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diff --git a/20.02/_activation_test_impl_8cpp_source.xhtml b/20.02/_activation_test_impl_8cpp_source.xhtml new file mode 100644 index 0000000000..5f05d183bc --- /dev/null +++ b/20.02/_activation_test_impl_8cpp_source.xhtml @@ -0,0 +1,213 @@ +<!-- Copyright (c) 2020 ARM Limited. --> +<!-- --> +<!-- SPDX-License-Identifier: MIT --> +<!-- --> +<!-- HTML header for doxygen 1.8.13--> +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> +<html xmlns="http://www.w3.org/1999/xhtml"> +<head> +<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/> +<meta http-equiv="X-UA-Compatible" content="IE=9"/> +<meta name="generator" content="Doxygen 1.8.13"/> +<meta name="robots" content="NOINDEX, NOFOLLOW" /> +<meta name="viewport" content="width=device-width, initial-scale=1"/> +<title>ArmNN: src/backends/backendsCommon/test/layerTests/ActivationTestImpl.cpp Source File</title> +<link href="tabs.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="jquery.js"></script> +<script type="text/javascript" src="dynsections.js"></script> +<link href="navtree.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="resize.js"></script> +<script type="text/javascript" src="navtreedata.js"></script> +<script type="text/javascript" src="navtree.js"></script> +<script type="text/javascript"> + $(document).ready(initResizable); +</script> +<link href="search/search.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="search/searchdata.js"></script> +<script type="text/javascript" src="search/search.js"></script> +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + extensions: ["tex2jax.js"], + jax: ["input/TeX","output/HTML-CSS"], +}); +</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script> +<link href="doxygen.css" rel="stylesheet" type="text/css" /> +<link href="stylesheet.css" rel="stylesheet" type="text/css"/> +</head> +<body> +<div id="top"><!-- do not remove this div, it is closed by doxygen! --> +<div id="titlearea"> +<table cellspacing="0" cellpadding="0"> + <tbody> + <tr style="height: 56px;"> + <img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 10rem; margin-top: .5rem; margin-left 10px"/> + <td style="padding-left: 0.5em;"> + <div id="projectname"> +  <span id="projectnumber">20.02</span> + </div> + </td> + </tr> + </tbody> +</table> +</div> +<!-- end header part --> +<!-- Generated by Doxygen 1.8.13 --> +<script type="text/javascript"> +var searchBox = new SearchBox("searchBox", "search",false,'Search'); +</script> +<script type="text/javascript" src="menudata.js"></script> +<script type="text/javascript" src="menu.js"></script> +<script type="text/javascript"> +$(function() { + initMenu('',true,false,'search.php','Search'); + $(document).ready(function() { init_search(); }); +}); +</script> +<div id="main-nav"></div> +</div><!-- top --> +<div id="side-nav" class="ui-resizable side-nav-resizable"> + <div id="nav-tree"> + <div id="nav-tree-contents"> + <div id="nav-sync" class="sync"></div> + </div> + </div> + <div id="splitbar" style="-moz-user-select:none;" + class="ui-resizable-handle"> + </div> +</div> +<script type="text/javascript"> +$(document).ready(function(){initNavTree('_activation_test_impl_8cpp_source.xhtml','');}); +</script> +<div id="doc-content"> +<!-- window showing the filter options --> +<div id="MSearchSelectWindow" + onmouseover="return searchBox.OnSearchSelectShow()" + onmouseout="return searchBox.OnSearchSelectHide()" + onkeydown="return searchBox.OnSearchSelectKey(event)"> +</div> + +<!-- iframe showing the search results (closed by default) --> +<div id="MSearchResultsWindow"> +<iframe src="javascript:void(0)" frameborder="0" + name="MSearchResults" id="MSearchResults"> +</iframe> +</div> + +<div class="header"> + <div class="headertitle"> +<div class="title">ActivationTestImpl.cpp</div> </div> +</div><!--header--> +<div class="contents"> +<a href="_activation_test_impl_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include "<a class="code" href="_activation_test_impl_8hpp.xhtml">ActivationTestImpl.hpp</a>"</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <<a class="code" href="_quantize_helper_8hpp.xhtml">QuantizeHelper.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> </div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <<a class="code" href="_activation_fixture_8hpp.xhtml">backendsCommon/test/ActivationFixture.hpp</a>></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <<a class="code" href="_tensor_copy_utils_8hpp.xhtml">backendsCommon/test/TensorCopyUtils.hpp</a>></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <<a class="code" href="_workload_test_utils_8hpp.xhtml">backendsCommon/test/WorkloadTestUtils.hpp</a>></span></div><div class="line"><a name="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="_tensor_helpers_8hpp.xhtml">test/TensorHelpers.hpp</a>></span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> </div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include <boost/multi_array.hpp></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 <algorithm></span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00023"></a><span class="lineno"><a class="line" href="_activation_test_impl_8cpp.xhtml#aaa4e43f7b9a9b14145accdc347bc0e18"> 23</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#aaa4e43f7b9a9b14145accdc347bc0e18">BoundedReLuTestCommon</a>(</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="keywordtype">float</span> upperBound,</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keywordtype">float</span> lowerBound,</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keywordtype">float</span> inputScale,</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  int32_t inputOffset,</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keywordtype">float</span> outputScale,</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  int32_t outputOffset,</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="keyword">const</span> std::vector<T>& inputData,</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keyword">const</span> std::vector<T>& outputExpectedData,</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth,</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight,</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels,</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatchSize)</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> {</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = inputWidth;</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = inputHeight;</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = inputChannels;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputBatchSize = inputBatchSize;</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, ArmnnType);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, ArmnnType);</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  {</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(inputScale);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  inputTensorInfo.SetQuantizationOffset(inputOffset);</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>  outputTensorInfo.SetQuantizationScale(outputScale);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  outputTensorInfo.SetQuantizationOffset(outputOffset);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  }</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> result(inputTensorInfo);</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> </div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo, inputData);</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> </div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="comment">// Setup bounded ReLu.</span></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <a class="code" href="structarmnn_1_1_activation_queue_descriptor.xhtml">armnn::ActivationQueueDescriptor</a> descriptor;</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">armnn::ActivationFunction::BoundedReLu</a>;</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = upperBound;</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = lowerBound;</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>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a4458d75c0db21c6abc941cd93a6a24c5">CreateActivation</a>(descriptor, workloadInfo);</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> </div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  inputHandle->Allocate();</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  outputHandle->Allocate();</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> </div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &input[0][0][0][0]);</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> </div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  workload->Execute();</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> </div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&result.<a class="code" href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a>[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<T, 4>(outputTensorInfo, outputExpectedData);</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> </div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> }</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a418191b7e7caba8173206c0870bc3684"> 91</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a418191b7e7caba8173206c0870bc3684">BoundedReLuUpperAndLowerBoundTest</a>(</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> {</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 4u;</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 5u;</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 1u;</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatchSize = 1;</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>  std::vector<float> input = std::vector<float>{</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  -2.0f, 0.1f, 0.5f, 1.25f,</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  0.786f, 0.9875f, -1.5f, 0.384f,</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  1.0001f, 3.5f, 7.5f, 0.896f,</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  2.126f, 2.0f, 0.3f, 0.15f,</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  0.999f, 1.2f, 0.89f, 6.1f,</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  };</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> </div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="comment">// Calculated manually.</span></div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  std::vector<float> output = std::vector<float>{</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  -1.0f, 0.1f, 0.5f, 1.0f,</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  0.786f, 0.9875f, -1.0f, 0.384f,</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  1.0f, 1.0f, 1.0f, 0.896f,</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  1.0f, 1.0f, 0.3f, 0.15f,</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  0.999f, 1.0f, 0.89f, 1.0f,</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> </div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keywordflow">return</span> BoundedReLuTestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  workloadFactory, memoryManager, 1.0f, -1.0f, 1.0f, 0, 1.0f, 0, input, output,</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  inputWidth, inputHeight, inputChannels, inputBatchSize);</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> }</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> </div><div class="line"><a name="l00122"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a359c1f734f9da1d6459e9d878e5612ba"> 122</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a359c1f734f9da1d6459e9d878e5612ba">BoundedReLuUpperBoundOnlyTest</a>(</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> {</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 4u;</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 5u;</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 1u;</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatchSize = 1;</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  std::vector<float> input = std::vector<float>{</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  -1.0f, 0.1f, 0.5f, 6.25f,</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  0.786f, 5.9875f, -0.5f, 0.384f,</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  6.0001f, 3.5f, 7.5f, 0.896f,</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  2.126f, 12.0f, 0.3f, 0.15f,</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  0.999f, 1.2f, 0.89f, 6.1f,</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  };</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> </div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="comment">// Calculated manually.</span></div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  std::vector<float> output = std::vector<float>{</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  0.0f, 0.1f, 0.5f, 6.0f,</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  0.786f, 5.9875f, 0.0f, 0.384f,</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  6.0f, 3.5f, 6.0f, 0.896f,</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  2.126f, 6.0f, 0.3f, 0.15f,</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  0.999f, 1.2f, 0.89f, 6.0f,</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> </div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keywordflow">return</span> BoundedReLuTestCommon<armnn::DataType::Float32>(</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  workloadFactory, memoryManager, 6.0f, 0.0f, 1.0f, 0, 1.0f, 0, input, output,</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  inputWidth, inputHeight, inputChannels, inputBatchSize);</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> </div><div class="line"><a name="l00153"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a7aa10bded0d26089e0bc4333ada10064"> 153</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a7aa10bded0d26089e0bc4333ada10064">BoundedReLuUint8UpperBoundOnlyTest</a>(</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> {</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 3u;</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 2u;</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 1u;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatchSize = 1;</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> </div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  std::vector<uint8_t> input = std::vector<uint8_t>{</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  51, 124, 28,</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  251, 8, 92</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  };</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> </div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="comment">// Calculated manually.</span></div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  std::vector<uint8_t> output = std::vector<uint8_t>{</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  0, 122, 0,</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  255, 0, 58</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  };</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> </div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keywordtype">float</span> inputScale = 12.0f / 255.0f;</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  int32_t inputOffset = 63;</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keywordtype">float</span> outputScale = 6.0f / 255.0f;</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  int32_t outputOffset = 0;</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> </div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordflow">return</span> BoundedReLuTestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  workloadFactory, memoryManager, 6.0f, 0.0f,</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  inputScale, inputOffset, outputScale, outputOffset,</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  input, output, inputWidth, inputHeight, inputChannels, inputBatchSize);</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> }</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> </div><div class="line"><a name="l00184"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a5b674a831a483affefe085d350094b8b"> 184</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a0e868e8fa03ce4c4674b007eae5dc1a2">BoundedReLuUint8UpperAndLowerBoundTest</a>(</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> {</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 3u;</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 2u;</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 1u;</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatchSize = 1;</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>  std::vector<uint8_t> input = std::vector<uint8_t>{</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  51, 230, 28,</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  251, 8, 92</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> </div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="comment">// Calculated manually.</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  std::vector<uint8_t> output = std::vector<uint8_t>{</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  51, 192, 32,</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  192, 32, 92</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> </div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  int32_t inputOffset = 112;</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keywordtype">float</span> inputScale = 0.0125f;</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> </div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="keywordflow">return</span> BoundedReLuTestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  workloadFactory, memoryManager, 1.0f, -1.0f,</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  inputScale, inputOffset, inputScale, inputOffset, <span class="comment">// Input/output scale & offset same.</span></div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  input, output, inputWidth, inputHeight, inputChannels, inputBatchSize);</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> }</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span> </div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span> <span class="keyword">namespace</span></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> </div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span> <span class="keyword">struct </span>BoundedReLuRandomInputTestTraits</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>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 31u;</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 19u;</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 4u;</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatchSize = 2;</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span> </div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = inputHeight;</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = inputWidth;</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = inputChannels;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputBatchSize = inputBatchSize;</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> </div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#ae52296dff1f4879854f320d59f92574e">GetInputTensorInfo</a>()</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  {</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ inputBatchSize, inputChannels, inputHeight, inputWidth },</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  }</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> </div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keyword">static</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> GetOutputTensorInfo()</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  {</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>({ outputBatchSize, outputChannels, outputHeight, outputWidth },</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  }</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> };</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> boost::multi_array<float, 4> BoundedReLuRandomInputTest(</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keywordtype">float</span> lowerBound,</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="keywordtype">float</span> upperBound,</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a>& activationDescriptor)</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span> {</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo = <a class="code" href="namespacearmnn.xhtml#ae52296dff1f4879854f320d59f92574e">BoundedReLuRandomInputTestTraits::GetInputTensorInfo</a>();</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo = BoundedReLuRandomInputTestTraits::GetOutputTensorInfo();</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> </div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  boost::multi_array<float, 4> output(GetTensorShapeAsArray<4>(outputTensorInfo));</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span> </div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="comment">// Min/max random values passed to MakeRandomTensor are purposely outside of the ReLu</span></div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="comment">// range [lowerBound, upperBound].</span></div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keyword">auto</span> input = MakeRandomTensor<float, 4>(inputTensorInfo, 4605828, lowerBound - 5.0f, upperBound * 2.0f);</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span> </div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> </div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="comment">// Set up bounded ReLu.</span></div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <a class="code" href="structarmnn_1_1_activation_queue_descriptor.xhtml">armnn::ActivationQueueDescriptor</a> descriptor;</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a> = activationDescriptor;</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> </div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a4458d75c0db21c6abc941cd93a6a24c5">CreateActivation</a>(descriptor, workloadInfo);</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> </div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  inputHandle->Allocate();</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  outputHandle->Allocate();</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span> </div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &input[0][0][0][0]);</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span> </div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  workload->Execute();</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span> </div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&output[0][0][0][0], outputHandle.get());</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="keywordflow">return</span> output;</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span> }</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> </div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> } <span class="comment">// namespace</span></div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> </div><div class="line"><a name="l00284"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a7aaeeaa0a8683fae56caa66849228a87"> 284</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a7aaeeaa0a8683fae56caa66849228a87">CompareBoundedReLuTest</a>(</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& refWorkloadFactory,</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="keywordtype">float</span> upperBound,</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="keywordtype">float</span> lowerBound)</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="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> result(BoundedReLuRandomInputTestTraits::GetOutputTensorInfo());</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span> </div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <a class="code" href="structarmnn_1_1_activation_descriptor.xhtml">armnn::ActivationDescriptor</a> activationDescriptor;</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">armnn::ActivationFunction::BoundedReLu</a>;</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = upperBound;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  activationDescriptor.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = lowerBound;</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span> </div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  result.<a class="code" href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a> = BoundedReLuRandomInputTest(</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  workloadFactory, memoryManager, 0.0f, upperBound, activationDescriptor);</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = BoundedReLuRandomInputTest(</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  refWorkloadFactory, <span class="keyword">nullptr</span>, 0.0f, upperBound, activationDescriptor);</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span> </div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span> }</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span> </div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00307"></a><span class="lineno"><a class="line" href="_activation_test_impl_8cpp.xhtml#a22562086e72d244fd7cf4156b958c134"> 307</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T,4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a22562086e72d244fd7cf4156b958c134">ConstantLinearActivationTestCommon</a>(</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="keywordtype">float</span> qScale = 0.0f,</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  int32_t qOffset = 0)</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span> {</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 20;</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 17;</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 3;</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 5;</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span> </div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span> </div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape[] = {batchSize, inputChannels, inputHeight, inputWidth};</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span> </div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape, ArmnnType);</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape, ArmnnType);</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="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  {</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  }</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span> </div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> ret(outputTensorInfo);</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span> </div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span> </div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="comment">// Do linear activation that should leave the tensor unchanged.</span></div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <a class="code" href="structarmnn_1_1_activation_queue_descriptor.xhtml">armnn::ActivationQueueDescriptor</a> data;</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = 1.0f;</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = 0.0f;</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa32a843da6ea40ab3b17a3421ccdf671b">armnn::ActivationFunction::Linear</a>;</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span> </div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a4458d75c0db21c6abc941cd93a6a24c5">CreateActivation</a>(data, info);</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span> </div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  inputHandle->Allocate();</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  outputHandle->Allocate();</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span> </div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 7123561);</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &input[0][0][0][0]);</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span> </div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  workload->Execute();</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span> </div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.<a class="code" href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a>[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span> </div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <span class="comment">// Ensure output equals input.</span></div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  ret.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = input;</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span> </div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span> }</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span> </div><div class="line"><a name="l00368"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a52af2639a8f96fbbc86343ea8914033a"> 368</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a52af2639a8f96fbbc86343ea8914033a">ConstantLinearActivationTest</a>(</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span> {</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  <span class="keywordflow">return</span> ConstantLinearActivationTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager);</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span> }</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span> </div><div class="line"><a name="l00375"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a34b322827b0d8ff9f8b3b8fb9410f7d3"> 375</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a34b322827b0d8ff9f8b3b8fb9410f7d3">ConstantLinearActivationUint8Test</a>(</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span> {</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <span class="keywordflow">return</span> ConstantLinearActivationTestCommon<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  workloadFactory, memoryManager, 4.0f, 3);</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span> }</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span> </div><div class="line"><a name="l00383"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a32a6595835f4cb5e93fec4182ada51bc"> 383</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a32a6595835f4cb5e93fec4182ada51bc">ConstantLinearActivationInt16Test</a>(</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span> {</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <span class="keywordflow">return</span> ConstantLinearActivationTestCommon<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  workloadFactory, memoryManager, 0.1f, 0);</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span> }</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span> </div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00392"></a><span class="lineno"><a class="line" href="_activation_test_impl_8cpp.xhtml#aaeea20fa5e5934ea49b8f764526a2d98"> 392</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#aaeea20fa5e5934ea49b8f764526a2d98">SimpleActivationTest</a>(</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9ea">armnn::ActivationFunction</a> activationFunction,</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <span class="keywordtype">float</span> activationParameterA,</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="keywordtype">float</span> activationParameterB,</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <span class="keywordtype">float</span> scale,</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  int32_t offset,</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keyword">const</span> std::vector<float>& inputData,</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="keywordtype">float</span> outScale,</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  int32_t outOffset,</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  <span class="keyword">const</span> std::vector<float>& outputExpectedData)</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span> {</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 16u;</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 1u;</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 1u;</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputBatchSize = 1u;</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span> </div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = inputWidth;</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = inputHeight;</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = inputChannels;</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  constexpr <span class="keyword">static</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputBatchSize = inputBatchSize;</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span> </div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, ArmnnType);</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, ArmnnType);</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span> </div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  {</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(scale);</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  inputTensorInfo.SetQuantizationOffset(offset);</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  outputTensorInfo.SetQuantizationScale(outScale);</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  outputTensorInfo.SetQuantizationOffset(outOffset);</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> </div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> result(inputTensorInfo);</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span> </div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <span class="keyword">auto</span> input = MakeTensor<T, 4>(inputTensorInfo, armnnUtils::QuantizedVector<T>(inputData, scale, offset));</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span> </div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span> </div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <span class="comment">// Setup bounded ReLu.</span></div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  <a class="code" href="structarmnn_1_1_activation_queue_descriptor.xhtml">armnn::ActivationQueueDescriptor</a> descriptor;</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());</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>  descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = activationFunction;</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = activationParameterA;</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = activationParameterB;</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span> </div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a4458d75c0db21c6abc941cd93a6a24c5">CreateActivation</a>(descriptor, workloadInfo);</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>  inputHandle->Allocate();</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  outputHandle->Allocate();</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span> </div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &input[0][0][0][0]);</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span> </div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  workload->Execute();</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span> </div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&result.<a class="code" href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a>[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span> </div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <span class="comment">// Calculated manually.</span></div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> =</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  MakeTensor<T, 4>(outputTensorInfo, armnnUtils::QuantizedVector<T>(outputExpectedData, outScale, outOffset));</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span> </div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <span class="keywordflow">return</span> result;</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> </div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00464"></a><span class="lineno"><a class="line" href="_activation_test_impl_8cpp.xhtml#a1020322feb8c6fe89ced59fcca8277c4"> 464</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a1020322feb8c6fe89ced59fcca8277c4">SimpleSigmoidTestCommon</a>(</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  int32_t qOffset)</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span> {</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  std::vector<float> inputData =</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  {</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  -0.1f, -0.2f, -0.3f, -0.4f,</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  0.1f, 0.2f, 0.3f, 0.4f,</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  -1.0f, -2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  1.0f, 2.0f, 3.0f, 4.0f</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  };</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span> </div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  <span class="comment">// Calculate output values for input.</span></div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="keyword">auto</span> f = [](<span class="keywordtype">float</span> value)</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="keywordflow">return</span> 1.0f / (1.0f + std::exp(-value));</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  };</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  std::vector<float> outputExpectedData(inputData.size());</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);</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>  <span class="keywordflow">return</span> SimpleActivationTest<ArmnnType>(workloadFactory,</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  memoryManager,</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">armnn::ActivationFunction::Sigmoid</a>,</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  0.f,</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  0.f,</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  qScale,</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  qOffset,</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  inputData,</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  1.f / 256.f,</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  0,</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  outputExpectedData);</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span> }</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span> </div><div class="line"><a name="l00499"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#aa87c451f7a773fd4ec9cdf11c20d7a58"> 499</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#aa87c451f7a773fd4ec9cdf11c20d7a58">SimpleSigmoidTest</a>(</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span> {</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  <span class="keywordflow">return</span> SimpleSigmoidTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0);</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> </div><div class="line"><a name="l00506"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a0889979f9ffb67b036c3928c6e94af50"> 506</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a0889979f9ffb67b036c3928c6e94af50">SimpleSigmoidUint8Test</a>(</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span> {</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  <span class="keywordflow">return</span> SimpleSigmoidTestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager, 0.1f, 50);</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span> }</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span> </div><div class="line"><a name="l00513"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a6558a4306d758625ab7804e9cb70b058"> 513</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a6558a4306d758625ab7804e9cb70b058">SimpleSigmoidInt16Test</a>(</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span> {</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  <span class="keywordflow">return</span> SimpleSigmoidTestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager, 0.1f, 0);</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span> }</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span> </div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00521"></a><span class="lineno"><a class="line" href="_activation_test_impl_8cpp.xhtml#acf22306b81aa054c64c48730b2786f96"> 521</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#acf22306b81aa054c64c48730b2786f96">ReLuTestCommon</a>(</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  int32_t qOffset)</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span> {</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  std::vector<float> inputData = {</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  -0.1f, -0.2f, -0.3f, -0.4f,</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  0.1f, 0.2f, 0.3f, 0.4f,</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  -1.0f, -2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  1.0f, 2.0f, 3.0f, 4.0f</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  };</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span> </div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <span class="comment">// Calculate output values for input.</span></div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <span class="keyword">auto</span> f = [](<span class="keywordtype">float</span> value)</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  {</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <span class="keywordflow">return</span> std::fmax(0.0f, value);</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  };</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  std::vector<float> outputExpectedData(inputData.size());</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span> </div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="keywordflow">return</span> SimpleActivationTest<ArmnnType>(workloadFactory,</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  memoryManager,</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">armnn::ActivationFunction::ReLu</a>,</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  0.f,</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  0.f,</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  qScale,</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  qOffset,</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  inputData,</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  qScale,</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  qOffset,</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  outputExpectedData);</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span> }</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span> </div><div class="line"><a name="l00555"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a20b01cc1552ab2c3abd70166fdd35faf"> 555</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a20b01cc1552ab2c3abd70166fdd35faf">ReLuInt16Test</a>(</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span> {</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  <span class="keywordflow">return</span> ReLuTestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager, 0.1f, 0);</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span> }</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span> </div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span> </div><div class="line"><a name="l00563"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#aa986502e638eba65543c1cbb01467d26"> 563</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#aa986502e638eba65543c1cbb01467d26">ReLuUint8Test</a>(</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span> {</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  <span class="keywordflow">return</span> ReLuTestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager, 0.1f, 0);</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span> }</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span> </div><div class="line"><a name="l00570"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a58872a37a87790e3a3f91ee254ce304a"> 570</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a58872a37a87790e3a3f91ee254ce304a">ReLuTest</a>(</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span> {</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  <span class="keywordflow">return</span> ReLuTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.1f, 0);</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span> }</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span> </div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span> </div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00579"></a><span class="lineno"><a class="line" href="_activation_test_impl_8cpp.xhtml#a2634923ff28734237c27fcc7c009ce9d"> 579</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#aaa4e43f7b9a9b14145accdc347bc0e18">BoundedReLuTestCommon</a>(</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  int32_t qOffset)</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span> {</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  std::vector<float> inputData = {</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  -0.1f, -0.2f, -0.3f, -0.4f,</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  0.1f, 0.2f, 0.3f, 0.4f,</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  -1.0f, -2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  1.0f, 2.0f, 3.0f, 4.0f</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  };</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> a = 1.0f;</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> b = -1.0f;</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <span class="comment">// Calculate output values for input.</span></div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  <span class="keyword">auto</span> f = [a, b](<span class="keywordtype">float</span> value)</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  {</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  <span class="keywordflow">return</span> std::min(a, std::max(b, value));</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  };</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  std::vector<float> outputExpectedData(inputData.size());</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span> </div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  <span class="keywordflow">return</span> SimpleActivationTest<ArmnnType>(workloadFactory,</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  memoryManager,</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">armnn::ActivationFunction::BoundedReLu</a>,</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  a,</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  b,</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  qScale,</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  qOffset,</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  inputData,</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  qScale,</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  qOffset,</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  outputExpectedData);</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span> }</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span> </div><div class="line"><a name="l00614"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#ae42bb4023d8578a27159c95dd4b33b28"> 614</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#ae42bb4023d8578a27159c95dd4b33b28">BoundedReLuInt16Test</a>(</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span> {</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  <span class="keywordflow">return</span> ReLuTestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager, 0.1f, 0);</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span> }</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span> </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> </div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00624"></a><span class="lineno"><a class="line" href="_activation_test_impl_8cpp.xhtml#a4b43ab0b58fc8d4ad51b1b71c0e35622"> 624</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a4b43ab0b58fc8d4ad51b1b71c0e35622">SoftReLuTestCommon</a>(</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  int32_t qOffset)</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>  std::vector<float> inputData = {</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  -0.1f, -0.2f, -0.3f, -0.4f,</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  0.1f, 0.2f, 0.3f, 0.4f,</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  -1.0f, -2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  1.0f, 2.0f, 3.0f, 4.0f</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  };</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span> </div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  <span class="comment">// Calculate output values for input.</span></div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <span class="keyword">auto</span> f = [](<span class="keywordtype">float</span> value)</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  {</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <span class="keywordflow">return</span> std::log(1.0f + std::exp(value));</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  };</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  std::vector<float> outputExpectedData(inputData.size());</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span> </div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  <span class="keywordflow">return</span> SimpleActivationTest<ArmnnType>(workloadFactory,</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  memoryManager,</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa6bba7052636d1740303b1b2ef3b53fef">armnn::ActivationFunction::SoftReLu</a>,</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  0.f,</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  0.f,</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  qScale,</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  qOffset,</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  inputData,</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  qScale,</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  qOffset,</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  outputExpectedData);</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span> }</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span> </div><div class="line"><a name="l00658"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a8bfdab68fed1467b8720cceb47881236"> 658</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a8bfdab68fed1467b8720cceb47881236">SoftReLuTest</a>(</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span> {</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  <span class="keywordflow">return</span> SoftReLuTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.1f, 0);</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span> }</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span> </div><div class="line"><a name="l00665"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a732229b22cff2a8f96798c38832cab92"> 665</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a732229b22cff2a8f96798c38832cab92">SoftReLuUint8Test</a>(</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span> {</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  <span class="keywordflow">return</span> SoftReLuTestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager, 0.0625f, 64);</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span> }</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span> </div><div class="line"><a name="l00672"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a641db2befcd47ac97af966e20b1c4c2c"> 672</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a641db2befcd47ac97af966e20b1c4c2c">SoftReLuInt16Test</a>(</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span> {</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  <span class="keywordflow">return</span> SoftReLuTestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager, 0.1f, 0);</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span> }</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span> </div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00680"></a><span class="lineno"><a class="line" href="_activation_test_impl_8cpp.xhtml#a6e45714708a6daf8688ef6ca58e54827"> 680</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a6e45714708a6daf8688ef6ca58e54827">LeakyReLuTestCommon</a>(</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  int32_t qOffset)</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span> {</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  std::vector<float> inputData = {</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  -0.1f, -0.2f, -0.3f, -0.4f,</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  0.1f, 0.2f, 0.3f, 0.4f,</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  -1.0f, -2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  1.0f, 2.0f, 3.0f, 4.0f</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  };</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span> </div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> a = 0.01f;</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  <span class="comment">// Calculate output values for input.</span></div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  <span class="keyword">auto</span> f = [a](<span class="keywordtype">float</span> value)</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  {</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <span class="keywordflow">return</span> value > 0.0f ? value : (value * a);</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  };</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  std::vector<float> outputExpectedData(inputData.size());</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span> </div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  <span class="keywordflow">return</span> SimpleActivationTest<ArmnnType>(workloadFactory,</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  memoryManager,</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaacb7667f5ec2f6e8a5943b781ba6c2735">armnn::ActivationFunction::LeakyReLu</a>,</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  a,</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  0.f,</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  qScale,</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  qOffset,</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  inputData,</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  qScale,</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  qOffset,</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  outputExpectedData);</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span> }</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span> </div><div class="line"><a name="l00715"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a0120909fa6b3032270399355f14654de"> 715</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a0120909fa6b3032270399355f14654de">LeakyReLuTest</a>(</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span> {</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  <span class="keywordflow">return</span> LeakyReLuTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.1f, 0);</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span> }</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span> </div><div class="line"><a name="l00722"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#af9293a4d81453abbe8cbdc788c290943"> 722</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#af9293a4d81453abbe8cbdc788c290943">LeakyReLuUint8Test</a>(</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span> {</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  <span class="keywordflow">return</span> LeakyReLuTestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager, 0.0625f, 64);</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span> }</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span> </div><div class="line"><a name="l00729"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#ac01b6901c3f2921c998aff77a8362f87"> 729</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#ac01b6901c3f2921c998aff77a8362f87">LeakyReLuInt16Test</a>(</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span> {</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  <span class="keywordflow">return</span> LeakyReLuTestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager, 0.1f, 0);</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span> }</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span> </div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00737"></a><span class="lineno"><a class="line" href="_activation_test_impl_8cpp.xhtml#aa8c2d170a4b51447f575183cee9579ab"> 737</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#aa8c2d170a4b51447f575183cee9579ab">AbsTestCommon</a>(</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>  int32_t qOffset)</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span> {</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  std::vector<float> inputData = {</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  -0.1f, -0.2f, -0.3f, -0.4f,</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  0.1f, 0.2f, 0.3f, 0.4f,</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  -1.0f, -2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  1.0f, 2.0f, 3.0f, 4.0f</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  };</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span> </div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  <span class="comment">// Calculate output values for input.</span></div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  <span class="keyword">auto</span> f = [](<span class="keywordtype">float</span> value)</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  {</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  <span class="keywordflow">return</span> std::abs(value);</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  };</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  std::vector<float> outputExpectedData(inputData.size());</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span> </div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  <span class="keywordflow">return</span> SimpleActivationTest<ArmnnType>(workloadFactory,</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  memoryManager,</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa1e34af023adeb7d5f484f8eb4b9826b6">armnn::ActivationFunction::Abs</a>,</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  0.f,</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  0.f,</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  qScale,</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  qOffset,</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  inputData,</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  qScale,</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  qOffset,</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  outputExpectedData);</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span> }</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span> </div><div class="line"><a name="l00771"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a31872d5729b4d7734c1eb0d189a0eece"> 771</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a31872d5729b4d7734c1eb0d189a0eece">AbsTest</a>(</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span> {</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  <span class="keywordflow">return</span> AbsTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.1f, 0);</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span> }</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span> </div><div class="line"><a name="l00778"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a11baf4886951944fcf149e2a92197e58"> 778</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a11baf4886951944fcf149e2a92197e58">AbsUint8Test</a>(</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span> {</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  <span class="keywordflow">return</span> AbsTestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager, 0.0625f, 64);</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span> }</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span> </div><div class="line"><a name="l00785"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a8dd4b2ac72e85dcfeb8540b7d5649b47"> 785</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a8dd4b2ac72e85dcfeb8540b7d5649b47">AbsInt16Test</a>(</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span> {</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  <span class="keywordflow">return</span> AbsTestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager, 0.1f, 0);</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> </div><div class="line"><a name="l00792"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a86f53855f5ab422f4e035b1aa11676f8"> 792</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 5></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a86f53855f5ab422f4e035b1aa11676f8">SqrtNNTest</a>(</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span> {</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> inputDataSize = 120;</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  std::vector<float> inputData(inputDataSize);</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span> </div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0u; i < inputDataSize; ++i)</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  {</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  inputData[i] = <span class="keyword">static_cast<</span><span class="keywordtype">float</span><span class="keyword">></span>(i) / 10;</div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  }</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span> </div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  <span class="keyword">auto</span> f = [](<span class="keywordtype">float</span> value)</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  {</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  <span class="keywordflow">return</span> std::sqrt(value);</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  };</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  std::vector<float> outputExpectedData(inputDataSize);</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo(</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  { 1u, 2u, 3u, 4u, 5u }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo(</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  { 1u, 2u, 3u, 4u, 5u }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span> </div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 5></a> result(inputTensorInfo);</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span> </div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  <span class="keyword">auto</span> input = MakeTensor<float, 5>(inputTensorInfo, inputData);</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span> </div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span> </div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  <a class="code" href="structarmnn_1_1_activation_queue_descriptor.xhtml">armnn::ActivationQueueDescriptor</a> descriptor;</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span> </div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  descriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaae77f3ad25595e35b327334d89410054">armnn::ActivationFunction::Sqrt</a>;</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span> </div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a4458d75c0db21c6abc941cd93a6a24c5">CreateActivation</a>(descriptor, workloadInfo);</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span> </div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>  inputHandle->Allocate();</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  outputHandle->Allocate();</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span> </div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &input[0][0][0][0][0]);</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span> </div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  workload->Execute();</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>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&result.output[0][0][0][0][0], outputHandle.get());</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span> </div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  <span class="comment">// Calculated manually.</span></div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  result.outputExpected = MakeTensor<float, 5>(outputTensorInfo, outputExpectedData);</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span> </div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span> };</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span> </div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00849"></a><span class="lineno"><a class="line" href="_activation_test_impl_8cpp.xhtml#aab2458914aa40f83ba027de7a8c07d06"> 849</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#aab2458914aa40f83ba027de7a8c07d06">SqrtTestCommon</a>(</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>  int32_t qOffset)</div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span> {</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  std::vector<float> inputData = {</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  0.1f, 0.2f, 0.3f, 0.4f,</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  0.1f, 0.2f, 0.3f, 0.4f,</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  1.0f, 2.0f, 3.0f, 4.0f,</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  1.0f, 2.0f, 3.0f, 4.0f</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  };</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>  <span class="comment">// Calculate output values for input.</span></div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  <span class="keyword">auto</span> f = [](<span class="keywordtype">float</span> value)</div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  {</div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  <span class="keywordflow">return</span> std::sqrt(value);</div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  };</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  std::vector<float> outputExpectedData(inputData.size());</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span> </div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  <span class="keywordflow">return</span> SimpleActivationTest<ArmnnType>(workloadFactory,</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>  memoryManager,</div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaae77f3ad25595e35b327334d89410054">armnn::ActivationFunction::Sqrt</a>,</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  0.f,</div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span>  0.f,</div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  qScale,</div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  qOffset,</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span>  inputData,</div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  qScale,</div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  qOffset,</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span>  outputExpectedData);</div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span> }</div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span> </div><div class="line"><a name="l00883"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#ad3928f2c56ed15642ff6306cc6823ebd"> 883</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#ad3928f2c56ed15642ff6306cc6823ebd">SqrtTest</a>(</div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span> {</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  <span class="keywordflow">return</span> SqrtTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.1f, 0);</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> </div><div class="line"><a name="l00890"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a6403e38cfee03672c164e3cba9863147"> 890</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a6403e38cfee03672c164e3cba9863147">SqrtUint8Test</a>(</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span> {</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span>  <span class="keywordflow">return</span> SqrtTestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager, 0.0625f, 64);</div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span> }</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span> </div><div class="line"><a name="l00897"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a8b855f5d3e8aab93decfa2bed46fc4cf"> 897</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a8b855f5d3e8aab93decfa2bed46fc4cf">SqrtInt16Test</a>(</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span> {</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  <span class="keywordflow">return</span> SqrtTestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager, 0.1f, 0);</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> </div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00905"></a><span class="lineno"><a class="line" href="_activation_test_impl_8cpp.xhtml#a26032da34ce1e283ae30d05ea3bbb103"> 905</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a26032da34ce1e283ae30d05ea3bbb103">SquareTestCommon</a>(</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  int32_t qOffset)</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span> {</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  std::vector<float> inputData = {</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span>  -0.1f, -0.2f, -0.3f, -0.4f,</div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span>  0.1f, 0.2f, 0.3f, 0.4f,</div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span>  -1.0f, -2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span>  1.0f, 2.0f, 3.0f, 4.0f</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> </div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  <span class="comment">// Calculate output values for input.</span></div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  <span class="keyword">auto</span> f = [](<span class="keywordtype">float</span> value)</div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  {</div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span>  <span class="keywordflow">return</span> std::pow(value,2);</div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  };</div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  std::vector<float> outputExpectedData(inputData.size());</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>  std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);</div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span> </div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  <span class="keywordflow">return</span> SimpleActivationTest<ArmnnType>(workloadFactory,</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  memoryManager,</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaceb46ca115d05c51aa5a16a8867c3304">armnn::ActivationFunction::Square</a>,</div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  0.f,</div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span>  0.f,</div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  qScale,</div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span>  qOffset,</div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span>  inputData,</div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  qScale,</div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  qOffset,</div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  outputExpectedData);</div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span> }</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span> </div><div class="line"><a name="l00939"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a6584d436388485a5bd9252430a0af5b6"> 939</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a6584d436388485a5bd9252430a0af5b6">SquareTest</a>(</div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span> {</div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  <span class="keywordflow">return</span> SquareTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.1f, 0);</div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span> }</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span> </div><div class="line"><a name="l00946"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a61fffaf40ad721073b70c350174d0ff3"> 946</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a61fffaf40ad721073b70c350174d0ff3">SquareUint8Test</a>(</div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span> {</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  <span class="keywordflow">return</span> SquareTestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager, 0.0625f, 64);</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span> }</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span> </div><div class="line"><a name="l00953"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a26219b66822d57b9fcce7a2504d1fca6"> 953</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a26219b66822d57b9fcce7a2504d1fca6">SquareInt16Test</a>(</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span> {</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span>  <span class="keywordflow">return</span> SquareTestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager, 0.1f, 0);</div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span> }</div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span> </div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00961"></a><span class="lineno"><a class="line" href="_activation_test_impl_8cpp.xhtml#a65aa329dc6abc6cf9dfb6177f42595de"> 961</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a65aa329dc6abc6cf9dfb6177f42595de">TanhTestCommon</a>(</div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span>  int32_t qOffset)</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span> {</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  std::vector<float> inputData = {</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  -0.1f, -0.2f, -0.3f, -0.4f,</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span>  0.1f, 0.2f, 0.3f, 0.4f,</div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  -1.0f, -2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  1.0f, 2.0f, 3.0f, 4.0f</div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span>  };</div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span> </div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> a = 2.0f;</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> b = 3.0f;</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  <span class="comment">// Calculate output values for input.</span></div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span>  <span class="keyword">auto</span> f = [a, b](<span class="keywordtype">float</span> value)</div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span>  {</div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  <span class="keywordflow">return</span> a * tanhf(b * value);</div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  };</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  std::vector<float> outputExpectedData(inputData.size());</div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);</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="keywordflow">return</span> SimpleActivationTest<ArmnnType>(workloadFactory,</div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  memoryManager,</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">armnn::ActivationFunction::TanH</a>,</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span>  a,</div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span>  b,</div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  qScale,</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span>  qOffset,</div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  inputData,</div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  qScale,</div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span>  qOffset,</div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span>  outputExpectedData);</div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span> }</div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span> </div><div class="line"><a name="l00997"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a923aa3e41cd11f5eeb7cc973fd8d3c76"> 997</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a923aa3e41cd11f5eeb7cc973fd8d3c76">TanhTest</a>(</div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span> {</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>  <span class="keywordflow">return</span> TanhTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.1f, 0);</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span> }</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span> </div><div class="line"><a name="l01004"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#abe9073d08e150e3dd5e156af7ea8faa5"> 1004</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#abe9073d08e150e3dd5e156af7ea8faa5">TanhUint8Test</a>(</div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span> {</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  <span class="keywordflow">return</span> TanhTestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager, 0.1f, 64);</div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span> }</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span> </div><div class="line"><a name="l01011"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#aacd820bdf2307a2aa667db2899283035"> 1011</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#aacd820bdf2307a2aa667db2899283035">TanhInt16Test</a>(</div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span> {</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>  <span class="keywordflow">return</span> TanhTestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager, 0.1f, 0);</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> </div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span> </div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l01020"></a><span class="lineno"><a class="line" href="_activation_test_impl_8cpp.xhtml#ab653abd1ebff85e413b3647d2408f3b1"> 1020</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#ab653abd1ebff85e413b3647d2408f3b1">EluTestCommon</a>(</div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span>  int32_t qOffset)</div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span> {</div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  std::vector<float> inputData = {</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  -0.1f, -0.2f, -0.3f, -0.4f,</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>  0.1f, 0.2f, 0.3f, 0.4f,</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  -1.0f, -2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span>  1.0f, 2.0f, 3.0f, 4.0f</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>  };</div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span> </div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span> </div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> a = 0.01f;</div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>  <span class="comment">// Calculate output values for input.</span></div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span>  <span class="keyword">auto</span> f = [a](<span class="keywordtype">float</span> value)</div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>  {</div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>  <span class="keywordflow">return</span> (value >= 0) ? value : a * (expf(value) - 1);</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>  std::vector<float> outputExpectedData(inputData.size());</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span>  std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);</div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span> </div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  <span class="keywordflow">return</span> SimpleActivationTest<ArmnnType>(workloadFactory,</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>  memoryManager,</div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaed67cf7d54c570e4c4891800f085f41d">armnn::ActivationFunction::Elu</a>,</div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>  a,</div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span>  0.0f,</div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span>  qScale,</div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>  qOffset,</div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span>  inputData,</div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>  qScale,</div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>  qOffset,</div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>  outputExpectedData);</div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span> }</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span> </div><div class="line"><a name="l01056"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a96ba4985a8fff8c04e6585e866256868"> 1056</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a96ba4985a8fff8c04e6585e866256868">EluTest</a>(</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span> {</div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>  <span class="keywordflow">return</span> EluTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.1f, 0);</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"><a class="line" href="_activation_test_impl_8hpp.xhtml#a4f95ca2d87f1d36d0a41d6a0cf56151b"> 1063</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a4f95ca2d87f1d36d0a41d6a0cf56151b">EluUint8Test</a>(</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span> {</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span>  <span class="keywordflow">return</span> EluTestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager, 0.1f, 64);</div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</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"><a class="line" href="_activation_test_impl_8hpp.xhtml#a205500c03971e6ed3aae6e07afdaf145"> 1070</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a205500c03971e6ed3aae6e07afdaf145">EluInt16Test</a>(</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span> {</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>  <span class="keywordflow">return</span> EluTestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager, 0.1f, 0);</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span> }</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span> </div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span> </div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l01079"></a><span class="lineno"><a class="line" href="_activation_test_impl_8cpp.xhtml#a1fcf6512fb0db8090ce19979a9ac6472"> 1079</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a1fcf6512fb0db8090ce19979a9ac6472">HardSwishTestCommon</a>(</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  int32_t qOffset)</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>  std::vector<float> inputData = {</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>  -0.1f, -0.2f, -0.3f, -0.4f,</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span>  0.1f, 0.2f, 0.3f, 0.4f,</div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>  -1.0f, -2.0f, -3.0f, -4.0f,</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  1.0f, 2.0f, 3.0f, 4.0f</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>  <span class="comment">// Calculate output values for input.</span></div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>  <span class="keyword">auto</span> f = [](<span class="keywordtype">float</span> x)</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>  {</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>  <span class="comment">// Break down the calculation to help with verification.</span></div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>  <span class="comment">// hard_swish(x) = x * relu6(x+3) / 6</span></div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>  <span class="comment">// relu6(x) = min(max(x,0),6)</span></div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>  <span class="keywordtype">float</span> reLu6_step1 = std::max((x + 3),0.0f);</div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>  <span class="keywordtype">float</span> reLu6Complete = std::min(reLu6_step1, 6.0f);</div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>  <span class="keywordtype">float</span> hardSwish_step1 = x * reLu6Complete;</div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span>  <span class="keywordtype">float</span> result = hardSwish_step1 / 6;</div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>  };</div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span>  std::vector<float> outputExpectedData(inputData.size());</div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>  std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);</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>  <span class="keywordflow">return</span> SimpleActivationTest<ArmnnType>(workloadFactory,</div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>  memoryManager,</div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa0877e5b3fbe9d7569df6399609ed0186">armnn::ActivationFunction::HardSwish</a>,</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span>  0.f,</div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>  0.f,</div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span>  qScale,</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span>  qOffset,</div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>  inputData,</div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>  qScale,</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span>  qOffset,</div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>  outputExpectedData);</div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span> }</div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span> </div><div class="line"><a name="l01119"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a630fd31472c64fa147a70b9be2d4911f"> 1119</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a630fd31472c64fa147a70b9be2d4911f">HardSwishTest</a>(</div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span> {</div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span>  <span class="keywordflow">return</span> HardSwishTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.1f, 0);</div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span> }</div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span> </div><div class="line"><a name="l01126"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a4cf5b253aee4ecf81d75797802c09604"> 1126</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a4cf5b253aee4ecf81d75797802c09604">HardSwishUint8Test</a>(</div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span> {</div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span>  <span class="keywordflow">return</span> HardSwishTestCommon<armnn::DataType::QAsymmU8>(workloadFactory, memoryManager, 0.1f, 64);</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span> }</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span> </div><div class="line"><a name="l01133"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#ad0ea8320eb816bc3b78ea9a18627d65a"> 1133</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t, 4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#ad0ea8320eb816bc3b78ea9a18627d65a">HardSwishInt16Test</a>(</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager)</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span> {</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>  <span class="keywordflow">return</span> HardSwishTestCommon<armnn::DataType::QSymmS16>(workloadFactory, memoryManager, 0.1f, 0);</div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span> }</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span> </div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span> </div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l01142"></a><span class="lineno"><a class="line" href="_activation_test_impl_8cpp.xhtml#a0758d9003f13b30d5e29eae6cd89c32b"> 1142</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T,4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a0758d9003f13b30d5e29eae6cd89c32b">CompareActivationTestImpl</a>(</div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& refWorkloadFactory,</div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9ea">armnn::ActivationFunction</a> f,</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 5,</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span>  <span class="keywordtype">float</span> qScale = 0.0f,</div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>  int32_t qOffset = 0)</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>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width = 17;</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height = 29;</div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> channels = 2;</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span> </div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  <span class="keywordtype">float</span> a = 0.234f;</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>  <span class="keywordtype">float</span> b = -12.345f;</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span> </div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape[] = {batchSize, channels, height, width};</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span> </div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>  inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape, ArmnnType);</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape, ArmnnType);</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span> </div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  <span class="comment">// Set quantization parameters if the requested type is a quantized type.</span></div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>  <span class="keywordflow">if</span>(armnn::IsQuantizedType<T>())</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  {</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(qScale);</div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">SetQuantizationOffset</a>(qOffset);</div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  }</div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span> </div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>  <span class="keywordtype">float</span> minVal = -10.f;</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>  <span class="keywordflow">if</span> (f == <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaae77f3ad25595e35b327334d89410054">armnn::ActivationFunction::Sqrt</a>)</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  {</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>  minVal = 0.f;</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>  }</div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span> </div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>  boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 21453, minVal, 10.f);</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span> </div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span> </div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T,4></a> ret(outputTensorInfo);</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>  <span class="keyword">auto</span> boostArrayExtents = boost::extents</div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>  [<a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><boost::multi_array_types::extent_gen::index>(batchSize)]</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>  [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(channels)]</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>  [<a class="code" href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">boost::numeric_cast</a><boost::multi_array_types::extent_gen::index>(height)]</div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(width)];</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>  ret.output.resize(boostArrayExtents);</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>  ret.outputExpected.resize(boostArrayExtents);</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span> </div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span> </div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span>  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span> </div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>  std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span> </div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>  <a class="code" href="structarmnn_1_1_activation_queue_descriptor.xhtml">armnn::ActivationQueueDescriptor</a> data;</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> info;</div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a017b2990003a014234f13e999dc7c689">m_A</a> = a;</div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#a28c4c9cb15f6be3499abbc46b356060b">m_B</a> = b;</div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_activation_descriptor.xhtml#af10fa7883e3579950f477bee92a64844">m_Function</a> = f;</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>  <a class="code" href="structarmnn_1_1_activation_queue_descriptor.xhtml">armnn::ActivationQueueDescriptor</a> refData = data;</div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a> refInfo = info;</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span>  SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());</div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>  SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span> </div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span>  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a4458d75c0db21c6abc941cd93a6a24c5">CreateActivation</a>(data, info);</div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span>  BOOST_ASSERT(workload != <span class="keyword">nullptr</span>);</div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>  std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a4458d75c0db21c6abc941cd93a6a24c5">CreateActivation</a>(refData, refInfo);</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span>  BOOST_ASSERT(workloadRef != <span class="keyword">nullptr</span>);</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>  inputHandle->Allocate();</div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span>  outputHandle->Allocate();</div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span>  inputHandleRef->Allocate();</div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span>  outputHandleRef->Allocate();</div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span> </div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), &input[0][0][0][0]);</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandleRef.get(), &input[0][0][0][0]);</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span> </div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>  workload->Execute();</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span>  workloadRef->Execute();</div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span> </div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.output[0][0][0][0], outputHandle.get());</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.outputExpected[0][0][0][0], outputHandleRef.get());</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span> </div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span> }</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span> </div><div class="line"><a name="l01236"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#ab48937c74230a7e804f6e5e225580bf4"> 1236</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float,4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#af226f71b992ee8076a3880def72b1f3f">CompareActivationTest</a>(</div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& refWorkloadFactory,</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9ea">armnn::ActivationFunction</a> f,</div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize)</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>  <span class="keywordflow">return</span> CompareActivationTestImpl<armnn::DataType::Float32>(</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  workloadFactory, memoryManager, refWorkloadFactory, f, batchSize);</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span> }</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span> </div><div class="line"><a name="l01247"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#a1e1abddc416db3041e9381b34f4c54bb"> 1247</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t,4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#addef260aaa3c7f7f1d08f821b823af33">CompareActivationUint8Test</a>(</div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& refWorkloadFactory,</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9ea">armnn::ActivationFunction</a> f)</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="keywordflow">return</span> CompareActivationTestImpl<armnn::DataType::QAsymmU8>(</div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  workloadFactory, memoryManager, refWorkloadFactory, f, 5, 0.1f, 50);</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span> }</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span> </div><div class="line"><a name="l01257"></a><span class="lineno"><a class="line" href="_activation_test_impl_8hpp.xhtml#ab08a7c7a7983fb0b7b66e7bf9c293a59"> 1257</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<int16_t,4></a> <a class="code" href="_activation_test_impl_8cpp.xhtml#a55dddf072af585903973b8e3398835dc">CompareActivationInt16Test</a>(</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a>& refWorkloadFactory,</div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span>  <a class="code" href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9ea">armnn::ActivationFunction</a> f)</div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span> {</div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>  <span class="keywordflow">return</span> CompareActivationTestImpl<armnn::DataType::QSymmS16>(</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>  workloadFactory, memoryManager, refWorkloadFactory, f, 5, 0.1f, 0);</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span> }</div><div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa3d90c0a5ab3fcf8e6f6608cb3d3a1559">armnn::ActivationFunction::ReLu</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a8b855f5d3e8aab93decfa2bed46fc4cf"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a8b855f5d3e8aab93decfa2bed46fc4cf">SqrtInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 4 > SqrtInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00897">ActivationTestImpl.cpp:897</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a20b01cc1552ab2c3abd70166fdd35faf"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a20b01cc1552ab2c3abd70166fdd35faf">ReLuInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 4 > ReLuInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00555">ActivationTestImpl.cpp:555</a></div></div> +<div class="ttc" id="_tensor_copy_utils_8hpp_xhtml"><div class="ttname"><a href="_tensor_copy_utils_8hpp.xhtml">TensorCopyUtils.hpp</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a4b43ab0b58fc8d4ad51b1b71c0e35622"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a4b43ab0b58fc8d4ad51b1b71c0e35622">SoftReLuTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > SoftReLuTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00624">ActivationTestImpl.cpp:624</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a0120909fa6b3032270399355f14654de"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a0120909fa6b3032270399355f14654de">LeakyReLuTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > LeakyReLuTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00715">ActivationTestImpl.cpp:715</a></div></div> +<div class="ttc" id="_activation_test_impl_8hpp_xhtml"><div class="ttname"><a href="_activation_test_impl_8hpp.xhtml">ActivationTestImpl.hpp</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a26032da34ce1e283ae30d05ea3bbb103"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a26032da34ce1e283ae30d05ea3bbb103">SquareTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > SquareTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00905">ActivationTestImpl.cpp:905</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a6584d436388485a5bd9252430a0af5b6"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a6584d436388485a5bd9252430a0af5b6">SquareTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > SquareTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00939">ActivationTestImpl.cpp:939</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_ae42bb4023d8578a27159c95dd4b33b28"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#ae42bb4023d8578a27159c95dd4b33b28">BoundedReLuInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 4 > BoundedReLuInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00614">ActivationTestImpl.cpp:614</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a8bfdab68fed1467b8720cceb47881236"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a8bfdab68fed1467b8720cceb47881236">SoftReLuTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > SoftReLuTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00658">ActivationTestImpl.cpp:658</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a58872a37a87790e3a3f91ee254ce304a"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a58872a37a87790e3a3f91ee254ce304a">ReLuTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > ReLuTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00570">ActivationTestImpl.cpp:570</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div> +<div class="ttc" id="_quantize_helper_8hpp_xhtml"><div class="ttname"><a href="_quantize_helper_8hpp.xhtml">QuantizeHelper.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8hpp_source.xhtml#l00021">WorkloadFactory.hpp:21</a></div></div> +<div class="ttc" id="_workload_test_utils_8hpp_xhtml"><div class="ttname"><a href="_workload_test_utils_8hpp.xhtml">WorkloadTestUtils.hpp</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a61fffaf40ad721073b70c350174d0ff3"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a61fffaf40ad721073b70c350174d0ff3">SquareUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > SquareUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00946">ActivationTestImpl.cpp:946</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a6403e38cfee03672c164e3cba9863147"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a6403e38cfee03672c164e3cba9863147">SqrtUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > SqrtUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00890">ActivationTestImpl.cpp:890</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaaae77f3ad25595e35b327334d89410054"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaae77f3ad25595e35b327334d89410054">armnn::ActivationFunction::Sqrt</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a26219b66822d57b9fcce7a2504d1fca6"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a26219b66822d57b9fcce7a2504d1fca6">SquareInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 4 > SquareInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00953">ActivationTestImpl.cpp:953</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a0758d9003f13b30d5e29eae6cd89c32b"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a0758d9003f13b30d5e29eae6cd89c32b">CompareActivationTestImpl</a></div><div class="ttdeci">LayerTestResult< T, 4 > CompareActivationTestImpl(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, armnn::IWorkloadFactory &refWorkloadFactory, armnn::ActivationFunction f, unsigned int batchSize=5, float qScale=0.0f, int32_t qOffset=0)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l01142">ActivationTestImpl.cpp:1142</a></div></div> +<div class="ttc" id="struct_layer_test_result_xhtml_a73610ea6c776cc66e5a78dd842a39b8b"><div class="ttname"><a href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">LayerTestResult::outputExpected</a></div><div class="ttdeci">boost::multi_array< T, n > outputExpected</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult.hpp:41</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaacb7667f5ec2f6e8a5943b781ba6c2735"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaacb7667f5ec2f6e8a5943b781ba6c2735">armnn::ActivationFunction::LeakyReLu</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a65aa329dc6abc6cf9dfb6177f42595de"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a65aa329dc6abc6cf9dfb6177f42595de">TanhTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > TanhTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00961">ActivationTestImpl.cpp:961</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="_activation_test_impl_8cpp_xhtml_a0e868e8fa03ce4c4674b007eae5dc1a2"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a0e868e8fa03ce4c4674b007eae5dc1a2">BoundedReLuUint8UpperAndLowerBoundTest</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > BoundedReLuUint8UpperAndLowerBoundTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00184">ActivationTestImpl.cpp:184</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a7aaeeaa0a8683fae56caa66849228a87"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a7aaeeaa0a8683fae56caa66849228a87">CompareBoundedReLuTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > CompareBoundedReLuTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, armnn::IWorkloadFactory &refWorkloadFactory, float upperBound, float lowerBound)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00284">ActivationTestImpl.cpp:284</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa21eebb164e4b8b9bcf64fdb4d8d5dff4">armnn::ActivationFunction::Sigmoid</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="_activation_test_impl_8cpp_xhtml_aab2458914aa40f83ba027de7a8c07d06"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#aab2458914aa40f83ba027de7a8c07d06">SqrtTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > SqrtTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00849">ActivationTestImpl.cpp:849</a></div></div> +<div class="ttc" id="_activation_fixture_8hpp_xhtml"><div class="ttname"><a href="_activation_fixture_8hpp.xhtml">ActivationFixture.hpp</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_i_workload_factory_xhtml_a4458d75c0db21c6abc941cd93a6a24c5"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a4458d75c0db21c6abc941cd93a6a24c5">armnn::IWorkloadFactory::CreateActivation</a></div><div class="ttdeci">virtual std::unique_ptr< IWorkload > CreateActivation(const ActivationQueueDescriptor &descriptor, const WorkloadInfo &info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01099">WorkloadFactory.cpp:1099</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_acf22306b81aa054c64c48730b2786f96"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#acf22306b81aa054c64c48730b2786f96">ReLuTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > ReLuTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00521">ActivationTestImpl.cpp:521</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a6e45714708a6daf8688ef6ca58e54827"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a6e45714708a6daf8688ef6ca58e54827">LeakyReLuTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > LeakyReLuTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00680">ActivationTestImpl.cpp:680</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a96ba4985a8fff8c04e6585e866256868"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a96ba4985a8fff8c04e6585e866256868">EluTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > EluTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l01056">ActivationTestImpl.cpp:1056</a></div></div> +<div class="ttc" id="structarmnn_1_1_queue_descriptor_with_parameters_xhtml_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00049">WorkloadData.hpp:49</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a6558a4306d758625ab7804e9cb70b058"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a6558a4306d758625ab7804e9cb70b058">SimpleSigmoidInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 4 > SimpleSigmoidInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00513">ActivationTestImpl.cpp:513</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a732229b22cff2a8f96798c38832cab92"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a732229b22cff2a8f96798c38832cab92">SoftReLuUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > SoftReLuUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00665">ActivationTestImpl.cpp:665</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a1fcf6512fb0db8090ce19979a9ac6472"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a1fcf6512fb0db8090ce19979a9ac6472">HardSwishTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > HardSwishTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l01079">ActivationTestImpl.cpp:1079</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a418191b7e7caba8173206c0870bc3684"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a418191b7e7caba8173206c0870bc3684">BoundedReLuUpperAndLowerBoundTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > BoundedReLuUpperAndLowerBoundTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00091">ActivationTestImpl.cpp:91</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_ac01b6901c3f2921c998aff77a8362f87"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#ac01b6901c3f2921c998aff77a8362f87">LeakyReLuInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 4 > LeakyReLuInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00729">ActivationTestImpl.cpp:729</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_aacd820bdf2307a2aa667db2899283035"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#aacd820bdf2307a2aa667db2899283035">TanhInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 4 > TanhInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l01011">ActivationTestImpl.cpp:1011</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_af9293a4d81453abbe8cbdc788c290943"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#af9293a4d81453abbe8cbdc788c290943">LeakyReLuUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > LeakyReLuUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00722">ActivationTestImpl.cpp:722</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_ad0ea8320eb816bc3b78ea9a18627d65a"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#ad0ea8320eb816bc3b78ea9a18627d65a">HardSwishInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 4 > HardSwishInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l01133">ActivationTestImpl.cpp:1133</a></div></div> +<div class="ttc" id="_tensor_helpers_8hpp_xhtml"><div class="ttname"><a href="_tensor_helpers_8hpp.xhtml">TensorHelpers.hpp</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a205500c03971e6ed3aae6e07afdaf145"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a205500c03971e6ed3aae6e07afdaf145">EluInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 4 > EluInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l01070">ActivationTestImpl.cpp:1070</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_backend_internal_xhtml_a693b40e6b94e958836aeb0410ca186bd"><div class="ttname"><a href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a></div><div class="ttdeci">std::shared_ptr< IMemoryManager > IMemoryManagerSharedPtr</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_backend_internal_8hpp_source.xhtml#l00090">IBackendInternal.hpp:90</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a923aa3e41cd11f5eeb7cc973fd8d3c76"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a923aa3e41cd11f5eeb7cc973fd8d3c76">TanhTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > TanhTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00997">ActivationTestImpl.cpp:997</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a11baf4886951944fcf149e2a92197e58"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a11baf4886951944fcf149e2a92197e58">AbsUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > AbsUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00778">ActivationTestImpl.cpp:778</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_addef260aaa3c7f7f1d08f821b823af33"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#addef260aaa3c7f7f1d08f821b823af33">CompareActivationUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > CompareActivationUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, armnn::IWorkloadFactory &refWorkloadFactory, armnn::ActivationFunction f)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l01247">ActivationTestImpl.cpp:1247</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_aaeea20fa5e5934ea49b8f764526a2d98"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#aaeea20fa5e5934ea49b8f764526a2d98">SimpleActivationTest</a></div><div class="ttdeci">LayerTestResult< T, 4 > SimpleActivationTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, armnn::ActivationFunction activationFunction, float activationParameterA, float activationParameterB, float scale, int32_t offset, const std::vector< float > &inputData, float outScale, int32_t outOffset, const std::vector< float > &outputExpectedData)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00392">ActivationTestImpl.cpp:392</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a641db2befcd47ac97af966e20b1c4c2c"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a641db2befcd47ac97af966e20b1c4c2c">SoftReLuInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 4 > SoftReLuInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00672">ActivationTestImpl.cpp:672</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa6bba7052636d1740303b1b2ef3b53fef"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa6bba7052636d1740303b1b2ef3b53fef">armnn::ActivationFunction::SoftReLu</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a31872d5729b4d7734c1eb0d189a0eece"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a31872d5729b4d7734c1eb0d189a0eece">AbsTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > AbsTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00771">ActivationTestImpl.cpp:771</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a685739c4eb65a580e075282cfe6787d6"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">armnn::TensorInfo::SetQuantizationScale</a></div><div class="ttdeci">void SetQuantizationScale(float scale)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00259">Tensor.cpp:259</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a37fa39012e90d568df7f774cd6d1e956"><div class="ttname"><a href="namespacearmnn.xhtml#a37fa39012e90d568df7f774cd6d1e956">armnn::numeric_cast</a></div><div class="ttdeci">std::enable_if_t< std::is_unsigned< Source >::value &&std::is_unsigned< Dest >::value, Dest > numeric_cast(Source source)</div><div class="ttdef"><b>Definition:</b> <a href="_numeric_cast_8hpp_source.xhtml#l00033">NumericCast.hpp:33</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a86f53855f5ab422f4e035b1aa11676f8"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a86f53855f5ab422f4e035b1aa11676f8">SqrtNNTest</a></div><div class="ttdeci">LayerTestResult< float, 5 > SqrtNNTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00792">ActivationTestImpl.cpp:792</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="_tensor_copy_utils_8cpp_xhtml_a99b626c58a926dc7d6df78d22ec186c8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a></div><div class="ttdeci">void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00014">TensorCopyUtils.cpp:14</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaabc5a0f0d6e7cf7fca73299dcd46c0f0d">armnn::ActivationFunction::BoundedReLu</a></div><div class="ttdoc">min(a, max(b, input)) ReLu1 & ReLu6. </div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_aa8c2d170a4b51447f575183cee9579ab"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#aa8c2d170a4b51447f575183cee9579ab">AbsTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > AbsTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00737">ActivationTestImpl.cpp:737</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a15c140be4ddceffee16436f009d3ed94"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a15c140be4ddceffee16436f009d3ed94">armnn::IWorkloadFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr< ITensorHandle > CreateTensorHandle(const TensorInfo &tensorInfo, const bool IsMemoryManaged=true) const =0</div></div> +<div class="ttc" id="struct_layer_test_result_xhtml_ac9d44d346bb7c89f7a7aa31d2bee947f"><div class="ttname"><a href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">LayerTestResult::output</a></div><div class="ttdeci">boost::multi_array< T, n > output</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00040">LayerTestResult.hpp:40</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a8dd4b2ac72e85dcfeb8540b7d5649b47"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a8dd4b2ac72e85dcfeb8540b7d5649b47">AbsInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 4 > AbsInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00785">ActivationTestImpl.cpp:785</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaaed67cf7d54c570e4c4891800f085f41d"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaed67cf7d54c570e4c4891800f085f41d">armnn::ActivationFunction::Elu</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="_activation_test_impl_8cpp_xhtml_a4f95ca2d87f1d36d0a41d6a0cf56151b"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a4f95ca2d87f1d36d0a41d6a0cf56151b">EluUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > EluUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l01063">ActivationTestImpl.cpp:1063</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_aa986502e638eba65543c1cbb01467d26"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#aa986502e638eba65543c1cbb01467d26">ReLuUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > ReLuUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00563">ActivationTestImpl.cpp:563</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a52af2639a8f96fbbc86343ea8914033a"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a52af2639a8f96fbbc86343ea8914033a">ConstantLinearActivationTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > ConstantLinearActivationTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00368">ActivationTestImpl.cpp:368</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a34b322827b0d8ff9f8b3b8fb9410f7d3"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a34b322827b0d8ff9f8b3b8fb9410f7d3">ConstantLinearActivationUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > ConstantLinearActivationUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00375">ActivationTestImpl.cpp:375</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_af226f71b992ee8076a3880def72b1f3f"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#af226f71b992ee8076a3880def72b1f3f">CompareActivationTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > CompareActivationTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, armnn::IWorkloadFactory &refWorkloadFactory, armnn::ActivationFunction f, unsigned int batchSize)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l01236">ActivationTestImpl.cpp:1236</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_ab653abd1ebff85e413b3647d2408f3b1"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#ab653abd1ebff85e413b3647d2408f3b1">EluTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > EluTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l01020">ActivationTestImpl.cpp:1020</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a32a6595835f4cb5e93fec4182ada51bc"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a32a6595835f4cb5e93fec4182ada51bc">ConstantLinearActivationInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 4 > ConstantLinearActivationInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00383">ActivationTestImpl.cpp:383</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_aa87c451f7a773fd4ec9cdf11c20d7a58"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#aa87c451f7a773fd4ec9cdf11c20d7a58">SimpleSigmoidTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > SimpleSigmoidTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00499">ActivationTestImpl.cpp:499</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaaceb46ca115d05c51aa5a16a8867c3304"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaaceb46ca115d05c51aa5a16a8867c3304">armnn::ActivationFunction::Square</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div> +<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div> +<div class="ttc" id="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00029">LayerTestResult.hpp:29</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a63cbc581012c957f9d68d224ddc3e43c"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a63cbc581012c957f9d68d224ddc3e43c">armnn::TensorInfo::SetQuantizationOffset</a></div><div class="ttdeci">void SetQuantizationOffset(int32_t offset)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00275">Tensor.cpp:275</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a359c1f734f9da1d6459e9d878e5612ba"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a359c1f734f9da1d6459e9d878e5612ba">BoundedReLuUpperBoundOnlyTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > BoundedReLuUpperBoundOnlyTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00122">ActivationTestImpl.cpp:122</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a22562086e72d244fd7cf4156b958c134"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a22562086e72d244fd7cf4156b958c134">ConstantLinearActivationTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > ConstantLinearActivationTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale=0.0f, int32_t qOffset=0)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00307">ActivationTestImpl.cpp:307</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a7aa10bded0d26089e0bc4333ada10064"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a7aa10bded0d26089e0bc4333ada10064">BoundedReLuUint8UpperBoundOnlyTest</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > BoundedReLuUint8UpperBoundOnlyTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00153">ActivationTestImpl.cpp:153</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a55dddf072af585903973b8e3398835dc"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a55dddf072af585903973b8e3398835dc">CompareActivationInt16Test</a></div><div class="ttdeci">LayerTestResult< int16_t, 4 > CompareActivationInt16Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, armnn::IWorkloadFactory &refWorkloadFactory, armnn::ActivationFunction f)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l01257">ActivationTestImpl.cpp:1257</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a4cf5b253aee4ecf81d75797802c09604"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a4cf5b253aee4ecf81d75797802c09604">HardSwishUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > HardSwishUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l01126">ActivationTestImpl.cpp:1126</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a630fd31472c64fa147a70b9be2d4911f"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a630fd31472c64fa147a70b9be2d4911f">HardSwishTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > HardSwishTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l01119">ActivationTestImpl.cpp:1119</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_aaa4e43f7b9a9b14145accdc347bc0e18"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#aaa4e43f7b9a9b14145accdc347bc0e18">BoundedReLuTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > BoundedReLuTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float upperBound, float lowerBound, float inputScale, int32_t inputOffset, float outputScale, int32_t outputOffset, const std::vector< T > &inputData, const std::vector< T > &outputExpectedData, unsigned int inputWidth, unsigned int inputHeight, unsigned int inputChannels, unsigned int inputBatchSize)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00023">ActivationTestImpl.cpp:23</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_abe9073d08e150e3dd5e156af7ea8faa5"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#abe9073d08e150e3dd5e156af7ea8faa5">TanhUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > TanhUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l01004">ActivationTestImpl.cpp:1004</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa32a843da6ea40ab3b17a3421ccdf671b"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa32a843da6ea40ab3b17a3421ccdf671b">armnn::ActivationFunction::Linear</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa0877e5b3fbe9d7569df6399609ed0186"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa0877e5b3fbe9d7569df6399609ed0186">armnn::ActivationFunction::HardSwish</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_a1020322feb8c6fe89ced59fcca8277c4"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a1020322feb8c6fe89ced59fcca8277c4">SimpleSigmoidTestCommon</a></div><div class="ttdeci">LayerTestResult< T, 4 > SimpleSigmoidTestCommon(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00464">ActivationTestImpl.cpp:464</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="namespacearmnn_xhtml_ae52296dff1f4879854f320d59f92574e"><div class="ttname"><a href="namespacearmnn.xhtml#ae52296dff1f4879854f320d59f92574e">armnn::GetInputTensorInfo</a></div><div class="ttdeci">TensorInfo GetInputTensorInfo(const Network *network)</div><div class="ttdef"><b>Definition:</b> <a href="_quantizer_test_8cpp_source.xhtml#l00335">QuantizerTest.cpp:335</a></div></div> +<div class="ttc" id="_activation_test_impl_8cpp_xhtml_ad3928f2c56ed15642ff6306cc6823ebd"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#ad3928f2c56ed15642ff6306cc6823ebd">SqrtTest</a></div><div class="ttdeci">LayerTestResult< float, 4 > SqrtTest(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00883">ActivationTestImpl.cpp:883</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="_activation_test_impl_8cpp_xhtml_a0889979f9ffb67b036c3928c6e94af50"><div class="ttname"><a href="_activation_test_impl_8cpp.xhtml#a0889979f9ffb67b036c3928c6e94af50">SimpleSigmoidUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > SimpleSigmoidUint8Test(armnn::IWorkloadFactory &workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr &memoryManager)</div><div class="ttdef"><b>Definition:</b> <a href="_activation_test_impl_8cpp_source.xhtml#l00506">ActivationTestImpl.cpp:506</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="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9ea"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9ea">armnn::ActivationFunction</a></div><div class="ttdeci">ActivationFunction</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00055">Types.hpp:55</a></div></div> +<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_ae15f1a3c55d2db87683577de9fa4437c"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a></div><div class="ttdeci">void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00009">TensorCopyUtils.cpp:9</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e"><div class="ttname"><a href="namespacearmnn.xhtml#a56297e0f7b215eea46c818cb7528d9eaa23b68da1de2b77d74da9da2635722a3e">armnn::ActivationFunction::TanH</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_0f3cdec46afbc61a1ded8e1687c9c9a0.xhtml">backends</a></li><li class="navelem"><a class="el" href="dir_797a213d7d01b98ef12d53b0820ea64e.xhtml">backendsCommon</a></li><li class="navelem"><a class="el" href="dir_28bfe507f7e135bdae07c2a6b7f66696.xhtml">test</a></li><li class="navelem"><a class="el" href="dir_99a30439342d160875b21dac3498ad7f.xhtml">layerTests</a></li><li class="navelem"><a class="el" href="_activation_test_impl_8cpp.xhtml">ActivationTestImpl.cpp</a></li> + <li class="footer">Generated on Fri Mar 13 2020 16:09:10 for ArmNN by + <a href="http://www.doxygen.org/index.html"> + <img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.13 </li> + </ul> +</div> +</body> +</html> |