outputData.end(), output.begin(), output.end());</div><div class="line"><a name="l01989"></a><span class="lineno"> 1989</span>  outputData.insert(outputData.end(), output.begin(), output.end());</div><div class="line"><a name="l01990"></a><span class="lineno"> 1990</span> </div><div class="line"><a name="l01991"></a><span class="lineno"> 1991</span>  <span class="comment">// at this point if we require it permute the expected output</span></div><div class="line"><a name="l01992"></a><span class="lineno"> 1992</span>  <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>)</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>  std::vector<T> tmp(outputData.size());</div><div class="line"><a name="l01995"></a><span class="lineno"> 1995</span>  <a class="code" href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a>(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(), NCHWToNHWC, outputData.data(), tmp.data(), <span class="keyword">sizeof</span>(T));</div><div class="line"><a name="l01996"></a><span class="lineno"> 1996</span>  outputData = tmp;</div><div class="line"><a name="l01997"></a><span class="lineno"> 1997</span>  }</div><div class="line"><a name="l01998"></a><span class="lineno"> 1998</span>  ret.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<T, 4>(outputTensorInfo, outputData);</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>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l02001"></a><span class="lineno"> 2001</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l02002"></a><span class="lineno"> 2002</span> </div><div class="line"><a name="l02003"></a><span class="lineno"> 2003</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">armnn::DepthwiseConvolution2dQueueDescriptor</a> data;</div><div class="line"><a name="l02004"></a><span class="lineno"> 2004</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l02005"></a><span class="lineno"> 2005</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> weightsTensor(kernelDesc);</div><div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> biasTensor(biasDesc);</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>  boost::multi_array<T, 4> kernel = boost::multi_array<T, 4>(originalKernel);</div><div class="line"><a name="l02009"></a><span class="lineno"> 2009</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&weightsTensor, &kernel[0][0][0][0]);</div><div class="line"><a name="l02010"></a><span class="lineno"> 2010</span> </div><div class="line"><a name="l02011"></a><span class="lineno"> 2011</span>  <span class="keywordflow">if</span>(biasEnabled)</div><div class="line"><a name="l02012"></a><span class="lineno"> 2012</span>  {</div><div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&biasTensor, &bias[0]);</div><div class="line"><a name="l02014"></a><span class="lineno"> 2014</span>  }</div><div class="line"><a name="l02015"></a><span class="lineno"> 2015</span> </div><div class="line"><a name="l02016"></a><span class="lineno"> 2016</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l02017"></a><span class="lineno"> 2017</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l02018"></a><span class="lineno"> 2018</span> </div><div class="line"><a name="l02019"></a><span class="lineno"> 2019</span>  data.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &weightsTensor;</div><div class="line"><a name="l02020"></a><span class="lineno"> 2020</span>  data.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &biasTensor; <span class="comment">// Still set this whether or not bias is enabled - can be a source of bugs.</span></div><div class="line"><a name="l02021"></a><span class="lineno"> 2021</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strideX;</div><div class="line"><a name="l02022"></a><span class="lineno"> 2022</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strideY;</div><div class="line"><a name="l02023"></a><span class="lineno"> 2023</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = padLeft;</div><div class="line"><a name="l02024"></a><span class="lineno"> 2024</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = padRight;</div><div class="line"><a name="l02025"></a><span class="lineno"> 2025</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = padTop;</div><div class="line"><a name="l02026"></a><span class="lineno"> 2026</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = padBottom;</div><div class="line"><a name="l02027"></a><span class="lineno"> 2027</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = biasEnabled;</div><div class="line"><a name="l02028"></a><span class="lineno"> 2028</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = layout;</div><div class="line"><a name="l02029"></a><span class="lineno"> 2029</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = dilationX;</div><div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = dilationY;</div><div class="line"><a name="l02031"></a><span class="lineno"> 2031</span> </div><div class="line"><a name="l02032"></a><span class="lineno"> 2032</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#accb9759dfd2880efe0f8d2705ddee448">CreateDepthwiseConvolution2d</a>(data, info);</div><div class="line"><a name="l02033"></a><span class="lineno"> 2033</span>  inputHandle->Allocate();</div><div class="line"><a name="l02034"></a><span class="lineno"> 2034</span>  outputHandle->Allocate();</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>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &batchedInput[0][0][0][0]);</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>  ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l02039"></a><span class="lineno"> 2039</span> </div><div class="line"><a name="l02040"></a><span class="lineno"> 2040</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.<a class="code" href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a>[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l02041"></a><span class="lineno"> 2041</span> </div><div class="line"><a name="l02042"></a><span class="lineno"> 2042</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l02043"></a><span class="lineno"> 2043</span> }</div><div class="line"><a name="l02044"></a><span class="lineno"> 2044</span> </div><div class="line"><a name="l02045"></a><span class="lineno"> 2045</span> <span class="keyword">template</span><<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnType, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnBType,</div><div class="line"><a name="l02046"></a><span class="lineno"> 2046</span>  <span class="keyword">typename</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<ArmnnType></a>></div><div class="line"><a name="l02047"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#a952b4460c66365d89ebb3df940bbd9bd"> 2047</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a952b4460c66365d89ebb3df940bbd9bd">DepthwiseConvolution2dAsymmetricTestCommon</a>(</div><div class="line"><a name="l02048"></a><span class="lineno"> 2048</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</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_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02050"></a><span class="lineno"> 2050</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l02051"></a><span class="lineno"> 2051</span>  int32_t qOffset,</div><div class="line"><a name="l02052"></a><span class="lineno"> 2052</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l02053"></a><span class="lineno"> 2053</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l02054"></a><span class="lineno"> 2054</span> {</div><div class="line"><a name="l02055"></a><span class="lineno"> 2055</span>  <span class="comment">// Use a single-batch 2-channel 5x5 image as input.</span></div><div class="line"><a name="l02056"></a><span class="lineno"> 2056</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, 2, 5, 5 }, ArmnnType);</div><div class="line"><a name="l02057"></a><span class="lineno"> 2057</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(</div><div class="line"><a name="l02058"></a><span class="lineno"> 2058</span>  QuantizedVector<T>({</div><div class="line"><a name="l02059"></a><span class="lineno"> 2059</span>  0, 1, 2, 3, 4,</div><div class="line"><a name="l02060"></a><span class="lineno"> 2060</span>  5, 6, 7, 8, 9,</div><div class="line"><a name="l02061"></a><span class="lineno"> 2061</span>  10, 11, 12, 13, 14,</div><div class="line"><a name="l02062"></a><span class="lineno"> 2062</span>  15, 16, 17, 18, 19,</div><div class="line"><a name="l02063"></a><span class="lineno"> 2063</span>  20, 21, 22, 23, 24,</div><div class="line"><a name="l02064"></a><span class="lineno"> 2064</span> </div><div class="line"><a name="l02065"></a><span class="lineno"> 2065</span>  25, 26, 27, 28, 29,</div><div class="line"><a name="l02066"></a><span class="lineno"> 2066</span>  30, 31, 32, 33, 34,</div><div class="line"><a name="l02067"></a><span class="lineno"> 2067</span>  35, 36, 37, 38, 39,</div><div class="line"><a name="l02068"></a><span class="lineno"> 2068</span>  40, 41, 42, 43, 44,</div><div class="line"><a name="l02069"></a><span class="lineno"> 2069</span>  45, 46, 47, 48, 49</div><div class="line"><a name="l02070"></a><span class="lineno"> 2070</span>  },</div><div class="line"><a name="l02071"></a><span class="lineno"> 2071</span>  inputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02072"></a><span class="lineno"> 2072</span>  inputTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02073"></a><span class="lineno"> 2073</span> </div><div class="line"><a name="l02074"></a><span class="lineno"> 2074</span>  <span class="comment">// Use a depth multiplier of 1 on a 2-channel 4x4 kernel.</span></div><div class="line"><a name="l02075"></a><span class="lineno"> 2075</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 1, 2, 4, 4 }, ArmnnType);</div><div class="line"><a name="l02076"></a><span class="lineno"> 2076</span>  <span class="keyword">auto</span> kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>(</div><div class="line"><a name="l02077"></a><span class="lineno"> 2077</span>  QuantizedVector<T>({</div><div class="line"><a name="l02078"></a><span class="lineno"> 2078</span>  32, 31, 30, 29,</div><div class="line"><a name="l02079"></a><span class="lineno"> 2079</span>  28, 27, 26, 25,</div><div class="line"><a name="l02080"></a><span class="lineno"> 2080</span>  24, 23, 22, 21,</div><div class="line"><a name="l02081"></a><span class="lineno"> 2081</span>  20, 19, 18, 17,</div><div class="line"><a name="l02082"></a><span class="lineno"> 2082</span> </div><div class="line"><a name="l02083"></a><span class="lineno"> 2083</span>  16, 15, 14, 13,</div><div class="line"><a name="l02084"></a><span class="lineno"> 2084</span>  12, 11, 10, 9,</div><div class="line"><a name="l02085"></a><span class="lineno"> 2085</span>  8, 7, 6, 5,</div><div class="line"><a name="l02086"></a><span class="lineno"> 2086</span>  4, 3, 2, 1</div><div class="line"><a name="l02087"></a><span class="lineno"> 2087</span>  },</div><div class="line"><a name="l02088"></a><span class="lineno"> 2088</span>  kernelTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02089"></a><span class="lineno"> 2089</span>  kernelTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02090"></a><span class="lineno"> 2090</span> </div><div class="line"><a name="l02091"></a><span class="lineno"> 2091</span>  <span class="comment">// Expected output is 1 batch of a 2-channel 5x5 image.</span></div><div class="line"><a name="l02092"></a><span class="lineno"> 2092</span>  <span class="comment">// Calculated using the python tensorflow library with strideX=1, strideY=1.</span></div><div class="line"><a name="l02093"></a><span class="lineno"> 2093</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 2, 5, 5 }, ArmnnType);</div><div class="line"><a name="l02094"></a><span class="lineno"> 2094</span>  boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>(</div><div class="line"><a name="l02095"></a><span class="lineno"> 2095</span>  QuantizedVector<T>({</div><div class="line"><a name="l02096"></a><span class="lineno"> 2096</span>  1062, 1580, 1850, 1530, 1117,</div><div class="line"><a name="l02097"></a><span class="lineno"> 2097</span>  2140, 3108, 3500, 2842, 2042,</div><div class="line"><a name="l02098"></a><span class="lineno"> 2098</span>  3580, 5068, 5460, 4342, 3062,</div><div class="line"><a name="l02099"></a><span class="lineno"> 2099</span>  3618, 5072, 5390, 4248, 2971,</div><div class="line"><a name="l02100"></a><span class="lineno"> 2100</span>  3074, 4282, 4510, 3533, 2457,</div><div class="line"><a name="l02101"></a><span class="lineno"> 2101</span> </div><div class="line"><a name="l02102"></a><span class="lineno"> 2102</span>  1550, 2284, 2362, 1955, 1428,</div><div class="line"><a name="l02103"></a><span class="lineno"> 2103</span>  2910, 4206, 4342, 3528, 2536,</div><div class="line"><a name="l02104"></a><span class="lineno"> 2104</span>  3390, 4886, 5022, 4068, 2916,</div><div class="line"><a name="l02105"></a><span class="lineno"> 2105</span>  3566, 5056, 5182, 4133, 2922,</div><div class="line"><a name="l02106"></a><span class="lineno"> 2106</span>  3100, 4352, 4452, 3517, 2465</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>  outputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02109"></a><span class="lineno"> 2109</span>  outputTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02110"></a><span class="lineno"> 2110</span> </div><div class="line"><a name="l02111"></a><span class="lineno"> 2111</span>  <span class="keywordflow">return</span> DepthwiseConvolution2dAsymmetricTestImpl<ArmnnType, ArmnnBType>(</div><div class="line"><a name="l02112"></a><span class="lineno"> 2112</span>  workloadFactory,</div><div class="line"><a name="l02113"></a><span class="lineno"> 2113</span>  memoryManager,</div><div class="line"><a name="l02114"></a><span class="lineno"> 2114</span>  input,</div><div class="line"><a name="l02115"></a><span class="lineno"> 2115</span>  kernel,</div><div class="line"><a name="l02116"></a><span class="lineno"> 2116</span>  GetBias2<ArmnnBType>(biasEnabled, qScale * qScale),</div><div class="line"><a name="l02117"></a><span class="lineno"> 2117</span>  expectedOutput,</div><div class="line"><a name="l02118"></a><span class="lineno"> 2118</span>  qScale,</div><div class="line"><a name="l02119"></a><span class="lineno"> 2119</span>  qOffset,</div><div class="line"><a name="l02120"></a><span class="lineno"> 2120</span>  layout,</div><div class="line"><a name="l02121"></a><span class="lineno"> 2121</span>  1, <span class="comment">// Padding left.</span></div><div class="line"><a name="l02122"></a><span class="lineno"> 2122</span>  1, <span class="comment">// Padding top.</span></div><div class="line"><a name="l02123"></a><span class="lineno"> 2123</span>  2, <span class="comment">// Padding right.</span></div><div class="line"><a name="l02124"></a><span class="lineno"> 2124</span>  2, <span class="comment">// Padding bottom.</span></div><div class="line"><a name="l02125"></a><span class="lineno"> 2125</span>  1, <span class="comment">// strideX</span></div><div class="line"><a name="l02126"></a><span class="lineno"> 2126</span>  1); <span class="comment">// strideY</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"> 2129</span> <span class="keyword">template</span><<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnType, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnBType,</div><div class="line"><a name="l02130"></a><span class="lineno"> 2130</span>  <span class="keyword">typename</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<ArmnnType></a>></div><div class="line"><a name="l02131"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#a6271caa80dbf6fc82f97081d3d99d987"> 2131</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a6271caa80dbf6fc82f97081d3d99d987">DepthwiseConvolution2dNhwcTestCommon</a>(</div><div class="line"><a name="l02132"></a><span class="lineno"> 2132</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02133"></a><span class="lineno"> 2133</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02134"></a><span class="lineno"> 2134</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l02135"></a><span class="lineno"> 2135</span>  int32_t qOffset,</div><div class="line"><a name="l02136"></a><span class="lineno"> 2136</span>  <span class="keywordtype">bool</span> biasEnabled)</div><div class="line"><a name="l02137"></a><span class="lineno"> 2137</span> {</div><div class="line"><a name="l02138"></a><span class="lineno"> 2138</span>  <span class="keyword">auto</span> layout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, 2, 5, 5}, ArmnnType);</div><div class="line"><a name="l02141"></a><span class="lineno"> 2141</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(</div><div class="line"><a name="l02142"></a><span class="lineno"> 2142</span>  QuantizedVector<T>({</div><div class="line"><a name="l02143"></a><span class="lineno"> 2143</span>  0, 1, 2, 3, 4,</div><div class="line"><a name="l02144"></a><span class="lineno"> 2144</span>  5, 6, 7, 8, 9,</div><div class="line"><a name="l02145"></a><span class="lineno"> 2145</span>  10, 11, 12, 13, 14,</div><div class="line"><a name="l02146"></a><span class="lineno"> 2146</span>  15, 16, 17, 18, 19,</div><div class="line"><a name="l02147"></a><span class="lineno"> 2147</span>  20, 21, 22, 23, 24,</div><div class="line"><a name="l02148"></a><span class="lineno"> 2148</span> </div><div class="line"><a name="l02149"></a><span class="lineno"> 2149</span>  25, 26, 27, 28, 29,</div><div class="line"><a name="l02150"></a><span class="lineno"> 2150</span>  30, 31, 32, 33, 34,</div><div class="line"><a name="l02151"></a><span class="lineno"> 2151</span>  35, 36, 37, 38, 39,</div><div class="line"><a name="l02152"></a><span class="lineno"> 2152</span>  40, 41, 42, 43, 44,</div><div class="line"><a name="l02153"></a><span class="lineno"> 2153</span>  45, 46, 47, 48, 49</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>  inputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02156"></a><span class="lineno"> 2156</span>  inputTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02157"></a><span class="lineno"> 2157</span> </div><div class="line"><a name="l02158"></a><span class="lineno"> 2158</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 1, 2, 4, 4 }, ArmnnType);</div><div class="line"><a name="l02159"></a><span class="lineno"> 2159</span>  <span class="keyword">auto</span> kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>(</div><div class="line"><a name="l02160"></a><span class="lineno"> 2160</span>  QuantizedVector<T>({</div><div class="line"><a name="l02161"></a><span class="lineno"> 2161</span>  32, 31, 30, 29,</div><div class="line"><a name="l02162"></a><span class="lineno"> 2162</span>  28, 27, 26, 25,</div><div class="line"><a name="l02163"></a><span class="lineno"> 2163</span>  24, 23, 22, 21,</div><div class="line"><a name="l02164"></a><span class="lineno"> 2164</span>  20, 19, 18, 17,</div><div class="line"><a name="l02165"></a><span class="lineno"> 2165</span> </div><div class="line"><a name="l02166"></a><span class="lineno"> 2166</span>  16, 15, 14, 13,</div><div class="line"><a name="l02167"></a><span class="lineno"> 2167</span>  12, 11, 10, 9,</div><div class="line"><a name="l02168"></a><span class="lineno"> 2168</span>  8, 7, 6, 5,</div><div class="line"><a name="l02169"></a><span class="lineno"> 2169</span>  4, 3, 2, 1</div><div class="line"><a name="l02170"></a><span class="lineno"> 2170</span>  },</div><div class="line"><a name="l02171"></a><span class="lineno"> 2171</span>  kernelTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02172"></a><span class="lineno"> 2172</span>  kernelTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02173"></a><span class="lineno"> 2173</span> </div><div class="line"><a name="l02174"></a><span class="lineno"> 2174</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 2, 5, 5}, ArmnnType);</div><div class="line"><a name="l02175"></a><span class="lineno"> 2175</span>  boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>(</div><div class="line"><a name="l02176"></a><span class="lineno"> 2176</span>  QuantizedVector<T>({</div><div class="line"><a name="l02177"></a><span class="lineno"> 2177</span>  1062, 1580, 1850, 1530, 1117,</div><div class="line"><a name="l02178"></a><span class="lineno"> 2178</span>  2140, 3108, 3500, 2842, 2042,</div><div class="line"><a name="l02179"></a><span class="lineno"> 2179</span>  3580, 5068, 5460, 4342, 3062,</div><div class="line"><a name="l02180"></a><span class="lineno"> 2180</span>  3618, 5072, 5390, 4248, 2971,</div><div class="line"><a name="l02181"></a><span class="lineno"> 2181</span>  3074, 4282, 4510, 3533, 2457,</div><div class="line"><a name="l02182"></a><span class="lineno"> 2182</span> </div><div class="line"><a name="l02183"></a><span class="lineno"> 2183</span>  1550, 2284, 2362, 1955, 1428,</div><div class="line"><a name="l02184"></a><span class="lineno"> 2184</span>  2910, 4206, 4342, 3528, 2536,</div><div class="line"><a name="l02185"></a><span class="lineno"> 2185</span>  3390, 4886, 5022, 4068, 2916,</div><div class="line"><a name="l02186"></a><span class="lineno"> 2186</span>  3566, 5056, 5182, 4133, 2922,</div><div class="line"><a name="l02187"></a><span class="lineno"> 2187</span>  3100, 4352, 4452, 3517, 2465</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>  outputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02190"></a><span class="lineno"> 2190</span>  outputTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02191"></a><span class="lineno"> 2191</span> </div><div class="line"><a name="l02192"></a><span class="lineno"> 2192</span>  <span class="keywordflow">return</span> DepthwiseConvolution2dTestImpl<ArmnnType, ArmnnBType>(</div><div class="line"><a name="l02193"></a><span class="lineno"> 2193</span>  workloadFactory,</div><div class="line"><a name="l02194"></a><span class="lineno"> 2194</span>  memoryManager,</div><div class="line"><a name="l02195"></a><span class="lineno"> 2195</span>  input,</div><div class="line"><a name="l02196"></a><span class="lineno"> 2196</span>  kernel,</div><div class="line"><a name="l02197"></a><span class="lineno"> 2197</span>  GetBias2<ArmnnBType>(biasEnabled, qScale * qScale),</div><div class="line"><a name="l02198"></a><span class="lineno"> 2198</span>  expectedOutput,</div><div class="line"><a name="l02199"></a><span class="lineno"> 2199</span>  qScale,</div><div class="line"><a name="l02200"></a><span class="lineno"> 2200</span>  qOffset,</div><div class="line"><a name="l02201"></a><span class="lineno"> 2201</span>  layout,</div><div class="line"><a name="l02202"></a><span class="lineno"> 2202</span>  1, <span class="comment">// Padding left.</span></div><div class="line"><a name="l02203"></a><span class="lineno"> 2203</span>  1, <span class="comment">// Padding top.</span></div><div class="line"><a name="l02204"></a><span class="lineno"> 2204</span>  2, <span class="comment">// Padding right.</span></div><div class="line"><a name="l02205"></a><span class="lineno"> 2205</span>  2, <span class="comment">// Padding bottom.</span></div><div class="line"><a name="l02206"></a><span class="lineno"> 2206</span>  1, <span class="comment">// strideX</span></div><div class="line"><a name="l02207"></a><span class="lineno"> 2207</span>  1); <span class="comment">// strideY</span></div><div class="line"><a name="l02208"></a><span class="lineno"> 2208</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> <span class="keyword">template</span><<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnType, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> ArmnnBType,</div><div class="line"><a name="l02211"></a><span class="lineno"> 2211</span>  <span class="keyword">typename</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<ArmnnType></a>></div><div class="line"><a name="l02212"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#ac7af28eafb5b583057bca4471ce22328"> 2212</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#ac7af28eafb5b583057bca4471ce22328">SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTestCommon</a>(</div><div class="line"><a name="l02213"></a><span class="lineno"> 2213</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</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_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02215"></a><span class="lineno"> 2215</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l02216"></a><span class="lineno"> 2216</span>  int32_t qOffset,</div><div class="line"><a name="l02217"></a><span class="lineno"> 2217</span>  <span class="keywordtype">bool</span> biasEnabled)</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="keyword">auto</span> layout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l02220"></a><span class="lineno"> 2220</span> </div><div class="line"><a name="l02221"></a><span class="lineno"> 2221</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 9, 9}, ArmnnType);</div><div class="line"><a name="l02222"></a><span class="lineno"> 2222</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(</div><div class="line"><a name="l02223"></a><span class="lineno"> 2223</span>  QuantizedVector<T>({</div><div class="line"><a name="l02224"></a><span class="lineno"> 2224</span>  0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02225"></a><span class="lineno"> 2225</span>  0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02226"></a><span class="lineno"> 2226</span>  0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02227"></a><span class="lineno"> 2227</span>  0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02228"></a><span class="lineno"> 2228</span>  0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02229"></a><span class="lineno"> 2229</span>  0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02230"></a><span class="lineno"> 2230</span>  0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02231"></a><span class="lineno"> 2231</span>  0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02232"></a><span class="lineno"> 2232</span>  0, 0, 0, 0, 0, 0, 0, 0, 0</div><div class="line"><a name="l02233"></a><span class="lineno"> 2233</span>  },</div><div class="line"><a name="l02234"></a><span class="lineno"> 2234</span>  inputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02235"></a><span class="lineno"> 2235</span>  inputTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02236"></a><span class="lineno"> 2236</span> </div><div class="line"><a name="l02237"></a><span class="lineno"> 2237</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 1, 1, 3, 3}, ArmnnType);</div><div class="line"><a name="l02238"></a><span class="lineno"> 2238</span>  <span class="keyword">auto</span> kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>(</div><div class="line"><a name="l02239"></a><span class="lineno"> 2239</span>  QuantizedVector<T>({</div><div class="line"><a name="l02240"></a><span class="lineno"> 2240</span>  1, 2, 3,</div><div class="line"><a name="l02241"></a><span class="lineno"> 2241</span>  4, 5, 6,</div><div class="line"><a name="l02242"></a><span class="lineno"> 2242</span>  7, 8, 9</div><div class="line"><a name="l02243"></a><span class="lineno"> 2243</span>  },</div><div class="line"><a name="l02244"></a><span class="lineno"> 2244</span>  kernelTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02245"></a><span class="lineno"> 2245</span>  kernelTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02246"></a><span class="lineno"> 2246</span> </div><div class="line"><a name="l02247"></a><span class="lineno"> 2247</span>  uint32_t padLeft = 0;</div><div class="line"><a name="l02248"></a><span class="lineno"> 2248</span>  uint32_t padTop = 0;</div><div class="line"><a name="l02249"></a><span class="lineno"> 2249</span>  uint32_t padRight = 0;</div><div class="line"><a name="l02250"></a><span class="lineno"> 2250</span>  uint32_t padBottom = 0;</div><div class="line"><a name="l02251"></a><span class="lineno"> 2251</span>  uint32_t strideX = 1;</div><div class="line"><a name="l02252"></a><span class="lineno"> 2252</span>  uint32_t strideY = 1;</div><div class="line"><a name="l02253"></a><span class="lineno"> 2253</span>  uint32_t dilationX = 3;</div><div class="line"><a name="l02254"></a><span class="lineno"> 2254</span>  uint32_t dilationY = 3;</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>  <span class="comment">// Since the dilation rate is 3 this will reduce the size of the output from 9x9 to 3x3 of all 5s.</span></div><div class="line"><a name="l02257"></a><span class="lineno"> 2257</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 3, 3}, ArmnnType);</div><div class="line"><a name="l02258"></a><span class="lineno"> 2258</span>  boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>(</div><div class="line"><a name="l02259"></a><span class="lineno"> 2259</span>  QuantizedVector<T>({</div><div class="line"><a name="l02260"></a><span class="lineno"> 2260</span>  5, 5, 5,</div><div class="line"><a name="l02261"></a><span class="lineno"> 2261</span>  5, 5, 5,</div><div class="line"><a name="l02262"></a><span class="lineno"> 2262</span>  5, 5, 5</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>  outputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02265"></a><span class="lineno"> 2265</span>  outputTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02266"></a><span class="lineno"> 2266</span> </div><div class="line"><a name="l02267"></a><span class="lineno"> 2267</span>  <span class="keywordflow">return</span> DepthwiseConvolution2dTestImpl<ArmnnType, ArmnnBType>(</div><div class="line"><a name="l02268"></a><span class="lineno"> 2268</span>  workloadFactory,</div><div class="line"><a name="l02269"></a><span class="lineno"> 2269</span>  memoryManager,</div><div class="line"><a name="l02270"></a><span class="lineno"> 2270</span>  input,</div><div class="line"><a name="l02271"></a><span class="lineno"> 2271</span>  kernel,</div><div class="line"><a name="l02272"></a><span class="lineno"> 2272</span>  GetBias2<ArmnnBType>(biasEnabled, qScale * qScale),</div><div class="line"><a name="l02273"></a><span class="lineno"> 2273</span>  expectedOutput,</div><div class="line"><a name="l02274"></a><span class="lineno"> 2274</span>  qScale,</div><div class="line"><a name="l02275"></a><span class="lineno"> 2275</span>  qOffset,</div><div class="line"><a name="l02276"></a><span class="lineno"> 2276</span>  layout,</div><div class="line"><a name="l02277"></a><span class="lineno"> 2277</span>  padLeft,</div><div class="line"><a name="l02278"></a><span class="lineno"> 2278</span>  padTop,</div><div class="line"><a name="l02279"></a><span class="lineno"> 2279</span>  padRight,</div><div class="line"><a name="l02280"></a><span class="lineno"> 2280</span>  padBottom,</div><div class="line"><a name="l02281"></a><span class="lineno"> 2281</span>  strideX,</div><div class="line"><a name="l02282"></a><span class="lineno"> 2282</span>  strideY,</div><div class="line"><a name="l02283"></a><span class="lineno"> 2283</span>  dilationX,</div><div class="line"><a name="l02284"></a><span class="lineno"> 2284</span>  dilationY);</div><div class="line"><a name="l02285"></a><span class="lineno"> 2285</span> }</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">template</span><armnn::DataType ArmnnType, armnn::DataType ArmnnBType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l02288"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#a80ee4cde34185af792db65aa40cf5c98"> 2288</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a80ee4cde34185af792db65aa40cf5c98">DepthwiseConvolution2d3x3DilationTestCommon</a>(</div><div class="line"><a name="l02289"></a><span class="lineno"> 2289</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02290"></a><span class="lineno"> 2290</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02291"></a><span class="lineno"> 2291</span>  <span class="keyword">const</span> std::vector<float>& inputNoQuantizedValues,</div><div class="line"><a name="l02292"></a><span class="lineno"> 2292</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& inputTensorInfo,</div><div class="line"><a name="l02293"></a><span class="lineno"> 2293</span>  <span class="keyword">const</span> std::vector<float>& kernelNoQuantizedValues,</div><div class="line"><a name="l02294"></a><span class="lineno"> 2294</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& kernelTensorInfo,</div><div class="line"><a name="l02295"></a><span class="lineno"> 2295</span>  <span class="keyword">const</span> std::vector<float>& outputExpectedNoQuantizedValues,</div><div class="line"><a name="l02296"></a><span class="lineno"> 2296</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>& outputTensorInfo,</div><div class="line"><a name="l02297"></a><span class="lineno"> 2297</span>  uint32_t dilationX,</div><div class="line"><a name="l02298"></a><span class="lineno"> 2298</span>  uint32_t dilationY,</div><div class="line"><a name="l02299"></a><span class="lineno"> 2299</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>,</div><div class="line"><a name="l02300"></a><span class="lineno"> 2300</span>  <span class="keywordtype">bool</span> biasEnabled = <span class="keyword">false</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>  <span class="keywordtype">float</span> qScale;</div><div class="line"><a name="l02303"></a><span class="lineno"> 2303</span>  int32_t qOffset;</div><div class="line"><a name="l02304"></a><span class="lineno"> 2304</span>  <span class="keywordflow">switch</span> (ArmnnType)</div><div class="line"><a name="l02305"></a><span class="lineno"> 2305</span>  {</div><div class="line"><a name="l02306"></a><span class="lineno"> 2306</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>:</div><div class="line"><a name="l02307"></a><span class="lineno"> 2307</span>  {</div><div class="line"><a name="l02308"></a><span class="lineno"> 2308</span>  qScale = 0.1f;</div><div class="line"><a name="l02309"></a><span class="lineno"> 2309</span>  qOffset = 128;</div><div class="line"><a name="l02310"></a><span class="lineno"> 2310</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02311"></a><span class="lineno"> 2311</span>  }</div><div class="line"><a name="l02312"></a><span class="lineno"> 2312</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>:</div><div class="line"><a name="l02313"></a><span class="lineno"> 2313</span>  {</div><div class="line"><a name="l02314"></a><span class="lineno"> 2314</span>  qScale = 0.1f;</div><div class="line"><a name="l02315"></a><span class="lineno"> 2315</span>  qOffset = 0;</div><div class="line"><a name="l02316"></a><span class="lineno"> 2316</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02317"></a><span class="lineno"> 2317</span>  }</div><div class="line"><a name="l02318"></a><span class="lineno"> 2318</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>:</div><div class="line"><a name="l02319"></a><span class="lineno"> 2319</span>  <span class="keywordflow">default</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>  qScale = 0.f;</div><div class="line"><a name="l02322"></a><span class="lineno"> 2322</span>  qOffset = 0;</div><div class="line"><a name="l02323"></a><span class="lineno"> 2323</span>  <span class="keywordflow">break</span>;</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> </div><div class="line"><a name="l02327"></a><span class="lineno"> 2327</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l02328"></a><span class="lineno"> 2328</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l02329"></a><span class="lineno"> 2329</span>  kernelTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l02330"></a><span class="lineno"> 2330</span>  kernelTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l02331"></a><span class="lineno"> 2331</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l02332"></a><span class="lineno"> 2332</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l02333"></a><span class="lineno"> 2333</span> </div><div class="line"><a name="l02334"></a><span class="lineno"> 2334</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo,</div><div class="line"><a name="l02335"></a><span class="lineno"> 2335</span>  std::vector<T>(QuantizedVector<T>(inputNoQuantizedValues,</div><div class="line"><a name="l02336"></a><span class="lineno"> 2336</span>  inputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02337"></a><span class="lineno"> 2337</span>  inputTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02338"></a><span class="lineno"> 2338</span>  <span class="keyword">auto</span> kernel = MakeTensor<T, 4>(kernelTensorInfo,</div><div class="line"><a name="l02339"></a><span class="lineno"> 2339</span>  std::vector<T>(QuantizedVector<T>(kernelNoQuantizedValues,</div><div class="line"><a name="l02340"></a><span class="lineno"> 2340</span>  kernelTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02341"></a><span class="lineno"> 2341</span>  kernelTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02342"></a><span class="lineno"> 2342</span>  <span class="keyword">auto</span> expectedOutput =</div><div class="line"><a name="l02343"></a><span class="lineno"> 2343</span>  MakeTensor<T, 4>(outputTensorInfo,</div><div class="line"><a name="l02344"></a><span class="lineno"> 2344</span>  std::vector<T>(QuantizedVector<T>(outputExpectedNoQuantizedValues,</div><div class="line"><a name="l02345"></a><span class="lineno"> 2345</span>  outputTensorInfo.GetQuantizationScale(),</div><div class="line"><a name="l02346"></a><span class="lineno"> 2346</span>  outputTensorInfo.GetQuantizationOffset())));</div><div class="line"><a name="l02347"></a><span class="lineno"> 2347</span> </div><div class="line"><a name="l02348"></a><span class="lineno"> 2348</span>  uint32_t padLeft = 0;</div><div class="line"><a name="l02349"></a><span class="lineno"> 2349</span>  uint32_t padTop = 0;</div><div class="line"><a name="l02350"></a><span class="lineno"> 2350</span>  uint32_t padRight = 0;</div><div class="line"><a name="l02351"></a><span class="lineno"> 2351</span>  uint32_t padBottom = 0;</div><div class="line"><a name="l02352"></a><span class="lineno"> 2352</span>  uint32_t strideX = 1;</div><div class="line"><a name="l02353"></a><span class="lineno"> 2353</span>  uint32_t strideY = 1;</div><div class="line"><a name="l02354"></a><span class="lineno"> 2354</span> </div><div class="line"><a name="l02355"></a><span class="lineno"> 2355</span>  <span class="keywordflow">return</span> DepthwiseConvolution2dTestImpl<ArmnnType, ArmnnBType>(</div><div class="line"><a name="l02356"></a><span class="lineno"> 2356</span>  workloadFactory,</div><div class="line"><a name="l02357"></a><span class="lineno"> 2357</span>  memoryManager,</div><div class="line"><a name="l02358"></a><span class="lineno"> 2358</span>  input,</div><div class="line"><a name="l02359"></a><span class="lineno"> 2359</span>  kernel,</div><div class="line"><a name="l02360"></a><span class="lineno"> 2360</span>  GetBias<ArmnnBType>(biasEnabled, qScale * qScale, outputTensorInfo, layout),</div><div class="line"><a name="l02361"></a><span class="lineno"> 2361</span>  expectedOutput,</div><div class="line"><a name="l02362"></a><span class="lineno"> 2362</span>  qScale,</div><div class="line"><a name="l02363"></a><span class="lineno"> 2363</span>  qOffset,</div><div class="line"><a name="l02364"></a><span class="lineno"> 2364</span>  layout,</div><div class="line"><a name="l02365"></a><span class="lineno"> 2365</span>  padLeft,</div><div class="line"><a name="l02366"></a><span class="lineno"> 2366</span>  padTop,</div><div class="line"><a name="l02367"></a><span class="lineno"> 2367</span>  padRight,</div><div class="line"><a name="l02368"></a><span class="lineno"> 2368</span>  padBottom,</div><div class="line"><a name="l02369"></a><span class="lineno"> 2369</span>  strideX,</div><div class="line"><a name="l02370"></a><span class="lineno"> 2370</span>  strideY,</div><div class="line"><a name="l02371"></a><span class="lineno"> 2371</span>  dilationX,</div><div class="line"><a name="l02372"></a><span class="lineno"> 2372</span>  dilationY);</div><div class="line"><a name="l02373"></a><span class="lineno"> 2373</span> }</div><div class="line"><a name="l02374"></a><span class="lineno"> 2374</span> </div><div class="line"><a name="l02375"></a><span class="lineno"> 2375</span> <span class="keyword">template</span><armnn::DataType ArmnnType, armnn::DataType ArmnnBType, <span class="keyword">typename</span> T></div><div class="line"><a name="l02376"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#a1c3398bdb48e4ce4643a1eeaf3e054a3"> 2376</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a1c3398bdb48e4ce4643a1eeaf3e054a3">DepthwiseConvolution2d3x3Dilation3x3Test</a>(</div><div class="line"><a name="l02377"></a><span class="lineno"> 2377</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02378"></a><span class="lineno"> 2378</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02379"></a><span class="lineno"> 2379</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l02380"></a><span class="lineno"> 2380</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l02381"></a><span class="lineno"> 2381</span> {</div><div class="line"><a name="l02382"></a><span class="lineno"> 2382</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({1, 1, 10, 10}, ArmnnType);</div><div class="line"><a name="l02383"></a><span class="lineno"> 2383</span>  std::vector<float> inputNoQuantizedValues =</div><div class="line"><a name="l02384"></a><span class="lineno"> 2384</span>  {</div><div class="line"><a name="l02385"></a><span class="lineno"> 2385</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02386"></a><span class="lineno"> 2386</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02387"></a><span class="lineno"> 2387</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02388"></a><span class="lineno"> 2388</span>  0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02389"></a><span class="lineno"> 2389</span>  0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02390"></a><span class="lineno"> 2390</span>  0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02391"></a><span class="lineno"> 2391</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02392"></a><span class="lineno"> 2392</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02393"></a><span class="lineno"> 2393</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02394"></a><span class="lineno"> 2394</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0</div><div class="line"><a name="l02395"></a><span class="lineno"> 2395</span>  };</div><div class="line"><a name="l02396"></a><span class="lineno"> 2396</span> </div><div class="line"><a name="l02397"></a><span class="lineno"> 2397</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 1, 1, 3, 3}, ArmnnType);</div><div class="line"><a name="l02398"></a><span class="lineno"> 2398</span>  std::vector<float> kernelNoQuantizedValues =</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>  1, 2, 3,</div><div class="line"><a name="l02401"></a><span class="lineno"> 2401</span>  4, 5, 6,</div><div class="line"><a name="l02402"></a><span class="lineno"> 2402</span>  7, 8, 9</div><div class="line"><a name="l02403"></a><span class="lineno"> 2403</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>  <span class="comment">// Since the dilation rate is 3 this will dilate the kernel to be like 7x7,</span></div><div class="line"><a name="l02406"></a><span class="lineno"> 2406</span>  <span class="comment">// therefore the output will be 4x4: (I−K+2P)/S +1 => (10-7 +0)/1 +1</span></div><div class="line"><a name="l02407"></a><span class="lineno"> 2407</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 4, 4}, ArmnnType);</div><div class="line"><a name="l02408"></a><span class="lineno"> 2408</span>  std::vector<float> outputExpectedNoQuantizedValues =</div><div class="line"><a name="l02409"></a><span class="lineno"> 2409</span>  {</div><div class="line"><a name="l02410"></a><span class="lineno"> 2410</span>  6., 5., 5., 5.,</div><div class="line"><a name="l02411"></a><span class="lineno"> 2411</span>  6., 5., 5., 5.,</div><div class="line"><a name="l02412"></a><span class="lineno"> 2412</span>  6., 5., 5., 5.,</div><div class="line"><a name="l02413"></a><span class="lineno"> 2413</span>  3., 2., 2., 2.</div><div class="line"><a name="l02414"></a><span class="lineno"> 2414</span>  };</div><div class="line"><a name="l02415"></a><span class="lineno"> 2415</span> </div><div class="line"><a name="l02416"></a><span class="lineno"> 2416</span>  <span class="keywordflow">return</span> DepthwiseConvolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(</div><div class="line"><a name="l02417"></a><span class="lineno"> 2417</span>  workloadFactory,</div><div class="line"><a name="l02418"></a><span class="lineno"> 2418</span>  memoryManager,</div><div class="line"><a name="l02419"></a><span class="lineno"> 2419</span>  inputNoQuantizedValues,</div><div class="line"><a name="l02420"></a><span class="lineno"> 2420</span>  inputTensorInfo,</div><div class="line"><a name="l02421"></a><span class="lineno"> 2421</span>  kernelNoQuantizedValues,</div><div class="line"><a name="l02422"></a><span class="lineno"> 2422</span>  kernelTensorInfo,</div><div class="line"><a name="l02423"></a><span class="lineno"> 2423</span>  outputExpectedNoQuantizedValues,</div><div class="line"><a name="l02424"></a><span class="lineno"> 2424</span>  outputTensorInfo,</div><div class="line"><a name="l02425"></a><span class="lineno"> 2425</span>  3,</div><div class="line"><a name="l02426"></a><span class="lineno"> 2426</span>  3,</div><div class="line"><a name="l02427"></a><span class="lineno"> 2427</span>  layout,</div><div class="line"><a name="l02428"></a><span class="lineno"> 2428</span>  biasEnabled);</div><div class="line"><a name="l02429"></a><span class="lineno"> 2429</span> }</div><div class="line"><a name="l02430"></a><span class="lineno"> 2430</span> </div><div class="line"><a name="l02431"></a><span class="lineno"> 2431</span> <span class="keyword">template</span><armnn::DataType ArmnnType, armnn::DataType ArmnnBType, <span class="keyword">typename</span> T></div><div class="line"><a name="l02432"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#acffa50ae3185e3e5302909f27e7e9a02"> 2432</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#acffa50ae3185e3e5302909f27e7e9a02">DepthwiseConvolution2d2x3x3Dilation3x3Test</a>(</div><div class="line"><a name="l02433"></a><span class="lineno"> 2433</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02434"></a><span class="lineno"> 2434</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02435"></a><span class="lineno"> 2435</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l02436"></a><span class="lineno"> 2436</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l02437"></a><span class="lineno"> 2437</span> {</div><div class="line"><a name="l02438"></a><span class="lineno"> 2438</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({1, 2, 10, 10}, ArmnnType);</div><div class="line"><a name="l02439"></a><span class="lineno"> 2439</span>  std::vector<float> inputNoQuantizedValues =</div><div class="line"><a name="l02440"></a><span class="lineno"> 2440</span>  {</div><div class="line"><a name="l02441"></a><span class="lineno"> 2441</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02442"></a><span class="lineno"> 2442</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02443"></a><span class="lineno"> 2443</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02444"></a><span class="lineno"> 2444</span>  0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02445"></a><span class="lineno"> 2445</span>  0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02446"></a><span class="lineno"> 2446</span>  0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02447"></a><span class="lineno"> 2447</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02448"></a><span class="lineno"> 2448</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02449"></a><span class="lineno"> 2449</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02450"></a><span class="lineno"> 2450</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02451"></a><span class="lineno"> 2451</span> </div><div class="line"><a name="l02452"></a><span class="lineno"> 2452</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02453"></a><span class="lineno"> 2453</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02454"></a><span class="lineno"> 2454</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02455"></a><span class="lineno"> 2455</span>  0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02456"></a><span class="lineno"> 2456</span>  0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02457"></a><span class="lineno"> 2457</span>  0, 0, 0, 0, 1, 1, 1, 0, 0, 0,</div><div class="line"><a name="l02458"></a><span class="lineno"> 2458</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02459"></a><span class="lineno"> 2459</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02460"></a><span class="lineno"> 2460</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,</div><div class="line"><a name="l02461"></a><span class="lineno"> 2461</span>  0, 0, 0, 0, 0, 0, 0, 0, 0, 0</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> </div><div class="line"><a name="l02464"></a><span class="lineno"> 2464</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 1, 2, 3, 3}, ArmnnType);</div><div class="line"><a name="l02465"></a><span class="lineno"> 2465</span>  std::vector<float> kernelNoQuantizedValues =</div><div class="line"><a name="l02466"></a><span class="lineno"> 2466</span>  {</div><div class="line"><a name="l02467"></a><span class="lineno"> 2467</span>  1, 2, 3,</div><div class="line"><a name="l02468"></a><span class="lineno"> 2468</span>  4, 5, 6,</div><div class="line"><a name="l02469"></a><span class="lineno"> 2469</span>  7, 8, 9,</div><div class="line"><a name="l02470"></a><span class="lineno"> 2470</span> </div><div class="line"><a name="l02471"></a><span class="lineno"> 2471</span>  1, 2, 3,</div><div class="line"><a name="l02472"></a><span class="lineno"> 2472</span>  4, 5, 6,</div><div class="line"><a name="l02473"></a><span class="lineno"> 2473</span>  7, 8, 9</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> </div><div class="line"><a name="l02476"></a><span class="lineno"> 2476</span>  <span class="comment">// Since the dilation rate is 3 this will dilate the kernel to be like 7x7,</span></div><div class="line"><a name="l02477"></a><span class="lineno"> 2477</span>  <span class="comment">// therefore the output will be 2x4x4: (I−K+2P)/S +1 => (10-7 +0)/1 +1</span></div><div class="line"><a name="l02478"></a><span class="lineno"> 2478</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 2, 4, 4}, ArmnnType);</div><div class="line"><a name="l02479"></a><span class="lineno"> 2479</span>  std::vector<float> outputExpectedNoQuantizedValues =</div><div class="line"><a name="l02480"></a><span class="lineno"> 2480</span>  {</div><div class="line"><a name="l02481"></a><span class="lineno"> 2481</span>  6., 5., 5., 5.,</div><div class="line"><a name="l02482"></a><span class="lineno"> 2482</span>  6., 5., 5., 5.,</div><div class="line"><a name="l02483"></a><span class="lineno"> 2483</span>  6., 5., 5., 5.,</div><div class="line"><a name="l02484"></a><span class="lineno"> 2484</span>  3., 2., 2., 2.,</div><div class="line"><a name="l02485"></a><span class="lineno"> 2485</span> </div><div class="line"><a name="l02486"></a><span class="lineno"> 2486</span>  6., 5., 5., 5.,</div><div class="line"><a name="l02487"></a><span class="lineno"> 2487</span>  6., 5., 5., 5.,</div><div class="line"><a name="l02488"></a><span class="lineno"> 2488</span>  6., 5., 5., 5.,</div><div class="line"><a name="l02489"></a><span class="lineno"> 2489</span>  3., 2., 2., 2.</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> </div><div class="line"><a name="l02492"></a><span class="lineno"> 2492</span>  <span class="keywordflow">return</span> DepthwiseConvolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(</div><div class="line"><a name="l02493"></a><span class="lineno"> 2493</span>  workloadFactory,</div><div class="line"><a name="l02494"></a><span class="lineno"> 2494</span>  memoryManager,</div><div class="line"><a name="l02495"></a><span class="lineno"> 2495</span>  inputNoQuantizedValues,</div><div class="line"><a name="l02496"></a><span class="lineno"> 2496</span>  inputTensorInfo,</div><div class="line"><a name="l02497"></a><span class="lineno"> 2497</span>  kernelNoQuantizedValues,</div><div class="line"><a name="l02498"></a><span class="lineno"> 2498</span>  kernelTensorInfo,</div><div class="line"><a name="l02499"></a><span class="lineno"> 2499</span>  outputExpectedNoQuantizedValues,</div><div class="line"><a name="l02500"></a><span class="lineno"> 2500</span>  outputTensorInfo,</div><div class="line"><a name="l02501"></a><span class="lineno"> 2501</span>  3,</div><div class="line"><a name="l02502"></a><span class="lineno"> 2502</span>  3,</div><div class="line"><a name="l02503"></a><span class="lineno"> 2503</span>  layout,</div><div class="line"><a name="l02504"></a><span class="lineno"> 2504</span>  biasEnabled);</div><div class="line"><a name="l02505"></a><span class="lineno"> 2505</span> }</div><div class="line"><a name="l02506"></a><span class="lineno"> 2506</span> </div><div class="line"><a name="l02507"></a><span class="lineno"> 2507</span> <span class="keyword">template</span><armnn::DataType ArmnnType, armnn::DataType ArmnnBType, <span class="keyword">typename</span> T></div><div class="line"><a name="l02508"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#a0da6534b3a5d2f923dcd73553950129a"> 2508</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a0da6534b3a5d2f923dcd73553950129a">DepthwiseConvolution2dMult4Test</a>(</div><div class="line"><a name="l02509"></a><span class="lineno"> 2509</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02510"></a><span class="lineno"> 2510</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02511"></a><span class="lineno"> 2511</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l02512"></a><span class="lineno"> 2512</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l02513"></a><span class="lineno"> 2513</span> {</div><div class="line"><a name="l02514"></a><span class="lineno"> 2514</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({1, 2, 3, 3}, ArmnnType);</div><div class="line"><a name="l02515"></a><span class="lineno"> 2515</span>  std::vector<float> inputNoQuantizedValues =</div><div class="line"><a name="l02516"></a><span class="lineno"> 2516</span>  {</div><div class="line"><a name="l02517"></a><span class="lineno"> 2517</span>  10.0, 10.0, 10.0,</div><div class="line"><a name="l02518"></a><span class="lineno"> 2518</span>  10.0, 10.0, 10.0,</div><div class="line"><a name="l02519"></a><span class="lineno"> 2519</span>  10.0, 10.0, 10.0,</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>  21.0, 22.0, 23.0,</div><div class="line"><a name="l02522"></a><span class="lineno"> 2522</span>  24.0, 25.0, 26.0,</div><div class="line"><a name="l02523"></a><span class="lineno"> 2523</span>  27.0, 28.0, 29.0</div><div class="line"><a name="l02524"></a><span class="lineno"> 2524</span>  };</div><div class="line"><a name="l02525"></a><span class="lineno"> 2525</span> </div><div class="line"><a name="l02526"></a><span class="lineno"> 2526</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 4, 2, 2, 2}, ArmnnType);</div><div class="line"><a name="l02527"></a><span class="lineno"> 2527</span> </div><div class="line"><a name="l02528"></a><span class="lineno"> 2528</span>  std::vector<float> kernelNoQuantizedValues =</div><div class="line"><a name="l02529"></a><span class="lineno"> 2529</span>  {</div><div class="line"><a name="l02530"></a><span class="lineno"> 2530</span>  0.25f, 0.25f,</div><div class="line"><a name="l02531"></a><span class="lineno"> 2531</span>  0.25f, 0.25f,</div><div class="line"><a name="l02532"></a><span class="lineno"> 2532</span> </div><div class="line"><a name="l02533"></a><span class="lineno"> 2533</span>  0.25f, 0.25f,</div><div class="line"><a name="l02534"></a><span class="lineno"> 2534</span>  0.25f, 0.25f,</div><div class="line"><a name="l02535"></a><span class="lineno"> 2535</span> </div><div class="line"><a name="l02536"></a><span class="lineno"> 2536</span>  0.0f , 0.0f,</div><div class="line"><a name="l02537"></a><span class="lineno"> 2537</span>  0.0f , 0.1f,</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>  0.0f , 0.0f,</div><div class="line"><a name="l02540"></a><span class="lineno"> 2540</span>  0.0f , 0.1f,</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>  0.2f , 0.0f,</div><div class="line"><a name="l02543"></a><span class="lineno"> 2543</span>  0.0f , 0.0f,</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>  0.2f , 0.0f,</div><div class="line"><a name="l02546"></a><span class="lineno"> 2546</span>  0.0f , 0.0f,</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>  0.0f , 0.3f,</div><div class="line"><a name="l02549"></a><span class="lineno"> 2549</span>  0.0f , 0.0f,</div><div class="line"><a name="l02550"></a><span class="lineno"> 2550</span> </div><div class="line"><a name="l02551"></a><span class="lineno"> 2551</span>  0.0f , 0.3f,</div><div class="line"><a name="l02552"></a><span class="lineno"> 2552</span>  0.0f , 0.0f</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> </div><div class="line"><a name="l02555"></a><span class="lineno"> 2555</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 8, 2, 2}, ArmnnType);</div><div class="line"><a name="l02556"></a><span class="lineno"> 2556</span>  std::vector<float> outputExpectedNoQuantizedValues =</div><div class="line"><a name="l02557"></a><span class="lineno"> 2557</span>  {</div><div class="line"><a name="l02558"></a><span class="lineno"> 2558</span>  10.f, 10.f,</div><div class="line"><a name="l02559"></a><span class="lineno"> 2559</span>  10.f, 10.f,</div><div class="line"><a name="l02560"></a><span class="lineno"> 2560</span> </div><div class="line"><a name="l02561"></a><span class="lineno"> 2561</span>  1.f, 1.f,</div><div class="line"><a name="l02562"></a><span class="lineno"> 2562</span>  1.f, 1.f,</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>  2.f, 2.f,</div><div class="line"><a name="l02565"></a><span class="lineno"> 2565</span>  2.f, 2.f,</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>  3.f, 3.f,</div><div class="line"><a name="l02568"></a><span class="lineno"> 2568</span>  3.f, 3.f,</div><div class="line"><a name="l02569"></a><span class="lineno"> 2569</span> </div><div class="line"><a name="l02570"></a><span class="lineno"> 2570</span>  23.f, 24.f,</div><div class="line"><a name="l02571"></a><span class="lineno"> 2571</span>  26.f, 27.f,</div><div class="line"><a name="l02572"></a><span class="lineno"> 2572</span> </div><div class="line"><a name="l02573"></a><span class="lineno"> 2573</span>  2.5f, 2.6000001f,</div><div class="line"><a name="l02574"></a><span class="lineno"> 2574</span>  2.8f, 2.9f,</div><div class="line"><a name="l02575"></a><span class="lineno"> 2575</span> </div><div class="line"><a name="l02576"></a><span class="lineno"> 2576</span>  4.2000003f, 4.4f,</div><div class="line"><a name="l02577"></a><span class="lineno"> 2577</span>  4.8f, 5.f,</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>  6.6000004f, 6.9f,</div><div class="line"><a name="l02580"></a><span class="lineno"> 2580</span>  7.5000005f, 7.8f</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> </div><div class="line"><a name="l02583"></a><span class="lineno"> 2583</span> </div><div class="line"><a name="l02584"></a><span class="lineno"> 2584</span>  <span class="keywordflow">return</span> DepthwiseConvolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(</div><div class="line"><a name="l02585"></a><span class="lineno"> 2585</span>  workloadFactory,</div><div class="line"><a name="l02586"></a><span class="lineno"> 2586</span>  memoryManager,</div><div class="line"><a name="l02587"></a><span class="lineno"> 2587</span>  inputNoQuantizedValues,</div><div class="line"><a name="l02588"></a><span class="lineno"> 2588</span>  inputTensorInfo,</div><div class="line"><a name="l02589"></a><span class="lineno"> 2589</span>  kernelNoQuantizedValues,</div><div class="line"><a name="l02590"></a><span class="lineno"> 2590</span>  kernelTensorInfo,</div><div class="line"><a name="l02591"></a><span class="lineno"> 2591</span>  outputExpectedNoQuantizedValues,</div><div class="line"><a name="l02592"></a><span class="lineno"> 2592</span>  outputTensorInfo,</div><div class="line"><a name="l02593"></a><span class="lineno"> 2593</span>  1,</div><div class="line"><a name="l02594"></a><span class="lineno"> 2594</span>  1,</div><div class="line"><a name="l02595"></a><span class="lineno"> 2595</span>  layout,</div><div class="line"><a name="l02596"></a><span class="lineno"> 2596</span>  biasEnabled);</div><div class="line"><a name="l02597"></a><span class="lineno"> 2597</span> }</div><div class="line"><a name="l02598"></a><span class="lineno"> 2598</span> </div><div class="line"><a name="l02599"></a><span class="lineno"> 2599</span> <span class="keyword">template</span><armnn::DataType ArmnnType, armnn::DataType ArmnnBType, <span class="keyword">typename</span> T></div><div class="line"><a name="l02600"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#aaed50a372a6b59b20e38469856a3ce6b"> 2600</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#aaed50a372a6b59b20e38469856a3ce6b">DepthwiseConvolution2dMult2Test</a>(</div><div class="line"><a name="l02601"></a><span class="lineno"> 2601</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02602"></a><span class="lineno"> 2602</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02603"></a><span class="lineno"> 2603</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l02604"></a><span class="lineno"> 2604</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l02605"></a><span class="lineno"> 2605</span> {</div><div class="line"><a name="l02606"></a><span class="lineno"> 2606</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({1, 2, 3, 3}, ArmnnType);</div><div class="line"><a name="l02607"></a><span class="lineno"> 2607</span>  std::vector<float> inputNoQuantizedValues =</div><div class="line"><a name="l02608"></a><span class="lineno"> 2608</span>  {</div><div class="line"><a name="l02609"></a><span class="lineno"> 2609</span>  10.0, 10.0, 10.0,</div><div class="line"><a name="l02610"></a><span class="lineno"> 2610</span>  10.0, 10.0, 10.0,</div><div class="line"><a name="l02611"></a><span class="lineno"> 2611</span>  10.0, 10.0, 10.0,</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>  21.0, 22.0, 23.0,</div><div class="line"><a name="l02614"></a><span class="lineno"> 2614</span>  24.0, 25.0, 26.0,</div><div class="line"><a name="l02615"></a><span class="lineno"> 2615</span>  27.0, 28.0, 29.0</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> </div><div class="line"><a name="l02618"></a><span class="lineno"> 2618</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 2, 2, 2, 2}, ArmnnType);</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>  std::vector<float> kernelNoQuantizedValues =</div><div class="line"><a name="l02621"></a><span class="lineno"> 2621</span>  {</div><div class="line"><a name="l02622"></a><span class="lineno"> 2622</span>  0.25f, 0.25f,</div><div class="line"><a name="l02623"></a><span class="lineno"> 2623</span>  0.25f, 0.25f,</div><div class="line"><a name="l02624"></a><span class="lineno"> 2624</span> </div><div class="line"><a name="l02625"></a><span class="lineno"> 2625</span>  0.2f , 0.0f,</div><div class="line"><a name="l02626"></a><span class="lineno"> 2626</span>  0.0f , 0.0f,</div><div class="line"><a name="l02627"></a><span class="lineno"> 2627</span> </div><div class="line"><a name="l02628"></a><span class="lineno"> 2628</span>  0.0f , 0.0f,</div><div class="line"><a name="l02629"></a><span class="lineno"> 2629</span>  0.0f , 0.1f,</div><div class="line"><a name="l02630"></a><span class="lineno"> 2630</span> </div><div class="line"><a name="l02631"></a><span class="lineno"> 2631</span>  0.0f , 0.3f,</div><div class="line"><a name="l02632"></a><span class="lineno"> 2632</span>  0.0f , 0.0f</div><div class="line"><a name="l02633"></a><span class="lineno"> 2633</span> </div><div class="line"><a name="l02634"></a><span class="lineno"> 2634</span>  };</div><div class="line"><a name="l02635"></a><span class="lineno"> 2635</span> </div><div class="line"><a name="l02636"></a><span class="lineno"> 2636</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 4, 2, 2}, ArmnnType);</div><div class="line"><a name="l02637"></a><span class="lineno"> 2637</span>  std::vector<float> outputExpectedNoQuantizedValues =</div><div class="line"><a name="l02638"></a><span class="lineno"> 2638</span>  {</div><div class="line"><a name="l02639"></a><span class="lineno"> 2639</span>  10.f, 10.f,</div><div class="line"><a name="l02640"></a><span class="lineno"> 2640</span>  10.f, 10.f,</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>  1.f, 1.f,</div><div class="line"><a name="l02643"></a><span class="lineno"> 2643</span>  1.f, 1.f,</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>  4.2000003f, 4.4f,</div><div class="line"><a name="l02646"></a><span class="lineno"> 2646</span>  4.8f, 5.f,</div><div class="line"><a name="l02647"></a><span class="lineno"> 2647</span> </div><div class="line"><a name="l02648"></a><span class="lineno"> 2648</span>  6.6000004f, 6.9f,</div><div class="line"><a name="l02649"></a><span class="lineno"> 2649</span>  7.5000005f, 7.8f</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> </div><div class="line"><a name="l02652"></a><span class="lineno"> 2652</span> </div><div class="line"><a name="l02653"></a><span class="lineno"> 2653</span>  <span class="keywordflow">return</span> DepthwiseConvolution2d3x3DilationTestCommon<ArmnnType, ArmnnBType>(</div><div class="line"><a name="l02654"></a><span class="lineno"> 2654</span>  workloadFactory,</div><div class="line"><a name="l02655"></a><span class="lineno"> 2655</span>  memoryManager,</div><div class="line"><a name="l02656"></a><span class="lineno"> 2656</span>  inputNoQuantizedValues,</div><div class="line"><a name="l02657"></a><span class="lineno"> 2657</span>  inputTensorInfo,</div><div class="line"><a name="l02658"></a><span class="lineno"> 2658</span>  kernelNoQuantizedValues,</div><div class="line"><a name="l02659"></a><span class="lineno"> 2659</span>  kernelTensorInfo,</div><div class="line"><a name="l02660"></a><span class="lineno"> 2660</span>  outputExpectedNoQuantizedValues,</div><div class="line"><a name="l02661"></a><span class="lineno"> 2661</span>  outputTensorInfo,</div><div class="line"><a name="l02662"></a><span class="lineno"> 2662</span>  1,</div><div class="line"><a name="l02663"></a><span class="lineno"> 2663</span>  1,</div><div class="line"><a name="l02664"></a><span class="lineno"> 2664</span>  layout,</div><div class="line"><a name="l02665"></a><span class="lineno"> 2665</span>  biasEnabled);</div><div class="line"><a name="l02666"></a><span class="lineno"> 2666</span> }</div><div class="line"><a name="l02667"></a><span class="lineno"> 2667</span> </div><div class="line"><a name="l02668"></a><span class="lineno"> 2668</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l02669"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8cpp.xhtml#acac29a0b58c3c3f2928e0d7ee258c066"> 2669</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#acac29a0b58c3c3f2928e0d7ee258c066">CompareDepthwiseConvolution2dTestImpl</a>(</div><div class="line"><a name="l02670"></a><span class="lineno"> 2670</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02671"></a><span class="lineno"> 2671</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02672"></a><span class="lineno"> 2672</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& refWorkloadFactory,</div><div class="line"><a name="l02673"></a><span class="lineno"> 2673</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">armnnUtils::DataLayoutIndexed</a>& layout)</div><div class="line"><a name="l02674"></a><span class="lineno"> 2674</span> {</div><div class="line"><a name="l02675"></a><span class="lineno"> 2675</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 8;</div><div class="line"><a name="l02676"></a><span class="lineno"> 2676</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 16;</div><div class="line"><a name="l02677"></a><span class="lineno"> 2677</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 3;</div><div class="line"><a name="l02678"></a><span class="lineno"> 2678</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 5;</div><div class="line"><a name="l02679"></a><span class="lineno"> 2679</span> </div><div class="line"><a name="l02680"></a><span class="lineno"> 2680</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelHeight = 3;</div><div class="line"><a name="l02681"></a><span class="lineno"> 2681</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> kernelWidth = 3;</div><div class="line"><a name="l02682"></a><span class="lineno"> 2682</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channelMultiplier = 1;</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>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideX = 2;</div><div class="line"><a name="l02685"></a><span class="lineno"> 2685</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> strideY = 3;</div><div class="line"><a name="l02686"></a><span class="lineno"> 2686</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padX = 1;</div><div class="line"><a name="l02687"></a><span class="lineno"> 2687</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> padY = 1;</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = inputNum;</div><div class="line"><a name="l02690"></a><span class="lineno"> 2690</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = inputChannels * channelMultiplier;</div><div class="line"><a name="l02691"></a><span class="lineno"> 2691</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = (inputHeight + 2 * padY - kernelHeight + strideY) / strideY;</div><div class="line"><a name="l02692"></a><span class="lineno"> 2692</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = (inputWidth + 2 * padX - kernelWidth + strideX) / strideX;</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_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l02695"></a><span class="lineno"> 2695</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l02696"></a><span class="lineno"> 2696</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelDesc;</div><div class="line"><a name="l02697"></a><span class="lineno"> 2697</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasDesc;</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> </div><div class="line"><a name="l02700"></a><span class="lineno"> 2700</span>  std::vector<unsigned int> inputShape;</div><div class="line"><a name="l02701"></a><span class="lineno"> 2701</span>  std::vector<unsigned int> outputShape;</div><div class="line"><a name="l02702"></a><span class="lineno"> 2702</span>  std::vector<unsigned int> kernelShape{ channelMultiplier, inputChannels, kernelHeight, kernelWidth };</div><div class="line"><a name="l02703"></a><span class="lineno"> 2703</span>  std::vector<unsigned int> biasShape{ outputChannels };</div><div class="line"><a name="l02704"></a><span class="lineno"> 2704</span>  <span class="keywordflow">switch</span> (layout.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a7d8b3d755b6ca8f5533657969efb06c4">GetDataLayout</a>())</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>:</div><div class="line"><a name="l02707"></a><span class="lineno"> 2707</span>  inputShape = { inputNum, inputChannels, inputHeight, inputWidth };</div><div class="line"><a name="l02708"></a><span class="lineno"> 2708</span>  outputShape = { outputNum, outputChannels, outputHeight, outputWidth };</div><div class="line"><a name="l02709"></a><span class="lineno"> 2709</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02710"></a><span class="lineno"> 2710</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout ::NHWC</a>:</div><div class="line"><a name="l02711"></a><span class="lineno"> 2711</span>  inputShape = { inputNum, inputHeight, inputWidth, inputChannels };</div><div class="line"><a name="l02712"></a><span class="lineno"> 2712</span>  outputShape = { outputNum, outputHeight, outputWidth, outputChannels };</div><div class="line"><a name="l02713"></a><span class="lineno"> 2713</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l02714"></a><span class="lineno"> 2714</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l02715"></a><span class="lineno"> 2715</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>(<span class="stringliteral">"unknown data layout ["</span></div><div class="line"><a name="l02716"></a><span class="lineno"> 2716</span>  + std::to_string(static_cast<int>(layout.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a7d8b3d755b6ca8f5533657969efb06c4">GetDataLayout</a>())) + <span class="stringliteral">"]"</span>);</div><div class="line"><a name="l02717"></a><span class="lineno"> 2717</span>  }</div><div class="line"><a name="l02718"></a><span class="lineno"> 2718</span> </div><div class="line"><a name="l02719"></a><span class="lineno"> 2719</span>  <span class="keywordtype">float</span> inputsQScale = armnn::IsQuantizedType<T>() ? 1.0f : 0;</div><div class="line"><a name="l02720"></a><span class="lineno"> 2720</span>  <span class="keywordtype">float</span> outputQScale = armnn::IsQuantizedType<T>() ? 2.0f : 0;</div><div class="line"><a name="l02721"></a><span class="lineno"> 2721</span>  int32_t qOffset = 0;</div><div class="line"><a name="l02722"></a><span class="lineno"> 2722</span> </div><div class="line"><a name="l02723"></a><span class="lineno"> 2723</span>  inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, inputShape.data(), ArmnnType, inputsQScale, qOffset);</div><div class="line"><a name="l02724"></a><span class="lineno"> 2724</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape.data(), ArmnnType, outputQScale, qOffset);</div><div class="line"><a name="l02725"></a><span class="lineno"> 2725</span>  kernelDesc = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, kernelShape.data(), ArmnnType, inputsQScale, qOffset);</div><div class="line"><a name="l02726"></a><span class="lineno"> 2726</span>  biasDesc = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(</div><div class="line"><a name="l02727"></a><span class="lineno"> 2727</span>  1, biasShape.data(), <a class="code" href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">armnn::GetBiasDataType</a>(ArmnnType), inputsQScale, qOffset);</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>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> ret(outputTensorInfo);</div><div class="line"><a name="l02730"></a><span class="lineno"> 2730</span> </div><div class="line"><a name="l02731"></a><span class="lineno"> 2731</span>  <span class="keyword">auto</span> input = MakeRandomTensor<T, 4>(inputTensorInfo, 124908, 0.0f, 255.0f);</div><div class="line"><a name="l02732"></a><span class="lineno"> 2732</span>  <span class="keyword">auto</span> kernel = MakeRandomTensor<T, 4>(kernelDesc, 891234, 0.0f, 255.0f);</div><div class="line"><a name="l02733"></a><span class="lineno"> 2733</span>  <span class="keyword">auto</span> bias = MakeRandomTensor<typename FullyConnectedBiasTypeForInputType<T>::Type, 1>(</div><div class="line"><a name="l02734"></a><span class="lineno"> 2734</span>  biasDesc, 1028, 0.0f, 255.0f);</div><div class="line"><a name="l02735"></a><span class="lineno"> 2735</span> </div><div class="line"><a name="l02736"></a><span class="lineno"> 2736</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l02737"></a><span class="lineno"> 2737</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l02738"></a><span class="lineno"> 2738</span> </div><div class="line"><a name="l02739"></a><span class="lineno"> 2739</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">armnn::DepthwiseConvolution2dQueueDescriptor</a> data;</div><div class="line"><a name="l02740"></a><span class="lineno"> 2740</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l02741"></a><span class="lineno"> 2741</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> weightsTensor(kernelDesc);</div><div class="line"><a name="l02742"></a><span class="lineno"> 2742</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> biasTensor(biasDesc);</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>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&weightsTensor, &kernel[0][0][0][0]);</div><div class="line"><a name="l02745"></a><span class="lineno"> 2745</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&biasTensor, &bias[0]);</div><div class="line"><a name="l02746"></a><span class="lineno"> 2746</span> </div><div class="line"><a name="l02747"></a><span class="lineno"> 2747</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l02748"></a><span class="lineno"> 2748</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l02749"></a><span class="lineno"> 2749</span>  data.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &weightsTensor;</div><div class="line"><a name="l02750"></a><span class="lineno"> 2750</span>  data.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &biasTensor;</div><div class="line"><a name="l02751"></a><span class="lineno"> 2751</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = strideX;</div><div class="line"><a name="l02752"></a><span class="lineno"> 2752</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = strideY;</div><div class="line"><a name="l02753"></a><span class="lineno"> 2753</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = padX;</div><div class="line"><a name="l02754"></a><span class="lineno"> 2754</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = padX;</div><div class="line"><a name="l02755"></a><span class="lineno"> 2755</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = padY;</div><div class="line"><a name="l02756"></a><span class="lineno"> 2756</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = padY;</div><div class="line"><a name="l02757"></a><span class="lineno"> 2757</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l02758"></a><span class="lineno"> 2758</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = layout.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a7d8b3d755b6ca8f5533657969efb06c4">GetDataLayout</a>();</div><div class="line"><a name="l02759"></a><span class="lineno"> 2759</span> </div><div class="line"><a name="l02760"></a><span class="lineno"> 2760</span>  std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l02761"></a><span class="lineno"> 2761</span>  std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l02762"></a><span class="lineno"> 2762</span> </div><div class="line"><a name="l02763"></a><span class="lineno"> 2763</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">armnn::DepthwiseConvolution2dQueueDescriptor</a> refData = data;</div><div class="line"><a name="l02764"></a><span class="lineno"> 2764</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> refInfo = info;</div><div class="line"><a name="l02765"></a><span class="lineno"> 2765</span>  SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());</div><div class="line"><a name="l02766"></a><span class="lineno"> 2766</span>  SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());</div><div class="line"><a name="l02767"></a><span class="lineno"> 2767</span> </div><div class="line"><a name="l02768"></a><span class="lineno"> 2768</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#accb9759dfd2880efe0f8d2705ddee448">CreateDepthwiseConvolution2d</a>(data, info);</div><div class="line"><a name="l02769"></a><span class="lineno"> 2769</span>  std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#accb9759dfd2880efe0f8d2705ddee448">CreateDepthwiseConvolution2d</a>(refData, refInfo);</div><div class="line"><a name="l02770"></a><span class="lineno"> 2770</span> </div><div class="line"><a name="l02771"></a><span class="lineno"> 2771</span>  outputHandleRef->Allocate();</div><div class="line"><a name="l02772"></a><span class="lineno"> 2772</span>  inputHandleRef->Allocate();</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>  inputHandle->Allocate();</div><div class="line"><a name="l02775"></a><span class="lineno"> 2775</span>  outputHandle->Allocate();</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="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &input[0][0][0][0]);</div><div class="line"><a name="l02778"></a><span class="lineno"> 2778</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandleRef.get(), &input[0][0][0][0]);</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>  ExecuteWorkload(*workload, memoryManager);</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>  workloadRef->PostAllocationConfigure();</div><div class="line"><a name="l02783"></a><span class="lineno"> 2783</span>  workloadRef->Execute();</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>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l02786"></a><span class="lineno"> 2786</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.outputExpected[0][0][0][0], outputHandleRef.get());</div><div class="line"><a name="l02787"></a><span class="lineno"> 2787</span> </div><div class="line"><a name="l02788"></a><span class="lineno"> 2788</span>  <span class="keywordflow">return</span> ret;</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> </div><div class="line"><a name="l02791"></a><span class="lineno"> 2791</span> <span class="comment">//</span></div><div class="line"><a name="l02792"></a><span class="lineno"> 2792</span> <span class="comment">// Explicit template specializations</span></div><div class="line"><a name="l02793"></a><span class="lineno"> 2793</span> <span class="comment">//</span></div><div class="line"><a name="l02794"></a><span class="lineno"> 2794</span> <span class="keyword">template</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<armnn::ResolveType<armnn::DataType::BFloat16></a>, 4></div><div class="line"><a name="l02795"></a><span class="lineno"> 2795</span> Convolution2d3x3Dilation3x3Test<armnn::DataType::BFloat16, armnn::DataType::BFloat16>(</div><div class="line"><a name="l02796"></a><span class="lineno"> 2796</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>&,</div><div class="line"><a name="l02797"></a><span class="lineno"> 2797</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>&,</div><div class="line"><a name="l02798"></a><span class="lineno"> 2798</span>  bool,</div><div class="line"><a name="l02799"></a><span class="lineno"> 2799</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>);</div><div class="line"><a name="l02800"></a><span class="lineno"> 2800</span> </div><div class="line"><a name="l02801"></a><span class="lineno"> 2801</span> <span class="keyword">template</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<armnn::ResolveType<armnn::DataType::Float32></a>, 4></div><div class="line"><a name="l02802"></a><span class="lineno"> 2802</span> Convolution2d3x3Dilation3x3Test<armnn::DataType::Float32, armnn::DataType::Float32>(</div><div class="line"><a name="l02803"></a><span class="lineno"> 2803</span>  armnn::IWorkloadFactory&,</div><div class="line"><a name="l02804"></a><span class="lineno"> 2804</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr&,</div><div class="line"><a name="l02805"></a><span class="lineno"> 2805</span>  bool,</div><div class="line"><a name="l02806"></a><span class="lineno"> 2806</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>);</div><div class="line"><a name="l02807"></a><span class="lineno"> 2807</span> </div><div class="line"><a name="l02808"></a><span class="lineno"> 2808</span> <span class="keyword">template</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<armnn::ResolveType<armnn::DataType::QAsymmU8></a>, 4></div><div class="line"><a name="l02809"></a><span class="lineno"> 2809</span> Convolution2d3x3Dilation3x3Test<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>(</div><div class="line"><a name="l02810"></a><span class="lineno"> 2810</span>  armnn::IWorkloadFactory&,</div><div class="line"><a name="l02811"></a><span class="lineno"> 2811</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr&,</div><div class="line"><a name="l02812"></a><span class="lineno"> 2812</span>  bool,</div><div class="line"><a name="l02813"></a><span class="lineno"> 2813</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>);</div><div class="line"><a name="l02814"></a><span class="lineno"> 2814</span> </div><div class="line"><a name="l02815"></a><span class="lineno"> 2815</span> <span class="keyword">template</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<armnn::ResolveType<armnn::DataType::QSymmS16></a>, 4></div><div class="line"><a name="l02816"></a><span class="lineno"> 2816</span> Convolution2d3x3Dilation3x3Test<armnn::DataType::QSymmS16, armnn::DataType::Signed32>(</div><div class="line"><a name="l02817"></a><span class="lineno"> 2817</span>  armnn::IWorkloadFactory&,</div><div class="line"><a name="l02818"></a><span class="lineno"> 2818</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr&,</div><div class="line"><a name="l02819"></a><span class="lineno"> 2819</span>  bool,</div><div class="line"><a name="l02820"></a><span class="lineno"> 2820</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>);</div><div class="line"><a name="l02821"></a><span class="lineno"> 2821</span> </div><div class="line"><a name="l02822"></a><span class="lineno"> 2822</span> <span class="keyword">template</span> LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4></div><div class="line"><a name="l02823"></a><span class="lineno"> 2823</span> Convolution2d2x3x3Dilation3x3Test<armnn::DataType::Float32, armnn::DataType::Float32>(</div><div class="line"><a name="l02824"></a><span class="lineno"> 2824</span>  armnn::IWorkloadFactory&,</div><div class="line"><a name="l02825"></a><span class="lineno"> 2825</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr&,</div><div class="line"><a name="l02826"></a><span class="lineno"> 2826</span>  bool,</div><div class="line"><a name="l02827"></a><span class="lineno"> 2827</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>);</div><div class="line"><a name="l02828"></a><span class="lineno"> 2828</span> </div><div class="line"><a name="l02829"></a><span class="lineno"> 2829</span> <span class="keyword">template</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<armnn::ResolveType<armnn::DataType::BFloat16></a>, 4></div><div class="line"><a name="l02830"></a><span class="lineno"> 2830</span> Convolution2d2x3x3Dilation3x3Test<armnn::DataType::BFloat16, armnn::DataType::BFloat16>(</div><div class="line"><a name="l02831"></a><span class="lineno"> 2831</span>  armnn::IWorkloadFactory&,</div><div class="line"><a name="l02832"></a><span class="lineno"> 2832</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr&,</div><div class="line"><a name="l02833"></a><span class="lineno"> 2833</span>  bool,</div><div class="line"><a name="l02834"></a><span class="lineno"> 2834</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>);</div><div class="line"><a name="l02835"></a><span class="lineno"> 2835</span> </div><div class="line"><a name="l02836"></a><span class="lineno"> 2836</span> <span class="keyword">template</span> LayerTestResult<armnn::ResolveType<armnn::DataType::QAsymmU8>, 4></div><div class="line"><a name="l02837"></a><span class="lineno"> 2837</span> Convolution2d2x3x3Dilation3x3Test<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>(</div><div class="line"><a name="l02838"></a><span class="lineno"> 2838</span>  armnn::IWorkloadFactory&,</div><div class="line"><a name="l02839"></a><span class="lineno"> 2839</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr&,</div><div class="line"><a name="l02840"></a><span class="lineno"> 2840</span>  bool,</div><div class="line"><a name="l02841"></a><span class="lineno"> 2841</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>);</div><div class="line"><a name="l02842"></a><span class="lineno"> 2842</span> </div><div class="line"><a name="l02843"></a><span class="lineno"> 2843</span> <span class="keyword">template</span> LayerTestResult<armnn::ResolveType<armnn::DataType::QSymmS16>, 4></div><div class="line"><a name="l02844"></a><span class="lineno"> 2844</span> Convolution2d2x3x3Dilation3x3Test<armnn::DataType::QSymmS16, armnn::DataType::Signed32>(</div><div class="line"><a name="l02845"></a><span class="lineno"> 2845</span>  armnn::IWorkloadFactory&,</div><div class="line"><a name="l02846"></a><span class="lineno"> 2846</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr&,</div><div class="line"><a name="l02847"></a><span class="lineno"> 2847</span>  bool,</div><div class="line"><a name="l02848"></a><span class="lineno"> 2848</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>);</div><div class="line"><a name="l02849"></a><span class="lineno"> 2849</span> </div><div class="line"><a name="l02850"></a><span class="lineno"> 2850</span> <span class="keyword">template</span> LayerTestResult<armnn::ResolveType<armnn::DataType::BFloat16>, 4></div><div class="line"><a name="l02851"></a><span class="lineno"> 2851</span> Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test<armnn::DataType::BFloat16, armnn::DataType::BFloat16>(</div><div class="line"><a name="l02852"></a><span class="lineno"> 2852</span>  armnn::IWorkloadFactory &workloadFactory,</div><div class="line"><a name="l02853"></a><span class="lineno"> 2853</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager,</div><div class="line"><a name="l02854"></a><span class="lineno"> 2854</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l02855"></a><span class="lineno"> 2855</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout);</div><div class="line"><a name="l02856"></a><span class="lineno"> 2856</span> </div><div class="line"><a name="l02857"></a><span class="lineno"> 2857</span> <span class="keyword">template</span> LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4></div><div class="line"><a name="l02858"></a><span class="lineno"> 2858</span> Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test<armnn::DataType::Float32, armnn::DataType::Float32>(</div><div class="line"><a name="l02859"></a><span class="lineno"> 2859</span>  armnn::IWorkloadFactory &workloadFactory,</div><div class="line"><a name="l02860"></a><span class="lineno"> 2860</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager,</div><div class="line"><a name="l02861"></a><span class="lineno"> 2861</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l02862"></a><span class="lineno"> 2862</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout);</div><div class="line"><a name="l02863"></a><span class="lineno"> 2863</span> </div><div class="line"><a name="l02864"></a><span class="lineno"> 2864</span> <span class="keyword">template</span> LayerTestResult<armnn::ResolveType<armnn::DataType::QAsymmU8>, 4></div><div class="line"><a name="l02865"></a><span class="lineno"> 2865</span> Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>(</div><div class="line"><a name="l02866"></a><span class="lineno"> 2866</span>  armnn::IWorkloadFactory &workloadFactory,</div><div class="line"><a name="l02867"></a><span class="lineno"> 2867</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager,</div><div class="line"><a name="l02868"></a><span class="lineno"> 2868</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l02869"></a><span class="lineno"> 2869</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout);</div><div class="line"><a name="l02870"></a><span class="lineno"> 2870</span> </div><div class="line"><a name="l02871"></a><span class="lineno"> 2871</span> <span class="keyword">template</span> LayerTestResult<armnn::ResolveType<armnn::DataType::QSymmS16>, 4></div><div class="line"><a name="l02872"></a><span class="lineno"> 2872</span> Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test<armnn::DataType::QSymmS16, armnn::DataType::Signed32>(</div><div class="line"><a name="l02873"></a><span class="lineno"> 2873</span>  armnn::IWorkloadFactory &workloadFactory,</div><div class="line"><a name="l02874"></a><span class="lineno"> 2874</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager,</div><div class="line"><a name="l02875"></a><span class="lineno"> 2875</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l02876"></a><span class="lineno"> 2876</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout);</div><div class="line"><a name="l02877"></a><span class="lineno"> 2877</span> </div><div class="line"><a name="l02878"></a><span class="lineno"> 2878</span> <span class="keyword">template</span> LayerTestResult<armnn::ResolveType<armnn::DataType::BFloat16>, 4></div><div class="line"><a name="l02879"></a><span class="lineno"> 2879</span> DepthwiseConvolution2d3x3Dilation3x3Test<armnn::DataType::BFloat16, armnn::DataType::BFloat16>(</div><div class="line"><a name="l02880"></a><span class="lineno"> 2880</span>  armnn::IWorkloadFactory&,</div><div class="line"><a name="l02881"></a><span class="lineno"> 2881</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr&,</div><div class="line"><a name="l02882"></a><span class="lineno"> 2882</span>  bool,</div><div class="line"><a name="l02883"></a><span class="lineno"> 2883</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>);</div><div class="line"><a name="l02884"></a><span class="lineno"> 2884</span> </div><div class="line"><a name="l02885"></a><span class="lineno"> 2885</span> <span class="keyword">template</span> LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4></div><div class="line"><a name="l02886"></a><span class="lineno"> 2886</span> DepthwiseConvolution2d3x3Dilation3x3Test<armnn::DataType::Float32, armnn::DataType::Float32>(</div><div class="line"><a name="l02887"></a><span class="lineno"> 2887</span>  armnn::IWorkloadFactory&,</div><div class="line"><a name="l02888"></a><span class="lineno"> 2888</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr&,</div><div class="line"><a name="l02889"></a><span class="lineno"> 2889</span>  bool,</div><div class="line"><a name="l02890"></a><span class="lineno"> 2890</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>);</div><div class="line"><a name="l02891"></a><span class="lineno"> 2891</span> </div><div class="line"><a name="l02892"></a><span class="lineno"> 2892</span> <span class="keyword">template</span> LayerTestResult<armnn::ResolveType<armnn::DataType::QAsymmU8>, 4></div><div class="line"><a name="l02893"></a><span class="lineno"> 2893</span> DepthwiseConvolution2d3x3Dilation3x3Test<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>(</div><div class="line"><a name="l02894"></a><span class="lineno"> 2894</span>  armnn::IWorkloadFactory&,</div><div class="line"><a name="l02895"></a><span class="lineno"> 2895</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr&,</div><div class="line"><a name="l02896"></a><span class="lineno"> 2896</span>  bool,</div><div class="line"><a name="l02897"></a><span class="lineno"> 2897</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>);</div><div class="line"><a name="l02898"></a><span class="lineno"> 2898</span> </div><div class="line"><a name="l02899"></a><span class="lineno"> 2899</span> <span class="keyword">template</span> LayerTestResult<armnn::ResolveType<armnn::DataType::QSymmS16>, 4></div><div class="line"><a name="l02900"></a><span class="lineno"> 2900</span> DepthwiseConvolution2d3x3Dilation3x3Test<armnn::DataType::QSymmS16, armnn::DataType::Signed32>(</div><div class="line"><a name="l02901"></a><span class="lineno"> 2901</span>  armnn::IWorkloadFactory&,</div><div class="line"><a name="l02902"></a><span class="lineno"> 2902</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr&,</div><div class="line"><a name="l02903"></a><span class="lineno"> 2903</span>  bool,</div><div class="line"><a name="l02904"></a><span class="lineno"> 2904</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>);</div><div class="line"><a name="l02905"></a><span class="lineno"> 2905</span> </div><div class="line"><a name="l02906"></a><span class="lineno"> 2906</span> <span class="keyword">template</span> LayerTestResult<armnn::ResolveType<armnn::DataType::BFloat16>, 4></div><div class="line"><a name="l02907"></a><span class="lineno"> 2907</span> DepthwiseConvolution2d2x3x3Dilation3x3Test<armnn::DataType::BFloat16, armnn::DataType::BFloat16>(</div><div class="line"><a name="l02908"></a><span class="lineno"> 2908</span>  armnn::IWorkloadFactory&,</div><div class="line"><a name="l02909"></a><span class="lineno"> 2909</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr&,</div><div class="line"><a name="l02910"></a><span class="lineno"> 2910</span>  bool,</div><div class="line"><a name="l02911"></a><span class="lineno"> 2911</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>);</div><div class="line"><a name="l02912"></a><span class="lineno"> 2912</span> </div><div class="line"><a name="l02913"></a><span class="lineno"> 2913</span> <span class="keyword">template</span> LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4></div><div class="line"><a name="l02914"></a><span class="lineno"> 2914</span> DepthwiseConvolution2d2x3x3Dilation3x3Test<armnn::DataType::Float32, armnn::DataType::Float32>(</div><div class="line"><a name="l02915"></a><span class="lineno"> 2915</span>  armnn::IWorkloadFactory&,</div><div class="line"><a name="l02916"></a><span class="lineno"> 2916</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr&,</div><div class="line"><a name="l02917"></a><span class="lineno"> 2917</span>  bool,</div><div class="line"><a name="l02918"></a><span class="lineno"> 2918</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>);</div><div class="line"><a name="l02919"></a><span class="lineno"> 2919</span> </div><div class="line"><a name="l02920"></a><span class="lineno"> 2920</span> <span class="keyword">template</span> LayerTestResult<armnn::ResolveType<armnn::DataType::QAsymmU8>, 4></div><div class="line"><a name="l02921"></a><span class="lineno"> 2921</span> DepthwiseConvolution2d2x3x3Dilation3x3Test<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>(</div><div class="line"><a name="l02922"></a><span class="lineno"> 2922</span>  armnn::IWorkloadFactory&,</div><div class="line"><a name="l02923"></a><span class="lineno"> 2923</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr&,</div><div class="line"><a name="l02924"></a><span class="lineno"> 2924</span>  bool,</div><div class="line"><a name="l02925"></a><span class="lineno"> 2925</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>);</div><div class="line"><a name="l02926"></a><span class="lineno"> 2926</span> </div><div class="line"><a name="l02927"></a><span class="lineno"> 2927</span> <span class="keyword">template</span> LayerTestResult<armnn::ResolveType<armnn::DataType::QSymmS16>, 4></div><div class="line"><a name="l02928"></a><span class="lineno"> 2928</span> DepthwiseConvolution2d2x3x3Dilation3x3Test<armnn::DataType::QSymmS16, armnn::DataType::Signed32>(</div><div class="line"><a name="l02929"></a><span class="lineno"> 2929</span>  armnn::IWorkloadFactory&,</div><div class="line"><a name="l02930"></a><span class="lineno"> 2930</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr&,</div><div class="line"><a name="l02931"></a><span class="lineno"> 2931</span>  bool,</div><div class="line"><a name="l02932"></a><span class="lineno"> 2932</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a>);</div><div class="line"><a name="l02933"></a><span class="lineno"> 2933</span> </div><div class="line"><a name="l02934"></a><span class="lineno"> 2934</span> <span class="keyword">template</span> LayerTestResult<armnn::ResolveType<armnn::DataType::BFloat16>, 4></div><div class="line"><a name="l02935"></a><span class="lineno"> 2935</span> DepthwiseConvolution2dMult4Test<armnn::DataType::BFloat16, armnn::DataType::BFloat16>(</div><div class="line"><a name="l02936"></a><span class="lineno"> 2936</span>  armnn::IWorkloadFactory &workloadFactory,</div><div class="line"><a name="l02937"></a><span class="lineno"> 2937</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager,</div><div class="line"><a name="l02938"></a><span class="lineno"> 2938</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l02939"></a><span class="lineno"> 2939</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout);</div><div class="line"><a name="l02940"></a><span class="lineno"> 2940</span> </div><div class="line"><a name="l02941"></a><span class="lineno"> 2941</span> <span class="keyword">template</span> LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4></div><div class="line"><a name="l02942"></a><span class="lineno"> 2942</span> DepthwiseConvolution2dMult4Test<armnn::DataType::Float32, armnn::DataType::Float32>(</div><div class="line"><a name="l02943"></a><span class="lineno"> 2943</span>  armnn::IWorkloadFactory &workloadFactory,</div><div class="line"><a name="l02944"></a><span class="lineno"> 2944</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager,</div><div class="line"><a name="l02945"></a><span class="lineno"> 2945</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l02946"></a><span class="lineno"> 2946</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout);</div><div class="line"><a name="l02947"></a><span class="lineno"> 2947</span> </div><div class="line"><a name="l02948"></a><span class="lineno"> 2948</span> <span class="keyword">template</span> LayerTestResult<armnn::ResolveType<armnn::DataType::BFloat16>, 4></div><div class="line"><a name="l02949"></a><span class="lineno"> 2949</span> DepthwiseConvolution2dMult2Test<armnn::DataType::BFloat16, armnn::DataType::BFloat16>(</div><div class="line"><a name="l02950"></a><span class="lineno"> 2950</span>  armnn::IWorkloadFactory &workloadFactory,</div><div class="line"><a name="l02951"></a><span class="lineno"> 2951</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager,</div><div class="line"><a name="l02952"></a><span class="lineno"> 2952</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l02953"></a><span class="lineno"> 2953</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout);</div><div class="line"><a name="l02954"></a><span class="lineno"> 2954</span> </div><div class="line"><a name="l02955"></a><span class="lineno"> 2955</span> <span class="keyword">template</span> LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4></div><div class="line"><a name="l02956"></a><span class="lineno"> 2956</span> DepthwiseConvolution2dMult2Test<armnn::DataType::Float32, armnn::DataType::Float32>(</div><div class="line"><a name="l02957"></a><span class="lineno"> 2957</span>  armnn::IWorkloadFactory &workloadFactory,</div><div class="line"><a name="l02958"></a><span class="lineno"> 2958</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager,</div><div class="line"><a name="l02959"></a><span class="lineno"> 2959</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l02960"></a><span class="lineno"> 2960</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout);</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="comment">//</span></div><div class="line"><a name="l02963"></a><span class="lineno"> 2963</span> <span class="comment">// Implementation functions</span></div><div class="line"><a name="l02964"></a><span class="lineno"> 2964</span> <span class="comment">//</span></div><div class="line"><a name="l02965"></a><span class="lineno"> 2965</span> </div><div class="line"><a name="l02966"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#afb5e7d86e241292d9cb899b960da54af"> 2966</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#afb5e7d86e241292d9cb899b960da54af">SimpleConvolution2d3x5Test</a>(</div><div class="line"><a name="l02967"></a><span class="lineno"> 2967</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l02968"></a><span class="lineno"> 2968</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l02969"></a><span class="lineno"> 2969</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l02970"></a><span class="lineno"> 2970</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l02971"></a><span class="lineno"> 2971</span> {</div><div class="line"><a name="l02972"></a><span class="lineno"> 2972</span>  <span class="keywordflow">return</span> SimpleConvolution2d3x5TestCommon<armnn::DataType::Float32, armnn::DataType::Float32>(</div><div class="line"><a name="l02973"></a><span class="lineno"> 2973</span>  workloadFactory, memoryManager, 0.f, 0, biasEnabled, layout);</div><div class="line"><a name="l02974"></a><span class="lineno"> 2974</span> }</div><div class="line"><a name="l02975"></a><span class="lineno"> 2975</span> </div><div class="line"><a name="l02976"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#a8ffca1c4b38a68b10ba06f4f1416660f"> 2976</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a8ffca1c4b38a68b10ba06f4f1416660f">SimpleConvolution2d3x5Uint8Test</a>(</div><div class="line"><a name="l02977"></a><span class="lineno"> 2977</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l02978"></a><span class="lineno"> 2978</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l02979"></a><span class="lineno"> 2979</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l02980"></a><span class="lineno"> 2980</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l02981"></a><span class="lineno"> 2981</span> {</div><div class="line"><a name="l02982"></a><span class="lineno"> 2982</span>  <span class="keywordflow">return</span> SimpleConvolution2d3x5TestCommon<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>(</div><div class="line"><a name="l02983"></a><span class="lineno"> 2983</span>  workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);</div><div class="line"><a name="l02984"></a><span class="lineno"> 2984</span> }</div><div class="line"><a name="l02985"></a><span class="lineno"> 2985</span> </div><div class="line"><a name="l02986"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#acbe1a2adccd9e0aad14fc0ccb9266b0d"> 2986</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#acbe1a2adccd9e0aad14fc0ccb9266b0d">SimpleConvolution2d3x3Test</a>(</div><div class="line"><a name="l02987"></a><span class="lineno"> 2987</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l02988"></a><span class="lineno"> 2988</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l02989"></a><span class="lineno"> 2989</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l02990"></a><span class="lineno"> 2990</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</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="keywordflow">return</span> SimpleConvolution2d3x3TestCommon<armnn::DataType::Float32, armnn::DataType::Float32>(</div><div class="line"><a name="l02993"></a><span class="lineno"> 2993</span>  workloadFactory, memoryManager, 0.f, 0, biasEnabled, layout);</div><div class="line"><a name="l02994"></a><span class="lineno"> 2994</span> }</div><div class="line"><a name="l02995"></a><span class="lineno"> 2995</span> </div><div class="line"><a name="l02996"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#ac7bae01fdca8edac70cc9bc722426b17"> 2996</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#ac7bae01fdca8edac70cc9bc722426b17">SimpleConvolution2d3x3NhwcTest</a>(</div><div class="line"><a name="l02997"></a><span class="lineno"> 2997</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l02998"></a><span class="lineno"> 2998</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l02999"></a><span class="lineno"> 2999</span>  <span class="keywordtype">bool</span> biasEnabled)</div><div class="line"><a name="l03000"></a><span class="lineno"> 3000</span> {</div><div class="line"><a name="l03001"></a><span class="lineno"> 3001</span>  <span class="keywordflow">return</span> SimpleConvolution2d3x3NhwcTestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l03002"></a><span class="lineno"> 3002</span>  workloadFactory,</div><div class="line"><a name="l03003"></a><span class="lineno"> 3003</span>  memoryManager,</div><div class="line"><a name="l03004"></a><span class="lineno"> 3004</span>  0.f,</div><div class="line"><a name="l03005"></a><span class="lineno"> 3005</span>  0,</div><div class="line"><a name="l03006"></a><span class="lineno"> 3006</span>  biasEnabled,</div><div class="line"><a name="l03007"></a><span class="lineno"> 3007</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>);</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> </div><div class="line"><a name="l03010"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#af4ac6874d18e1cb59873a17073512873"> 3010</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#af4ac6874d18e1cb59873a17073512873">SimpleConvolution2d3x3Stride2x2Test</a>(</div><div class="line"><a name="l03011"></a><span class="lineno"> 3011</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03012"></a><span class="lineno"> 3012</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03013"></a><span class="lineno"> 3013</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l03014"></a><span class="lineno"> 3014</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l03015"></a><span class="lineno"> 3015</span> {</div><div class="line"><a name="l03016"></a><span class="lineno"> 3016</span>  <span class="keywordflow">return</span> SimpleConvolution2d3x3Stride2x2TestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l03017"></a><span class="lineno"> 3017</span>  workloadFactory,</div><div class="line"><a name="l03018"></a><span class="lineno"> 3018</span>  memoryManager,</div><div class="line"><a name="l03019"></a><span class="lineno"> 3019</span>  0.f,</div><div class="line"><a name="l03020"></a><span class="lineno"> 3020</span>  0,</div><div class="line"><a name="l03021"></a><span class="lineno"> 3021</span>  biasEnabled,</div><div class="line"><a name="l03022"></a><span class="lineno"> 3022</span>  layout);</div><div class="line"><a name="l03023"></a><span class="lineno"> 3023</span> }</div><div class="line"><a name="l03024"></a><span class="lineno"> 3024</span> </div><div class="line"><a name="l03025"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#ad45f359d9d4bee360bee857faa79d292"> 3025</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#ad45f359d9d4bee360bee857faa79d292">SimpleConvolution2d3x3Uint8Test</a>(</div><div class="line"><a name="l03026"></a><span class="lineno"> 3026</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03027"></a><span class="lineno"> 3027</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03028"></a><span class="lineno"> 3028</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l03029"></a><span class="lineno"> 3029</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l03030"></a><span class="lineno"> 3030</span> {</div><div class="line"><a name="l03031"></a><span class="lineno"> 3031</span>  <span class="keywordflow">return</span> SimpleConvolution2d3x3TestCommon<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>(</div><div class="line"><a name="l03032"></a><span class="lineno"> 3032</span>  workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);</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> </div><div class="line"><a name="l03035"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#a9dcd2fb98f5c3284c74f65a7c7a69da1"> 3035</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a9dcd2fb98f5c3284c74f65a7c7a69da1">SimpleConvolution2d3x5QSymm16Test</a>(</div><div class="line"><a name="l03036"></a><span class="lineno"> 3036</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03037"></a><span class="lineno"> 3037</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03038"></a><span class="lineno"> 3038</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l03039"></a><span class="lineno"> 3039</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l03040"></a><span class="lineno"> 3040</span> {</div><div class="line"><a name="l03041"></a><span class="lineno"> 3041</span>  <span class="keywordflow">return</span> SimpleConvolution2d3x5TestCommon<armnn::DataType::QSymmS16, armnn::DataType::Signed32>(</div><div class="line"><a name="l03042"></a><span class="lineno"> 3042</span>  workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);</div><div class="line"><a name="l03043"></a><span class="lineno"> 3043</span> }</div><div class="line"><a name="l03044"></a><span class="lineno"> 3044</span> </div><div class="line"><a name="l03045"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#abac8f73ae590a93fe91115371ae4ced3"> 3045</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#abac8f73ae590a93fe91115371ae4ced3">SimpleConvolution2d3x3QSymm16Test</a>(</div><div class="line"><a name="l03046"></a><span class="lineno"> 3046</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03047"></a><span class="lineno"> 3047</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03048"></a><span class="lineno"> 3048</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l03049"></a><span class="lineno"> 3049</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l03050"></a><span class="lineno"> 3050</span> {</div><div class="line"><a name="l03051"></a><span class="lineno"> 3051</span>  <span class="keywordflow">return</span> SimpleConvolution2d3x3TestCommon<armnn::DataType::QSymmS16, armnn::DataType::Signed32>(</div><div class="line"><a name="l03052"></a><span class="lineno"> 3052</span>  workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);</div><div class="line"><a name="l03053"></a><span class="lineno"> 3053</span> }</div><div class="line"><a name="l03054"></a><span class="lineno"> 3054</span> </div><div class="line"><a name="l03055"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#af7f2cd23423130ebdd916de12bc0eb1d"> 3055</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#af7f2cd23423130ebdd916de12bc0eb1d">Convolution2dAsymmetricPaddingTest</a>(</div><div class="line"><a name="l03056"></a><span class="lineno"> 3056</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03057"></a><span class="lineno"> 3057</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03058"></a><span class="lineno"> 3058</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l03059"></a><span class="lineno"> 3059</span> {</div><div class="line"><a name="l03060"></a><span class="lineno"> 3060</span>  <span class="keywordflow">return</span> SimpleConvolution2dAsymmetricPaddingTestCommon<armnn::DataType::Float32, armnn::DataType::Float32>(</div><div class="line"><a name="l03061"></a><span class="lineno"> 3061</span>  workloadFactory, memoryManager, layout, 0.0f, 0);</div><div class="line"><a name="l03062"></a><span class="lineno"> 3062</span> }</div><div class="line"><a name="l03063"></a><span class="lineno"> 3063</span> </div><div class="line"><a name="l03064"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#a48884a37a6b783185c608a68cfce752f"> 3064</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a48884a37a6b783185c608a68cfce752f">Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest</a>(</div><div class="line"><a name="l03065"></a><span class="lineno"> 3065</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03066"></a><span class="lineno"> 3066</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03067"></a><span class="lineno"> 3067</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l03068"></a><span class="lineno"> 3068</span> {</div><div class="line"><a name="l03069"></a><span class="lineno"> 3069</span>  <span class="keywordflow">return</span> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a35ad1225c524b4594b461e613695ee4a">Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon</a></div><div class="line"><a name="l03070"></a><span class="lineno"> 3070</span>  <<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>, armnn::DataType::Float32>(</div><div class="line"><a name="l03071"></a><span class="lineno"> 3071</span>  workloadFactory, memoryManager, layout, 0.0f, 0);</div><div class="line"><a name="l03072"></a><span class="lineno"> 3072</span> }</div><div class="line"><a name="l03073"></a><span class="lineno"> 3073</span> </div><div class="line"><a name="l03074"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#ac7fac5767dabd650d3d8829572717064"> 3074</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#ac7fac5767dabd650d3d8829572717064">Convolution1dTest</a>(</div><div class="line"><a name="l03075"></a><span class="lineno"> 3075</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03076"></a><span class="lineno"> 3076</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03077"></a><span class="lineno"> 3077</span>  <span class="keywordtype">bool</span> biasEnabled)</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>  <span class="keywordflow">return</span> Convolution1dTestImpl<armnn::DataType::Float32, armnn::DataType::Float32>(</div><div class="line"><a name="l03080"></a><span class="lineno"> 3080</span>  workloadFactory, memoryManager, 0.0f, 0, biasEnabled);</div><div class="line"><a name="l03081"></a><span class="lineno"> 3081</span> }</div><div class="line"><a name="l03082"></a><span class="lineno"> 3082</span> </div><div class="line"><a name="l03083"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#a40bc412ed2a6d2f764655070c02c036b"> 3083</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a40bc412ed2a6d2f764655070c02c036b">Convolution1dUint8Test</a>(</div><div class="line"><a name="l03084"></a><span class="lineno"> 3084</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03085"></a><span class="lineno"> 3085</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03086"></a><span class="lineno"> 3086</span>  <span class="keywordtype">bool</span> biasEnabled)</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="keywordflow">return</span> Convolution1dTestImpl<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>(</div><div class="line"><a name="l03089"></a><span class="lineno"> 3089</span>  workloadFactory, memoryManager, 0.1f, 128, biasEnabled);</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> </div><div class="line"><a name="l03092"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#a370a5216668b507284677234264a22a2"> 3092</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a370a5216668b507284677234264a22a2">Convolution2dPerAxisQuantTest</a>(</div><div class="line"><a name="l03093"></a><span class="lineno"> 3093</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03094"></a><span class="lineno"> 3094</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03095"></a><span class="lineno"> 3095</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l03096"></a><span class="lineno"> 3096</span> {</div><div class="line"><a name="l03097"></a><span class="lineno"> 3097</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l03098"></a><span class="lineno"> 3098</span> </div><div class="line"><a name="l03099"></a><span class="lineno"> 3099</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> inputType = DataType::QAsymmU8;</div><div class="line"><a name="l03100"></a><span class="lineno"> 3100</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> kernelType = DataType::QSymmS8;</div><div class="line"><a name="l03101"></a><span class="lineno"> 3101</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> biasType = DataType::Signed32;</div><div class="line"><a name="l03102"></a><span class="lineno"> 3102</span> </div><div class="line"><a name="l03103"></a><span class="lineno"> 3103</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo ({ 1, 3, 1, 2 }, inputType, 0.5f, 128);</div><div class="line"><a name="l03104"></a><span class="lineno"> 3104</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo({ 1, 3, 1, 3 }, inputType, 1.0f, 128);</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="keyword">const</span> std::vector<float> quantScales{ 0.5f, 0.75f, 1.0f };</div><div class="line"><a name="l03107"></a><span class="lineno"> 3107</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> quantDimension = 0;</div><div class="line"><a name="l03108"></a><span class="lineno"> 3108</span> </div><div class="line"><a name="l03109"></a><span class="lineno"> 3109</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> kernelInfo({ 3, 1, 1, 2 }, kernelType, quantScales, quantDimension);</div><div class="line"><a name="l03110"></a><span class="lineno"> 3110</span> </div><div class="line"><a name="l03111"></a><span class="lineno"> 3111</span>  <span class="keyword">const</span> std::vector<float> biasQuantScales{ 0.25f, 0.375f, 0.5f };</div><div class="line"><a name="l03112"></a><span class="lineno"> 3112</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo({ 3 }, biasType, biasQuantScales, quantDimension);</div><div class="line"><a name="l03113"></a><span class="lineno"> 3113</span> </div><div class="line"><a name="l03114"></a><span class="lineno"> 3114</span>  std::vector<uint8_t> inputData =</div><div class="line"><a name="l03115"></a><span class="lineno"> 3115</span>  {</div><div class="line"><a name="l03116"></a><span class="lineno"> 3116</span>  138, 108, 138, 108, 138, 108</div><div class="line"><a name="l03117"></a><span class="lineno"> 3117</span>  };</div><div class="line"><a name="l03118"></a><span class="lineno"> 3118</span> </div><div class="line"><a name="l03119"></a><span class="lineno"> 3119</span>  std::vector<int8_t> kernelData =</div><div class="line"><a name="l03120"></a><span class="lineno"> 3120</span>  {</div><div class="line"><a name="l03121"></a><span class="lineno"> 3121</span>  1, 2, 1, 2, 1, 2</div><div class="line"><a name="l03122"></a><span class="lineno"> 3122</span>  };</div><div class="line"><a name="l03123"></a><span class="lineno"> 3123</span> </div><div class="line"><a name="l03124"></a><span class="lineno"> 3124</span>  std::vector<int32_t> biasData =</div><div class="line"><a name="l03125"></a><span class="lineno"> 3125</span>  {</div><div class="line"><a name="l03126"></a><span class="lineno"> 3126</span>  4, 4, 4</div><div class="line"><a name="l03127"></a><span class="lineno"> 3127</span>  };</div><div class="line"><a name="l03128"></a><span class="lineno"> 3128</span> </div><div class="line"><a name="l03129"></a><span class="lineno"> 3129</span>  std::vector<uint8_t> expectedOutputData =</div><div class="line"><a name="l03130"></a><span class="lineno"> 3130</span>  {</div><div class="line"><a name="l03131"></a><span class="lineno"> 3131</span>  121, 118, 115, 121, 118, 115, 121, 118, 115</div><div class="line"><a name="l03132"></a><span class="lineno"> 3132</span>  };</div><div class="line"><a name="l03133"></a><span class="lineno"> 3133</span> </div><div class="line"><a name="l03134"></a><span class="lineno"> 3134</span>  <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l03135"></a><span class="lineno"> 3135</span>  {</div><div class="line"><a name="l03136"></a><span class="lineno"> 3136</span>  <a class="code" href="_data_layout_utils_8hpp.xhtml#a1452f049aef30409b3b649af2be7ff82">PermuteTensorNhwcToNchw</a>(inputInfo, inputData);</div><div class="line"><a name="l03137"></a><span class="lineno"> 3137</span>  <a class="code" href="_data_layout_utils_8hpp.xhtml#a1452f049aef30409b3b649af2be7ff82">PermuteTensorNhwcToNchw</a>(kernelInfo, kernelData);</div><div class="line"><a name="l03138"></a><span class="lineno"> 3138</span>  <a class="code" href="_data_layout_utils_8hpp.xhtml#a1452f049aef30409b3b649af2be7ff82">PermuteTensorNhwcToNchw</a>(outputInfo, expectedOutputData);</div><div class="line"><a name="l03139"></a><span class="lineno"> 3139</span>  }</div><div class="line"><a name="l03140"></a><span class="lineno"> 3140</span> </div><div class="line"><a name="l03141"></a><span class="lineno"> 3141</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l03142"></a><span class="lineno"> 3142</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l03143"></a><span class="lineno"> 3143</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l03144"></a><span class="lineno"> 3144</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 0;</div><div class="line"><a name="l03145"></a><span class="lineno"> 3145</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 0;</div><div class="line"><a name="l03146"></a><span class="lineno"> 3146</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 0;</div><div class="line"><a name="l03147"></a><span class="lineno"> 3147</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 0;</div><div class="line"><a name="l03148"></a><span class="lineno"> 3148</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l03149"></a><span class="lineno"> 3149</span>  descriptor.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = layout;</div><div class="line"><a name="l03150"></a><span class="lineno"> 3150</span> </div><div class="line"><a name="l03151"></a><span class="lineno"> 3151</span>  std::unique_ptr<ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputInfo);</div><div class="line"><a name="l03152"></a><span class="lineno"> 3152</span>  std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputInfo);</div><div class="line"><a name="l03153"></a><span class="lineno"> 3153</span> </div><div class="line"><a name="l03154"></a><span class="lineno"> 3154</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l03155"></a><span class="lineno"> 3155</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> weightTensor(kernelInfo);</div><div class="line"><a name="l03156"></a><span class="lineno"> 3156</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> biasTensor(biasInfo);</div><div class="line"><a name="l03157"></a><span class="lineno"> 3157</span> </div><div class="line"><a name="l03158"></a><span class="lineno"> 3158</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&weightTensor, kernelData.data());</div><div class="line"><a name="l03159"></a><span class="lineno"> 3159</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&biasTensor, biasData.data());</div><div class="line"><a name="l03160"></a><span class="lineno"> 3160</span> </div><div class="line"><a name="l03161"></a><span class="lineno"> 3161</span>  <a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">Convolution2dQueueDescriptor</a> queueDescriptor;</div><div class="line"><a name="l03162"></a><span class="lineno"> 3162</span>  queueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a> = descriptor;</div><div class="line"><a name="l03163"></a><span class="lineno"> 3163</span>  queueDescriptor.m_Weight = &weightTensor;</div><div class="line"><a name="l03164"></a><span class="lineno"> 3164</span>  queueDescriptor.m_Bias = &biasTensor;</div><div class="line"><a name="l03165"></a><span class="lineno"> 3165</span> </div><div class="line"><a name="l03166"></a><span class="lineno"> 3166</span>  AddInputToWorkload(queueDescriptor, workloadInfo, inputInfo, inputHandle.get());</div><div class="line"><a name="l03167"></a><span class="lineno"> 3167</span>  AddOutputToWorkload(queueDescriptor, workloadInfo, outputInfo, outputHandle.get());</div><div class="line"><a name="l03168"></a><span class="lineno"> 3168</span> </div><div class="line"><a name="l03169"></a><span class="lineno"> 3169</span>  std::unique_ptr<IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a2184995027cd2c9f9980206de9658855">CreateConvolution2d</a>(queueDescriptor, workloadInfo);</div><div class="line"><a name="l03170"></a><span class="lineno"> 3170</span>  inputHandle->Allocate();</div><div class="line"><a name="l03171"></a><span class="lineno"> 3171</span>  outputHandle->Allocate();</div><div class="line"><a name="l03172"></a><span class="lineno"> 3172</span> </div><div class="line"><a name="l03173"></a><span class="lineno"> 3173</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), inputData.data());</div><div class="line"><a name="l03174"></a><span class="lineno"> 3174</span> </div><div class="line"><a name="l03175"></a><span class="lineno"> 3175</span>  ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l03176"></a><span class="lineno"> 3176</span> </div><div class="line"><a name="l03177"></a><span class="lineno"> 3177</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> ret(outputInfo);</div><div class="line"><a name="l03178"></a><span class="lineno"> 3178</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(ret.<a class="code" href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a>.origin(), outputHandle.get());</div><div class="line"><a name="l03179"></a><span class="lineno"> 3179</span>  ret.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<uint8_t, 4>(outputInfo, expectedOutputData);</div><div class="line"><a name="l03180"></a><span class="lineno"> 3180</span> </div><div class="line"><a name="l03181"></a><span class="lineno"> 3181</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l03182"></a><span class="lineno"> 3182</span> }</div><div class="line"><a name="l03183"></a><span class="lineno"> 3183</span> </div><div class="line"><a name="l03184"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#a15fe73bad57133008945807f7a5b4783"> 3184</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float,4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a2b2c2f8f89d96932e62b95e7a22961d4">CompareConvolution2dTest</a>(</div><div class="line"><a name="l03185"></a><span class="lineno"> 3185</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03186"></a><span class="lineno"> 3186</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03187"></a><span class="lineno"> 3187</span>  armnn::IWorkloadFactory& refWorkloadFactory)</div><div class="line"><a name="l03188"></a><span class="lineno"> 3188</span> {</div><div class="line"><a name="l03189"></a><span class="lineno"> 3189</span>  <span class="keywordflow">return</span> CompareConvolution2dTestImpl<armnn::DataType::Float32>(</div><div class="line"><a name="l03190"></a><span class="lineno"> 3190</span>  workloadFactory, memoryManager, refWorkloadFactory);</div><div class="line"><a name="l03191"></a><span class="lineno"> 3191</span> }</div><div class="line"><a name="l03192"></a><span class="lineno"> 3192</span> </div><div class="line"><a name="l03193"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#a11fbd94028ab646528b42d0c8c55eee1"> 3193</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a11fbd94028ab646528b42d0c8c55eee1">DepthwiseConvolution2dTest</a>(</div><div class="line"><a name="l03194"></a><span class="lineno"> 3194</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03195"></a><span class="lineno"> 3195</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03196"></a><span class="lineno"> 3196</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l03197"></a><span class="lineno"> 3197</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l03198"></a><span class="lineno"> 3198</span> {</div><div class="line"><a name="l03199"></a><span class="lineno"> 3199</span>  <span class="keywordflow">return</span> DepthwiseConvolution2dTestImpl<armnn::DataType::Float32, armnn::DataType::Float32>(</div><div class="line"><a name="l03200"></a><span class="lineno"> 3200</span>  workloadFactory, memoryManager, 0.0f, 0, biasEnabled, layout);</div><div class="line"><a name="l03201"></a><span class="lineno"> 3201</span> }</div><div class="line"><a name="l03202"></a><span class="lineno"> 3202</span> </div><div class="line"><a name="l03203"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#a0cccb5cffee89004bc8d9fb309ed6636"> 3203</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a0cccb5cffee89004bc8d9fb309ed6636">DepthwiseConvolution2dDepthNhwcTest</a>(</div><div class="line"><a name="l03204"></a><span class="lineno"> 3204</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03205"></a><span class="lineno"> 3205</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03206"></a><span class="lineno"> 3206</span>  <span class="keywordtype">bool</span> biasEnabled)</div><div class="line"><a name="l03207"></a><span class="lineno"> 3207</span> {</div><div class="line"><a name="l03208"></a><span class="lineno"> 3208</span>  <span class="keywordflow">return</span> DepthwiseConvolution2dNhwcTestCommon<armnn::DataType::Float32, armnn::DataType::Float32>(</div><div class="line"><a name="l03209"></a><span class="lineno"> 3209</span>  workloadFactory, memoryManager, 0.0f, 0, biasEnabled);</div><div class="line"><a name="l03210"></a><span class="lineno"> 3210</span> }</div><div class="line"><a name="l03211"></a><span class="lineno"> 3211</span> </div><div class="line"><a name="l03212"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#a8b32d950a40903f502f5e1ec0dcab0bd"> 3212</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a8b32d950a40903f502f5e1ec0dcab0bd">DepthwiseConvolution2dDepthMul1Test</a>(</div><div class="line"><a name="l03213"></a><span class="lineno"> 3213</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03214"></a><span class="lineno"> 3214</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03215"></a><span class="lineno"> 3215</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l03216"></a><span class="lineno"> 3216</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l03217"></a><span class="lineno"> 3217</span> {</div><div class="line"><a name="l03218"></a><span class="lineno"> 3218</span>  <span class="keywordflow">return</span> DepthwiseConvolution2dDepthMul1TestImpl<armnn::DataType::Float32, armnn::DataType::Float32>(</div><div class="line"><a name="l03219"></a><span class="lineno"> 3219</span>  workloadFactory, memoryManager, 0.0f, 0, biasEnabled, layout);</div><div class="line"><a name="l03220"></a><span class="lineno"> 3220</span> }</div><div class="line"><a name="l03221"></a><span class="lineno"> 3221</span> </div><div class="line"><a name="l03222"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#ab020b4a99bf905b61a1c5e03332b63a6"> 3222</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#ab020b4a99bf905b61a1c5e03332b63a6">DepthwiseConvolution2dDepthMul64Test</a>(</div><div class="line"><a name="l03223"></a><span class="lineno"> 3223</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03224"></a><span class="lineno"> 3224</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)</div><div class="line"><a name="l03225"></a><span class="lineno"> 3225</span> {</div><div class="line"><a name="l03226"></a><span class="lineno"> 3226</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, 1, 2, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03227"></a><span class="lineno"> 3227</span>  <span class="keyword">auto</span> input = MakeTensor<float, 4>(inputTensorInfo, { 1.f, 2.f, 3.f, 4.f });</div><div class="line"><a name="l03228"></a><span class="lineno"> 3228</span> </div><div class="line"><a name="l03229"></a><span class="lineno"> 3229</span>  std::vector<float> kernelData;</div><div class="line"><a name="l03230"></a><span class="lineno"> 3230</span>  std::vector<float> singleDepthKernel{ 1.f, -1.f, -1.f, 1.f };</div><div class="line"><a name="l03231"></a><span class="lineno"> 3231</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < 64; ++i)</div><div class="line"><a name="l03232"></a><span class="lineno"> 3232</span>  {</div><div class="line"><a name="l03233"></a><span class="lineno"> 3233</span>  kernelData.insert(kernelData.end(), singleDepthKernel.begin(), singleDepthKernel.end());</div><div class="line"><a name="l03234"></a><span class="lineno"> 3234</span>  }</div><div class="line"><a name="l03235"></a><span class="lineno"> 3235</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> kernelTensorInfo({ 64, 1, 2, 2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03236"></a><span class="lineno"> 3236</span>  <span class="keyword">auto</span> kernel = MakeTensor<float, 4>(kernelTensorInfo, kernelData);</div><div class="line"><a name="l03237"></a><span class="lineno"> 3237</span> </div><div class="line"><a name="l03238"></a><span class="lineno"> 3238</span>  std::vector<float> expectedOutputData(64, 0.f);</div><div class="line"><a name="l03239"></a><span class="lineno"> 3239</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 64, 1, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l03240"></a><span class="lineno"> 3240</span>  <span class="keyword">auto</span> expectedOutput = MakeTensor<float, 4>(outputTensorInfo, expectedOutputData);</div><div class="line"><a name="l03241"></a><span class="lineno"> 3241</span> </div><div class="line"><a name="l03242"></a><span class="lineno"> 3242</span>  <span class="keywordflow">return</span> DepthwiseConvolution2dTestImpl<armnn::DataType::Float32, armnn::DataType::Float32>(</div><div class="line"><a name="l03243"></a><span class="lineno"> 3243</span>  workloadFactory,</div><div class="line"><a name="l03244"></a><span class="lineno"> 3244</span>  memoryManager,</div><div class="line"><a name="l03245"></a><span class="lineno"> 3245</span>  input,</div><div class="line"><a name="l03246"></a><span class="lineno"> 3246</span>  kernel,</div><div class="line"><a name="l03247"></a><span class="lineno"> 3247</span>  boost::multi_array<float, 1>(),</div><div class="line"><a name="l03248"></a><span class="lineno"> 3248</span>  expectedOutput,</div><div class="line"><a name="l03249"></a><span class="lineno"> 3249</span>  0.f,</div><div class="line"><a name="l03250"></a><span class="lineno"> 3250</span>  0,</div><div class="line"><a name="l03251"></a><span class="lineno"> 3251</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a>);</div><div class="line"><a name="l03252"></a><span class="lineno"> 3252</span> }</div><div class="line"><a name="l03253"></a><span class="lineno"> 3253</span> </div><div class="line"><a name="l03254"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#abf326cbf49ec19c6272fe7c244b7817c"> 3254</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#abf326cbf49ec19c6272fe7c244b7817c">DepthwiseConvolution2dAsymmetricTest</a>(</div><div class="line"><a name="l03255"></a><span class="lineno"> 3255</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03256"></a><span class="lineno"> 3256</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03257"></a><span class="lineno"> 3257</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l03258"></a><span class="lineno"> 3258</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l03259"></a><span class="lineno"> 3259</span> {</div><div class="line"><a name="l03260"></a><span class="lineno"> 3260</span>  <span class="keywordflow">return</span> DepthwiseConvolution2dAsymmetricTestCommon<armnn::DataType::Float32, armnn::DataType::Float32>(</div><div class="line"><a name="l03261"></a><span class="lineno"> 3261</span>  workloadFactory, memoryManager, 0.0f, 0, biasEnabled, layout);</div><div class="line"><a name="l03262"></a><span class="lineno"> 3262</span> }</div><div class="line"><a name="l03263"></a><span class="lineno"> 3263</span> </div><div class="line"><a name="l03264"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#a8076c31bd6e9eae629994a89a5fa18c3"> 3264</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a8076c31bd6e9eae629994a89a5fa18c3">DepthwiseConvolution2dUint8Test</a>(</div><div class="line"><a name="l03265"></a><span class="lineno"> 3265</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03266"></a><span class="lineno"> 3266</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03267"></a><span class="lineno"> 3267</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l03268"></a><span class="lineno"> 3268</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l03269"></a><span class="lineno"> 3269</span> {</div><div class="line"><a name="l03270"></a><span class="lineno"> 3270</span>  <span class="keywordflow">return</span> DepthwiseConvolution2dTestImpl<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>(</div><div class="line"><a name="l03271"></a><span class="lineno"> 3271</span>  workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);</div><div class="line"><a name="l03272"></a><span class="lineno"> 3272</span> }</div><div class="line"><a name="l03273"></a><span class="lineno"> 3273</span> </div><div class="line"><a name="l03274"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#ae797be34b659db2afe183f0c762fb9b7"> 3274</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#ae797be34b659db2afe183f0c762fb9b7">DepthwiseConvolution2dDepthMul1Uint8Test</a>(</div><div class="line"><a name="l03275"></a><span class="lineno"> 3275</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03276"></a><span class="lineno"> 3276</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03277"></a><span class="lineno"> 3277</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l03278"></a><span class="lineno"> 3278</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l03279"></a><span class="lineno"> 3279</span> {</div><div class="line"><a name="l03280"></a><span class="lineno"> 3280</span>  <span class="keywordflow">return</span> DepthwiseConvolution2dDepthMul1TestImpl<armnn::DataType::QAsymmU8, armnn::DataType::Signed32>(</div><div class="line"><a name="l03281"></a><span class="lineno"> 3281</span>  workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);</div><div class="line"><a name="l03282"></a><span class="lineno"> 3282</span> }</div><div class="line"><a name="l03283"></a><span class="lineno"> 3283</span> </div><div class="line"><a name="l03284"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#a77a29527216d36bce78e88354462ede8"> 3284</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a77a29527216d36bce78e88354462ede8">SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTest</a>(</div><div class="line"><a name="l03285"></a><span class="lineno"> 3285</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03286"></a><span class="lineno"> 3286</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)</div><div class="line"><a name="l03287"></a><span class="lineno"> 3287</span> {</div><div class="line"><a name="l03288"></a><span class="lineno"> 3288</span>  <span class="keywordflow">return</span> SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTestCommon<armnn::DataType::Float32, armnn::DataType::Float32>(</div><div class="line"><a name="l03289"></a><span class="lineno"> 3289</span>  workloadFactory,</div><div class="line"><a name="l03290"></a><span class="lineno"> 3290</span>  memoryManager,</div><div class="line"><a name="l03291"></a><span class="lineno"> 3291</span>  0.f,</div><div class="line"><a name="l03292"></a><span class="lineno"> 3292</span>  0,</div><div class="line"><a name="l03293"></a><span class="lineno"> 3293</span>  <span class="keyword">false</span>);</div><div class="line"><a name="l03294"></a><span class="lineno"> 3294</span> }</div><div class="line"><a name="l03295"></a><span class="lineno"> 3295</span> </div><div class="line"><a name="l03296"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#a2ae97c2dd6621f4972c571cf1ec2a005"> 3296</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a2ae97c2dd6621f4972c571cf1ec2a005">DepthwiseConvolution2dInt16Test</a>(</div><div class="line"><a name="l03297"></a><span class="lineno"> 3297</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03298"></a><span class="lineno"> 3298</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03299"></a><span class="lineno"> 3299</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l03300"></a><span class="lineno"> 3300</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l03301"></a><span class="lineno"> 3301</span> {</div><div class="line"><a name="l03302"></a><span class="lineno"> 3302</span>  <span class="keywordflow">return</span> DepthwiseConvolution2dTestImpl<armnn::DataType::QSymmS16, armnn::DataType::Signed32>(</div><div class="line"><a name="l03303"></a><span class="lineno"> 3303</span>  workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);</div><div class="line"><a name="l03304"></a><span class="lineno"> 3304</span> }</div><div class="line"><a name="l03305"></a><span class="lineno"> 3305</span> </div><div class="line"><a name="l03306"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#a74346a72d64f7fa3463473424c3098ab"> 3306</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a74346a72d64f7fa3463473424c3098ab">DepthwiseConvolution2dDepthMul1Int16Test</a>(</div><div class="line"><a name="l03307"></a><span class="lineno"> 3307</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03308"></a><span class="lineno"> 3308</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03309"></a><span class="lineno"> 3309</span>  <span class="keywordtype">bool</span> biasEnabled,</div><div class="line"><a name="l03310"></a><span class="lineno"> 3310</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l03311"></a><span class="lineno"> 3311</span> {</div><div class="line"><a name="l03312"></a><span class="lineno"> 3312</span>  <span class="keywordflow">return</span> DepthwiseConvolution2dDepthMul1TestImpl<armnn::DataType::QSymmS16, armnn::DataType::Signed32>(</div><div class="line"><a name="l03313"></a><span class="lineno"> 3313</span>  workloadFactory, memoryManager, 0.5f, 50, biasEnabled, layout);</div><div class="line"><a name="l03314"></a><span class="lineno"> 3314</span> }</div><div class="line"><a name="l03315"></a><span class="lineno"> 3315</span> </div><div class="line"><a name="l03316"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#a8a51827c480f827c1e29f9347d7433c3"> 3316</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a8a51827c480f827c1e29f9347d7433c3">DepthwiseConvolution2dPerAxisQuantTest</a>(</div><div class="line"><a name="l03317"></a><span class="lineno"> 3317</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03318"></a><span class="lineno"> 3318</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03319"></a><span class="lineno"> 3319</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l03320"></a><span class="lineno"> 3320</span> {</div><div class="line"><a name="l03321"></a><span class="lineno"> 3321</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l03322"></a><span class="lineno"> 3322</span> </div><div class="line"><a name="l03323"></a><span class="lineno"> 3323</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> inputType = DataType::QAsymmU8;</div><div class="line"><a name="l03324"></a><span class="lineno"> 3324</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> kernelType = DataType::QSymmS8;</div><div class="line"><a name="l03325"></a><span class="lineno"> 3325</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> biasType = DataType::Signed32;</div><div class="line"><a name="l03326"></a><span class="lineno"> 3326</span> </div><div class="line"><a name="l03327"></a><span class="lineno"> 3327</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo ({ 1, 3, 3, 2 }, inputType, 0.5f, 128); <span class="comment">// N H W C</span></div><div class="line"><a name="l03328"></a><span class="lineno"> 3328</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo({ 1, 2, 2, 4 }, inputType, 1.0f, 128); <span class="comment">// N H W C</span></div><div class="line"><a name="l03329"></a><span class="lineno"> 3329</span> </div><div class="line"><a name="l03330"></a><span class="lineno"> 3330</span>  <span class="keyword">const</span> std::vector<float> quantScales{ 1.0f, 0.5f, 1.0f, 0.5f };</div><div class="line"><a name="l03331"></a><span class="lineno"> 3331</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> quantDimension = 0;</div><div class="line"><a name="l03332"></a><span class="lineno"> 3332</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> kernelInfo({ 2, 2, 2, 2 }, kernelType, quantScales, quantDimension); <span class="comment">// M I H W</span></div><div class="line"><a name="l03333"></a><span class="lineno"> 3333</span> </div><div class="line"><a name="l03334"></a><span class="lineno"> 3334</span>  <span class="keyword">const</span> std::vector<float> biasQuantScales{ 0.5f, 0.25f, 0.5f, 0.25f };</div><div class="line"><a name="l03335"></a><span class="lineno"> 3335</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasQuantDimension = 0;</div><div class="line"><a name="l03336"></a><span class="lineno"> 3336</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo({ 4 }, biasType, biasQuantScales, biasQuantDimension);</div><div class="line"><a name="l03337"></a><span class="lineno"> 3337</span> </div><div class="line"><a name="l03338"></a><span class="lineno"> 3338</span>  std::vector<uint8_t> inputData =</div><div class="line"><a name="l03339"></a><span class="lineno"> 3339</span>  {</div><div class="line"><a name="l03340"></a><span class="lineno"> 3340</span>  129, 130,</div><div class="line"><a name="l03341"></a><span class="lineno"> 3341</span>  129, 130,</div><div class="line"><a name="l03342"></a><span class="lineno"> 3342</span>  129, 130,</div><div class="line"><a name="l03343"></a><span class="lineno"> 3343</span>  129, 130,</div><div class="line"><a name="l03344"></a><span class="lineno"> 3344</span>  129, 130,</div><div class="line"><a name="l03345"></a><span class="lineno"> 3345</span>  129, 130,</div><div class="line"><a name="l03346"></a><span class="lineno"> 3346</span>  129, 130,</div><div class="line"><a name="l03347"></a><span class="lineno"> 3347</span>  129, 130,</div><div class="line"><a name="l03348"></a><span class="lineno"> 3348</span>  129, 130</div><div class="line"><a name="l03349"></a><span class="lineno"> 3349</span>  };</div><div class="line"><a name="l03350"></a><span class="lineno"> 3350</span> </div><div class="line"><a name="l03351"></a><span class="lineno"> 3351</span>  std::vector<int8_t> kernelData =</div><div class="line"><a name="l03352"></a><span class="lineno"> 3352</span>  {</div><div class="line"><a name="l03353"></a><span class="lineno"> 3353</span>  1, 1, 1, 1,</div><div class="line"><a name="l03354"></a><span class="lineno"> 3354</span>  1, 1, 1, 1,</div><div class="line"><a name="l03355"></a><span class="lineno"> 3355</span>  1, 1, 1, 1,</div><div class="line"><a name="l03356"></a><span class="lineno"> 3356</span>  1, 1, 1, 1</div><div class="line"><a name="l03357"></a><span class="lineno"> 3357</span>  };</div><div class="line"><a name="l03358"></a><span class="lineno"> 3358</span> </div><div class="line"><a name="l03359"></a><span class="lineno"> 3359</span>  std::vector<int32_t> biasData =</div><div class="line"><a name="l03360"></a><span class="lineno"> 3360</span>  {</div><div class="line"><a name="l03361"></a><span class="lineno"> 3361</span>  4, 4, 4, 4</div><div class="line"><a name="l03362"></a><span class="lineno"> 3362</span>  };</div><div class="line"><a name="l03363"></a><span class="lineno"> 3363</span> </div><div class="line"><a name="l03364"></a><span class="lineno"> 3364</span>  std::vector<uint8_t> expectedOutputData =</div><div class="line"><a name="l03365"></a><span class="lineno"> 3365</span>  {</div><div class="line"><a name="l03366"></a><span class="lineno"> 3366</span>  132, 130, 134, 131,</div><div class="line"><a name="l03367"></a><span class="lineno"> 3367</span>  132, 130, 134, 131,</div><div class="line"><a name="l03368"></a><span class="lineno"> 3368</span>  132, 130, 134, 131,</div><div class="line"><a name="l03369"></a><span class="lineno"> 3369</span>  132, 130, 134, 131</div><div class="line"><a name="l03370"></a><span class="lineno"> 3370</span>  };</div><div class="line"><a name="l03371"></a><span class="lineno"> 3371</span> </div><div class="line"><a name="l03372"></a><span class="lineno"> 3372</span>  <span class="keywordflow">if</span> (layout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l03373"></a><span class="lineno"> 3373</span>  {</div><div class="line"><a name="l03374"></a><span class="lineno"> 3374</span>  <a class="code" href="_data_layout_utils_8hpp.xhtml#a1452f049aef30409b3b649af2be7ff82">PermuteTensorNhwcToNchw</a>(inputInfo, inputData);</div><div class="line"><a name="l03375"></a><span class="lineno"> 3375</span>  <a class="code" href="_data_layout_utils_8hpp.xhtml#a1452f049aef30409b3b649af2be7ff82">PermuteTensorNhwcToNchw</a>(outputInfo, expectedOutputData);</div><div class="line"><a name="l03376"></a><span class="lineno"> 3376</span>  }</div><div class="line"><a name="l03377"></a><span class="lineno"> 3377</span> </div><div class="line"><a name="l03378"></a><span class="lineno"> 3378</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a> descriptor;</div><div class="line"><a name="l03379"></a><span class="lineno"> 3379</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l03380"></a><span class="lineno"> 3380</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l03381"></a><span class="lineno"> 3381</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 0;</div><div class="line"><a name="l03382"></a><span class="lineno"> 3382</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 0;</div><div class="line"><a name="l03383"></a><span class="lineno"> 3383</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 0;</div><div class="line"><a name="l03384"></a><span class="lineno"> 3384</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 0;</div><div class="line"><a name="l03385"></a><span class="lineno"> 3385</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">m_DilationX</a> = 1;</div><div class="line"><a name="l03386"></a><span class="lineno"> 3386</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">m_DilationY</a> = 1;</div><div class="line"><a name="l03387"></a><span class="lineno"> 3387</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l03388"></a><span class="lineno"> 3388</span>  descriptor.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = layout;</div><div class="line"><a name="l03389"></a><span class="lineno"> 3389</span> </div><div class="line"><a name="l03390"></a><span class="lineno"> 3390</span>  std::unique_ptr<ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputInfo);</div><div class="line"><a name="l03391"></a><span class="lineno"> 3391</span>  std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputInfo);</div><div class="line"><a name="l03392"></a><span class="lineno"> 3392</span> </div><div class="line"><a name="l03393"></a><span class="lineno"> 3393</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l03394"></a><span class="lineno"> 3394</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> weightTensor(kernelInfo);</div><div class="line"><a name="l03395"></a><span class="lineno"> 3395</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> biasTensor(biasInfo);</div><div class="line"><a name="l03396"></a><span class="lineno"> 3396</span> </div><div class="line"><a name="l03397"></a><span class="lineno"> 3397</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&weightTensor, kernelData.data());</div><div class="line"><a name="l03398"></a><span class="lineno"> 3398</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a>(&biasTensor, biasData.data());</div><div class="line"><a name="l03399"></a><span class="lineno"> 3399</span> </div><div class="line"><a name="l03400"></a><span class="lineno"> 3400</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">DepthwiseConvolution2dQueueDescriptor</a> queueDescriptor;</div><div class="line"><a name="l03401"></a><span class="lineno"> 3401</span>  queueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a> = descriptor;</div><div class="line"><a name="l03402"></a><span class="lineno"> 3402</span>  queueDescriptor.m_Weight = &weightTensor;</div><div class="line"><a name="l03403"></a><span class="lineno"> 3403</span>  queueDescriptor.m_Bias = &biasTensor;</div><div class="line"><a name="l03404"></a><span class="lineno"> 3404</span> </div><div class="line"><a name="l03405"></a><span class="lineno"> 3405</span>  AddInputToWorkload(queueDescriptor, workloadInfo, inputInfo, inputHandle.get());</div><div class="line"><a name="l03406"></a><span class="lineno"> 3406</span>  AddOutputToWorkload(queueDescriptor, workloadInfo, outputInfo, outputHandle.get());</div><div class="line"><a name="l03407"></a><span class="lineno"> 3407</span> </div><div class="line"><a name="l03408"></a><span class="lineno"> 3408</span>  std::unique_ptr<IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#accb9759dfd2880efe0f8d2705ddee448">CreateDepthwiseConvolution2d</a>(queueDescriptor, workloadInfo);</div><div class="line"><a name="l03409"></a><span class="lineno"> 3409</span>  inputHandle->Allocate();</div><div class="line"><a name="l03410"></a><span class="lineno"> 3410</span>  outputHandle->Allocate();</div><div class="line"><a name="l03411"></a><span class="lineno"> 3411</span> </div><div class="line"><a name="l03412"></a><span class="lineno"> 3412</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), inputData.data());</div><div class="line"><a name="l03413"></a><span class="lineno"> 3413</span> </div><div class="line"><a name="l03414"></a><span class="lineno"> 3414</span>  ExecuteWorkload(*workload, memoryManager);</div><div class="line"><a name="l03415"></a><span class="lineno"> 3415</span> </div><div class="line"><a name="l03416"></a><span class="lineno"> 3416</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> ret(outputInfo);</div><div class="line"><a name="l03417"></a><span class="lineno"> 3417</span> </div><div class="line"><a name="l03418"></a><span class="lineno"> 3418</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(ret.<a class="code" href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a>.origin(), outputHandle.get());</div><div class="line"><a name="l03419"></a><span class="lineno"> 3419</span>  ret.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<uint8_t, 4>(outputInfo, expectedOutputData);</div><div class="line"><a name="l03420"></a><span class="lineno"> 3420</span> </div><div class="line"><a name="l03421"></a><span class="lineno"> 3421</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l03422"></a><span class="lineno"> 3422</span> }</div><div class="line"><a name="l03423"></a><span class="lineno"> 3423</span> </div><div class="line"><a name="l03424"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#a09705f5e38cfc0d5bccc64791eb4f231"> 3424</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a09705f5e38cfc0d5bccc64791eb4f231">CompareDepthwiseConvolution2dFloatTest</a>(</div><div class="line"><a name="l03425"></a><span class="lineno"> 3425</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03426"></a><span class="lineno"> 3426</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03427"></a><span class="lineno"> 3427</span>  armnn::IWorkloadFactory& refWorkloadFactory,</div><div class="line"><a name="l03428"></a><span class="lineno"> 3428</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l03429"></a><span class="lineno"> 3429</span> {</div><div class="line"><a name="l03430"></a><span class="lineno"> 3430</span>  <span class="keywordflow">return</span> CompareDepthwiseConvolution2dTestImpl<armnn::DataType::Float32>(</div><div class="line"><a name="l03431"></a><span class="lineno"> 3431</span>  workloadFactory, memoryManager, refWorkloadFactory, layout);</div><div class="line"><a name="l03432"></a><span class="lineno"> 3432</span> }</div><div class="line"><a name="l03433"></a><span class="lineno"> 3433</span> </div><div class="line"><a name="l03434"></a><span class="lineno"><a class="line" href="_conv2d_test_impl_8hpp.xhtml#a21af5850bca4df2ea0315afb407e7900"> 3434</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_conv2d_test_impl_8cpp.xhtml#a21af5850bca4df2ea0315afb407e7900">CompareDepthwiseConvolution2dUint8Test</a>(</div><div class="line"><a name="l03435"></a><span class="lineno"> 3435</span>  armnn::IWorkloadFactory& workloadFactory,</div><div class="line"><a name="l03436"></a><span class="lineno"> 3436</span>  <span class="keyword">const</span> armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,</div><div class="line"><a name="l03437"></a><span class="lineno"> 3437</span>  armnn::IWorkloadFactory& refWorkloadFactory,</div><div class="line"><a name="l03438"></a><span class="lineno"> 3438</span>  <span class="keyword">const</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a> layout)</div><div class="line"><a name="l03439"></a><span class="lineno"> 3439</span> {</div><div class="line"><a name="l03440"></a><span class="lineno"> 3440</span>  <span class="keywordflow">return</span> CompareDepthwiseConvolution2dTestImpl<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l03441"></a><span class="lineno"> 3441</span>  workloadFactory, memoryManager, refWorkloadFactory, layout);</div><div class="line"><a name="l03442"></a><span class="lineno"> 3442</span> }</div><div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_ab020b4a99bf905b61a1c5e03332b63a6"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#ab020b4a99bf905b61a1c5e03332b63a6">DepthwiseConvolution2dDepthMul64Test</a></div><div class="ttdeci">LayerTestResult< float, 4 > DepthwiseConvolution2dDepthMul64Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03222">Conv2dTestImpl.cpp:3222</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Convolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00428">Descriptors.hpp:428</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::Convolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00438">Descriptors.hpp:438</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a5070a9bac7ba582ed116a8b2323ed2a5"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a5070a9bac7ba582ed116a8b2323ed2a5">SimpleConvolution2d3x3TestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > SimpleConvolution2d3x3TestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00790">Conv2dTestImpl.cpp:790</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a09705f5e38cfc0d5bccc64791eb4f231"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a09705f5e38cfc0d5bccc64791eb4f231">CompareDepthwiseConvolution2dFloatTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > CompareDepthwiseConvolution2dFloatTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, armnn::IWorkloadFactory &refWorkloadFactory, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03424">Conv2dTestImpl.cpp:3424</a></div></div> +<div class="ttc" id="_ignore_unused_8hpp_xhtml"><div class="ttname"><a href="_ignore_unused_8hpp.xhtml">IgnoreUnused.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml_ab3437cee6b0687812104fc1b37cbe8b3"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">armnn::Convolution2dQueueDescriptor::m_Bias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Bias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00177">WorkloadData.hpp:177</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Convolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00440">Descriptors.hpp:440</a></div></div> +<div class="ttc" id="_tensor_copy_utils_8hpp_xhtml"><div class="ttname"><a href="_tensor_copy_utils_8hpp.xhtml">TensorCopyUtils.hpp</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_ae797be34b659db2afe183f0c762fb9b7"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#ae797be34b659db2afe183f0c762fb9b7">DepthwiseConvolution2dDepthMul1Uint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > DepthwiseConvolution2dDepthMul1Uint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03274">Conv2dTestImpl.cpp:3274</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_af32b0642214e3129d8e93fa45a12e704"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#af32b0642214e3129d8e93fa45a12e704">SimpleConvolution2dAsymmetricPaddingTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > SimpleConvolution2dAsymmetricPaddingTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::DataLayout layout, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00936">Conv2dTestImpl.cpp:936</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_depthwise_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::DepthwiseConvolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00490">Descriptors.hpp:490</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_afb5e7d86e241292d9cb899b960da54af"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#afb5e7d86e241292d9cb899b960da54af">SimpleConvolution2d3x5Test</a></div><div class="ttdeci">LayerTestResult< float, 4 > SimpleConvolution2d3x5Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l02966">Conv2dTestImpl.cpp:2966</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00049">Types.hpp:49</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00088">Tensor.hpp:88</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::DepthwiseConvolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00480">Descriptors.hpp:480</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a8a51827c480f827c1e29f9347d7433c3"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a8a51827c480f827c1e29f9347d7433c3">DepthwiseConvolution2dPerAxisQuantTest</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > DepthwiseConvolution2dPerAxisQuantTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03316">Conv2dTestImpl.cpp:3316</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_aa1f4ce02e0904dc8cf1b7f42bc34d346"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#aa1f4ce02e0904dc8cf1b7f42bc34d346">ApplyBias</a></div><div class="ttdeci">void ApplyBias(std::vector< T > &v, float vScale, int32_t vOffset, const std::vector< B > &bias, float bScale, int32_t bOffset, uint32_t w, uint32_t h)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00169">Conv2dTestImpl.cpp:169</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_ac7fac5767dabd650d3d8829572717064"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#ac7fac5767dabd650d3d8829572717064">Convolution1dTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > Convolution1dTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03074">Conv2dTestImpl.cpp:3074</a></div></div> +<div class="ttc" id="_inference_test_image_8hpp_xhtml_a65983f8cb907d873f2328bb8307c296aa9d5ed678fe57bcca610140957afab571"><div class="ttname"><a href="_inference_test_image_8hpp.xhtml#a65983f8cb907d873f2328bb8307c296aa9d5ed678fe57bcca610140957afab571">ImageChannel::B</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::DepthwiseConvolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00492">Descriptors.hpp:492</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_af7f2cd23423130ebdd916de12bc0eb1d"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#af7f2cd23423130ebdd916de12bc0eb1d">Convolution2dAsymmetricPaddingTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > Convolution2dAsymmetricPaddingTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03055">Conv2dTestImpl.cpp:3055</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="_quantize_helper_8hpp_xhtml"><div class="ttname"><a href="_quantize_helper_8hpp.xhtml">QuantizeHelper.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8hpp_source.xhtml#l00021">WorkloadFactory.hpp:21</a></div></div> +<div class="ttc" id="_workload_test_utils_8hpp_xhtml"><div class="ttname"><a href="_workload_test_utils_8hpp.xhtml">WorkloadTestUtils.hpp</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a8076c31bd6e9eae629994a89a5fa18c3"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a8076c31bd6e9eae629994a89a5fa18c3">DepthwiseConvolution2dUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > DepthwiseConvolution2dUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03264">Conv2dTestImpl.cpp:3264</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8hpp_xhtml"><div class="ttname"><a href="_conv2d_test_impl_8hpp.xhtml">Conv2dTestImpl.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml_ab3437cee6b0687812104fc1b37cbe8b3"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">armnn::DepthwiseConvolution2dQueueDescriptor::m_Bias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Bias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00192">WorkloadData.hpp:192</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a></div><div class="ttdoc">A Convolution2dDescriptor for the Convolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00392">Descriptors.hpp:392</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::DepthwiseConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00474">Descriptors.hpp:474</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_ae3cc54b77789d10caeb5a438a0821ba0"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#ae3cc54b77789d10caeb5a438a0821ba0">DepthwiseConvolution2dTestImpl</a></div><div class="ttdeci">LayerTestResult< T, 4 > DepthwiseConvolution2dTestImpl(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l01671">Conv2dTestImpl.cpp:1671</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a01eae690cbfa5359968f4b8ee13b8814"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a01eae690cbfa5359968f4b8ee13b8814">DepthwiseConvolution2dDepthMul1TestImpl</a></div><div class="ttdeci">LayerTestResult< T, 4 > DepthwiseConvolution2dDepthMul1TestImpl(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l01518">Conv2dTestImpl.cpp:1518</a></div></div> +<div class="ttc" id="struct_layer_test_result_xhtml_a73610ea6c776cc66e5a78dd842a39b8b"><div class="ttname"><a href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">LayerTestResult::outputExpected</a></div><div class="ttdeci">boost::multi_array< T, n > outputExpected</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult.hpp:41</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_acac29a0b58c3c3f2928e0d7ee258c066"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#acac29a0b58c3c3f2928e0d7ee258c066">CompareDepthwiseConvolution2dTestImpl</a></div><div class="ttdeci">LayerTestResult< T, 4 > CompareDepthwiseConvolution2dTestImpl(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, armnn::IWorkloadFactory &refWorkloadFactory, const armnnUtils::DataLayoutIndexed &layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l02669">Conv2dTestImpl.cpp:2669</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_ad45f359d9d4bee360bee857faa79d292"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#ad45f359d9d4bee360bee857faa79d292">SimpleConvolution2d3x3Uint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > SimpleConvolution2d3x3Uint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03025">Conv2dTestImpl.cpp:3025</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a80ee4cde34185af792db65aa40cf5c98"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a80ee4cde34185af792db65aa40cf5c98">DepthwiseConvolution2d3x3DilationTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > DepthwiseConvolution2d3x3DilationTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const std::vector< float > &inputNoQuantizedValues, armnn::TensorInfo &inputTensorInfo, const std::vector< float > &kernelNoQuantizedValues, armnn::TensorInfo &kernelTensorInfo, const std::vector< float > &outputExpectedNoQuantizedValues, armnn::TensorInfo &outputTensorInfo, uint32_t dilationX, uint32_t dilationY, armnn::DataLayout layout=armnn::DataLayout::NCHW, bool biasEnabled=false)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l02288">Conv2dTestImpl.cpp:2288</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_ac79e75b3bcb6cb8c34f0bd4e3e35f73e"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#ac79e75b3bcb6cb8c34f0bd4e3e35f73e">SimpleConvolution2dNhwcTestImpl</a></div><div class="ttdeci">LayerTestResult< T, 4 > SimpleConvolution2dNhwcTestImpl(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const boost::multi_array< T, 4 > &input, const boost::multi_array< T, 4 > &kernel, const boost::multi_array< B, 1 > &bias, const boost::multi_array< T, 4 > &outputExpected, const armnn::DataLayout dataLayout, float qScale, int32_t qOffset, uint32_t padLeft=1, uint32_t padTop=1, uint32_t padRight=1, uint32_t padBottom=1, uint32_t strideX=1, uint32_t strideY=1)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00367">Conv2dTestImpl.cpp:367</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a7bd1547ceefdc1acedbb1fa6445b2968"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a7bd1547ceefdc1acedbb1fa6445b2968">SimpleConvolution2dTestImpl</a></div><div class="ttdeci">LayerTestResult< T, 4 > SimpleConvolution2dTestImpl(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const boost::multi_array< T, 4 > &originalInput, const boost::multi_array< T, 4 > &originalKernel, const boost::multi_array< B, 1 > &bias, const boost::multi_array< T, 4 > &originalOutputExpected, float qScale, int32_t qOffset, const armnn::DataLayout layout=armnn::DataLayout::NCHW, uint32_t padLeft=0, uint32_t padTop=0, uint32_t padRight=0, uint32_t padBottom=0, uint32_t strideX=1, uint32_t strideY=1, uint32_t dilationX=1, uint32_t dilationY=1)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00201">Conv2dTestImpl.cpp:201</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a0743ed5e860c316a20b68ca96301b411"><div class="ttname"><a href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a></div><div class="ttdeci">typename ResolveTypeImpl< DT >::Type ResolveType</div><div class="ttdef"><b>Definition:</b> <a href="_resolve_type_8hpp_source.xhtml#l00073">ResolveType.hpp:73</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_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Convolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00424">Descriptors.hpp:424</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_abac8f73ae590a93fe91115371ae4ced3"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#abac8f73ae590a93fe91115371ae4ced3">SimpleConvolution2d3x3QSymm16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 4 > SimpleConvolution2d3x3QSymm16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03045">Conv2dTestImpl.cpp:3045</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="_conv2d_test_impl_8cpp_xhtml_af541f19e3d1ad345cc9208fc2d2e7b19"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#af541f19e3d1ad345cc9208fc2d2e7b19">Convolution1dTestImpl</a></div><div class="ttdeci">LayerTestResult< T, 4 > Convolution1dTestImpl(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset, bool biasEnabled)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00460">Conv2dTestImpl.cpp:460</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &&...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a302b688d88dd73cde0fb1faef6679907"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">armnn::Convolution2dDescriptor::m_DilationY</a></div><div class="ttdeci">uint32_t m_DilationY</div><div class="ttdoc">Dilation along y axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00436">Descriptors.hpp:436</a></div></div> +<div class="ttc" id="structarmnn_1_1_queue_descriptor_with_parameters_xhtml_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00049">WorkloadData.hpp:49</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a302b688d88dd73cde0fb1faef6679907"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a302b688d88dd73cde0fb1faef6679907">armnn::DepthwiseConvolution2dDescriptor::m_DilationY</a></div><div class="ttdeci">uint32_t m_DilationY</div><div class="ttdoc">Dilation factor value for height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00488">Descriptors.hpp:488</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml_a3369b66d9316a773a41711e3f590c041"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">armnn::DepthwiseConvolution2dQueueDescriptor::m_Weight</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Weight</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00191">WorkloadData.hpp:191</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a77a29527216d36bce78e88354462ede8"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a77a29527216d36bce78e88354462ede8">SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03284">Conv2dTestImpl.cpp:3284</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a0cccb5cffee89004bc8d9fb309ed6636"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a0cccb5cffee89004bc8d9fb309ed6636">DepthwiseConvolution2dDepthNhwcTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > DepthwiseConvolution2dDepthNhwcTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03203">Conv2dTestImpl.cpp:3203</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a90abce368d7f16012bef5ee461329484"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a90abce368d7f16012bef5ee461329484">Convolution2d3x3Dilation3x3Test</a></div><div class="ttdeci">LayerTestResult< T, 4 > Convolution2d3x3Dilation3x3Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l01083">Conv2dTestImpl.cpp:1083</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Convolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00426">Descriptors.hpp:426</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a370a5216668b507284677234264a22a2"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a370a5216668b507284677234264a22a2">Convolution2dPerAxisQuantTest</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > Convolution2dPerAxisQuantTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03092">Conv2dTestImpl.cpp:3092</a></div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml_af3c74017185773dd61d8ca6662d65d43"><div class="ttname"><a href="namespacearmnn_utils.xhtml#af3c74017185773dd61d8ca6662d65d43">armnnUtils::Permute</a></div><div class="ttdeci">void Permute(const armnn::TensorShape &dstShape, const armnn::PermutationVector &mappings, const void *src, void *dst, size_t dataTypeSize)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.xhtml#l00121">Permute.cpp:121</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_acf553288e3b5060768fb91e064993678"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#acf553288e3b5060768fb91e064993678">Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test</a></div><div class="ttdeci">LayerTestResult< T, 4 > Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l01210">Conv2dTestImpl.cpp:1210</a></div></div> +<div class="ttc" id="_tensor_helpers_8hpp_xhtml"><div class="ttname"><a href="_tensor_helpers_8hpp.xhtml">TensorHelpers.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Convolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00430">Descriptors.hpp:430</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a638295d292bfdcf71899b57396703c80"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a638295d292bfdcf71899b57396703c80">CompareConvolution2dTestImpl</a></div><div class="ttdeci">LayerTestResult< T, 4 > CompareConvolution2dTestImpl(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, armnn::IWorkloadFactory &refWorkloadFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l01277">Conv2dTestImpl.cpp:1277</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::DepthwiseConvolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00482">Descriptors.hpp:482</a></div></div> +<div class="ttc" id="_permute_8hpp_xhtml"><div class="ttname"><a href="_permute_8hpp.xhtml">Permute.hpp</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00032">Types.hpp:32</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aa3c6a77a963a98ccb8ea7b8fd008a8c1"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">armnn::DepthwiseConvolution2dDescriptor::m_DilationX</a></div><div class="ttdeci">uint32_t m_DilationX</div><div class="ttdoc">Dilation factor value for width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00486">Descriptors.hpp:486</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::DepthwiseConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00478">Descriptors.hpp:478</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a9dcd2fb98f5c3284c74f65a7c7a69da1"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a9dcd2fb98f5c3284c74f65a7c7a69da1">SimpleConvolution2d3x5QSymm16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 4 > SimpleConvolution2d3x5QSymm16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03035">Conv2dTestImpl.cpp:3035</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_backend_internal_xhtml_a693b40e6b94e958836aeb0410ca186bd"><div class="ttname"><a href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a></div><div class="ttdeci">std::shared_ptr< IMemoryManager > IMemoryManagerSharedPtr</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_backend_internal_8hpp_source.xhtml#l00090">IBackendInternal.hpp:90</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="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml_a3369b66d9316a773a41711e3f590c041"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">armnn::Convolution2dQueueDescriptor::m_Weight</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Weight</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00176">WorkloadData.hpp:176</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_aa405363108e52032fb1e23c3f5a03a57"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#aa405363108e52032fb1e23c3f5a03a57">DepthwiseConvolution2dAsymmetricTestImpl</a></div><div class="ttdeci">LayerTestResult< T, 4 > DepthwiseConvolution2dAsymmetricTestImpl(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const boost::multi_array< T, 4 > &input, const boost::multi_array< T, 4 > &kernel, const boost::multi_array< B, 1 > &bias, const boost::multi_array< T, 4 > &outputExpected, float qScale, int32_t qOffset, const armnn::DataLayout layout, uint32_t padLeft=0, uint32_t padTop=0, uint32_t padRight=0, uint32_t padBottom=0, uint32_t strideX=1, uint32_t strideY=1)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l01381">Conv2dTestImpl.cpp:1381</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_acffa50ae3185e3e5302909f27e7e9a02"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#acffa50ae3185e3e5302909f27e7e9a02">DepthwiseConvolution2d2x3x3Dilation3x3Test</a></div><div class="ttdeci">LayerTestResult< T, 4 > DepthwiseConvolution2d2x3x3Dilation3x3Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l02432">Conv2dTestImpl.cpp:2432</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_aaed50a372a6b59b20e38469856a3ce6b"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#aaed50a372a6b59b20e38469856a3ce6b">DepthwiseConvolution2dMult2Test</a></div><div class="ttdeci">LayerTestResult< T, 4 > DepthwiseConvolution2dMult2Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l02600">Conv2dTestImpl.cpp:2600</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_ad80bc46727797692d35f94d5935469cb"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#ad80bc46727797692d35f94d5935469cb">GetBias2</a></div><div class="ttdeci">boost::multi_array< T, 1 > GetBias2(bool biasEnabled, float qScale)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00074">Conv2dTestImpl.cpp:74</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a685739c4eb65a580e075282cfe6787d6"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">armnn::TensorInfo::SetQuantizationScale</a></div><div class="ttdeci">void SetQuantizationScale(float scale)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00259">Tensor.cpp:259</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a74346a72d64f7fa3463473424c3098ab"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a74346a72d64f7fa3463473424c3098ab">DepthwiseConvolution2dDepthMul1Int16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 4 > DepthwiseConvolution2dDepthMul1Int16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03306">Conv2dTestImpl.cpp:3306</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_ac7af28eafb5b583057bca4471ce22328"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#ac7af28eafb5b583057bca4471ce22328">SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > SimpleDepthwiseConvolution2d3x3Dilation3x3NhwcTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset, bool biasEnabled)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l02212">Conv2dTestImpl.cpp:2212</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="_conv2d_test_impl_8cpp_xhtml_acbe1a2adccd9e0aad14fc0ccb9266b0d"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#acbe1a2adccd9e0aad14fc0ccb9266b0d">SimpleConvolution2d3x3Test</a></div><div class="ttdeci">LayerTestResult< float, 4 > SimpleConvolution2d3x3Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l02986">Conv2dTestImpl.cpp:2986</a></div></div> +<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_afaaca8c3f3a467d124bba44067d2afa8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#afaaca8c3f3a467d124bba44067d2afa8">AllocateAndCopyDataToITensorHandle</a></div><div class="ttdeci">void AllocateAndCopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00019">TensorCopyUtils.cpp:19</a></div></div> +<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml_a7d8b3d755b6ca8f5533657969efb06c4"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a7d8b3d755b6ca8f5533657969efb06c4">armnnUtils::DataLayoutIndexed::GetDataLayout</a></div><div class="ttdeci">armnn::DataLayout GetDataLayout() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00022">DataLayoutIndexed.hpp:22</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a6271caa80dbf6fc82f97081d3d99d987"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a6271caa80dbf6fc82f97081d3d99d987">DepthwiseConvolution2dNhwcTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > DepthwiseConvolution2dNhwcTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset, bool biasEnabled)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l02131">Conv2dTestImpl.cpp:2131</a></div></div> +<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_a99b626c58a926dc7d6df78d22ec186c8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a></div><div class="ttdeci">void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00014">TensorCopyUtils.cpp:14</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_aa794621b8665d1df93a1c9aa95d5a90d"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#aa794621b8665d1df93a1c9aa95d5a90d">GetBias4</a></div><div class="ttdeci">boost::multi_array< T, 1 > GetBias4(bool biasEnabled, float qScale)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00090">Conv2dTestImpl.cpp:90</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="_conv2d_test_impl_8cpp_xhtml_a99ef3f48cbd057e0169bc80dc77331ef"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a99ef3f48cbd057e0169bc80dc77331ef">Convolution2d2x3x3Dilation3x3Test</a></div><div class="ttdeci">LayerTestResult< T, 4 > Convolution2d2x3x3Dilation3x3Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l01139">Conv2dTestImpl.cpp:1139</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="_conv2d_test_impl_8cpp_xhtml_a2b2c2f8f89d96932e62b95e7a22961d4"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a2b2c2f8f89d96932e62b95e7a22961d4">CompareConvolution2dTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > CompareConvolution2dTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, armnn::IWorkloadFactory &refWorkloadFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03184">Conv2dTestImpl.cpp:3184</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Convolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00432">Descriptors.hpp:432</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a15c140be4ddceffee16436f009d3ed94"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">armnn::IWorkloadFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr< ITensorHandle > CreateTensorHandle(const TensorInfo &tensorInfo, const bool IsMemoryManaged=true) const =0</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="_conv2d_test_impl_8cpp_xhtml_abf326cbf49ec19c6272fe7c244b7817c"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#abf326cbf49ec19c6272fe7c244b7817c">DepthwiseConvolution2dAsymmetricTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > DepthwiseConvolution2dAsymmetricTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03254">Conv2dTestImpl.cpp:3254</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="struct_layer_test_result_xhtml_ac9d44d346bb7c89f7a7aa31d2bee947f"><div class="ttname"><a href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">LayerTestResult::output</a></div><div class="ttdeci">boost::multi_array< T, n > output</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00040">LayerTestResult.hpp:40</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">armnn::Convolution2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00168">WorkloadData.hpp:168</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a48884a37a6b783185c608a68cfce752f"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a48884a37a6b783185c608a68cfce752f">Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03064">Conv2dTestImpl.cpp:3064</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_ad12c52b6d41931219bdfec5fbf5990bd"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#ad12c52b6d41931219bdfec5fbf5990bd">Convolution2d3x3DilationTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > Convolution2d3x3DilationTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const std::vector< float > &inputNoQuantizedValues, armnn::TensorInfo &inputTensorInfo, const std::vector< float > &kernelNoQuantizedValues, armnn::TensorInfo &kernelTensorInfo, const std::vector< float > &outputExpectedNoQuantizedValues, armnn::TensorInfo &outputTensorInfo, uint32_t dilationX, uint32_t dilationY, armnn::DataLayout layout=armnn::DataLayout::NCHW, uint32_t padLeft=0, uint32_t padTop=0, uint32_t padRight=0, uint32_t padBottom=0, uint32_t strideX=1, uint32_t strideY=1, bool biasEnabled=false)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00995">Conv2dTestImpl.cpp:995</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aa3c6a77a963a98ccb8ea7b8fd008a8c1"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa3c6a77a963a98ccb8ea7b8fd008a8c1">armnn::Convolution2dDescriptor::m_DilationX</a></div><div class="ttdeci">uint32_t m_DilationX</div><div class="ttdoc">Dilation along x axis. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00434">Descriptors.hpp:434</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_ae04bff4e44deed6908feae29e57ffe0c"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#ae04bff4e44deed6908feae29e57ffe0c">GetBias8</a></div><div class="ttdeci">boost::multi_array< T, 1 > GetBias8(bool biasEnabled, float qScale)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00106">Conv2dTestImpl.cpp:106</a></div></div> +<div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_ac7bae01fdca8edac70cc9bc722426b17"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#ac7bae01fdca8edac70cc9bc722426b17">SimpleConvolution2d3x3NhwcTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > SimpleConvolution2d3x3NhwcTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l02996">Conv2dTestImpl.cpp:2996</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_depthwise_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::DepthwiseConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00484">Descriptors.hpp:484</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a35ad1225c524b4594b461e613695ee4a"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a35ad1225c524b4594b461e613695ee4a">Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::DataLayout layout, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00869">Conv2dTestImpl.cpp:869</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="_conv2d_test_impl_8cpp_xhtml_aafa5b575d2bc27ec7229f1d87ab8efdb"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#aafa5b575d2bc27ec7229f1d87ab8efdb">SimpleConvolution2d3x3Stride2x2TestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > SimpleConvolution2d3x3Stride2x2TestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset, bool biasEnabled, const armnn::DataLayout &dataLayout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00635">Conv2dTestImpl.cpp:635</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a3481304dfd3e941b809c64979b940ad5"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a3481304dfd3e941b809c64979b940ad5">GetBias</a></div><div class="ttdeci">boost::multi_array< T, 1 > GetBias(bool biasEnabled, float qScale, armnn::TensorInfo outputInfo, armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00122">Conv2dTestImpl.cpp:122</a></div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml"><div class="ttname"><a href="namespacearmnn_utils.xhtml">armnnUtils</a></div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00013">DataLayoutIndexed.hpp:13</a></div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml_acee63cd08da47910fc166a1990988fa8"><div class="ttname"><a href="namespacearmnn_utils.xhtml#acee63cd08da47910fc166a1990988fa8">armnnUtils::GetTensorInfo</a></div><div class="ttdeci">armnn::TensorInfo GetTensorInfo(unsigned int numberOfBatches, unsigned int numberOfChannels, unsigned int height, unsigned int width, const armnn::DataLayout dataLayout, const armnn::DataType dataType)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_utils_8cpp_source.xhtml#l00038">TensorUtils.cpp:38</a></div></div> +<div class="ttc" id="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="_conv2d_test_impl_8cpp_xhtml_a11fbd94028ab646528b42d0c8c55eee1"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a11fbd94028ab646528b42d0c8c55eee1">DepthwiseConvolution2dTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > DepthwiseConvolution2dTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03193">Conv2dTestImpl.cpp:3193</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a3660079f1e20e5b1618402dfc5214441"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a3660079f1e20e5b1618402dfc5214441">SimpleConvolution2d3x5TestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > SimpleConvolution2d3x5TestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00703">Conv2dTestImpl.cpp:703</a></div></div> +<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div> +<div class="ttc" id="namespacearmnn_utils_xhtml_a5135dc1ce7a8aeb97623c1a92c5a3543"><div class="ttname"><a href="namespacearmnn_utils.xhtml#a5135dc1ce7a8aeb97623c1a92c5a3543">armnnUtils::SelectiveDequantize</a></div><div class="ttdeci">float SelectiveDequantize(T value, float scale, int32_t offset)</div><div class="ttdef"><b>Definition:</b> <a href="_quantize_helper_8hpp_source.xhtml#l00092">QuantizeHelper.hpp:92</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a63cbc581012c957f9d68d224ddc3e43c"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">armnn::TensorInfo::SetQuantizationOffset</a></div><div class="ttdeci">void SetQuantizationOffset(int32_t offset)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00275">Tensor.cpp:275</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a1c3398bdb48e4ce4643a1eeaf3e054a3"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a1c3398bdb48e4ce4643a1eeaf3e054a3">DepthwiseConvolution2d3x3Dilation3x3Test</a></div><div class="ttdeci">LayerTestResult< T, 4 > DepthwiseConvolution2d3x3Dilation3x3Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l02376">Conv2dTestImpl.cpp:2376</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a40bc412ed2a6d2f764655070c02c036b"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a40bc412ed2a6d2f764655070c02c036b">Convolution1dUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > Convolution1dUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03083">Conv2dTestImpl.cpp:3083</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a952b4460c66365d89ebb3df940bbd9bd"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a952b4460c66365d89ebb3df940bbd9bd">DepthwiseConvolution2dAsymmetricTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > DepthwiseConvolution2dAsymmetricTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l02047">Conv2dTestImpl.cpp:2047</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a21af5850bca4df2ea0315afb407e7900"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a21af5850bca4df2ea0315afb407e7900">CompareDepthwiseConvolution2dUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > CompareDepthwiseConvolution2dUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, armnn::IWorkloadFactory &refWorkloadFactory, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03434">Conv2dTestImpl.cpp:3434</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a8ffca1c4b38a68b10ba06f4f1416660f"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a8ffca1c4b38a68b10ba06f4f1416660f">SimpleConvolution2d3x5Uint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > SimpleConvolution2d3x5Uint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l02976">Conv2dTestImpl.cpp:2976</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a8225effadfc56a5d831ae0f7f686a6cf"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a8225effadfc56a5d831ae0f7f686a6cf">SimpleConvolution2d3x3NhwcTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > SimpleConvolution2d3x3NhwcTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset, bool biasEnabled, armnn::DataLayout dataLayout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l00582">Conv2dTestImpl.cpp:582</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="_conv2d_test_impl_8cpp_xhtml_a2ae97c2dd6621f4972c571cf1ec2a005"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a2ae97c2dd6621f4972c571cf1ec2a005">DepthwiseConvolution2dInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 4 > DepthwiseConvolution2dInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03296">Conv2dTestImpl.cpp:3296</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_accb9759dfd2880efe0f8d2705ddee448"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#accb9759dfd2880efe0f8d2705ddee448">armnn::IWorkloadFactory::CreateDepthwiseConvolution2d</a></div><div class="ttdeci">virtual std::unique_ptr< IWorkload > CreateDepthwiseConvolution2d(const DepthwiseConvolution2dQueueDescriptor &descriptor, const WorkloadInfo &info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01177">WorkloadFactory.cpp:1177</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="_data_layout_utils_8hpp_xhtml"><div class="ttname"><a href="_data_layout_utils_8hpp.xhtml">DataLayoutUtils.hpp</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a8b32d950a40903f502f5e1ec0dcab0bd"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a8b32d950a40903f502f5e1ec0dcab0bd">DepthwiseConvolution2dDepthMul1Test</a></div><div class="ttdeci">LayerTestResult< float, 4 > DepthwiseConvolution2dDepthMul1Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03212">Conv2dTestImpl.cpp:3212</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_a0da6534b3a5d2f923dcd73553950129a"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#a0da6534b3a5d2f923dcd73553950129a">DepthwiseConvolution2dMult4Test</a></div><div class="ttdeci">LayerTestResult< T, 4 > DepthwiseConvolution2dMult4Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l02508">Conv2dTestImpl.cpp:2508</a></div></div> +<div class="ttc" id="_conv2d_test_impl_8cpp_xhtml_af4ac6874d18e1cb59873a17073512873"><div class="ttname"><a href="_conv2d_test_impl_8cpp.xhtml#af4ac6874d18e1cb59873a17073512873">SimpleConvolution2d3x3Stride2x2Test</a></div><div class="ttdeci">LayerTestResult< float, 4 > SimpleConvolution2d3x3Stride2x2Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, bool biasEnabled, const armnn::DataLayout layout)</div><div class="ttdef"><b>Definition:</b> <a href="_conv2d_test_impl_8cpp_source.xhtml#l03010">Conv2dTestImpl.cpp:3010</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">armnn::DepthwiseConvolution2dDescriptor</a></div><div class="ttdoc">A DepthwiseConvolution2dDescriptor for the DepthwiseConvolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00444">Descriptors.hpp:444</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">armnn::DepthwiseConvolution2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00183">WorkloadData.hpp:183</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Convolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00422">Descriptors.hpp:422</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a2184995027cd2c9f9980206de9658855"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a2184995027cd2c9f9980206de9658855">armnn::IWorkloadFactory::CreateConvolution2d</a></div><div class="ttdeci">virtual std::unique_ptr< IWorkload > CreateConvolution2d(const Convolution2dQueueDescriptor &descriptor, const WorkloadInfo &info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01159">WorkloadFactory.cpp:1159</a></div></div> +<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_ae15f1a3c55d2db87683577de9fa4437c"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a></div><div class="ttdeci">void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00009">TensorCopyUtils.cpp:9</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::DepthwiseConvolution2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00476">Descriptors.hpp:476</a></div></div> +<div class="ttc" id="_data_layout_utils_8hpp_xhtml_a1452f049aef30409b3b649af2be7ff82"><div class="ttname"><a href="_data_layout_utils_8hpp.xhtml#a1452f049aef30409b3b649af2be7ff82">PermuteTensorNhwcToNchw</a></div><div class="ttdeci">void PermuteTensorNhwcToNchw(armnn::TensorInfo &tensorInfo, std::vector< T > &tensorData)</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_utils_8hpp_source.xhtml#l00026">DataLayoutUtils.hpp:26</a></div></div> +</div><!-- fragment --></div><!-- contents --> +</div><!-- doc-content --> +<!-- start footer part --> +<div id="nav-path" class="navpath"><!-- id is needed for treeview function! --> + <ul> + <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.xhtml">src</a></li><li class="navelem"><a class="el" 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