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diff --git a/20.02/_create_workload_8hpp_source.xhtml b/20.02/_create_workload_8hpp_source.xhtml new file mode 100644 index 0000000000..dbd64dc836 --- /dev/null +++ b/20.02/_create_workload_8hpp_source.xhtml @@ -0,0 +1,331 @@ +<!-- 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/armnn/test/CreateWorkload.hpp 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('_create_workload_8hpp_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">CreateWorkload.hpp</div> </div> +</div><!--header--> +<div class="contents"> +<a href="_create_workload_8hpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="preprocessor">#pragma once</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> </div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include "<a class="code" href="_test_utils_8hpp.xhtml">TestUtils.hpp</a>"</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> </div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_graph_8hpp.xhtml">Graph.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_network_8hpp.xhtml">Network.hpp</a>></span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> </div><div class="line"><a name="l00013"></a><span class="lineno"> 13</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="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <<a class="code" href="_ignore_unused_8hpp.xhtml">armnn/utility/IgnoreUnused.hpp</a>></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> </div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <<a class="code" href="_workload_data_8hpp.xhtml">backendsCommon/WorkloadData.hpp</a>></span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include <<a class="code" href="_workload_factory_8hpp.xhtml">backendsCommon/WorkloadFactory.hpp</a>></span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</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="l00019"></a><span class="lineno"> 19</span> </div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="preprocessor">#include <boost/test/unit_test.hpp></span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="preprocessor">#include <boost/cast.hpp></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 <utility></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="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</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="keyword">namespace</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> {</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="keyword">using namespace </span><a class="code" href="namespacestd.xhtml">std</a>;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> </div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="comment">// Calls CreateWorkload for a layer, and checks the returned pointer is of the correct type.</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="keyword">template</span><<span class="keyword">typename</span> Workload></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> std::unique_ptr<Workload> MakeAndCheckWorkload(<a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>& layer, <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& factory)</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>  std::unique_ptr<IWorkload> workload = layer.<a class="code" href="classarmnn_1_1_layer.xhtml#a08d1e10a45f15cd0bd02557be35a3864">CreateWorkload</a>(factory);</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  BOOST_TEST(workload.get() == boost::polymorphic_downcast<Workload*>(workload.get()),</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="stringliteral">"Cannot convert to derived class"</span>);</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  std::string reasonIfUnsupported;</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  layer.<a class="code" href="classarmnn_1_1_layer.xhtml#a3f6ad59212fa8a47c9265162fff8a274">SetBackendId</a>(factory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a9f7e4296485d2812e7996089149c96d1">GetBackendId</a>());</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  BOOST_TEST(factory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a74dc9ec1a223eab8b072368b2dacee87">IsLayerSupported</a>(layer, layer.<a class="code" href="classarmnn_1_1_layer.xhtml#aea909c7327109228ef618d459015def3">GetDataType</a>(), reasonIfUnsupported));</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keywordflow">return</span> std::unique_ptr<Workload>(<span class="keyword">static_cast<</span>Workload*<span class="keyword">></span>(workload.release()));</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> }</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> </div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="comment">// Helper function to create tensor handlers for workloads, assuming they all use the same factory.</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="keywordtype">void</span> CreateTensorHandles(<a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph,</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory)</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> {</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <a class="code" href="classarmnn_1_1_tensor_handle_factory_registry.xhtml">TensorHandleFactoryRegistry</a> tmpRegistry;</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>&& layer : graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a9a7209345edfdb2b066b0ceb66414d7c">TopologicalSort</a>())</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  {</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  layer-><a class="code" href="classarmnn_1_1_layer.xhtml#a3ff62126ec713a2708e5fbaa6146a7de">CreateTensorHandles</a>(tmpRegistry, factory);</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  }</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> }</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> <span class="comment"></span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> <span class="comment">/////////////////////////////////////////////////////////////////////////////////////////////</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> <span class="comment"></span><span class="comment">// The following functions are called by backendsCommon/test/CreateWorkload*.cpp</span></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> <span class="comment">// They build very simple graphs, and then create a workload.</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> <span class="comment">// Some checks are performed on the workload to ensure parameters have been passed correctly.</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> <span class="comment">// They return the created workloads so that backend-specific checks can be performed.</span><span class="comment"></span></div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> <span class="comment">/////////////////////////////////////////////////////////////////////////////////////////////</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> <span class="comment"></span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ActivationWorkload, armnn::DataType DataType></div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> std::unique_ptr<ActivationWorkload> CreateActivationWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory,</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph)</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> {</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="comment">// Creates the layer we're testing.</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">ActivationDescriptor</a> layerDesc;</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  layerDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa1e34af023adeb7d5f484f8eb4b9826b6">ActivationFunction::Abs</a>;</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  layerDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 3.5f;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  layerDesc.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = -10.0f;</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <a class="code" href="classarmnn_1_1_activation_layer.xhtml">ActivationLayer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_activation_layer.xhtml">ActivationLayer</a>>(layerDesc, <span class="stringliteral">"layer"</span>);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> </div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="comment">// Creates extra layers.</span></div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="comment">// Connects up.</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo({1, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</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>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, tensorInfo);</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, tensorInfo);</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> </div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> </div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="comment">// Makes the workload and checks it.</span></div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<ActivationWorkload>(*layer, factory);</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> </div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <a class="code" href="structarmnn_1_1_activation_queue_descriptor.xhtml">ActivationQueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_A == 3.5f);</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_B == -10.0f);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  BOOST_TEST((queueDescriptor.m_Parameters.m_Function == <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa1e34af023adeb7d5f484f8eb4b9826b6">ActivationFunction::Abs</a>));</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> </div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="comment">// Returns so we can do extra, backend-specific tests.</span></div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keywordflow">return</span> workload;</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> </div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> <span class="keyword">template</span> <<span class="keyword">typename</span> WorkloadType,</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="keyword">typename</span> DescriptorType,</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keyword">typename</span> <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4">LayerType</a>,</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>></div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> std::unique_ptr<WorkloadType> CreateElementwiseWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> & factory,</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a> & graph)</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> {</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="comment">// Creates the layer we're testing.</span></div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><LayerType>(<span class="stringliteral">"layer"</span>);</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> </div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="comment">// Creates extra layers.</span></div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input1 = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(1, <span class="stringliteral">"input1"</span>);</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input2 = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(2, <span class="stringliteral">"input2"</span>);</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</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="comment">// Connects up.</span></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo({2, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input1, layer, tensorInfo, 0, 0);</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input2, layer, tensorInfo, 0, 1);</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, tensorInfo);</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> </div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="comment">// Makes the workload and checks it.</span></div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<WorkloadType>(*layer, factory);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> </div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  DescriptorType queueDescriptor = workload->GetData();</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 2);</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);</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="comment">// Returns so we can do extra, backend-specific tests.</span></div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordflow">return</span> workload;</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> }</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> </div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> <span class="keyword">template</span> <<span class="keyword">typename</span> WorkloadType, </div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keyword">typename</span> DescriptorType,</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> std::unique_ptr<WorkloadType> CreateElementwiseUnaryWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> & factory,</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a> & graph,</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <a class="code" href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8">armnn::UnaryOperation</a> op)</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>  <a class="code" href="structarmnn_1_1_elementwise_unary_descriptor.xhtml">ElementwiseUnaryDescriptor</a> desc = <a class="code" href="structarmnn_1_1_elementwise_unary_descriptor.xhtml">ElementwiseUnaryDescriptor</a>(op);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_elementwise_unary_layer.xhtml">armnn::ElementwiseUnaryLayer</a>>(desc, <span class="stringliteral">"layer"</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>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> </div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo({ 2, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, tensorInfo, 0, 0);</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, tensorInfo, 0, 0);</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> </div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<WorkloadType>(*layer, factory);</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  DescriptorType queueDescriptor = workload->GetData();</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>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);</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="keywordflow">return</span> workload;</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> <<span class="keyword">typename</span> BatchNormalizationWorkloadType, armnn::DataType DataType></div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> std::unique_ptr<BatchNormalizationWorkloadType> CreateBatchNormalizationWorkloadTest(</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory, <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph, <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> {</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> tensorShape;</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="keywordflow">switch</span> (dataLayout)</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  {</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>:</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  tensorShape = { 2, 4, 4, 3 };</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>:</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  tensorShape = { 2, 3, 4, 4 };</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  }</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> </div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="comment">// Creates the layer we're testing.</span></div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml">BatchNormalizationDescriptor</a> layerDesc;</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  layerDesc.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">m_Eps</a> = 0.05f;</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  layerDesc.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> </div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <a class="code" href="classarmnn_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>>(layerDesc, <span class="stringliteral">"layer"</span>);</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightInfo({3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  layer-><a class="code" href="classarmnn_1_1_batch_normalization_layer.xhtml#a3540afac8fad99bbe68b3f7b57590160">m_Mean</a> = std::make_unique<ScopedCpuTensorHandle>(weightInfo);</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  layer-><a class="code" href="classarmnn_1_1_batch_normalization_layer.xhtml#aa5685ee78433980cf535d745d1fcab55">m_Variance</a> = std::make_unique<ScopedCpuTensorHandle>(weightInfo);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  layer-><a class="code" href="classarmnn_1_1_batch_normalization_layer.xhtml#a77c30d191e7ee8917e2c0ff5e97f5640">m_Beta</a> = std::make_unique<ScopedCpuTensorHandle>(weightInfo);</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  layer-><a class="code" href="classarmnn_1_1_batch_normalization_layer.xhtml#aec22bddf14a932c4a72796c30669066b">m_Gamma</a> = std::make_unique<ScopedCpuTensorHandle>(weightInfo);</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  layer-><a class="code" href="classarmnn_1_1_batch_normalization_layer.xhtml#a3540afac8fad99bbe68b3f7b57590160">m_Mean</a>->Allocate();</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  layer-><a class="code" href="classarmnn_1_1_batch_normalization_layer.xhtml#aa5685ee78433980cf535d745d1fcab55">m_Variance</a>->Allocate();</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  layer-><a class="code" href="classarmnn_1_1_batch_normalization_layer.xhtml#a77c30d191e7ee8917e2c0ff5e97f5640">m_Beta</a>->Allocate();</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  layer-><a class="code" href="classarmnn_1_1_batch_normalization_layer.xhtml#aec22bddf14a932c4a72796c30669066b">m_Gamma</a>->Allocate();</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>  <span class="comment">// Creates extra layers.</span></div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> </div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="comment">// Connects up.</span></div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo(tensorShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, tensorInfo);</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, tensorInfo);</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> </div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="comment">// Makes the workload and checks it.</span></div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<BatchNormalizationWorkloadType>(*layer, factory);</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml">BatchNormalizationQueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_Eps == 0.05f);</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  BOOST_TEST((queueDescriptor.m_Mean->GetTensorInfo() == <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>)));</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  BOOST_TEST((queueDescriptor.m_Variance->GetTensorInfo() == <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>)));</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  BOOST_TEST((queueDescriptor.m_Gamma->GetTensorInfo() == <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>)));</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  BOOST_TEST((queueDescriptor.m_Beta->GetTensorInfo() == <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>)));</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout == dataLayout));</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span> </div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="comment">// Returns so we can do extra, backend-specific tests.</span></div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keywordflow">return</span> workload;</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> }</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> </div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span> <span class="keyword">template</span> <<span class="keyword">typename</span> Convolution2dWorkload, armnn::DataType DataType></div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span> std::unique_ptr<Convolution2dWorkload> CreateConvolution2dWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory,</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph,</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span> {</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <span class="comment">// Creates the layer we're testing.</span></div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> layerDesc;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  layerDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 3;</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  layerDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 3;</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  layerDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  layerDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  layerDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 2;</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  layerDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 4;</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  layerDesc.<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="l00233"></a><span class="lineno"> 233</span>  layerDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span> </div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">Convolution2dLayer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">Convolution2dLayer</a>>(layerDesc, <span class="stringliteral">"layer"</span>);</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> </div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> weightShape = (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>) ? <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{2, 3, 5, 3} : <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{2, 5, 3, 3};</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape = (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>) ? <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{2, 3, 8, 16} : <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{2, 8, 16, 3};</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape = (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>) ? <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{2, 2, 2, 10} : <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{2, 2, 10, 2};</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span> </div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  layer-><a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml#a2664044e28e69309ea08ef385fe53903">m_Weight</a> = std::make_unique<ScopedCpuTensorHandle>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(weightShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>));</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  layer-><a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml#a39925bc24d3afcfb322a46a5884fadb9">m_Bias</a> = std::make_unique<ScopedCpuTensorHandle>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({2}, <a class="code" href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">GetBiasDataType</a>(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>)));</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span> </div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  layer-><a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml#a2664044e28e69309ea08ef385fe53903">m_Weight</a>->Allocate();</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  layer-><a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml#a39925bc24d3afcfb322a46a5884fadb9">m_Bias</a>->Allocate();</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span> </div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="comment">// Creates extra layers.</span></div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> </div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="comment">// Connects up.</span></div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>));</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>));</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> </div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="comment">// Makes the workload and checks it.</span></div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<Convolution2dWorkload>(*layer, factory);</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> </div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">Convolution2dQueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_StrideX == 2);</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_StrideY == 4);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_PadLeft == 3);</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_PadRight == 3);</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_PadTop == 1);</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_PadBottom == 1);</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_BiasEnabled);</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout == dataLayout));</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span> </div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  BOOST_TEST((queueDescriptor.m_Weight->GetTensorInfo() == <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(weightShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>)));</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  BOOST_TEST((queueDescriptor.m_Bias->GetTensorInfo() ==</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({2}, <a class="code" href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">GetBiasDataType</a>(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>))));</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span> </div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="comment">// Returns so we can do extra, backend-specific tests.</span></div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="keywordflow">return</span> workload;</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span> }</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span> </div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span> <span class="keyword">template</span> <<span class="keyword">typename</span> LstmWorkload></div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span> std::unique_ptr<LstmWorkload> CreateLstmWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory, <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph)</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> {</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <span class="comment">// This parameter setting is for withCifgWithPeepholeNoProjection</span></div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml">LstmDescriptor</a> layerDesc;</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  layerDesc.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">m_ActivationFunc</a> = 4;</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  layerDesc.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">m_ClippingThresCell</a> = 0.0f;</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  layerDesc.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">m_ClippingThresProj</a> = 0.0f;</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  layerDesc.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">m_CifgEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  layerDesc.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">m_PeepholeEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  layerDesc.<a class="code" href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">m_ProjectionEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span> </div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <a class="code" href="classarmnn_1_1_lstm_layer.xhtml">LstmLayer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_lstm_layer.xhtml">LstmLayer</a>>(layerDesc, <span class="stringliteral">"layer"</span>);</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 2;</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 2;</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numUnits = 4;</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 4;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span> </div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a5a0d8af26a6aad1e5be521ea7dc550eb">m_InputToForgetWeights</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits, inputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a14ab2bc78421c417c4f97a65b0bd78f9">m_InputToCellWeights</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits, inputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#ae8d897b8d282f25a6eb784c4aaa98df6">m_InputToOutputWeights</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits, inputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a3d5f129421bbe6479a66d4ed1356bf68">m_RecurrentToForgetWeights</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a6e8c3db3c5474f0760553ff93fbc39e6">m_RecurrentToCellWeights</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a91dda74af4085ae43913746ad817795a">m_RecurrentToOutputWeights</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a0e0e17d5b494993407cb75d614455ddd">m_ForgetGateBias</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a51255889cbc063130a3d691c1781c5d3">m_CellBias</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#aacb55e0992b6781a7bd3225ab6e6bb2f">m_OutputGateBias</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</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>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a5a0d8af26a6aad1e5be521ea7dc550eb">m_InputToForgetWeights</a>->Allocate();</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a14ab2bc78421c417c4f97a65b0bd78f9">m_InputToCellWeights</a>->Allocate();</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#ae8d897b8d282f25a6eb784c4aaa98df6">m_InputToOutputWeights</a>->Allocate();</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a3d5f129421bbe6479a66d4ed1356bf68">m_RecurrentToForgetWeights</a>->Allocate();</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a6e8c3db3c5474f0760553ff93fbc39e6">m_RecurrentToCellWeights</a>->Allocate();</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a91dda74af4085ae43913746ad817795a">m_RecurrentToOutputWeights</a>->Allocate();</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a0e0e17d5b494993407cb75d614455ddd">m_ForgetGateBias</a>->Allocate();</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#a51255889cbc063130a3d691c1781c5d3">m_CellBias</a>->Allocate();</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">m_BasicParameters</a>.<a class="code" href="structarmnn_1_1_lstm_basic_parameters.xhtml#aacb55e0992b6781a7bd3225ab6e6bb2f">m_OutputGateBias</a>->Allocate();</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span> </div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span> </div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <span class="keywordflow">if</span> (layerDesc.m_PeepholeEnabled)</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>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a4efa0f4d46817ab94e36c8507c26f276">m_PeepholeParameters</a>.<a class="code" href="structarmnn_1_1_lstm_opt_peephole_parameters.xhtml#a5d0ebbbb11b727a67877df40b59a628c">m_CellToForgetWeights</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a4efa0f4d46817ab94e36c8507c26f276">m_PeepholeParameters</a>.<a class="code" href="structarmnn_1_1_lstm_opt_peephole_parameters.xhtml#a310e133b0b51b93a74b83008893792e9">m_CellToOutputWeights</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>));</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a4efa0f4d46817ab94e36c8507c26f276">m_PeepholeParameters</a>.<a class="code" href="structarmnn_1_1_lstm_opt_peephole_parameters.xhtml#a5d0ebbbb11b727a67877df40b59a628c">m_CellToForgetWeights</a>->Allocate();</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  layer-><a class="code" href="classarmnn_1_1_lstm_layer.xhtml#a4efa0f4d46817ab94e36c8507c26f276">m_PeepholeParameters</a>.<a class="code" href="structarmnn_1_1_lstm_opt_peephole_parameters.xhtml#a310e133b0b51b93a74b83008893792e9">m_CellToOutputWeights</a>->Allocate();</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> </div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <span class="comment">// create input and output layers</span></div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> outputStateIn = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(1, <span class="stringliteral">"outputStateIn"</span>);</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> cellStateIn = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(2, <span class="stringliteral">"cellStateIn"</span>);</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> scratchBuffer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"scratchBuffer"</span>);</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> outputStateOut = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(1, <span class="stringliteral">"outputStateOut"</span>);</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> cellStateOut = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(2, <span class="stringliteral">"cellStateOut"</span>);</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(3, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span> </div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <span class="comment">// connect up</span></div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> lstmTensorInfo1({ batchSize, inputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> lstmTensorInfo2({ batchSize, numUnits}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> lstmTensorInfo3({ batchSize, outputSize }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> lstmTensorInfoScratchBuff({ batchSize, numUnits * (layerDesc.m_CifgEnabled ? 3 : 4) },</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, lstmTensorInfo1, 0, 0);</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(cellStateIn, layer, lstmTensorInfo2, 0, 1);</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(outputStateIn, layer, lstmTensorInfo3, 0, 2);</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, scratchBuffer, lstmTensorInfoScratchBuff, 0, 0);</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, outputStateOut, lstmTensorInfo3, 1, 0);</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, cellStateOut, lstmTensorInfo2, 2, 0);</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, lstmTensorInfo3, 3, 0);</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span> </div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  CreateTensorHandles(graph, factory);</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="comment">// make the workload and check it</span></div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<LstmWorkload>(*layer, factory);</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  <a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml">LstmQueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_ActivationFunc == 4);</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_ClippingThresCell == 0.0f);</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_ClippingThresProj == 0.0f);</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 3);</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 4);</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span> </div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  BOOST_TEST((queueDescriptor.m_InputToForgetWeights->GetTensorInfo() == <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits, inputSize },</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>)));</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  BOOST_TEST((queueDescriptor.m_OutputGateBias->GetTensorInfo() == <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits },</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>)));</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  BOOST_TEST((queueDescriptor.m_CellBias->GetTensorInfo() == <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ numUnits }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>)));</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  <span class="keywordflow">return</span> workload;</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span> }</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span> </div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span> <span class="keyword">template</span> <<span class="keyword">typename</span> QuantizedLstmWorkload></div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span> std::unique_ptr<QuantizedLstmWorkload> CreateQuantizedLstmWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory,</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph)</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span> {</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="keyword">auto</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_quantized_lstm_layer.xhtml">QuantizedLstmLayer</a>>(<span class="stringliteral">"quantizedLstmlayer"</span>);</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numBatches = 2;</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 2;</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 4;</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span> </div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  <span class="comment">// Scale/Offset for input/output, cellState In/Out, weights, bias</span></div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="keywordtype">float</span> inputOutputScale = 0.0078125f;</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  int32_t inputOutputOffset = 128;</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span> </div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keywordtype">float</span> cellStateScale = 0.00048828125f;</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  int32_t cellStateOffset = 0;</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">float</span> weightsScale = 0.00408021f;</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  int32_t weightsOffset = 100;</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span> </div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <span class="keywordtype">float</span> biasScale = 3.1876640625e-05f;</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  int32_t biasOffset = 0;</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span> </div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="comment">// Weights and bias tensor and quantization info</span></div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputWeightsInfo({outputSize, inputSize},</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  weightsScale,</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  weightsOffset);</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span> </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> recurrentWeightsInfo({outputSize, outputSize},</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  weightsScale,</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  weightsOffset);</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span> </div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasInfo({outputSize},</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a>,</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  biasScale,</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  biasOffset);</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span> </div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <span class="comment">// Weights and bias</span></div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  layer->m_QuantizedLstmParameters.m_InputToInputWeights =</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  std::make_unique<ScopedCpuTensorHandle>(inputWeightsInfo);</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  layer->m_QuantizedLstmParameters.m_InputToForgetWeights =</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  std::make_unique<ScopedCpuTensorHandle>(inputWeightsInfo);</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  layer->m_QuantizedLstmParameters.m_InputToCellWeights =</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  std::make_unique<ScopedCpuTensorHandle>(inputWeightsInfo);</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  layer->m_QuantizedLstmParameters.m_InputToOutputWeights =</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  std::make_unique<ScopedCpuTensorHandle>(inputWeightsInfo);</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>  layer->m_QuantizedLstmParameters.m_RecurrentToInputWeights =</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  std::make_unique<ScopedCpuTensorHandle>(recurrentWeightsInfo);</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  layer->m_QuantizedLstmParameters.m_RecurrentToForgetWeights =</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  std::make_unique<ScopedCpuTensorHandle>(recurrentWeightsInfo);</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  layer->m_QuantizedLstmParameters.m_RecurrentToCellWeights =</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  std::make_unique<ScopedCpuTensorHandle>(recurrentWeightsInfo);</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  layer->m_QuantizedLstmParameters.m_RecurrentToOutputWeights =</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  std::make_unique<ScopedCpuTensorHandle>(recurrentWeightsInfo);</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span> </div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  layer->m_QuantizedLstmParameters.m_InputGateBias = std::make_unique<ScopedCpuTensorHandle>(biasInfo);</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  layer->m_QuantizedLstmParameters.m_ForgetGateBias = std::make_unique<ScopedCpuTensorHandle>(biasInfo);</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  layer->m_QuantizedLstmParameters.m_CellBias = std::make_unique<ScopedCpuTensorHandle>(biasInfo);</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  layer->m_QuantizedLstmParameters.m_OutputGateBias = std::make_unique<ScopedCpuTensorHandle>(biasInfo);</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span> </div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <span class="comment">// Allocate weights and bias</span></div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  layer->m_QuantizedLstmParameters.m_InputToInputWeights->Allocate();</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  layer->m_QuantizedLstmParameters.m_InputToForgetWeights->Allocate();</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  layer->m_QuantizedLstmParameters.m_InputToCellWeights->Allocate();</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  layer->m_QuantizedLstmParameters.m_InputToOutputWeights->Allocate();</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span> </div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  layer->m_QuantizedLstmParameters.m_RecurrentToInputWeights->Allocate();</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  layer->m_QuantizedLstmParameters.m_RecurrentToForgetWeights->Allocate();</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  layer->m_QuantizedLstmParameters.m_RecurrentToCellWeights->Allocate();</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  layer->m_QuantizedLstmParameters.m_RecurrentToOutputWeights->Allocate();</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>  layer->m_QuantizedLstmParameters.m_InputGateBias->Allocate();</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  layer->m_QuantizedLstmParameters.m_ForgetGateBias->Allocate();</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  layer->m_QuantizedLstmParameters.m_CellBias->Allocate();</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  layer->m_QuantizedLstmParameters.m_OutputGateBias->Allocate();</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span> </div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <span class="comment">// Create input and output layers</span></div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> cellStateIn = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(1, <span class="stringliteral">"cellStateIn"</span>);</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> outputStateIn = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(2, <span class="stringliteral">"outputStateIn"</span>);</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span> </div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> cellStateOut = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"cellStateOut"</span>);</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> outputStateOut = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(1, <span class="stringliteral">"outputStateOut"</span>);</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span> </div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <span class="comment">// Input/output tensor info and quantization info</span></div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputInfo({numBatches , inputSize},</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  inputOutputScale,</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  inputOutputOffset);</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span> </div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateInfo({numBatches , outputSize},</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a>,</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  cellStateScale,</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  cellStateOffset);</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span> </div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateInfo({numBatches , outputSize},</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>,</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  inputOutputScale,</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  inputOutputOffset);</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span> </div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  <span class="comment">// Connect input/output slots</span></div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, inputInfo, 0, 0);</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(cellStateIn, layer, cellStateInfo, 0, 1);</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(outputStateIn, layer, outputStateInfo, 0, 2);</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span> </div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, cellStateOut, cellStateInfo, 0, 0);</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, outputStateOut, outputStateInfo, 1, 0);</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span> </div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span> </div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <span class="comment">// Create workload and check layer support</span></div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<QuantizedLstmWorkload>(*layer, factory);</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  <a class="code" href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml">QuantizedLstmQueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span> </div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  <span class="comment">// Validate input/output sizes</span></div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 3);</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 2);</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span> </div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  <span class="comment">// Validate weight tensor info</span></div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  BOOST_TEST((queueDescriptor.m_InputToInputWeights->GetTensorInfo() == inputWeightsInfo));</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  BOOST_TEST((queueDescriptor.m_InputToForgetWeights->GetTensorInfo() == inputWeightsInfo));</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  BOOST_TEST((queueDescriptor.m_InputToCellWeights->GetTensorInfo() == inputWeightsInfo));</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  BOOST_TEST((queueDescriptor.m_InputToOutputWeights->GetTensorInfo() == inputWeightsInfo));</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>  BOOST_TEST((queueDescriptor.m_RecurrentToInputWeights->GetTensorInfo() == recurrentWeightsInfo));</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  BOOST_TEST((queueDescriptor.m_RecurrentToForgetWeights->GetTensorInfo() == recurrentWeightsInfo));</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  BOOST_TEST((queueDescriptor.m_RecurrentToCellWeights->GetTensorInfo() == recurrentWeightsInfo));</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  BOOST_TEST((queueDescriptor.m_RecurrentToOutputWeights->GetTensorInfo() == recurrentWeightsInfo));</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>  BOOST_TEST((queueDescriptor.m_InputGateBias->GetTensorInfo() == biasInfo));</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  BOOST_TEST((queueDescriptor.m_ForgetGateBias->GetTensorInfo() == biasInfo));</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  BOOST_TEST((queueDescriptor.m_CellBias->GetTensorInfo() == biasInfo));</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  BOOST_TEST((queueDescriptor.m_OutputGateBias->GetTensorInfo() == biasInfo));</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span> </div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  <span class="keywordflow">return</span> workload;</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span> }</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span> </div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span> <span class="keyword">template</span> <<span class="keyword">typename</span> Convolution2dWorkload, armnn::DataType DataType></div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span> std::unique_ptr<Convolution2dWorkload> CreateDirectConvolution2dWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory,</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph)</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>  <span class="comment">// Creates the layer we're testing.</span></div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">Convolution2dDescriptor</a> layerDesc;</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  layerDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  layerDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 1;</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  layerDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  layerDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  layerDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  layerDesc.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  layerDesc.<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="l00531"></a><span class="lineno"> 531</span> </div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">Convolution2dLayer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml">Convolution2dLayer</a>>(layerDesc, <span class="stringliteral">"layer"</span>);</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span> </div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <span class="keywordtype">float</span> inputsQScale = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> == armnn::DataType::QAsymmU8 ? 1.0f : 0.0;</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <span class="keywordtype">float</span> outputQScale = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> == armnn::DataType::QAsymmU8 ? 2.0f : 0.0;</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>  layer-><a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml#a2664044e28e69309ea08ef385fe53903">m_Weight</a> = std::make_unique<ScopedCpuTensorHandle>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({ 2, 3, 3, 3 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>, inputsQScale));</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  layer-><a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml#a39925bc24d3afcfb322a46a5884fadb9">m_Bias</a> = std::make_unique<ScopedCpuTensorHandle></div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({2}, <a class="code" href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">GetBiasDataType</a>(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>), inputsQScale));</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  layer-><a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml#a2664044e28e69309ea08ef385fe53903">m_Weight</a>->Allocate();</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  layer-><a class="code" href="classarmnn_1_1_convolution2d_layer.xhtml#a39925bc24d3afcfb322a46a5884fadb9">m_Bias</a>->Allocate();</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span> </div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <span class="comment">// Creates extra layers.</span></div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span> </div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <span class="comment">// Connects up.</span></div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({2, 3, 6, 6}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>, inputsQScale));</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({2, 2, 6, 6}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>, outputQScale));</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span> </div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  <span class="comment">// Makes the workload and checks it.</span></div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<Convolution2dWorkload>(*layer, factory);</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span> </div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  <a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">Convolution2dQueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_StrideX == 1);</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_StrideY == 1);</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_PadLeft == 1);</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_PadRight == 1);</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_PadTop == 1);</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_PadBottom == 1);</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_BiasEnabled == <span class="keyword">true</span>);</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span> </div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  BOOST_TEST((queueDescriptor.m_Weight->GetTensorInfo() == <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({2, 3, 3, 3},</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>, inputsQScale)));</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  BOOST_TEST((queueDescriptor.m_Bias->GetTensorInfo()</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  == <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({2}, <a class="code" href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">GetBiasDataType</a>(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>), inputsQScale)));</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span> </div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <span class="comment">// Returns so we can do extra, backend-specific tests.</span></div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <span class="keywordflow">return</span> workload;</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> </div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span> <span class="keyword">template</span> <<span class="keyword">typename</span> DepthwiseConvolution2dFloat32Workload, armnn::DataType DataType></div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span> std::unique_ptr<DepthwiseConvolution2dFloat32Workload> CreateDepthwiseConvolution2dWorkloadTest(</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory, <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph, <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span> {</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  <span class="comment">// Creates the layer we're testing.</span></div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">DepthwiseConvolution2dDescriptor</a> layerDesc;</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  layerDesc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 1;</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  layerDesc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 2;</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  layerDesc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  layerDesc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 2;</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  layerDesc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  layerDesc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  layerDesc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  layerDesc.<a class="code" href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_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="classarmnn_1_1_depthwise_convolution2d_layer.xhtml">DepthwiseConvolution2dLayer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml">DepthwiseConvolution2dLayer</a>>(layerDesc, <span class="stringliteral">"layer"</span>);</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span> </div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  layer-><a class="code" href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml#a2664044e28e69309ea08ef385fe53903">m_Weight</a> = std::make_unique<ScopedCpuTensorHandle>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({1, 2, 4, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>)); <span class="comment">// [ M, I, H, W ]</span></div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  layer-><a class="code" href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml#a2664044e28e69309ea08ef385fe53903">m_Weight</a>->Allocate();</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span> </div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  <span class="comment">// Creates extra layers.</span></div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span> </div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape = (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>) ?</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{ 2, 2, 5, 5 } : <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{ 2, 5, 5, 2 };</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape = (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>) ?</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{ 2, 2, 5, 5 } : <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{ 2, 5, 5, 2 };</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span> </div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  <span class="comment">// Connects up.</span></div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>));</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>));</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  CreateTensorHandles(graph, factory);</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>  <span class="comment">// Makes the workload and checks it.</span></div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<DepthwiseConvolution2dFloat32Workload>(*layer, factory);</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span> </div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  <a class="code" href="structarmnn_1_1_depthwise_convolution2d_queue_descriptor.xhtml">DepthwiseConvolution2dQueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_StrideX == 1);</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_StrideY == 1);</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_PadLeft == 1);</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_PadRight == 2);</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_PadTop == 1);</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_PadBottom == 2);</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_BiasEnabled == <span class="keyword">false</span>);</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout == dataLayout));</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>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  BOOST_TEST((queueDescriptor.m_Weight->GetTensorInfo() == <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({1, 2, 4, 4}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>)));</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span> </div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  <span class="comment">// Returns so we can do extra, backend-specific tests.</span></div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  <span class="keywordflow">return</span> workload;</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span> }</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span> </div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span> <span class="keyword">template</span> <<span class="keyword">typename</span> FullyConnectedWorkload, armnn::DataType DataType></div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span> std::unique_ptr<FullyConnectedWorkload> CreateFullyConnectedWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory,</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph)</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="comment">// Creates the layer we're testing.</span></div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  <a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml">FullyConnectedDescriptor</a> layerDesc;</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  layerDesc.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  layerDesc.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">m_TransposeWeightMatrix</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span> </div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  <a class="code" href="classarmnn_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>>(layerDesc, <span class="stringliteral">"layer"</span>);</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span> </div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  <span class="keywordtype">float</span> inputsQScale = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> == armnn::DataType::QAsymmU8 ? 1.0f : 0.0;</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  <span class="keywordtype">float</span> outputQScale = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> == armnn::DataType::QAsymmU8 ? 2.0f : 0.0;</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span> </div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  layer-><a class="code" href="classarmnn_1_1_fully_connected_layer.xhtml#a2664044e28e69309ea08ef385fe53903">m_Weight</a> = std::make_unique<ScopedCpuTensorHandle>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({7, 20}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>, inputsQScale, 0));</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  layer-><a class="code" href="classarmnn_1_1_fully_connected_layer.xhtml#a39925bc24d3afcfb322a46a5884fadb9">m_Bias</a> = std::make_unique<ScopedCpuTensorHandle>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({7}, <a class="code" href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">GetBiasDataType</a>(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>), inputsQScale));</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  layer-><a class="code" href="classarmnn_1_1_fully_connected_layer.xhtml#a2664044e28e69309ea08ef385fe53903">m_Weight</a>->Allocate();</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  layer-><a class="code" href="classarmnn_1_1_fully_connected_layer.xhtml#a39925bc24d3afcfb322a46a5884fadb9">m_Bias</a>->Allocate();</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>  <span class="comment">// Creates extra layers.</span></div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span> </div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  <span class="comment">// Connects up.</span></div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({3, 1, 4, 5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>, inputsQScale));</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({3, 7}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>, outputQScale));</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span> </div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  <span class="comment">// Makes the workload and checks it.</span></div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<FullyConnectedWorkload>(*layer, factory);</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span> </div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  <a class="code" href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml">FullyConnectedQueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_BiasEnabled == <span class="keyword">true</span>);</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_TransposeWeightMatrix == <span class="keyword">true</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>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  BOOST_TEST((queueDescriptor.m_Weight->GetTensorInfo() == <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({7, 20}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>, inputsQScale)));</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  BOOST_TEST((queueDescriptor.m_Bias->GetTensorInfo() == <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>({7}, <a class="code" href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">GetBiasDataType</a>(<a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>), inputsQScale)));</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span> </div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  <span class="comment">// Returns so we can do extra, backend-specific tests.</span></div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  <span class="keywordflow">return</span> workload;</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span> }</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span> </div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span> <span class="keyword">template</span> <<span class="keyword">typename</span> NormalizationWorkload, armnn::DataType DataType></div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span> std::unique_ptr<NormalizationWorkload> CreateNormalizationWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory,</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph,</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span> {</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  <span class="comment">// Creates the layer we're testing.</span></div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  <a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml">NormalizationDescriptor</a> layerDesc;</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  layerDesc.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#afe1f0f09d49ad2befc01f8789187b7dd">m_NormChannelType</a> = <a class="code" href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437a810f43f3996922151c39b76143faeecc">NormalizationAlgorithmChannel::Across</a>;</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  layerDesc.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a05945f080edf694b631960728b87aadb">m_NormMethodType</a> = <a class="code" href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d">NormalizationAlgorithmMethod::LocalBrightness</a>;</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  layerDesc.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#aa70c05f1aad12fbd9d9ec43ea4557b03">m_NormSize</a> = 3;</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  layerDesc.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a174279be57d7596eeb04c6b7f7510f99">m_Alpha</a> = 0.5f;</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  layerDesc.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = -1.0f;</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  layerDesc.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a8526ea7cf860d8e7f8340e9f9354f9f0">m_K</a> = 0.2f;</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  layerDesc.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span> </div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  <a class="code" href="classarmnn_1_1_normalization_layer.xhtml">NormalizationLayer</a>* layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_normalization_layer.xhtml">NormalizationLayer</a>>(layerDesc, <span class="stringliteral">"layer"</span>);</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span> </div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  <span class="comment">// Creates extra layers.</span></div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span> </div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape = (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>) ?</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{ 3, 5, 5, 1 } : <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{ 3, 1, 5, 5 };</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape = (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>) ?</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{ 3, 5, 5, 1 } : <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{ 3, 1, 5, 5 };</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span> </div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  <span class="comment">// Connects up.</span></div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, inputTensorInfo);</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, outputTensorInfo);</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span> </div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  <span class="comment">// Makes the workload and checks it.</span></div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<NormalizationWorkload>(*layer, factory);</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span> </div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  <a class="code" href="structarmnn_1_1_normalization_queue_descriptor.xhtml">NormalizationQueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  BOOST_TEST((queueDescriptor.m_Parameters.m_NormChannelType == <a class="code" href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437a810f43f3996922151c39b76143faeecc">NormalizationAlgorithmChannel::Across</a>));</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  BOOST_TEST((queueDescriptor.m_Parameters.m_NormMethodType == <a class="code" href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d">NormalizationAlgorithmMethod::LocalBrightness</a>));</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_NormSize == 3);</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_Alpha == 0.5f);</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_Beta == -1.0f);</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_K == 0.2f);</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout == dataLayout));</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span> </div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span> </div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  <span class="comment">// Returns so we can do extra, backend-specific tests.</span></div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  <span class="keywordflow">return</span> workload;</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span> }</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span> </div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span> <span class="keyword">template</span> <<span class="keyword">typename</span> Pooling2dWorkload, armnn::DataType DataType></div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span> std::unique_ptr<Pooling2dWorkload> CreatePooling2dWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory,</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph,</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</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>  <span class="comment">// Creates the layer we're testing.</span></div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  <a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml">Pooling2dDescriptor</a> layerDesc;</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  layerDesc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">m_PoolType</a> = <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">PoolingAlgorithm::Average</a>;</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  layerDesc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">m_PoolWidth</a> = 3;</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  layerDesc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">m_PoolHeight</a> = 3;</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  layerDesc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">m_PadLeft</a> = 2;</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  layerDesc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">m_PadRight</a> = 2;</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  layerDesc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">m_PadTop</a> = 1;</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  layerDesc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">m_PadBottom</a> = 1;</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  layerDesc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 2;</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  layerDesc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 3;</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  layerDesc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#affb5b68b3eba3ed45a06c7cde7781962">m_OutputShapeRounding</a> = <a class="code" href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">OutputShapeRounding::Floor</a>;</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  layerDesc.<a class="code" href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span> </div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  <a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_pooling2d_layer.xhtml">Pooling2dLayer</a>>(layerDesc, <span class="stringliteral">"layer"</span>);</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span> </div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  <span class="comment">// Create extra layers</span></div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span> </div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape = (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>) ? <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{3, 2, 5, 5} : <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{3, 5, 5, 2};</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape = (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>) ? <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{3, 2, 2, 4} : <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{3, 2, 4, 2};</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span> </div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  <span class="comment">// Connect up</span></div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>));</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>));</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span> </div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  <span class="comment">// Make the workload and checks it</span></div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<Pooling2dWorkload>(*layer, factory);</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span> </div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  <a class="code" href="structarmnn_1_1_pooling2d_queue_descriptor.xhtml">Pooling2dQueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  BOOST_TEST((queueDescriptor.m_Parameters.m_PoolType == <a class="code" href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">PoolingAlgorithm::Average</a>));</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  BOOST_TEST((queueDescriptor.m_Parameters.m_OutputShapeRounding == <a class="code" href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">OutputShapeRounding::Floor</a>));</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_PoolWidth == 3);</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_PoolHeight == 3);</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_StrideX == 2);</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_StrideY == 3);</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_PadLeft == 2);</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_PadRight == 2);</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_PadTop == 1);</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_PadBottom == 1);</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout == dataLayout));</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span> </div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span> </div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  <span class="comment">// Return so we can do extra, backend-specific tests</span></div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  <span class="keywordflow">return</span> workload;</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span> }</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span> </div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span> <span class="keyword">template</span> <<span class="keyword">typename</span> SoftmaxWorkload, armnn::DataType DataType></div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span> std::unique_ptr<SoftmaxWorkload> CreateSoftmaxWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory,</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph)</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span> {</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  <span class="comment">// Create the layer we're testing.</span></div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  <a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml">SoftmaxDescriptor</a> softmaxDescriptor;</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  <span class="comment">// Set Axis to 1 if CL or Neon until further Axes are supported.</span></div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  <span class="keywordflow">if</span> (factory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a9f7e4296485d2812e7996089149c96d1">GetBackendId</a>() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a> || factory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a9f7e4296485d2812e7996089149c96d1">GetBackendId</a>() == <a class="code" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a>)</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>  {</div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  softmaxDescriptor.<a class="code" href="structarmnn_1_1_softmax_descriptor.xhtml#a214c3636fdf0ea5bac8edb42d0e6c7f0">m_Axis</a> = 1;</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  }</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span> </div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_softmax_layer.xhtml">SoftmaxLayer</a>>(softmaxDescriptor, <span class="stringliteral">"layer"</span>);</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  <span class="comment">// Create extra layers.</span></div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span> </div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  <span class="comment">// Connect up</span></div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo({4, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, tensorInfo);</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, tensorInfo);</div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span> </div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  <span class="comment">// Make the workload and checks it.</span></div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<SoftmaxWorkload>(*layer, factory);</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span> </div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  <a class="code" href="structarmnn_1_1_softmax_queue_descriptor.xhtml">SoftmaxQueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 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>  <span class="comment">// Return so we can do extra, backend-specific tests.</span></div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  <span class="keywordflow">return</span> workload;</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span> }</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span> </div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span> <span class="keyword">template</span><<span class="keyword">typename</span> SplitterWorkload, armnn::DataType DataType></div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span> std::unique_ptr<SplitterWorkload></div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  CreateSplitterWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory, <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph)</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span> {</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  <span class="comment">// Create the layer we're testing.</span></div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  <span class="comment">// NOTE: need three dimensions channels, height/y, width/x because the Compute</span></div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  <span class="comment">// library restricts subtensors to have the same x and y dimensions as</span></div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  <span class="comment">// their parent tensors, and therefore the origin on the x and y dimension</span></div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  <span class="comment">// has to be zero for any view. So we need a third dimension to split...</span></div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  <span class="comment">// NOTE: arguments are: number of views, number of dimensions.</span></div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">ViewsDescriptor</a> layerDesc(3, 3);</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  <span class="comment">// NOTE: arguments are: view, dimension, value.</span></div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  layerDesc.SetViewOriginCoord(0, 0, 0);</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  layerDesc.SetViewOriginCoord(1, 0, 1);</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  layerDesc.SetViewOriginCoord(2, 0, 3);</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span> </div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_splitter_layer.xhtml">SplitterLayer</a>>(layerDesc, <span class="stringliteral">"layer"</span>);</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span> </div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  <span class="comment">// Adds extra layers.</span></div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output0 = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output0"</span>);</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output1 = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(1, <span class="stringliteral">"output1"</span>);</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output2 = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(2, <span class="stringliteral">"output2"</span>);</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span> </div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  <span class="comment">// Connects up.</span></div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo({5, 7, 7}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, tensorInfo);</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span> </div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> output0Info({1, 7, 7}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> output1Info({2, 7, 7}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> output2Info({2, 7, 7}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span> </div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output0, output0Info, 0, 0);</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output1, output1Info, 1, 0);</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output2, output2Info, 2, 0);</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span> </div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span> </div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  <span class="comment">// Makes the workload and checks it.</span></div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<SplitterWorkload>(*layer, factory);</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span> </div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  <a class="code" href="structarmnn_1_1_splitter_queue_descriptor.xhtml">SplitterQueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 3);</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  BOOST_TEST(queueDescriptor.m_ViewOrigins.size() == 3);</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span> </div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  BOOST_TEST(queueDescriptor.m_ViewOrigins[0].m_Origin[0] == 0);</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  BOOST_TEST(queueDescriptor.m_ViewOrigins[1].m_Origin[0] == 1);</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  BOOST_TEST(queueDescriptor.m_ViewOrigins[2].m_Origin[0] == 3);</div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  BOOST_TEST(queueDescriptor.m_ViewOrigins[0].m_Origin[1] == 0);</div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  BOOST_TEST(queueDescriptor.m_ViewOrigins[1].m_Origin[1] == 0);</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  BOOST_TEST(queueDescriptor.m_ViewOrigins[2].m_Origin[1] == 0);</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  BOOST_TEST(queueDescriptor.m_ViewOrigins[0].m_Origin[2] == 0);</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  BOOST_TEST(queueDescriptor.m_ViewOrigins[1].m_Origin[2] == 0);</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  BOOST_TEST(queueDescriptor.m_ViewOrigins[2].m_Origin[2] == 0);</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span> </div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  <span class="comment">// Returns so we can do extra, backend-specific tests.</span></div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  <span class="keywordflow">return</span> workload;</div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span> }</div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span> <span class="comment"></span></div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span> <span class="comment">/// This function constructs a graph with both a splitter and a concat, and returns a pair of the workloads.</span></div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span> <span class="comment"></span><span class="keyword">template</span><<span class="keyword">typename</span> SplitterWorkload, <span class="keyword">typename</span> ConcatWorkload, armnn::DataType DataType></div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span> std::pair<std::unique_ptr<SplitterWorkload>, std::unique_ptr<ConcatWorkload>></div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  CreateSplitterConcatWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &factory, <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a> &graph)</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span> {</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, 2, 100, 10 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span> </div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> splitTensorInfo1({ 1, 1, 100, 10 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> splitTensorInfo2({ 1, 1, 100, 10 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</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">//Constructs the graph.</span></div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span> </div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">armnn::ViewsDescriptor</a> splitterViews(2);</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span>  splitterViews.SetViewOriginCoord(0, 0, 0);</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span>  splitterViews.SetViewOriginCoord(0, 1, 0);</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  splitterViews.SetViewOriginCoord(0, 2, 0);</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  splitterViews.SetViewOriginCoord(0, 3, 0);</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>  splitterViews.SetViewOriginCoord(1, 0, 0);</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span>  splitterViews.SetViewOriginCoord(1, 1, 1);</div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>  splitterViews.SetViewOriginCoord(1, 2, 0);</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span>  splitterViews.SetViewOriginCoord(1, 3, 0);</div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span> </div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> splitter = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_splitter_layer.xhtml">SplitterLayer</a>>(splitterViews, <span class="stringliteral">"splitter"</span>);</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"created splitter layer"</span>);</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span> </div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a> concatViews(2);</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span>  concatViews.SetViewOriginCoord(0, 0, 0);</div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span>  concatViews.SetViewOriginCoord(0, 1, 1);</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  concatViews.SetViewOriginCoord(0, 2, 0);</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  concatViews.SetViewOriginCoord(0, 3, 0);</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span> </div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  concatViews.SetViewOriginCoord(1, 0, 0);</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span>  concatViews.SetViewOriginCoord(1, 1, 0);</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  concatViews.SetViewOriginCoord(1, 2, 0);</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span>  concatViews.SetViewOriginCoord(1, 3, 0);</div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span> </div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> concat = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_concat_layer.xhtml">ConcatLayer</a>>(concatViews, <span class="stringliteral">"concat"</span>);</div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"created concat layer"</span>);</div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span> </div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span> </div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  <span class="comment">// Adds connections.</span></div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, splitter, inputTensorInfo, 0, 0);</div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"connect input to splitter"</span>);</div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(splitter, concat, splitTensorInfo1, 0, 1); <span class="comment">// The splitter & concat are connected up.</span></div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"connect splitter[0] to concat[1]"</span>);</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(splitter, concat, splitTensorInfo2, 1, 0); <span class="comment">// So that the outputs are flipped round.</span></div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"connect splitter[1] to concat[0]"</span>);</div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(concat, output, inputTensorInfo, 0, 0);</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"connect concat to output"</span>);</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span> </div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"created tensor handles"</span>);</div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span> </div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span>  <span class="keyword">auto</span> workloadSplitter = MakeAndCheckWorkload<SplitterWorkload>(*splitter, factory);</div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"created splitter workload"</span>);</div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  <span class="keyword">auto</span> workloadConcat = MakeAndCheckWorkload<ConcatWorkload>(*concat, factory);</div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"created concat workload"</span>);</div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span> </div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  <span class="keywordflow">return</span> {std::move(workloadSplitter), std::move(workloadConcat)};</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span> }</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span> </div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span> <span class="comment"></span></div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span> <span class="comment">/// This function constructs a graph with a splitter with two outputs. Each of the outputs is then</span></div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span> <span class="comment">/// connected to two different activation layers</span></div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span> <span class="comment"></span><span class="keyword">template</span><<span class="keyword">typename</span> SplitterWorkload, <span class="keyword">typename</span> ActivationWorkload, armnn::DataType DataType></div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span> <span class="keywordtype">void</span> CreateSplitterMultipleInputsOneOutputWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory, <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph,</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  std::unique_ptr<SplitterWorkload>& wlSplitter,</div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  std::unique_ptr<ActivationWorkload>& wlActiv0_0,</div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  std::unique_ptr<ActivationWorkload>& wlActiv0_1,</div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span>  std::unique_ptr<ActivationWorkload>& wlActiv1_0,</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  std::unique_ptr<ActivationWorkload>& wlActiv1_1)</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span> {</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo ({ 1, 3, 100, 50 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> splitTensorInfo1({ 1, 1, 100, 50 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> splitTensorInfo2({ 1, 2, 100, 50 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span> </div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  <span class="comment">//Constructs the graph.</span></div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span> </div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">armnn::ViewsDescriptor</a> splitterViews(2);</div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span> </div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  splitterViews.SetViewOriginCoord(0, 0, 0);</div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  splitterViews.SetViewOriginCoord(0, 1, 0);</div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  splitterViews.SetViewOriginCoord(0, 2, 0);</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  splitterViews.SetViewOriginCoord(0, 3, 0);</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>  splitterViews.SetViewOriginCoord(1, 0, 0);</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span>  splitterViews.SetViewOriginCoord(1, 1, 1);</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  splitterViews.SetViewOriginCoord(1, 2, 0);</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  splitterViews.SetViewOriginCoord(1, 3, 0);</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span> </div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> splitter = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_splitter_layer.xhtml">SplitterLayer</a>>(splitterViews, <span class="stringliteral">"splitter"</span>);</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span> </div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a> activationDesc;</div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span> </div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> activ0_0 = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_activation_layer.xhtml">ActivationLayer</a>>(activationDesc, <span class="stringliteral">"activ0_0"</span>);</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> activ0_1 = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_activation_layer.xhtml">ActivationLayer</a>>(activationDesc, <span class="stringliteral">"activ0_1"</span>);</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> activ1_0 = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_activation_layer.xhtml">ActivationLayer</a>>(activationDesc, <span class="stringliteral">"activ1_0"</span>);</div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> activ1_1 = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_activation_layer.xhtml">ActivationLayer</a>>(activationDesc, <span class="stringliteral">"activ1_1"</span>);</div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span> </div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output1 = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(1, <span class="stringliteral">"output1"</span>);</div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output2 = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(2, <span class="stringliteral">"output2"</span>);</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output3 = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(3, <span class="stringliteral">"output3"</span>);</div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output4 = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(4, <span class="stringliteral">"output4"</span>);</div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span> </div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  <span class="comment">// Adds connections.</span></div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, splitter, inputTensorInfo, 0, 0);</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(splitter, activ0_0, splitTensorInfo1, 0, 0);</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(splitter, activ0_1, splitTensorInfo1, 0, 0);</div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span> </div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(splitter, activ1_0, splitTensorInfo2, 1, 0);</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(splitter, activ1_1, splitTensorInfo2, 1, 0);</div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span> </div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(activ0_0, output1, splitTensorInfo1, 0, 0);</div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(activ0_1, output2, splitTensorInfo1, 0, 0);</div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(activ1_0, output3, splitTensorInfo2, 0, 0);</div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(activ1_1, output4, splitTensorInfo2, 0, 0);</div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span> </div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span> </div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  <span class="keyword">auto</span> workloadSplitter = MakeAndCheckWorkload<SplitterWorkload>(*splitter, factory);</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  <span class="keyword">auto</span> workloadActiv0_0 = MakeAndCheckWorkload<ActivationWorkload>(*activ0_0, factory);</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>  <span class="keyword">auto</span> workloadActiv0_1 = MakeAndCheckWorkload<ActivationWorkload>(*activ0_1, factory);</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  <span class="keyword">auto</span> workloadActiv1_0 = MakeAndCheckWorkload<ActivationWorkload>(*activ1_0, factory);</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>  <span class="keyword">auto</span> workloadActiv1_1 = MakeAndCheckWorkload<ActivationWorkload>(*activ1_1, factory);</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span> </div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  wlSplitter = std::move(workloadSplitter);</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>  wlActiv0_0 = std::move(workloadActiv0_0);</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>  wlActiv0_1 = std::move(workloadActiv0_1);</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  wlActiv1_0 = std::move(workloadActiv1_0);</div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  wlActiv1_1 = std::move(workloadActiv1_1);</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span> }</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span> </div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ResizeWorkload, armnn::DataType DataType></div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span> std::unique_ptr<ResizeWorkload> CreateResizeBilinearWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory,</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph,</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>  <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span> {</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape;</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape;</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">switch</span> (dataLayout) {</div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>:</div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>  inputShape = { 2, 4, 4, 3 };</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>  outputShape = { 2, 2, 2, 3 };</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>  <span class="keywordflow">case</span> <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>:</div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  inputShape = { 2, 3, 4, 4 };</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>  outputShape = { 2, 3, 2, 2 };</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  }</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span> </div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>  <span class="comment">// Creates the layer we're testing.</span></div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span>  <a class="code" href="structarmnn_1_1_resize_descriptor.xhtml">ResizeDescriptor</a> resizeDesc;</div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span>  <a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml">armnnUtils::DataLayoutIndexed</a> dimensionIndices = dataLayout;</div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>  resizeDesc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a869254cb56968986a78a79e1d6d4a86b">m_Method</a> = <a class="code" href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f">ResizeMethod::Bilinear</a>;</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>  resizeDesc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">m_TargetWidth</a> = outputShape[dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">GetWidthIndex</a>()];</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>  resizeDesc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a46c3fa15c46fb0d1dcdc24d0ea5cb5cd">m_TargetHeight</a> = outputShape[dimensionIndices.<a class="code" href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">GetHeightIndex</a>()];</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>  resizeDesc.<a class="code" href="structarmnn_1_1_resize_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_resize_layer.xhtml">ResizeLayer</a>>(resizeDesc, <span class="stringliteral">"resize"</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>  <span class="comment">// Creates extra layers.</span></div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span> </div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>  <span class="comment">// Connects up.</span></div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, inputTensorInfo);</div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, outputTensorInfo);</div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span> </div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>  <span class="comment">// Makes the workload and checks it.</span></div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<ResizeWorkload>(*layer, factory);</div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span> </div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>  <span class="keyword">auto</span> queueDescriptor = workload->GetData();</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(queueDescriptor.m_Inputs.size() == 1);</div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(queueDescriptor.m_Outputs.size() == 1);</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(queueDescriptor.m_Parameters.m_DataLayout == dataLayout);</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span> </div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>  <span class="comment">// Returns so we can do extra, backend-specific tests.</span></div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>  <span class="keywordflow">return</span> workload;</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span> }</div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span> </div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span> <span class="keyword">template</span> <<span class="keyword">typename</span> BatchToSpaceNdWorkload, armnn::DataType DataType></div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span> std::unique_ptr<BatchToSpaceNdWorkload> CreateBatchToSpaceNdWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory,</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph)</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span> {</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>  <a class="code" href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">BatchToSpaceNdDescriptor</a> desc;</div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_batch_to_space_nd_layer.xhtml">BatchToSpaceNdLayer</a>>(desc, <span class="stringliteral">"batchToSpace"</span>);</div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span> </div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>  <span class="comment">// Creates extra layers.</span></div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span> </div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>  <span class="comment">// Connects up.</span></div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo({1, 1, 1, 1}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span> </div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, tensorInfo);</div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, tensorInfo);</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span> </div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>  CreateTensorHandles(graph, factory);</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="comment">// Makes the workload and checks it.</span></div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<BatchToSpaceNdWorkload>(*layer, factory);</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span> </div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>  <a class="code" href="structarmnn_1_1_batch_to_space_nd_queue_descriptor.xhtml">BatchToSpaceNdQueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);</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>  <span class="keywordflow">return</span> workload;</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span> }</div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span> </div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span> <span class="keyword">template</span> <<span class="keyword">typename</span> L2NormalizationWorkload, armnn::DataType DataType></div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span> std::unique_ptr<L2NormalizationWorkload> CreateL2NormalizationWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory,</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph, <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> dataLayout = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span> {</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>  <span class="comment">// Creates the layer we're testing.</span></div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>  <a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml">L2NormalizationDescriptor</a> layerDesc;</div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>  layerDesc.<a class="code" href="structarmnn_1_1_l2_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = dataLayout;</div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span> </div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_l2_normalization_layer.xhtml">L2NormalizationLayer</a>>(layerDesc, <span class="stringliteral">"l2norm"</span>);</div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span> </div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>  <span class="comment">// Creates extra layers.</span></div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span> </div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape = (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>) ?</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{ 5, 20, 50, 67 } : <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{ 5, 50, 67, 20 };</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape = (dataLayout == <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>) ?</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{ 5, 20, 50, 67 } : <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>{ 5, 50, 67, 20 };</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>  <span class="comment">// Connects up.</span></div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, inputTensorInfo);</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, outputTensorInfo);</div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span> </div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span>  <span class="comment">// Makes the workload and checks it.</span></div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<L2NormalizationWorkload>(*layer, factory);</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span> </div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>  <a class="code" href="structarmnn_1_1_l2_normalization_queue_descriptor.xhtml">L2NormalizationQueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>  BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout == dataLayout));</div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);</div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);</div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span> </div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>  <span class="comment">// Returns so we can do extra, backend-specific tests.</span></div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>  <span class="keywordflow">return</span> workload;</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span> }</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span> </div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ReshapeWorkload, armnn::DataType DataType></div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span> std::unique_ptr<ReshapeWorkload> CreateReshapeWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory,</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph)</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span> {</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>  <span class="comment">// Creates the layer we're testing.</span></div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape({ 1, 4 });</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  <a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml">ReshapeDescriptor</a> reshapeDesc;</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>  reshapeDesc.<a class="code" href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">m_TargetShape</a> = outputShape;</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_reshape_layer.xhtml">ReshapeLayer</a>>(reshapeDesc, <span class="stringliteral">"layer"</span>);</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span> </div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>  <span class="comment">// Creates extra layers.</span></div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span> </div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>  <span class="comment">// Connects up.</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({ 4, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, inputTensorInfo);</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, outputTensorInfo);</div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span> </div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>  <span class="comment">// Makes the workload and checks it.</span></div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<ReshapeWorkload>(*layer, factory);</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span> </div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  <a class="code" href="structarmnn_1_1_reshape_queue_descriptor.xhtml">ReshapeQueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span> </div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>  <span class="comment">// Returns so we can do extra, backend-specific tests.</span></div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>  <span class="keywordflow">return</span> workload;</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span> }</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span> </div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ConvertFp16ToFp32Float32Workload></div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span> std::unique_ptr<ConvertFp16ToFp32Float32Workload> CreateConvertFp16ToFp32WorkloadTest(</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory, <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph)</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span> {</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>  <span class="comment">// Creates the layer we're testing.</span></div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  <a class="code" href="classarmnn_1_1_convert_fp16_to_fp32_layer.xhtml">ConvertFp16ToFp32Layer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_convert_fp16_to_fp32_layer.xhtml">ConvertFp16ToFp32Layer</a>>(<span class="stringliteral">"Fp16ToFp32Converter"</span>);</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span> </div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  <span class="comment">// Creates extra layers.</span></div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span> </div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>  <span class="comment">// Connects up.</span></div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({1, 3, 2, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a>);</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({1, 3, 2, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, inputTensorInfo);</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, outputTensorInfo);</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span> </div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>  <span class="comment">// Makes the workload and checks it.</span></div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<ConvertFp16ToFp32Float32Workload>(*layer, factory);</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span> </div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  <a class="code" href="structarmnn_1_1_convert_fp16_to_fp32_queue_descriptor.xhtml">ConvertFp16ToFp32QueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span> </div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>  <span class="comment">// Returns so we can do extra, backend-specific tests.</span></div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>  <span class="keywordflow">return</span> workload;</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span> }</div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span> </div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span> <span class="keyword">template</span> <<span class="keyword">typename</span> ConvertFp32ToFp16Float16Workload></div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span> std::unique_ptr<ConvertFp32ToFp16Float16Workload> CreateConvertFp32ToFp16WorkloadTest(</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory, <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph)</div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span> {</div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>  <span class="comment">// Creates the layer we're testing.</span></div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span>  <a class="code" href="classarmnn_1_1_convert_fp32_to_fp16_layer.xhtml">ConvertFp32ToFp16Layer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_convert_fp32_to_fp16_layer.xhtml">ConvertFp32ToFp16Layer</a>>(<span class="stringliteral">"Fp32ToFp16Converter"</span>);</div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span> </div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  <span class="comment">// Creates extra layers.</span></div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span> </div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>  <span class="comment">// Connects up.</span></div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({1, 3, 2, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({1, 3, 2, 3}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a>);</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, inputTensorInfo);</div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, outputTensorInfo);</div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>  CreateTensorHandles(graph, factory);</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="comment">// Makes the workload and checks it.</span></div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<ConvertFp32ToFp16Float16Workload>(*layer, factory);</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span> </div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>  <a class="code" href="structarmnn_1_1_convert_fp32_to_fp16_queue_descriptor.xhtml">ConvertFp32ToFp16QueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);</div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span> </div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>  <span class="comment">// Returns so we can do extra, backend-specific tests.</span></div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span>  <span class="keywordflow">return</span> workload;</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> </div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span> <span class="keyword">template</span> <<span class="keyword">typename</span> MeanWorkload, armnn::DataType DataType></div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span> std::unique_ptr<MeanWorkload> CreateMeanWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory, <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph)</div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span> {</div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>  <span class="comment">// Reduce along the first and second dimensions, and do not keep the reduced dimensions.</span></div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>  <a class="code" href="structarmnn_1_1_mean_descriptor.xhtml">MeanDescriptor</a> descriptor({ 1, 2 }, <span class="keyword">false</span>);</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span> </div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>  <span class="comment">// Creates the layer we're testing.</span></div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_mean_layer.xhtml">MeanLayer</a>>(descriptor, <span class="stringliteral">"mean"</span>);</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span> </div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span>  <span class="comment">// Creates extra layers.</span></div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span> </div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>  <span class="comment">// Connects up.</span></div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, 3, 7, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, inputTensorInfo);</div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, outputTensorInfo);</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span> </div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  <span class="comment">// Makes the workload and checks it.</span></div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<MeanWorkload>(*layer, factory);</div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span> </div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  <a class="code" href="structarmnn_1_1_mean_queue_descriptor.xhtml">MeanQueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_Axis == descriptor.m_Axis);</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>  BOOST_TEST(queueDescriptor.m_Parameters.m_KeepDims == descriptor.m_KeepDims);</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);</div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 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>  <span class="comment">// Returns so we can do extra, backend-specific tests.</span></div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>  <span class="keywordflow">return</span> workload;</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span> }</div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span> </div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span> <span class="keyword">template</span><<span class="keyword">typename</span> ConcatWorkload, armnn::DataType DataType></div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span> std::unique_ptr<ConcatWorkload> CreateConcatWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a> &factory,</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a> &graph,</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a> &outputShape,</div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> concatAxis)</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span> {</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 2, 3, 2, 5 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span> </div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>  <span class="comment">// Constructs the graph.</span></div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input0 = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input0"</span>);</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input1 = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(1, <span class="stringliteral">"input1"</span>);</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>  <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a> descriptor;</div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span> </div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>  std::vector<armnn::TensorShape> inputShapes{{ 2, 3, 2, 5 }, { 2, 3, 2, 5 }};</div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span> </div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span>  descriptor = <a class="code" href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">CreateDescriptorForConcatenation</a>(inputShapes.begin(),</div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span>  inputShapes.end(),</div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span>  concatAxis);</div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span> </div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> concat = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_concat_layer.xhtml">ConcatLayer</a>>(descriptor, <span class="stringliteral">"concat"</span>);</div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"created concat layer"</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>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span> </div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>  <span class="comment">// Adds connections.</span></div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input0, concat, inputTensorInfo, 0, 0);</div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"connect input0 to concat"</span>);</div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input1, concat, inputTensorInfo, 0, 1);</div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"connect input1 to concat"</span>);</div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(concat, output, outputTensorInfo, 0, 0);</div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"connect concat to output"</span>);</div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span> </div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"created tensor handles"</span>);</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span> </div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>  <span class="keyword">auto</span> workloadConcat = MakeAndCheckWorkload<ConcatWorkload>(*concat, factory);</div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"created concat workload"</span>);</div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span> </div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>  <span class="keywordflow">return</span> workloadConcat;</div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span> }</div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span> </div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span> <span class="keyword">template</span> <<span class="keyword">typename</span> PreCompiledWorkload, armnn::DataType dataType></div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span> std::pair<armnn::IOptimizedNetworkPtr, std::unique_ptr<PreCompiledWorkload>> CreatePreCompiledWorkloadTest(</div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory,</div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph,</div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>  <span class="keywordtype">bool</span> biasEnabled = <span class="keyword">false</span>)</div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span> {</div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(graph);</div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span> </div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span>  <span class="comment">// To create a PreCompiled layer, create a network and Optimize it.</span></div><div class="line"><a name="l01304"></a><span class="lineno"> 1304</span>  <a class="code" href="classarmnn_1_1_network.xhtml">armnn::Network</a> net;</div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span> </div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>  <span class="comment">// Add an input layer</span></div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> inputLayer = net.<a class="code" href="classarmnn_1_1_network.xhtml#a90d8841cfbbc82ab02328f33fed24ac6">AddInputLayer</a>(0, <span class="stringliteral">"input layer"</span>);</div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</span>  BOOST_TEST(inputLayer);</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>  <span class="comment">// ArmNN weights tensor shape is OIHW (out channels, in channels, height, width) for NCHW</span></div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span>  <span class="comment">// ArmNN weights tensor shape is OHWI (out channels, height, width, in channels) for NHWC</span></div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>  <span class="comment">// this test is using NHWC, so the weights shape is OHWI</span></div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> weightsTensorInfo(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>({16, 1, 1, 16}), dataType, 0.9f, 0);</div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsLength = weightsTensorInfo.GetNumElements();</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span> </div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>  <span class="keyword">using</span> WeightType = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<dataType></a>;</div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span>  std::vector<WeightType> convWeightsData(weightsLength);</div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < weightsLength; ++i)</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>  {</div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span>  convWeightsData[i] = <span class="keyword">static_cast<</span>WeightType<span class="keyword">></span>(i);</div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>  }</div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span> </div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> weights(weightsTensorInfo, convWeightsData);</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span> </div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>  <span class="comment">// Add a layer that can be used in the PreCompiled layer</span></div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span>  <a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a> convDesc2d;</div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>  convDesc2d.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">m_StrideX</a> = 1;</div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span>  convDesc2d.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">m_StrideY</a> = 1;</div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span>  convDesc2d.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = biasEnabled;</div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>  convDesc2d.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = <a class="code" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a>;</div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span> </div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* convLayer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>  <span class="keyword">const</span> std::string convLayerName(<span class="stringliteral">"conv layer"</span>);</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span> </div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>  <span class="keywordflow">if</span> (biasEnabled)</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>  {</div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>  constexpr <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> biasDataType = ( dataType == <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>) ?</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span>  armnn::DataType::Signed32 : <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>;</div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span> </div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasTensorInfo(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>({16}), biasDataType, 0.9f * 0.9f, 0);</div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasLength = biasTensorInfo.GetNumElements();</div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span> </div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span>  <span class="keyword">using</span> BiasType = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType<biasDataType></a>;</div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span>  std::vector<BiasType> biasData(biasLength);</div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span>  std::fill(biasData.begin(), biasData.end(), <span class="keyword">static_cast<</span>BiasType<span class="keyword">></span>(0));</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="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a> biases(biasTensorInfo, biasData);</div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span> </div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span>  <span class="comment">// Create convolution layer with biases</span></div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>  convLayer = net.<a class="code" href="classarmnn_1_1_network.xhtml#a865189c08aa64d448d05efc92a43725a">AddConvolution2dLayer</a>(convDesc2d,</div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>  weights,</div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(biases),</div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span>  convLayerName.c_str());</div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span>  }</div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>  {</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span>  <span class="comment">// Create convolution layer without biases</span></div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>  convLayer = net.<a class="code" href="classarmnn_1_1_network.xhtml#a865189c08aa64d448d05efc92a43725a">AddConvolution2dLayer</a>(convDesc2d,</div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span>  weights,</div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span>  <a class="code" href="structarmnn_1_1_empty_optional.xhtml">EmptyOptional</a>(),</div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>  convLayerName.c_str());</div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>  }</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>  BOOST_TEST(convLayer);</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>  <span class="comment">// Add an output layer</span></div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a>* <span class="keyword">const</span> outputLayer = net.<a class="code" href="classarmnn_1_1_network.xhtml#ad55ff20f4c7e60c18b849e61f28f0e2e">AddOutputLayer</a>(0, <span class="stringliteral">"output layer"</span>);</div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span>  BOOST_TEST(outputLayer);</div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span> </div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>  <span class="comment">// set the tensors in the network (NHWC format)</span></div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>({ 1, 16, 16, 16 }), dataType);</div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span>  <span class="keywordflow">if</span> (dataType == armnn::DataType::QAsymmU8)</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>  inputTensorInfo.SetQuantizationOffset(0);</div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span>  inputTensorInfo.SetQuantizationScale(0.9f);</div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span>  }</div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span> </div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo(<a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>({1, 16, 16, 16}), dataType);</div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>  <span class="keywordflow">if</span> (dataType == armnn::DataType::QAsymmU8)</div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>  {</div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span>  outputTensorInfo.SetQuantizationOffset(0);</div><div class="line"><a name="l01382"></a><span class="lineno"> 1382</span>  outputTensorInfo.SetQuantizationScale(0.9f);</div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span>  }</div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</span> </div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span>  <span class="comment">// Connect the layers</span></div><div class="line"><a name="l01386"></a><span class="lineno"> 1386</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>  inputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputTensorInfo);</div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</span> </div><div class="line"><a name="l01389"></a><span class="lineno"> 1389</span>  convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l01390"></a><span class="lineno"> 1390</span>  convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputTensorInfo);</div><div class="line"><a name="l01391"></a><span class="lineno"> 1391</span> </div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span>  <span class="comment">// Optimize the network for the backend supported by the factory</span></div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>  std::vector<armnn::BackendId> backends = {factory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a9f7e4296485d2812e7996089149c96d1">GetBackendId</a>()};</div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span>  <a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a> <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a>;</div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a> runtime(<a class="code" href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a>(options));</div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>  <a class="code" href="structarmnn_1_1_optimizer_options.xhtml">armnn::OptimizerOptions</a> optimizerOptions;</div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a> optimizedNet = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a>(net, backends, runtime->GetDeviceSpec(),</div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span>  optimizerOptions);</div><div class="line"><a name="l01399"></a><span class="lineno"> 1399</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(optimizedNet != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</span> </div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span>  <span class="comment">// Find the PreCompiled layer in the optimised graph</span></div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& optimisedGraph = <span class="keyword">static_cast<</span><a class="code" href="classarmnn_1_1_optimized_network.xhtml">armnn::OptimizedNetwork</a>*<span class="keyword">></span>(optimizedNet.get())->GetGraph();</div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* preCompiledLayer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>& layer : optimisedGraph)</div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span>  {</div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>  <span class="keywordflow">if</span> (layer->GetType() == <a class="code" href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a1ba143ebe524d46181a4b53470693278">LayerType::PreCompiled</a>)</div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>  {</div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>  preCompiledLayer = layer;</div><div class="line"><a name="l01409"></a><span class="lineno"> 1409</span>  }</div><div class="line"><a name="l01410"></a><span class="lineno"> 1410</span>  }</div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(preCompiledLayer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span> </div><div class="line"><a name="l01413"></a><span class="lineno"> 1413</span>  <span class="comment">// Create the TensorHandles.</span></div><div class="line"><a name="l01414"></a><span class="lineno"> 1414</span>  CreateTensorHandles(optimisedGraph, factory);</div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span> </div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span>  <span class="comment">// Make the workload and check it.</span></div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<PreCompiledWorkload>(*preCompiledLayer, factory);</div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span> </div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span>  <a class="code" href="structarmnn_1_1_pre_compiled_queue_descriptor.xhtml">PreCompiledQueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);</div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);</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">// Returns the workload so we can do extra, backend-specific tests.</span></div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>  <span class="comment">// NOTE: We need to return the optimised network as well, otherwise it gets</span></div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>  <span class="comment">// out of scope and the tensor handles get destructed</span></div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>  <span class="keywordflow">return</span> std::make_pair(std::move(optimizedNet), std::move(workload));</div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span> }</div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</span> </div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span> <span class="keyword">template</span><<span class="keyword">typename</span> ConstantWorkload, armnn::DataType DataType></div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span> std::unique_ptr<ConstantWorkload> CreateConstantWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory,</div><div class="line"><a name="l01431"></a><span class="lineno"> 1431</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph,</div><div class="line"><a name="l01432"></a><span class="lineno"> 1432</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& outputShape)</div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</span> {</div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</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="keyword">auto</span> constant = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_constant_layer.xhtml">ConstantLayer</a>>(<span class="stringliteral">"constant"</span>);</div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</span>  constant-><a class="code" href="classarmnn_1_1_constant_layer.xhtml#a67ccc257eeefce0964c1cafc4b255c9f">m_LayerOutput</a> = std::make_unique<ScopedCpuTensorHandle>(outputTensorInfo);</div><div class="line"><a name="l01438"></a><span class="lineno"> 1438</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"created constant layer"</span>);</div><div class="line"><a name="l01439"></a><span class="lineno"> 1439</span> </div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</span> </div><div class="line"><a name="l01442"></a><span class="lineno"> 1442</span>  <span class="comment">// Adds connections.</span></div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(constant, output, outputTensorInfo, 0, 0);</div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"connect constant to output"</span>);</div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span> </div><div class="line"><a name="l01446"></a><span class="lineno"> 1446</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l01447"></a><span class="lineno"> 1447</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"created tensor handles"</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> workloadConstant = MakeAndCheckWorkload<ConstantWorkload>(*constant, factory);</div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"created Constant workload"</span>);</div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span> </div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>  <span class="keywordflow">return</span> workloadConstant;</div><div class="line"><a name="l01453"></a><span class="lineno"> 1453</span> }</div><div class="line"><a name="l01454"></a><span class="lineno"> 1454</span> </div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span> <span class="keyword">template</span> <<span class="keyword">typename</span> PreluWorkload></div><div class="line"><a name="l01456"></a><span class="lineno"> 1456</span> std::unique_ptr<PreluWorkload> CreatePreluWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory,</div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph,</div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& inputShape,</div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& alphaShape,</div><div class="line"><a name="l01460"></a><span class="lineno"> 1460</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& outputShape,</div><div class="line"><a name="l01461"></a><span class="lineno"> 1461</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> dataType)</div><div class="line"><a name="l01462"></a><span class="lineno"> 1462</span> {</div><div class="line"><a name="l01463"></a><span class="lineno"> 1463</span>  <span class="comment">// Creates the PReLU layer</span></div><div class="line"><a name="l01464"></a><span class="lineno"> 1464</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_prelu_layer.xhtml">PreluLayer</a>>(<span class="stringliteral">"prelu"</span>);</div><div class="line"><a name="l01465"></a><span class="lineno"> 1465</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(layer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01466"></a><span class="lineno"> 1466</span> </div><div class="line"><a name="l01467"></a><span class="lineno"> 1467</span>  <span class="comment">// Creates extra layers</span></div><div class="line"><a name="l01468"></a><span class="lineno"> 1468</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>> (0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> alpha = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>> (1, <span class="stringliteral">"alpha"</span>);</div><div class="line"><a name="l01470"></a><span class="lineno"> 1470</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01471"></a><span class="lineno"> 1471</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(input != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01472"></a><span class="lineno"> 1472</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(alpha != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01473"></a><span class="lineno"> 1473</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(output != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</span> </div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span>  <span class="comment">// Connects up</span></div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo (inputShape, dataType);</div><div class="line"><a name="l01477"></a><span class="lineno"> 1477</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> alphaTensorInfo (alphaShape, dataType);</div><div class="line"><a name="l01478"></a><span class="lineno"> 1478</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo(outputShape, dataType);</div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, inputTensorInfo, 0, 0);</div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(alpha, layer, alphaTensorInfo, 0, 1);</div><div class="line"><a name="l01481"></a><span class="lineno"> 1481</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, outputTensorInfo, 0, 0);</div><div class="line"><a name="l01482"></a><span class="lineno"> 1482</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l01483"></a><span class="lineno"> 1483</span> </div><div class="line"><a name="l01484"></a><span class="lineno"> 1484</span>  <span class="comment">// Makes the workload and checks it</span></div><div class="line"><a name="l01485"></a><span class="lineno"> 1485</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<PreluWorkload>(*layer, factory);</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>  <a class="code" href="structarmnn_1_1_prelu_queue_descriptor.xhtml">PreluQueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l01488"></a><span class="lineno"> 1488</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 2);</div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);</div><div class="line"><a name="l01490"></a><span class="lineno"> 1490</span> </div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span>  <span class="comment">// Returns so we can do extra, backend-specific tests.</span></div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span>  <span class="keywordflow">return</span> workload;</div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span> }</div><div class="line"><a name="l01494"></a><span class="lineno"> 1494</span> </div><div class="line"><a name="l01495"></a><span class="lineno"> 1495</span> <span class="keyword">template</span> <<span class="keyword">typename</span> SpaceToDepthWorkload, armnn::DataType DataType></div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span> std::unique_ptr<SpaceToDepthWorkload> CreateSpaceToDepthWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory,</div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph)</div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span> {</div><div class="line"><a name="l01499"></a><span class="lineno"> 1499</span>  <a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml">SpaceToDepthDescriptor</a> desc;</div><div class="line"><a name="l01500"></a><span class="lineno"> 1500</span>  desc.<a class="code" href="structarmnn_1_1_space_to_depth_descriptor.xhtml#a6c6b8957f1e176867e5fb05b1a1a1486">m_BlockSize</a> = 2;</div><div class="line"><a name="l01501"></a><span class="lineno"> 1501</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> layer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_space_to_depth_layer.xhtml">SpaceToDepthLayer</a>>(desc, <span class="stringliteral">"spaceToDepth"</span>);</div><div class="line"><a name="l01502"></a><span class="lineno"> 1502</span> </div><div class="line"><a name="l01503"></a><span class="lineno"> 1503</span>  <span class="comment">// Creates extra layers.</span></div><div class="line"><a name="l01504"></a><span class="lineno"> 1504</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> input = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l01505"></a><span class="lineno"> 1505</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01506"></a><span class="lineno"> 1506</span> </div><div class="line"><a name="l01507"></a><span class="lineno"> 1507</span>  <span class="comment">// Connects up.</span></div><div class="line"><a name="l01508"></a><span class="lineno"> 1508</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ 1, 2, 2, 1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l01509"></a><span class="lineno"> 1509</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ 1, 1, 1, 4 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</span> </div><div class="line"><a name="l01511"></a><span class="lineno"> 1511</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, layer, inputTensorInfo);</div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(layer, output, outputTensorInfo);</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>  CreateTensorHandles(graph, factory);</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>  <span class="comment">// Makes the workload and checks it.</span></div><div class="line"><a name="l01517"></a><span class="lineno"> 1517</span>  <span class="keyword">auto</span> workload = MakeAndCheckWorkload<SpaceToDepthWorkload>(*layer, factory);</div><div class="line"><a name="l01518"></a><span class="lineno"> 1518</span> </div><div class="line"><a name="l01519"></a><span class="lineno"> 1519</span>  <a class="code" href="structarmnn_1_1_space_to_depth_queue_descriptor.xhtml">SpaceToDepthQueueDescriptor</a> queueDescriptor = workload->GetData();</div><div class="line"><a name="l01520"></a><span class="lineno"> 1520</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == 1);</div><div class="line"><a name="l01521"></a><span class="lineno"> 1521</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);</div><div class="line"><a name="l01522"></a><span class="lineno"> 1522</span> </div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span>  <span class="keywordflow">return</span> workload;</div><div class="line"><a name="l01524"></a><span class="lineno"> 1524</span> }</div><div class="line"><a name="l01525"></a><span class="lineno"> 1525</span> </div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span> <span class="keyword">template</span> <<span class="keyword">typename</span> StackWorkload, armnn::DataType DataType></div><div class="line"><a name="l01527"></a><span class="lineno"> 1527</span> std::unique_ptr<StackWorkload> CreateStackWorkloadTest(<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& factory,</div><div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">armnn::Graph</a>& graph,</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& inputShape,</div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a>& outputShape,</div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> axis,</div><div class="line"><a name="l01532"></a><span class="lineno"> 1532</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numInputs)</div><div class="line"><a name="l01533"></a><span class="lineno"> 1533</span> {</div><div class="line"><a name="l01534"></a><span class="lineno"> 1534</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l01535"></a><span class="lineno"> 1535</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>);</div><div class="line"><a name="l01536"></a><span class="lineno"> 1536</span> </div><div class="line"><a name="l01537"></a><span class="lineno"> 1537</span>  <span class="comment">// Constructs the Stack layer.</span></div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span>  <a class="code" href="structarmnn_1_1_stack_descriptor.xhtml">armnn::StackDescriptor</a> descriptor(axis, numInputs, inputShape);</div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> stackLayer = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_stack_layer.xhtml">StackLayer</a>>(descriptor, <span class="stringliteral">"stack"</span>);</div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(stackLayer != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span> </div><div class="line"><a name="l01542"></a><span class="lineno"> 1542</span>  <span class="comment">// Constructs layer inputs and output.</span></div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span>  std::vector<Layer*> inputs;</div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i=0; i<numInputs; ++i)</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span>  {</div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span>  inputs.push_back(graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_input_layer.xhtml">InputLayer</a>>(</div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span>  static_cast<int>(i),</div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span>  (<span class="stringliteral">"input"</span> + std::to_string(i)).c_str()</div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span>  ));</div><div class="line"><a name="l01550"></a><span class="lineno"> 1550</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(inputs[i] != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</span>  }</div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>  <a class="code" href="classarmnn_1_1_layer.xhtml">Layer</a>* <span class="keyword">const</span> output = graph.<a class="code" href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">AddLayer</a><<a class="code" href="classarmnn_1_1_output_layer.xhtml">OutputLayer</a>>(0, <span class="stringliteral">"output"</span>);</div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>  <a class="code" href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a>(output != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01554"></a><span class="lineno"> 1554</span> </div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>  <span class="comment">// Adds connections.</span></div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i=0; i<numInputs; ++i)</div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>  {</div><div class="line"><a name="l01558"></a><span class="lineno"> 1558</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(inputs[i], stackLayer, inputTensorInfo, 0, i);</div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span>  }</div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(stackLayer, output, outputTensorInfo, 0, 0);</div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span> </div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span>  CreateTensorHandles(graph, factory);</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span> </div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span>  <span class="keyword">auto</span> stackWorkload = MakeAndCheckWorkload<StackWorkload>(*stackLayer, factory);</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span>  <a class="code" href="structarmnn_1_1_stack_queue_descriptor.xhtml">StackQueueDescriptor</a> queueDescriptor = stackWorkload->GetData();</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span>  BOOST_TEST(queueDescriptor.m_Inputs.size() == numInputs);</div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span>  BOOST_TEST(queueDescriptor.m_Outputs.size() == 1);</div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span> </div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span>  <span class="keywordflow">return</span> stackWorkload;</div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span> }</div><div class="line"><a name="l01571"></a><span class="lineno"> 1571</span> </div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span> } <span class="comment">// Anonymous namespace</span></div><div class="ttc" id="classarmnn_1_1_constant_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_constant_layer.xhtml">armnn::ConstantLayer</a></div><div class="ttdoc">A layer that the constant data can be bound to. </div><div class="ttdef"><b>Definition:</b> <a href="_constant_layer_8hpp_source.xhtml#l00015">ConstantLayer.hpp:15</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_basic_parameters_xhtml_a0e0e17d5b494993407cb75d614455ddd"><div class="ttname"><a href="structarmnn_1_1_lstm_basic_parameters.xhtml#a0e0e17d5b494993407cb75d614455ddd">armnn::LstmBasicParameters::m_ForgetGateBias</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_ForgetGateBias</div><div class="ttdoc">A unique pointer to represent 1D weights tensor with dimensions [num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00069">LstmLayer.hpp:69</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="structarmnn_1_1_lstm_basic_parameters_xhtml_ae8d897b8d282f25a6eb784c4aaa98df6"><div class="ttname"><a href="structarmnn_1_1_lstm_basic_parameters.xhtml#ae8d897b8d282f25a6eb784c4aaa98df6">armnn::LstmBasicParameters::m_InputToOutputWeights</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_InputToOutputWeights</div><div class="ttdoc">A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00061">LstmLayer.hpp:61</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_lstm_basic_parameters_xhtml_a6e8c3db3c5474f0760553ff93fbc39e6"><div class="ttname"><a href="structarmnn_1_1_lstm_basic_parameters.xhtml#a6e8c3db3c5474f0760553ff93fbc39e6">armnn::LstmBasicParameters::m_RecurrentToCellWeights</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_RecurrentToCellWeights</div><div class="ttdoc">A unique pointer to represent 2D weights tensor with dimensions [output_size, num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00065">LstmLayer.hpp:65</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a6c9de81fc65b3c4924cab11907075a17"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a6c9de81fc65b3c4924cab11907075a17">armnn::LstmDescriptor::m_ProjectionEnabled</a></div><div class="ttdeci">bool m_ProjectionEnabled</div><div class="ttdoc">Enable/disable the projection layer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00871">Descriptors.hpp:871</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="_workload_data_8hpp_xhtml"><div class="ttname"><a href="_workload_data_8hpp.xhtml">WorkloadData.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_splitter_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_splitter_layer.xhtml">armnn::SplitterLayer</a></div><div class="ttdoc">This layer represents a split operation. </div><div class="ttdef"><b>Definition:</b> <a href="_splitter_layer_8hpp_source.xhtml#l00013">SplitterLayer.hpp:13</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_runtime_xhtml_ad44ecd3700748dc30dc4bbe34ba5bde7"><div class="ttname"><a href="classarmnn_1_1_i_runtime.xhtml#ad44ecd3700748dc30dc4bbe34ba5bde7">armnn::IRuntime::Create</a></div><div class="ttdeci">static IRuntimePtr Create(const CreationOptions &options)</div><div class="ttdef"><b>Definition:</b> <a href="_runtime_8cpp_source.xhtml#l00032">Runtime.cpp:32</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a9f7e4296485d2812e7996089149c96d1"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a9f7e4296485d2812e7996089149c96d1">armnn::IWorkloadFactory::GetBackendId</a></div><div class="ttdeci">virtual const BackendId & GetBackendId() const =0</div></div> +<div class="ttc" id="classarmnn_1_1_lstm_layer_xhtml_a8838b317568861294a9df608221f185e"><div class="ttname"><a href="classarmnn_1_1_lstm_layer.xhtml#a8838b317568861294a9df608221f185e">armnn::LstmLayer::m_BasicParameters</a></div><div class="ttdeci">LstmBasicParameters m_BasicParameters</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00081">LstmLayer.hpp:81</a></div></div> +<div class="ttc" id="classarmnn_1_1_batch_normalization_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_batch_normalization_layer.xhtml">armnn::BatchNormalizationLayer</a></div><div class="ttdoc">This layer represents a batch normalization operation. </div><div class="ttdef"><b>Definition:</b> <a href="_batch_normalization_layer_8hpp_source.xhtml#l00015">BatchNormalizationLayer.hpp:15</a></div></div> +<div class="ttc" id="_data_layout_indexed_8hpp_xhtml"><div class="ttname"><a href="_data_layout_indexed_8hpp.xhtml">DataLayoutIndexed.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml">armnn::ViewsDescriptor</a></div><div class="ttdoc">A ViewsDescriptor for the SplitterLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00190">Descriptors.hpp:190</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00061">INetwork.hpp:61</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::Pooling2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00355">Descriptors.hpp:355</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::DepthwiseConvolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00490">Descriptors.hpp:490</a></div></div> +<div class="ttc" id="classarmnn_1_1_network_xhtml_ad55ff20f4c7e60c18b849e61f28f0e2e"><div class="ttname"><a href="classarmnn_1_1_network.xhtml#ad55ff20f4c7e60c18b849e61f28f0e2e">armnn::Network::AddOutputLayer</a></div><div class="ttdeci">IConnectableLayer * AddOutputLayer(LayerBindingId id, const char *name=nullptr) override</div><div class="ttdoc">Adds an output layer to the network. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l01310">Network.cpp:1310</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">armnn::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00049">Types.hpp:49</a></div></div> +<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml_a414e6f95548e6f7a01d5028b55ad3941"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a414e6f95548e6f7a01d5028b55ad3941">armnnUtils::DataLayoutIndexed::GetWidthIndex</a></div><div class="ttdeci">unsigned int GetWidthIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00025">DataLayoutIndexed.hpp:25</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a8526ea7cf860d8e7f8340e9f9354f9f0"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a8526ea7cf860d8e7f8340e9f9354f9f0">armnn::NormalizationDescriptor::m_K</a></div><div class="ttdeci">float m_K</div><div class="ttdoc">Kappa value used for the across channel normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00585">Descriptors.hpp:585</a></div></div> +<div class="ttc" id="structarmnn_1_1_softmax_descriptor_xhtml_a214c3636fdf0ea5bac8edb42d0e6c7f0"><div class="ttname"><a href="structarmnn_1_1_softmax_descriptor.xhtml#a214c3636fdf0ea5bac8edb42d0e6c7f0">armnn::SoftmaxDescriptor::m_Axis</a></div><div class="ttdeci">int m_Axis</div><div class="ttdoc">Scalar, defaulted to the last index (-1), specifying the dimension the activation will be performed o...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00138">Descriptors.hpp:138</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_aa9e49717ebdb741e8c767741647fc618"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#aa9e49717ebdb741e8c767741647fc618">armnn::DepthwiseConvolution2dDescriptor::m_PadBottom</a></div><div class="ttdeci">uint32_t m_PadBottom</div><div class="ttdoc">Padding bottom value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00480">Descriptors.hpp:480</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Pooling2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00349">Descriptors.hpp:349</a></div></div> +<div class="ttc" id="classarmnn_1_1_fully_connected_layer_xhtml_a2664044e28e69309ea08ef385fe53903"><div class="ttname"><a href="classarmnn_1_1_fully_connected_layer.xhtml#a2664044e28e69309ea08ef385fe53903">armnn::FullyConnectedLayer::m_Weight</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_Weight</div><div class="ttdoc">A unique pointer to store Weight values. </div><div class="ttdef"><b>Definition:</b> <a href="_fully_connected_layer_8hpp_source.xhtml#l00019">FullyConnectedLayer.hpp:19</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a86e88bef0df4df96df752b4b8955a3af"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a86e88bef0df4df96df752b4b8955a3af">armnn::LstmDescriptor::m_ClippingThresProj</a></div><div class="ttdeci">float m_ClippingThresProj</div><div class="ttdoc">Clipping threshold value for the projection. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00865">Descriptors.hpp:865</a></div></div> +<div class="ttc" id="classarmnn_1_1_optional_xhtml"><div class="ttname"><a href="classarmnn_1_1_optional.xhtml">armnn::Optional</a></div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00270">Optional.hpp:270</a></div></div> +<div class="ttc" id="structarmnn_1_1_splitter_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_splitter_queue_descriptor.xhtml">armnn::SplitterQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00083">WorkloadData.hpp:83</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3"><div class="ttname"><a href="namespacearmnn.xhtml#adf2e5515c4c36a3e7e46bb8b83c6754eaf3f6d0343d56ce88ce7958170ed05cb3">armnn::OutputShapeRounding::Floor</a></div></div> +<div class="ttc" id="structarmnn_1_1_reshape_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_reshape_descriptor.xhtml">armnn::ReshapeDescriptor</a></div><div class="ttdoc">A ReshapeDescriptor for the ReshapeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00758">Descriptors.hpp:758</a></div></div> +<div class="ttc" id="structarmnn_1_1_quantized_lstm_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_quantized_lstm_queue_descriptor.xhtml">armnn::QuantizedLstmQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00512">WorkloadData.hpp:512</a></div></div> +<div class="ttc" id="_workload_factory_8hpp_xhtml"><div class="ttname"><a href="_workload_factory_8hpp.xhtml">WorkloadFactory.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::DepthwiseConvolution2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00492">Descriptors.hpp:492</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_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="classarmnn_1_1_depthwise_convolution2d_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml">armnn::DepthwiseConvolution2dLayer</a></div><div class="ttdoc">This layer represents a depthwise convolution 2d operation. </div><div class="ttdef"><b>Definition:</b> <a href="_depthwise_convolution2d_layer_8hpp_source.xhtml#l00015">DepthwiseConvolution2dLayer.hpp:15</a></div></div> +<div class="ttc" id="classarmnn_1_1_graph_xhtml_a7563c5b899e7d0ada08fd0fdb202f205"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a7563c5b899e7d0ada08fd0fdb202f205">armnn::Graph::AddLayer</a></div><div class="ttdeci">LayerT * AddLayer(Args &&... args)</div><div class="ttdoc">Adds a new layer, of type LayerType, to the graph constructed with the arguments passed. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00397">Graph.hpp:397</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml_a281fcaec86e17c97f7b8402633f6b55a"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">armnn::FullyConnectedDescriptor::m_TransposeWeightMatrix</a></div><div class="ttdeci">bool m_TransposeWeightMatrix</div><div class="ttdoc">Enable/disable transpose weight matrix. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00388">Descriptors.hpp:388</a></div></div> +<div class="ttc" id="classarmnn_1_1_convolution2d_layer_xhtml_a39925bc24d3afcfb322a46a5884fadb9"><div class="ttname"><a href="classarmnn_1_1_convolution2d_layer.xhtml#a39925bc24d3afcfb322a46a5884fadb9">armnn::Convolution2dLayer::m_Bias</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_Bias</div><div class="ttdoc">A unique pointer to store Bias values. </div><div class="ttdef"><b>Definition:</b> <a href="_convolution2d_layer_8hpp_source.xhtml#l00022">Convolution2dLayer.hpp:22</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a6d8fb685cc1ff224f25aa127fcf62c86"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6d8fb685cc1ff224f25aa127fcf62c86">armnn::Pooling2dDescriptor::m_PoolWidth</a></div><div class="ttdeci">uint32_t m_PoolWidth</div><div class="ttdoc">Pooling width value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00357">Descriptors.hpp:357</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml">armnn::Convolution2dDescriptor</a></div><div class="ttdoc">A Convolution2dDescriptor for the Convolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00392">Descriptors.hpp:392</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a174279be57d7596eeb04c6b7f7510f99"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a174279be57d7596eeb04c6b7f7510f99">armnn::NormalizationDescriptor::m_Alpha</a></div><div class="ttdeci">float m_Alpha</div><div class="ttdoc">Alpha value for the normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00581">Descriptors.hpp:581</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::DepthwiseConvolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00474">Descriptors.hpp:474</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_basic_parameters_xhtml_a3d5f129421bbe6479a66d4ed1356bf68"><div class="ttname"><a href="structarmnn_1_1_lstm_basic_parameters.xhtml#a3d5f129421bbe6479a66d4ed1356bf68">armnn::LstmBasicParameters::m_RecurrentToForgetWeights</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_RecurrentToForgetWeights</div><div class="ttdoc">A unique pointer to represent 2D weights tensor with dimensions [output_size, num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00063">LstmLayer.hpp:63</a></div></div> +<div class="ttc" id="classarmnn_1_1_convert_fp16_to_fp32_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_convert_fp16_to_fp32_layer.xhtml">armnn::ConvertFp16ToFp32Layer</a></div><div class="ttdoc">This layer converts data type Float 16 to Float 32. </div><div class="ttdef"><b>Definition:</b> <a href="_convert_fp16_to_fp32_layer_8hpp_source.xhtml#l00014">ConvertFp16ToFp32Layer.hpp:14</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a869254cb56968986a78a79e1d6d4a86b"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a869254cb56968986a78a79e1d6d4a86b">armnn::ResizeDescriptor::m_Method</a></div><div class="ttdeci">ResizeMethod m_Method</div><div class="ttdoc">The Interpolation method to use (Bilinear, NearestNeighbor). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00749">Descriptors.hpp:749</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a150468a02bd7b2d2d061c4aaaee939f0"><div class="ttname"><a href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a></div><div class="ttdeci">std::unique_ptr< IRuntime, void(*)(IRuntime *runtime)> IRuntimePtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00024">IRuntime.hpp:24</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml_a11c821c7524251004a72ed13c510853c"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a11c821c7524251004a72ed13c510853c">armnn::BatchNormalizationDescriptor::m_Eps</a></div><div class="ttdeci">float m_Eps</div><div class="ttdoc">Value to add to the variance. Used to avoid dividing by zero. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00623">Descriptors.hpp:623</a></div></div> +<div class="ttc" id="classarmnn_1_1_space_to_depth_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_space_to_depth_layer.xhtml">armnn::SpaceToDepthLayer</a></div><div class="ttdoc">This layer represents a SpaceToDepth operation. </div><div class="ttdef"><b>Definition:</b> <a href="_space_to_depth_layer_8hpp_source.xhtml#l00014">SpaceToDepthLayer.hpp:14</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::BatchNormalizationDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00625">Descriptors.hpp:625</a></div></div> +<div class="ttc" id="classarmnn_1_1_network_xhtml_a90d8841cfbbc82ab02328f33fed24ac6"><div class="ttname"><a href="classarmnn_1_1_network.xhtml#a90d8841cfbbc82ab02328f33fed24ac6">armnn::Network::AddInputLayer</a></div><div class="ttdeci">IConnectableLayer * AddInputLayer(LayerBindingId id, const char *name=nullptr) override</div><div class="ttdoc">Adds an input layer to the network. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l01041">Network.cpp:1041</a></div></div> +<div class="ttc" id="classarmnn_1_1_batch_normalization_layer_xhtml_aec22bddf14a932c4a72796c30669066b"><div class="ttname"><a href="classarmnn_1_1_batch_normalization_layer.xhtml#aec22bddf14a932c4a72796c30669066b">armnn::BatchNormalizationLayer::m_Gamma</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_Gamma</div><div class="ttdoc">A unique pointer to store Gamma values. </div><div class="ttdef"><b>Definition:</b> <a href="_batch_normalization_layer_8hpp_source.xhtml#l00025">BatchNormalizationLayer.hpp:25</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_basic_parameters_xhtml_aacb55e0992b6781a7bd3225ab6e6bb2f"><div class="ttname"><a href="structarmnn_1_1_lstm_basic_parameters.xhtml#aacb55e0992b6781a7bd3225ab6e6bb2f">armnn::LstmBasicParameters::m_OutputGateBias</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_OutputGateBias</div><div class="ttdoc">A unique pointer to represent 1D weights tensor with dimensions [num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00073">LstmLayer.hpp:73</a></div></div> +<div class="ttc" id="structarmnn_1_1_stack_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_stack_queue_descriptor.xhtml">armnn::StackQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00124">WorkloadData.hpp:124</a></div></div> +<div class="ttc" id="classarmnn_1_1_reshape_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_reshape_layer.xhtml">armnn::ReshapeLayer</a></div><div class="ttdoc">This layer represents a reshape operation. </div><div class="ttdef"><b>Definition:</b> <a href="_reshape_layer_8hpp_source.xhtml#l00013">ReshapeLayer.hpp:13</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa1e34af023adeb7d5f484f8eb4b9826b6"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa1e34af023adeb7d5f484f8eb4b9826b6">armnn::ActivationFunction::Abs</a></div></div> +<div class="ttc" id="classarmnn_1_1_batch_normalization_layer_xhtml_aa5685ee78433980cf535d745d1fcab55"><div class="ttname"><a href="classarmnn_1_1_batch_normalization_layer.xhtml#aa5685ee78433980cf535d745d1fcab55">armnn::BatchNormalizationLayer::m_Variance</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_Variance</div><div class="ttdoc">A unique pointer to store Variance values. </div><div class="ttdef"><b>Definition:</b> <a href="_batch_normalization_layer_8hpp_source.xhtml#l00021">BatchNormalizationLayer.hpp:21</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="classarmnn_1_1_activation_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_activation_layer.xhtml">armnn::ActivationLayer</a></div><div class="ttdoc">This layer represents an activation operation with the specified activation function. </div><div class="ttdef"><b>Definition:</b> <a href="_activation_layer_8hpp_source.xhtml#l00012">ActivationLayer.hpp:12</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Pooling2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00353">Descriptors.hpp:353</a></div></div> +<div class="ttc" id="_resolve_type_8hpp_xhtml"><div class="ttname"><a href="_resolve_type_8hpp.xhtml">ResolveType.hpp</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="structarmnn_1_1_normalization_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::NormalizationDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00587">Descriptors.hpp:587</a></div></div> +<div class="ttc" id="_test_utils_8hpp_xhtml"><div class="ttname"><a href="_test_utils_8hpp.xhtml">TestUtils.hpp</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2020 ARM Limited. </div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00025">00_introduction.dox:25</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml">armnn::FullyConnectedQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00141">WorkloadData.hpp:141</a></div></div> +<div class="ttc" id="classarmnn_1_1_lstm_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_lstm_layer.xhtml">armnn::LstmLayer</a></div><div class="ttdoc">This layer represents a LSTM operation. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00077">LstmLayer.hpp:77</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="classarmnn_1_1_layer_xhtml_a3f6ad59212fa8a47c9265162fff8a274"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a3f6ad59212fa8a47c9265162fff8a274">armnn::Layer::SetBackendId</a></div><div class="ttdeci">void SetBackendId(const BackendId &id)</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00264">Layer.hpp:264</a></div></div> +<div class="ttc" id="structarmnn_1_1_prelu_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_prelu_queue_descriptor.xhtml">armnn::PreluQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00489">WorkloadData.hpp:489</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_queue_descriptor.xhtml">armnn::BatchToSpaceNdQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00420">WorkloadData.hpp:420</a></div></div> +<div class="ttc" id="structarmnn_1_1_space_to_depth_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_space_to_depth_descriptor.xhtml">armnn::SpaceToDepthDescriptor</a></div><div class="ttdoc">A SpaceToDepthDescriptor for the SpaceToDepthLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00810">Descriptors.hpp:810</a></div></div> +<div class="ttc" id="structarmnn_1_1_softmax_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_softmax_queue_descriptor.xhtml">armnn::SoftmaxQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00077">WorkloadData.hpp:77</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_to_space_nd_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_to_space_nd_descriptor.xhtml">armnn::BatchToSpaceNdDescriptor</a></div><div class="ttdoc">A BatchToSpaceNdDescriptor for the BatchToSpaceNdLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00657">Descriptors.hpp:657</a></div></div> +<div class="ttc" id="_file_only_profiling_decorator_tests_8cpp_xhtml_a0c262ba6f6c189a2d092d127c1b7627b"><div class="ttname"><a href="_file_only_profiling_decorator_tests_8cpp.xhtml#a0c262ba6f6c189a2d092d127c1b7627b">BOOST_CHECK</a></div><div class="ttdeci">BOOST_CHECK(profilingService.GetCurrentState()==ProfilingState::WaitingForAck)</div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Pooling2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00361">Descriptors.hpp:361</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_basic_parameters_xhtml_a51255889cbc063130a3d691c1781c5d3"><div class="ttname"><a href="structarmnn_1_1_lstm_basic_parameters.xhtml#a51255889cbc063130a3d691c1781c5d3">armnn::LstmBasicParameters::m_CellBias</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_CellBias</div><div class="ttdoc">A unique pointer to represent 1D weights tensor with dimensions [num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00071">LstmLayer.hpp:71</a></div></div> +<div class="ttc" id="classarmnn_utils_1_1_data_layout_indexed_xhtml_a61c00316c443adc233c24e85c6c5b740"><div class="ttname"><a href="classarmnn_utils_1_1_data_layout_indexed.xhtml#a61c00316c443adc233c24e85c6c5b740">armnnUtils::DataLayoutIndexed::GetHeightIndex</a></div><div class="ttdeci">unsigned int GetHeightIndex() const</div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00024">DataLayoutIndexed.hpp:24</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a5ee4a6c9a2481245487b1b1a70d20fd0"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">armnn::IOutputSlot::SetTensorInfo</a></div><div class="ttdeci">virtual void SetTensorInfo(const TensorInfo &tensorInfo)=0</div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a05945f080edf694b631960728b87aadb"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a05945f080edf694b631960728b87aadb">armnn::NormalizationDescriptor::m_NormMethodType</a></div><div class="ttdeci">NormalizationAlgorithmMethod m_NormMethodType</div><div class="ttdoc">Normalization method algorithm to use (LocalBrightness, LocalContrast). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00577">Descriptors.hpp:577</a></div></div> +<div class="ttc" id="classarmnn_1_1_elementwise_unary_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_elementwise_unary_layer.xhtml">armnn::ElementwiseUnaryLayer</a></div><div class="ttdoc">This layer represents a elementwiseUnary operation. </div><div class="ttdef"><b>Definition:</b> <a href="_elementwise_unary_layer_8hpp_source.xhtml#l00014">ElementwiseUnaryLayer.hpp:14</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml">armnn::ResizeDescriptor</a></div><div class="ttdoc">A ResizeDescriptor for the ResizeLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00724">Descriptors.hpp:724</a></div></div> +<div class="ttc" id="classarmnn_1_1_batch_normalization_layer_xhtml_a77c30d191e7ee8917e2c0ff5e97f5640"><div class="ttname"><a href="classarmnn_1_1_batch_normalization_layer.xhtml#a77c30d191e7ee8917e2c0ff5e97f5640">armnn::BatchNormalizationLayer::m_Beta</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_Beta</div><div class="ttdoc">A unique pointer to store Beta values. </div><div class="ttdef"><b>Definition:</b> <a href="_batch_normalization_layer_8hpp_source.xhtml#l00023">BatchNormalizationLayer.hpp:23</a></div></div> +<div class="ttc" id="structarmnn_1_1_l2_normalization_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_l2_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::L2NormalizationDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00606">Descriptors.hpp:606</a></div></div> +<div class="ttc" id="classarmnn_1_1_network_xhtml_a865189c08aa64d448d05efc92a43725a"><div class="ttname"><a href="classarmnn_1_1_network.xhtml#a865189c08aa64d448d05efc92a43725a">armnn::Network::AddConvolution2dLayer</a></div><div class="ttdeci">IConnectableLayer * AddConvolution2dLayer(const Convolution2dDescriptor &convolution2dDescriptor, const ConstTensor &weights, const Optional< ConstTensor > &biases, const char *name=nullptr) override</div><div class="ttdoc">Adds a 2D convolution layer to the network. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l01139">Network.cpp:1139</a></div></div> +<div class="ttc" id="structarmnn_1_1_space_to_depth_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_space_to_depth_queue_descriptor.xhtml">armnn::SpaceToDepthQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00348">WorkloadData.hpp:348</a></div></div> +<div class="ttc" id="structarmnn_1_1_stack_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_stack_descriptor.xhtml">armnn::StackDescriptor</a></div><div class="ttdoc">A StackDescriptor for the StackLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00950">Descriptors.hpp:950</a></div></div> +<div class="ttc" id="structarmnn_1_1_reshape_descriptor_xhtml_a1178f4dafdda81f59c15145ec327f7d9"><div class="ttname"><a href="structarmnn_1_1_reshape_descriptor.xhtml#a1178f4dafdda81f59c15145ec327f7d9">armnn::ReshapeDescriptor::m_TargetShape</a></div><div class="ttdeci">TensorShape m_TargetShape</div><div class="ttdoc">Target shape value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00774">Descriptors.hpp:774</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a5699e8606c37d18c03910b242cd1b010"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a5699e8606c37d18c03910b242cd1b010">armnn::Pooling2dDescriptor::m_PoolHeight</a></div><div class="ttdeci">uint32_t m_PoolHeight</div><div class="ttdoc">Pooling height value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00359">Descriptors.hpp:359</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::Convolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00426">Descriptors.hpp:426</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_opt_peephole_parameters_xhtml_a5d0ebbbb11b727a67877df40b59a628c"><div class="ttname"><a href="structarmnn_1_1_lstm_opt_peephole_parameters.xhtml#a5d0ebbbb11b727a67877df40b59a628c">armnn::LstmOptPeepholeParameters::m_CellToForgetWeights</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_CellToForgetWeights</div><div class="ttdoc">A unique pointer to represent 1D weights tensor with dimensions [num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00049">LstmLayer.hpp:49</a></div></div> +<div class="ttc" id="namespacestd_xhtml"><div class="ttname"><a href="namespacestd.xhtml">std</a></div><div class="ttdef"><b>Definition:</b> <a href="_backend_id_8hpp_source.xhtml#l00147">BackendId.hpp:147</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_afe6a3377c4531315354def9023c8fdda"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#afe6a3377c4531315354def9023c8fdda">armnn::Convolution2dDescriptor::m_StrideX</a></div><div class="ttdeci">uint32_t m_StrideX</div><div class="ttdoc">Stride value when proceeding through input for the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00430">Descriptors.hpp:430</a></div></div> +<div class="ttc" id="classarmnn_1_1_constant_layer_xhtml_a67ccc257eeefce0964c1cafc4b255c9f"><div class="ttname"><a href="classarmnn_1_1_constant_layer.xhtml#a67ccc257eeefce0964c1cafc4b255c9f">armnn::ConstantLayer::m_LayerOutput</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_LayerOutput</div><div class="ttdef"><b>Definition:</b> <a href="_constant_layer_8hpp_source.xhtml#l00043">ConstantLayer.hpp:43</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="classarmnn_1_1_output_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_output_layer.xhtml">armnn::OutputLayer</a></div><div class="ttdoc">A layer user-provided data can be bound to (e.g. inputs, outputs). </div><div class="ttdef"><b>Definition:</b> <a href="_output_layer_8hpp_source.xhtml#l00013">OutputLayer.hpp:13</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="classarmnn_1_1_fully_connected_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_fully_connected_layer.xhtml">armnn::FullyConnectedLayer</a></div><div class="ttdoc">This layer represents a fully connected operation. </div><div class="ttdef"><b>Definition:</b> <a href="_fully_connected_layer_8hpp_source.xhtml#l00015">FullyConnectedLayer.hpp:15</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml">armnn::LstmDescriptor</a></div><div class="ttdoc">An LstmDescriptor for the LstmLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00837">Descriptors.hpp:837</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a002bc30e590d78cbb4f4d12171055ca7"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a002bc30e590d78cbb4f4d12171055ca7">armnn::Pooling2dDescriptor::m_PadRight</a></div><div class="ttdeci">uint32_t m_PadRight</div><div class="ttdoc">Padding right value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00351">Descriptors.hpp:351</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_a56b51f56cef50cdfa554258eecdab046"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#a56b51f56cef50cdfa554258eecdab046">armnn::DepthwiseConvolution2dDescriptor::m_PadTop</a></div><div class="ttdeci">uint32_t m_PadTop</div><div class="ttdoc">Padding top value in the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00478">Descriptors.hpp:478</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a82e98ef05fd67036d1195ba17174d685"><div class="ttname"><a href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a></div><div class="ttdeci">IOptimizedNetworkPtr Optimize(const INetwork &network, const std::vector< BackendId > &backendPreferences, const IDeviceSpec &deviceSpec, const OptimizerOptions &options=OptimizerOptions(), Optional< std::vector< std::string > &> messages=EmptyOptional())</div><div class="ttdoc">Create an optimized version of the network. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00890">Network.cpp:890</a></div></div> +<div class="ttc" id="classarmnn_1_1_quantized_lstm_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_quantized_lstm_layer.xhtml">armnn::QuantizedLstmLayer</a></div><div class="ttdoc">This layer represents a QuantizedLstm operation. </div><div class="ttdef"><b>Definition:</b> <a href="_quantized_lstm_layer_8hpp_source.xhtml#l00045">QuantizedLstmLayer.hpp:45</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml">armnn::LstmQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00358">WorkloadData.hpp:358</a></div></div> +<div class="ttc" id="classarmnn_1_1_batch_normalization_layer_xhtml_a3540afac8fad99bbe68b3f7b57590160"><div class="ttname"><a href="classarmnn_1_1_batch_normalization_layer.xhtml#a3540afac8fad99bbe68b3f7b57590160">armnn::BatchNormalizationLayer::m_Mean</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_Mean</div><div class="ttdoc">A unique pointer to store Mean values. </div><div class="ttdef"><b>Definition:</b> <a href="_batch_normalization_layer_8hpp_source.xhtml#l00019">BatchNormalizationLayer.hpp:19</a></div></div> +<div class="ttc" id="structarmnn_1_1_l2_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_l2_normalization_descriptor.xhtml">armnn::L2NormalizationDescriptor</a></div><div class="ttdoc">A L2NormalizationDescriptor for the L2NormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00591">Descriptors.hpp:591</a></div></div> +<div class="ttc" id="_graph_8hpp_xhtml"><div class="ttname"><a href="_graph_8hpp.xhtml">Graph.hpp</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="structarmnn_1_1_origins_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a></div><div class="ttdoc">An OriginsDescriptor for the ConcatLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00147">Descriptors.hpp:147</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml">armnn::FullyConnectedDescriptor</a></div><div class="ttdoc">A FullyConnectedDescriptor for the FullyConnectedLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00373">Descriptors.hpp:373</a></div></div> +<div class="ttc" id="classarmnn_1_1_convolution2d_layer_xhtml_a2664044e28e69309ea08ef385fe53903"><div class="ttname"><a href="classarmnn_1_1_convolution2d_layer.xhtml#a2664044e28e69309ea08ef385fe53903">armnn::Convolution2dLayer::m_Weight</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_Weight</div><div class="ttdoc">A unique pointer to store Weight values. </div><div class="ttdef"><b>Definition:</b> <a href="_convolution2d_layer_8hpp_source.xhtml#l00020">Convolution2dLayer.hpp:20</a></div></div> +<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::FullyConnectedDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00386">Descriptors.hpp:386</a></div></div> +<div class="ttc" id="classarmnn_1_1_stack_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_stack_layer.xhtml">armnn::StackLayer</a></div><div class="ttdoc">This layer represents a stack operation. </div><div class="ttdef"><b>Definition:</b> <a href="_stack_layer_8hpp_source.xhtml#l00013">StackLayer.hpp:13</a></div></div> +<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00199">Tensor.hpp:199</a></div></div> +<div class="ttc" id="classarmnn_1_1_concat_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_concat_layer.xhtml">armnn::ConcatLayer</a></div><div class="ttdoc">This layer represents a merge operation. </div><div class="ttdef"><b>Definition:</b> <a href="_concat_layer_8hpp_source.xhtml#l00013">ConcatLayer.hpp:13</a></div></div> +<div class="ttc" id="classarmnn_1_1_softmax_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_softmax_layer.xhtml">armnn::SoftmaxLayer</a></div><div class="ttdoc">This layer represents a softmax operation. </div><div class="ttdef"><b>Definition:</b> <a href="_softmax_layer_8hpp_source.xhtml#l00013">SoftmaxLayer.hpp:13</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_adcf5037208faac36c0788239a073f75c"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#adcf5037208faac36c0788239a073f75c">armnn::ResizeDescriptor::m_TargetWidth</a></div><div class="ttdeci">uint32_t m_TargetWidth</div><div class="ttdoc">Target width value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00744">Descriptors.hpp:744</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a26e6ed77470c6f2f830ecf874e6c0d55">armnn::DataType::Float16</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a2837b4396f20c956952d1a7286cab5f8"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a2837b4396f20c956952d1a7286cab5f8">armnn::LstmDescriptor::m_PeepholeEnabled</a></div><div class="ttdeci">bool m_PeepholeEnabled</div><div class="ttdoc">Enable/disable peephole. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00869">Descriptors.hpp:869</a></div></div> +<div class="ttc" id="classarmnn_1_1_batch_to_space_nd_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_batch_to_space_nd_layer.xhtml">armnn::BatchToSpaceNdLayer</a></div><div class="ttdoc">This layer represents a BatchToSpaceNd operation. </div><div class="ttdef"><b>Definition:</b> <a href="_batch_to_space_nd_layer_8hpp_source.xhtml#l00013">BatchToSpaceNdLayer.hpp:13</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a674efcf6cbdb9e831d653ff0e821fb38"><div class="ttname"><a href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a></div><div class="ttdeci">std::unique_ptr< IOptimizedNetwork, void(*)(IOptimizedNetwork *network)> IOptimizedNetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00566">INetwork.hpp:566</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021"><div class="ttname"><a href="namespacearmnn.xhtml#a961bbfe1db71a848eff5a1f0ab775718ab1897515d548a960afe49ecf66a29021">armnn::PoolingAlgorithm::Average</a></div></div> +<div class="ttc" id="structarmnn_1_1_convert_fp16_to_fp32_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convert_fp16_to_fp32_queue_descriptor.xhtml">armnn::ConvertFp16ToFp32QueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00410">WorkloadData.hpp:410</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4a1ba143ebe524d46181a4b53470693278"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4a1ba143ebe524d46181a4b53470693278">armnn::LayerType::PreCompiled</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aeafaa4524e3df19ada32643ce9a222362b">armnn::Compute::GpuAcc</a></div><div class="ttdoc">GPU Execution: OpenCL: ArmCompute. </div></div> +<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a74dc9ec1a223eab8b072368b2dacee87"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a74dc9ec1a223eab8b072368b2dacee87">armnn::IWorkloadFactory::IsLayerSupported</a></div><div class="ttdeci">static bool IsLayerSupported(const BackendId &backendId, const IConnectableLayer &layer, Optional< DataType > dataType, std::string &outReasonIfUnsupported)</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l00045">WorkloadFactory.cpp:45</a></div></div> +<div class="ttc" id="structarmnn_1_1_optimizer_options_xhtml"><div class="ttname"><a href="structarmnn_1_1_optimizer_options.xhtml">armnn::OptimizerOptions</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00582">INetwork.hpp:582</a></div></div> +<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a></div><div class="ttdoc">An ActivationDescriptor for the ActivationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00020">Descriptors.hpp:20</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a46c3fa15c46fb0d1dcdc24d0ea5cb5cd"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a46c3fa15c46fb0d1dcdc24d0ea5cb5cd">armnn::ResizeDescriptor::m_TargetHeight</a></div><div class="ttdeci">uint32_t m_TargetHeight</div><div class="ttdoc">Target height value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00746">Descriptors.hpp:746</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_ae1b07ed928036004bd257169e5aeeef4"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#ae1b07ed928036004bd257169e5aeeef4">armnn::LstmDescriptor::m_ActivationFunc</a></div><div class="ttdeci">uint32_t m_ActivationFunc</div><div class="ttdoc">The activation function to use. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00861">Descriptors.hpp:861</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_graph_xhtml"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml">armnn::Graph</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00029">Graph.hpp:29</a></div></div> +<div class="ttc" id="classarmnn_1_1_normalization_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_normalization_layer.xhtml">armnn::NormalizationLayer</a></div><div class="ttdoc">This layer represents a normalization operation. </div><div class="ttdef"><b>Definition:</b> <a href="_normalization_layer_8hpp_source.xhtml#l00013">NormalizationLayer.hpp:13</a></div></div> +<div class="ttc" id="classarmnn_1_1_pooling2d_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_pooling2d_layer.xhtml">armnn::Pooling2dLayer</a></div><div class="ttdoc">This layer represents a pooling 2d operation. </div><div class="ttdef"><b>Definition:</b> <a href="_pooling2d_layer_8hpp_source.xhtml#l00013">Pooling2dLayer.hpp:13</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_a531a3907ec13d3772370da88030191a5"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#a531a3907ec13d3772370da88030191a5">armnn::LstmDescriptor::m_ClippingThresCell</a></div><div class="ttdeci">float m_ClippingThresCell</div><div class="ttdoc">Clipping threshold value for the cell state. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00863">Descriptors.hpp:863</a></div></div> +<div class="ttc" id="classarmnn_1_1_convert_fp32_to_fp16_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_convert_fp32_to_fp16_layer.xhtml">armnn::ConvertFp32ToFp16Layer</a></div><div class="ttdoc">This layer converts data type Float 32 to Float 16. </div><div class="ttdef"><b>Definition:</b> <a href="_convert_fp32_to_fp16_layer_8hpp_source.xhtml#l00013">ConvertFp32ToFp16Layer.hpp:13</a></div></div> +<div class="ttc" id="structarmnn_1_1_space_to_depth_descriptor_xhtml_a6c6b8957f1e176867e5fb05b1a1a1486"><div class="ttname"><a href="structarmnn_1_1_space_to_depth_descriptor.xhtml#a6c6b8957f1e176867e5fb05b1a1a1486">armnn::SpaceToDepthDescriptor::m_BlockSize</a></div><div class="ttdeci">unsigned int m_BlockSize</div><div class="ttdoc">Scalar specifying the input block size. It must be >= 1. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00827">Descriptors.hpp:827</a></div></div> +<div class="ttc" id="structarmnn_1_1_convert_fp32_to_fp16_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convert_fp32_to_fp16_queue_descriptor.xhtml">armnn::ConvertFp32ToFp16QueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00415">WorkloadData.hpp:415</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a872803f5667392efc3c8e5607bd453ad"><div class="ttname"><a href="namespacearmnn.xhtml#a872803f5667392efc3c8e5607bd453ad">armnn::GetBiasDataType</a></div><div class="ttdeci">DataType GetBiasDataType(DataType inputDataType)</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l00025">WorkloadData.cpp:25</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::Pooling2dDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00369">Descriptors.hpp:369</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_handle_factory_registry_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_handle_factory_registry.xhtml">armnn::TensorHandleFactoryRegistry</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_handle_factory_registry_8hpp_source.xhtml#l00020">TensorHandleFactoryRegistry.hpp:20</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_basic_parameters_xhtml_a91dda74af4085ae43913746ad817795a"><div class="ttname"><a href="structarmnn_1_1_lstm_basic_parameters.xhtml#a91dda74af4085ae43913746ad817795a">armnn::LstmBasicParameters::m_RecurrentToOutputWeights</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_RecurrentToOutputWeights</div><div class="ttdoc">A unique pointer to represent 2D weights tensor with dimensions [output_size, num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00067">LstmLayer.hpp:67</a></div></div> +<div class="ttc" id="structarmnn_1_1_i_runtime_1_1_creation_options_xhtml"><div class="ttname"><a href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00041">IRuntime.hpp:41</a></div></div> +<div class="ttc" id="classarmnn_1_1_lstm_layer_xhtml_a4efa0f4d46817ab94e36c8507c26f276"><div class="ttname"><a href="classarmnn_1_1_lstm_layer.xhtml#a4efa0f4d46817ab94e36c8507c26f276">armnn::LstmLayer::m_PeepholeParameters</a></div><div class="ttdeci">LstmOptPeepholeParameters m_PeepholeParameters</div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00084">LstmLayer.hpp:84</a></div></div> +<div class="ttc" id="classarmnn_1_1_network_xhtml"><div class="ttname"><a href="classarmnn_1_1_network.xhtml">armnn::Network</a></div><div class="ttdoc">Private implementation of INetwork. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8hpp_source.xhtml#l00028">Network.hpp:28</a></div></div> +<div class="ttc" id="_cpu_tensor_handle_8hpp_xhtml"><div class="ttname"><a href="_cpu_tensor_handle_8hpp.xhtml">CpuTensorHandle.hpp</a></div></div> +<div class="ttc" id="structarmnn_1_1_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="structarmnn_1_1_normalization_descriptor_xhtml_afe1f0f09d49ad2befc01f8789187b7dd"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#afe1f0f09d49ad2befc01f8789187b7dd">armnn::NormalizationDescriptor::m_NormChannelType</a></div><div class="ttdeci">NormalizationAlgorithmChannel m_NormChannelType</div><div class="ttdoc">Normalization channel algorithm to use (Across, Within). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00575">Descriptors.hpp:575</a></div></div> +<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml_a017b2990003a014234f13e999dc7c689"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">armnn::ActivationDescriptor::m_A</a></div><div class="ttdeci">float m_A</div><div class="ttdoc">Alpha upper bound value used by the activation functions. (BoundedReLu, Linear, TanH). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00037">Descriptors.hpp:37</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_descriptor_xhtml_ad474e5c51a0b194ef32e812b86c0cbdb"><div class="ttname"><a href="structarmnn_1_1_lstm_descriptor.xhtml#ad474e5c51a0b194ef32e812b86c0cbdb">armnn::LstmDescriptor::m_CifgEnabled</a></div><div class="ttdeci">bool m_CifgEnabled</div><div class="ttdoc">Enable/disable cifg (coupled input & forget gate). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00867">Descriptors.hpp:867</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_basic_parameters_xhtml_a14ab2bc78421c417c4f97a65b0bd78f9"><div class="ttname"><a href="structarmnn_1_1_lstm_basic_parameters.xhtml#a14ab2bc78421c417c4f97a65b0bd78f9">armnn::LstmBasicParameters::m_InputToCellWeights</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_InputToCellWeights</div><div class="ttdoc">A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00059">LstmLayer.hpp:59</a></div></div> +<div class="ttc" id="structarmnn_1_1_empty_optional_xhtml"><div class="ttname"><a href="structarmnn_1_1_empty_optional.xhtml">armnn::EmptyOptional</a></div><div class="ttdoc">EmptyOptional is used to initialize the Optional class in case we want to have default value for an O...</div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00032">Optional.hpp:32</a></div></div> +<div class="ttc" id="structarmnn_1_1_elementwise_unary_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_elementwise_unary_descriptor.xhtml">armnn::ElementwiseUnaryDescriptor</a></div><div class="ttdoc">A ElementwiseUnaryDescriptor for the ElementwiseUnaryLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00082">Descriptors.hpp:82</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_a0031997bf43bd2747656c31e4977793a"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#a0031997bf43bd2747656c31e4977793a">armnn::Pooling2dDescriptor::m_PoolType</a></div><div class="ttdeci">PoolingAlgorithm m_PoolType</div><div class="ttdoc">The pooling algorithm to use (Max. Average, L2). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00347">Descriptors.hpp:347</a></div></div> +<div class="ttc" id="structarmnn_1_1_l2_normalization_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_l2_normalization_queue_descriptor.xhtml">armnn::L2NormalizationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00316">WorkloadData.hpp:316</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::DepthwiseConvolution2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00484">Descriptors.hpp:484</a></div></div> +<div class="ttc" id="classarmnn_1_1_l2_normalization_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_l2_normalization_layer.xhtml">armnn::L2NormalizationLayer</a></div><div class="ttdoc">This layer represents a L2 normalization operation. </div><div class="ttdef"><b>Definition:</b> <a href="_l2_normalization_layer_8hpp_source.xhtml#l00013">L2NormalizationLayer.hpp:13</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1"><div class="ttname"><a href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456aea39f8662ca778258e9c6a14f26fec5ec1">armnn::Compute::CpuAcc</a></div><div class="ttdoc">CPU Execution: NEON: ArmCompute. </div></div> +<div class="ttc" id="namespacearmnn_xhtml_a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f"><div class="ttname"><a href="namespacearmnn.xhtml#a9a2af2f8c4af4f9efa8e79417d505ac4aaf17c98bbd83c27d6426d2ff3fa81d7f">armnn::ResizeMethod::Bilinear</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_affb5b68b3eba3ed45a06c7cde7781962"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#affb5b68b3eba3ed45a06c7cde7781962">armnn::Pooling2dDescriptor::m_OutputShapeRounding</a></div><div class="ttdeci">OutputShapeRounding m_OutputShapeRounding</div><div class="ttdoc">The rounding method for the output shape. (Floor, Ceiling). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00365">Descriptors.hpp:365</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml">armnn::BatchNormalizationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00269">WorkloadData.hpp:269</a></div></div> +<div class="ttc" id="classarmnn_1_1_input_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_input_layer.xhtml">armnn::InputLayer</a></div><div class="ttdoc">A layer user-provided data can be bound to (e.g. inputs, outputs). </div><div class="ttdef"><b>Definition:</b> <a href="_input_layer_8hpp_source.xhtml#l00013">InputLayer.hpp:13</a></div></div> +<div class="ttc" id="_network_8hpp_xhtml"><div class="ttname"><a href="_network_8hpp.xhtml">Network.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_fully_connected_layer_xhtml_a39925bc24d3afcfb322a46a5884fadb9"><div class="ttname"><a href="classarmnn_1_1_fully_connected_layer.xhtml#a39925bc24d3afcfb322a46a5884fadb9">armnn::FullyConnectedLayer::m_Bias</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_Bias</div><div class="ttdoc">A unique pointer to store Bias values. </div><div class="ttdef"><b>Definition:</b> <a href="_fully_connected_layer_8hpp_source.xhtml#l00021">FullyConnectedLayer.hpp:21</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a6ec9e0eb66d7d6a01240492a0b18104c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">armnn::IConnectableLayer::GetInputSlot</a></div><div class="ttdeci">virtual const IInputSlot & GetInputSlot(unsigned int index) const =0</div><div class="ttdoc">Get a const input slot handle by slot index. </div></div> +<div class="ttc" id="structarmnn_1_1_mean_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_mean_descriptor.xhtml">armnn::MeanDescriptor</a></div><div class="ttdoc">A MeanDescriptor for the MeanLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00877">Descriptors.hpp:877</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a1cfaa710db2a54673b21d2ea2da757c8"><div class="ttname"><a href="namespacearmnn.xhtml#a1cfaa710db2a54673b21d2ea2da757c8">armnn::UnaryOperation</a></div><div class="ttdeci">UnaryOperation</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00087">Types.hpp:87</a></div></div> +<div class="ttc" id="structarmnn_1_1_lstm_opt_peephole_parameters_xhtml_a310e133b0b51b93a74b83008893792e9"><div class="ttname"><a href="structarmnn_1_1_lstm_opt_peephole_parameters.xhtml#a310e133b0b51b93a74b83008893792e9">armnn::LstmOptPeepholeParameters::m_CellToOutputWeights</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_CellToOutputWeights</div><div class="ttdoc">A unique pointer to represent 1D weights tensor with dimensions [num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00051">LstmLayer.hpp:51</a></div></div> +<div class="ttc" id="classarmnn_1_1_layer_xhtml_aea909c7327109228ef618d459015def3"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#aea909c7327109228ef618d459015def3">armnn::Layer::GetDataType</a></div><div class="ttdeci">DataType GetDataType() const</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00273">Layer.cpp:273</a></div></div> +<div class="ttc" id="classarmnn_1_1_prelu_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_prelu_layer.xhtml">armnn::PreluLayer</a></div><div class="ttdef"><b>Definition:</b> <a href="_prelu_layer_8hpp_source.xhtml#l00014">PreluLayer.hpp:14</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a80ac4eda2e7f2757ec9dd96fc96dbd16"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">armnn::IConnectableLayer::GetOutputSlot</a></div><div class="ttdeci">virtual const IOutputSlot & GetOutputSlot(unsigned int index) const =0</div><div class="ttdoc">Get the const output slot handle by slot index. </div></div> +<div class="ttc" id="classarmnn_1_1_convolution2d_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_convolution2d_layer.xhtml">armnn::Convolution2dLayer</a></div><div class="ttdoc">This layer represents a convolution 2d operation. </div><div class="ttdef"><b>Definition:</b> <a href="_convolution2d_layer_8hpp_source.xhtml#l00015">Convolution2dLayer.hpp:15</a></div></div> +<div class="ttc" id="_test_utils_8cpp_xhtml_a0b295acb179f15eb3fb862b32008f782"><div class="ttname"><a href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a></div><div class="ttdeci">void Connect(armnn::IConnectableLayer *from, armnn::IConnectableLayer *to, const armnn::TensorInfo &tensorInfo, unsigned int fromIndex, unsigned int toIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8cpp_source.xhtml#l00012">TestUtils.cpp:12</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a733ae6b70d0bfa43433c3e7606992328"><div class="ttname"><a href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">armnn::CreateDescriptorForConcatenation</a></div><div class="ttdeci">OriginsDescriptor CreateDescriptorForConcatenation(TensorShapeIt first, TensorShapeIt last, unsigned int concatenationDimension)</div><div class="ttdoc">Convenience template to create an OriginsDescriptor to use when creating a ConcatLayer for performing...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00242">Descriptors.hpp:242</a></div></div> +<div class="ttc" id="classarmnn_1_1_graph_xhtml_a9a7209345edfdb2b066b0ceb66414d7c"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml#a9a7209345edfdb2b066b0ceb66414d7c">armnn::Graph::TopologicalSort</a></div><div class="ttdeci">Graph & TopologicalSort()</div><div class="ttdoc">Sorts layers in topological order and return this. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00173">Graph.hpp:173</a></div></div> +<div class="ttc" id="classarmnn_1_1_mean_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_mean_layer.xhtml">armnn::MeanLayer</a></div><div class="ttdoc">This layer represents a mean operation. </div><div class="ttdef"><b>Definition:</b> <a href="_mean_layer_8hpp_source.xhtml#l00014">MeanLayer.hpp:14</a></div></div> +<div class="ttc" id="structarmnn_1_1_pre_compiled_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pre_compiled_queue_descriptor.xhtml">armnn::PreCompiledQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00462">WorkloadData.hpp:462</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_ac1835f8756a9f03c02fcf9664e3a0fce"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">armnn::IOutputSlot::Connect</a></div><div class="ttdeci">virtual int Connect(IInputSlot &destination)=0</div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">armnn::DataLayout::NCHW</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d"><div class="ttname"><a href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d">armnn::NormalizationAlgorithmMethod::LocalBrightness</a></div><div class="ttdoc">Krichevsky 2012: Local Brightness Normalization. </div></div> +<div class="ttc" id="classarmnn_1_1_depthwise_convolution2d_layer_xhtml_a2664044e28e69309ea08ef385fe53903"><div class="ttname"><a href="classarmnn_1_1_depthwise_convolution2d_layer.xhtml#a2664044e28e69309ea08ef385fe53903">armnn::DepthwiseConvolution2dLayer::m_Weight</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_Weight</div><div class="ttdoc">A unique pointer to store Weight values. </div><div class="ttdef"><b>Definition:</b> <a href="_depthwise_convolution2d_layer_8hpp_source.xhtml#l00019">DepthwiseConvolution2dLayer.hpp:19</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling2d_queue_descriptor.xhtml">armnn::Pooling2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00162">WorkloadData.hpp:162</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml">armnn::Pooling2dDescriptor</a></div><div class="ttdoc">A Pooling2dDescriptor for the Pooling2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00313">Descriptors.hpp:313</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_abe18a5033f2ab9c0de82c676b48f5437a810f43f3996922151c39b76143faeecc"><div class="ttname"><a href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437a810f43f3996922151c39b76143faeecc">armnn::NormalizationAlgorithmChannel::Across</a></div></div> +<div class="ttc" id="_file_only_profiling_decorator_tests_8cpp_xhtml_a6560146509197f3e197d8d36f76c1347"><div class="ttname"><a href="_file_only_profiling_decorator_tests_8cpp.xhtml#a6560146509197f3e197d8d36f76c1347">options</a></div><div class="ttdeci">armnn::Runtime::CreationOptions::ExternalProfilingOptions options</div><div class="ttdef"><b>Definition:</b> <a href="_file_only_profiling_decorator_tests_8cpp_source.xhtml#l00045">FileOnlyProfilingDecoratorTests.cpp:45</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml">armnn::NormalizationDescriptor</a></div><div class="ttdoc">A NormalizationDescriptor for the NormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00551">Descriptors.hpp:551</a></div></div> +<div class="ttc" id="structarmnn_1_1_resize_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_resize_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::ResizeDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00751">Descriptors.hpp:751</a></div></div> +<div class="ttc" id="structarmnn_1_1_mean_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_mean_queue_descriptor.xhtml">armnn::MeanQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00246">WorkloadData.hpp:246</a></div></div> +<div class="ttc" id="classarmnn_1_1_layer_xhtml_a3ff62126ec713a2708e5fbaa6146a7de"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a3ff62126ec713a2708e5fbaa6146a7de">armnn::Layer::CreateTensorHandles</a></div><div class="ttdeci">virtual void CreateTensorHandles(const TensorHandleFactoryRegistry &registry, const IWorkloadFactory &factory, const bool IsMemoryManaged=true)</div><div class="ttdef"><b>Definition:</b> <a href="_layer_8cpp_source.xhtml#l00240">Layer.cpp:240</a></div></div> +<div class="ttc" id="classarmnn_1_1_layer_xhtml_a08d1e10a45f15cd0bd02557be35a3864"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml#a08d1e10a45f15cd0bd02557be35a3864">armnn::Layer::CreateWorkload</a></div><div class="ttdeci">virtual std::unique_ptr< IWorkload > CreateWorkload(const IWorkloadFactory &factory) const =0</div></div> +<div class="ttc" id="structarmnn_1_1_lstm_basic_parameters_xhtml_a5a0d8af26a6aad1e5be521ea7dc550eb"><div class="ttname"><a href="structarmnn_1_1_lstm_basic_parameters.xhtml#a5a0d8af26a6aad1e5be521ea7dc550eb">armnn::LstmBasicParameters::m_InputToForgetWeights</a></div><div class="ttdeci">std::unique_ptr< ScopedCpuTensorHandle > m_InputToForgetWeights</div><div class="ttdoc">A unique pointer to represent 2D weights tensor with dimensions [input_size, num_units]. </div><div class="ttdef"><b>Definition:</b> <a href="_lstm_layer_8hpp_source.xhtml#l00057">LstmLayer.hpp:57</a></div></div> +<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml_a28c4c9cb15f6be3499abbc46b356060b"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">armnn::ActivationDescriptor::m_B</a></div><div class="ttdeci">float m_B</div><div class="ttdoc">Beta lower bound value used by the activation functions. (BoundedReLu, Linear, TanH). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00039">Descriptors.hpp:39</a></div></div> +<div class="ttc" id="structarmnn_1_1_softmax_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_softmax_descriptor.xhtml">armnn::SoftmaxDescriptor</a></div><div class="ttdoc">A SoftmaxDescriptor for the SoftmaxLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00123">Descriptors.hpp:123</a></div></div> +<div class="ttc" id="structarmnn_1_1_reshape_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_reshape_queue_descriptor.xhtml">armnn::ReshapeQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00338">WorkloadData.hpp:338</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a8275d51ef9a584feb95726ea0522f6e5"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">armnn::NormalizationDescriptor::m_Beta</a></div><div class="ttdeci">float m_Beta</div><div class="ttdoc">Beta value for the normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00583">Descriptors.hpp:583</a></div></div> +<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_aa70c05f1aad12fbd9d9ec43ea4557b03"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#aa70c05f1aad12fbd9d9ec43ea4557b03">armnn::NormalizationDescriptor::m_NormSize</a></div><div class="ttdeci">uint32_t m_NormSize</div><div class="ttdoc">Depth radius value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00579">Descriptors.hpp:579</a></div></div> +<div class="ttc" id="structarmnn_1_1_activation_descriptor_xhtml_af10fa7883e3579950f477bee92a64844"><div class="ttname"><a href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">armnn::ActivationDescriptor::m_Function</a></div><div class="ttdeci">ActivationFunction m_Function</div><div class="ttdoc">The activation function to use (Sigmoid, TanH, Linear, ReLu, BoundedReLu, SoftReLu, LeakyReLu, Abs, Sqrt, Square). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00035">Descriptors.hpp:35</a></div></div> +<div class="ttc" id="structarmnn_1_1_pooling2d_descriptor_xhtml_ac1fe174bbadfb39a2b636940c2e647c8"><div class="ttname"><a href="structarmnn_1_1_pooling2d_descriptor.xhtml#ac1fe174bbadfb39a2b636940c2e647c8">armnn::Pooling2dDescriptor::m_StrideY</a></div><div class="ttdeci">uint32_t m_StrideY</div><div class="ttdoc">Stride value when proceeding through input for the height dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00363">Descriptors.hpp:363</a></div></div> +<div class="ttc" id="structarmnn_1_1_depthwise_convolution2d_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_depthwise_convolution2d_descriptor.xhtml">armnn::DepthwiseConvolution2dDescriptor</a></div><div class="ttdoc">A DepthwiseConvolution2dDescriptor for the DepthwiseConvolution2dLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00444">Descriptors.hpp:444</a></div></div> +<div class="ttc" id="classarmnn_1_1_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml">armnn::Layer</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00209">Layer.hpp:209</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_activation_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_activation_queue_descriptor.xhtml">armnn::ActivationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00130">WorkloadData.hpp:130</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml">armnn::BatchNormalizationDescriptor</a></div><div class="ttdoc">A BatchNormalizationDescriptor for the BatchNormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00610">Descriptors.hpp:610</a></div></div> +<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_ac18546ebbebbb32fe0a03baa9bf2c600"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#ac18546ebbebbb32fe0a03baa9bf2c600">armnn::Convolution2dDescriptor::m_PadLeft</a></div><div class="ttdeci">uint32_t m_PadLeft</div><div class="ttdoc">Padding left value in the width dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00422">Descriptors.hpp:422</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::DataLayout::NHWC</a></div></div> +<div class="ttc" id="classarmnn_1_1_resize_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_resize_layer.xhtml">armnn::ResizeLayer</a></div><div class="ttdoc">This layer represents a resize operation. </div><div class="ttdef"><b>Definition:</b> <a href="_resize_layer_8hpp_source.xhtml#l00013">ResizeLayer.hpp:13</a></div></div> +<div class="ttc" id="classarmnn_1_1_optimized_network_xhtml"><div class="ttname"><a href="classarmnn_1_1_optimized_network.xhtml">armnn::OptimizedNetwork</a></div><div class="ttdef"><b>Definition:</b> <a href="_network_8hpp_source.xhtml#l00265">Network.hpp:265</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56943a0946e5f15e5e58054b8e7a04a4"><div class="ttname"><a href="namespacearmnn.xhtml#a56943a0946e5f15e5e58054b8e7a04a4">armnn::LayerType</a></div><div class="ttdeci">LayerType</div><div class="ttdef"><b>Definition:</b> <a href="_internal_types_8hpp_source.xhtml#l00014">InternalTypes.hpp:14</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="structarmnn_1_1_normalization_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_normalization_queue_descriptor.xhtml">armnn::NormalizationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00210">WorkloadData.hpp:210</a></div></div> +</div><!-- fragment --></div><!-- contents --> +</div><!-- doc-content --> +<!-- start footer part --> +<div id="nav-path" class="navpath"><!-- id is needed for treeview function! --> + <ul> + <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.xhtml">src</a></li><li class="navelem"><a class="el" href="dir_e0a84d05c80a2ef4231141dcbbeac5c8.xhtml">armnn</a></li><li class="navelem"><a class="el" href="dir_9d86fd1fbecbedf5bdb69c7e7235fe5f.xhtml">test</a></li><li class="navelem"><a class="el" href="_create_workload_8hpp.xhtml">CreateWorkload.hpp</a></li> + <li class="footer">Generated on Fri Mar 13 2020 16:09:08 for ArmNN by + <a href="http://www.doxygen.org/index.html"> + <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.13 </li> + </ul> +</div> +</body> +</html> |