diff options
Diffstat (limited to '21.02/_fuse_batch_norm_tests_8cpp.xhtml')
| -rw-r--r-- | 21.02/_fuse_batch_norm_tests_8cpp.xhtml | 227 |
1 files changed, 227 insertions, 0 deletions
diff --git a/21.02/_fuse_batch_norm_tests_8cpp.xhtml b/21.02/_fuse_batch_norm_tests_8cpp.xhtml new file mode 100644 index 0000000000..0e9c9a7a7d --- /dev/null +++ b/21.02/_fuse_batch_norm_tests_8cpp.xhtml @@ -0,0 +1,227 @@ +<!-- Copyright (c) 2020 ARM Limited. --> +<!-- --> +<!-- SPDX-License-Identifier: MIT --> +<!-- --> +<!-- HTML header for doxygen 1.8.13--> +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> +<html xmlns="http://www.w3.org/1999/xhtml"> +<head> +<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/> +<meta http-equiv="X-UA-Compatible" content="IE=9"/> +<meta name="generator" content="Doxygen 1.8.13"/> +<meta name="robots" content="NOINDEX, NOFOLLOW" /> +<meta name="viewport" content="width=device-width, initial-scale=1"/> +<title>ArmNN: src/armnn/test/optimizations/FuseBatchNormTests.cpp File Reference</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"> + $(document).ready(initResizable); +</script> +<link href="search/search.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="search/searchdata.js"></script> +<script type="text/javascript" src="search/search.js"></script> +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + extensions: ["tex2jax.js"], + jax: ["input/TeX","output/HTML-CSS"], +}); +</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script> +<link href="doxygen.css" rel="stylesheet" type="text/css" /> +<link href="stylesheet.css" rel="stylesheet" type="text/css"/> +</head> +<body> +<div id="top"><!-- do not remove this div, it is closed by doxygen! --> +<div id="titlearea"> +<table cellspacing="0" cellpadding="0"> + <tbody> + <tr style="height: 56px;"> + <img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 10rem; margin-top: .5rem; margin-left 10px"/> + <td style="padding-left: 0.5em;"> + <div id="projectname"> +  <span id="projectnumber">21.02</span> + </div> + </td> + </tr> + </tbody> +</table> +</div> +<!-- end header part --> +<!-- Generated by Doxygen 1.8.13 --> +<script type="text/javascript"> +var searchBox = new SearchBox("searchBox", "search",false,'Search'); +</script> +<script type="text/javascript" src="menudata.js"></script> +<script type="text/javascript" src="menu.js"></script> +<script type="text/javascript"> +$(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('_fuse_batch_norm_tests_8cpp.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="summary"> +<a href="#func-members">Functions</a> </div> + <div class="headertitle"> +<div class="title">FuseBatchNormTests.cpp File Reference</div> </div> +</div><!--header--> +<div class="contents"> +<div class="textblock"><code>#include "<a class="el" href="_layers_fwd_8hpp_source.xhtml">LayersFwd.hpp</a>"</code><br /> +<code>#include <<a class="el" href="_network_8hpp_source.xhtml">Network.hpp</a>></code><br /> +<code>#include <<a class="el" href="_resolve_type_8hpp_source.xhtml">ResolveType.hpp</a>></code><br /> +<code>#include <<a class="el" href="_i_network_8hpp_source.xhtml">armnn/INetwork.hpp</a>></code><br /> +<code>#include <<a class="el" href="_test_utils_8hpp_source.xhtml">test/TestUtils.hpp</a>></code><br /> +<code>#include <boost/test/unit_test.hpp></code><br /> +</div> +<p><a href="_fuse_batch_norm_tests_8cpp_source.xhtml">Go to the source code of this file.</a></p> +<table class="memberdecls"> +<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a> +Functions</h2></td></tr> +<tr class="memitem:a007cb6b1e66b629cdff3e267a81f42e4"><td class="memTemplParams" colspan="2">template<typename Conv2dTest , armnn::DataType ArmnnType, typename ConvDescriptorType = typename Conv2dTest::ConvDescriptorType, typename T = armnn::ResolveType<ArmnnType>> </td></tr> +<tr class="memitem:a007cb6b1e66b629cdff3e267a81f42e4"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_fuse_batch_norm_tests_8cpp.xhtml#a007cb6b1e66b629cdff3e267a81f42e4">CreatNetwork</a> (bool depthwise, bool preventFusing)</td></tr> +<tr class="separator:a007cb6b1e66b629cdff3e267a81f42e4"><td class="memSeparator" colspan="2"> </td></tr> +<tr class="memitem:aed39023dc42b609fa4ab7de2a502ab25"><td class="memTemplParams" colspan="2">template<typename Conv2dTest , armnn::DataType ArmnnType, typename ConvDescriptorType = typename Conv2dTest::ConvDescriptorType, typename ConvLayerType = typename Conv2dTest::ConvLayerType, typename T = armnn::ResolveType<ArmnnType>> </td></tr> +<tr class="memitem:aed39023dc42b609fa4ab7de2a502ab25"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="_fuse_batch_norm_tests_8cpp.xhtml#aed39023dc42b609fa4ab7de2a502ab25">FuseBatchNormIntoConvTest</a> (bool depthwise, float tolerance, <a class="el" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456ae">armnn::Compute</a> backendId)</td></tr> +<tr class="separator:aed39023dc42b609fa4ab7de2a502ab25"><td class="memSeparator" colspan="2"> </td></tr> +</table> +<h2 class="groupheader">Function Documentation</h2> +<a id="a007cb6b1e66b629cdff3e267a81f42e4"></a> +<h2 class="memtitle"><span class="permalink"><a href="#a007cb6b1e66b629cdff3e267a81f42e4">◆ </a></span>CreatNetwork()</h2> + +<div class="memitem"> +<div class="memproto"> + <table class="memname"> + <tr> + <td class="memname"><a class="el" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> CreatNetwork </td> + <td>(</td> + <td class="paramtype">bool </td> + <td class="paramname"><em>depthwise</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">bool </td> + <td class="paramname"><em>preventFusing</em> </td> + </tr> + <tr> + <td></td> + <td>)</td> + <td></td><td></td> + </tr> + </table> +</div><div class="memdoc"> + +<p class="definition">Definition at line <a class="el" href="_fuse_batch_norm_tests_8cpp_source.xhtml#l00076">76</a> of file <a class="el" href="_fuse_batch_norm_tests_8cpp_source.xhtml">FuseBatchNormTests.cpp</a>.</p> + +<p class="reference">References <a class="el" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">IOutputSlot::Connect()</a>, <a class="el" href="_network_8cpp_source.xhtml#l00510">INetwork::Create()</a>, <a class="el" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">IConnectableLayer::GetInputSlot()</a>, <a class="el" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">IConnectableLayer::GetOutputSlot()</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00641">BatchNormalizationDescriptor::m_DataLayout</a>, <a class="el" href="namespacearmnn.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">armnn::NHWC</a>, and <a class="el" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">IOutputSlot::SetTensorInfo()</a>.</p> +<div class="fragment"><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> {</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="comment">// Define layers information</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  ConvDescriptorType convolution2dDescriptor;</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  convolution2dDescriptor.m_BiasEnabled = <span class="keyword">false</span>;</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  convolution2dDescriptor.m_DataLayout = DataLayout::NHWC;</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  convolution2dDescriptor.m_StrideX = 1;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  convolution2dDescriptor.m_StrideY = 1;</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml">BatchNormalizationDescriptor</a> batchNormDescriptor;</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  batchNormDescriptor.<a class="code" href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">m_DataLayout</a> = DataLayout::NHWC;</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> </div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputDimensionSizes[] = {1, 4, 4, 3}; <span class="comment">// NHWCin</span></div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsDimensionSizes[] = {4, 2, 2, 3}; <span class="comment">// CoutHWCin</span></div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputDimensionSizes[] = {1, 3, 3, 4}; <span class="comment">// NHWCout</span></div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keywordflow">if</span> (depthwise)</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  {</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="comment">//M Cin H W</span></div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  weightsDimensionSizes[0] = 4;</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  weightsDimensionSizes[1] = 3;</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  weightsDimensionSizes[2] = 2;</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  weightsDimensionSizes[3] = 2;</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  outputDimensionSizes[3] = weightsDimensionSizes[0] * weightsDimensionSizes[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>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannelSize[] = {outputDimensionSizes[3]}; <span class="comment">// Cout</span></div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> </div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo(4, inputDimensionSizes, ArmnnType);</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(4, outputDimensionSizes, ArmnnType);</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> </div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  std::vector<int> weightsIntVector = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42};</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  std::vector<T> weightsVector(begin(weightsIntVector), end(weightsIntVector));</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> weightsInfo(4, weightsDimensionSizes, ArmnnType);</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> weights(weightsInfo, weightsVector);</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> </div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  std::vector<T> biasVector = GetVector<T>(outputDimensionSizes[3], 3.3f, 0.1f);</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo(1, outputChannelSize, ArmnnType);</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> bias(biasInfo, biasVector);</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a> optionalBias = <a class="code" href="classarmnn_1_1_optional.xhtml">Optional<ConstTensor></a>(bias);</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  std::vector<T> betaVector = GetVector<T>(outputDimensionSizes[3], 0.0f, 0.2f);</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  std::vector<T> gammaVector = GetVector<T>(outputDimensionSizes[3], 0.5f, 0.1f);</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  std::vector<T> meanVector = GetVector<T>(outputDimensionSizes[3], 0.1f, 0.1f);</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  std::vector<T> varianceVector = GetVector<T>(outputDimensionSizes[3], 1.0f, 0.1f);</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> </div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> beta (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, outputChannelSize, ArmnnType), betaVector);</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> gamma (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, outputChannelSize, ArmnnType), gammaVector);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> mean (<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, outputChannelSize, ArmnnType), meanVector);</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> variance(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>(1, outputChannelSize, ArmnnType), varianceVector);</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> </div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="comment">// Create a network</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> network = INetwork::Create();</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* inputLayer = network->AddInputLayer(0);</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> </div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* convLayer = Conv2dTest::AddConvolution(network.get(),</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  convolution2dDescriptor,</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  weights,</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  optionalBias,</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="stringliteral">"convolution"</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>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* batchNormLayer = network->AddBatchNormalizationLayer(batchNormDescriptor,</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  mean,</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  variance,</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  beta,</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  gamma,</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="stringliteral">"batchNorm"</span>);</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> </div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* outputLayer = network->AddOutputLayer(0);</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output2Layer = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> </div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <span class="keywordflow">if</span> (preventFusing)</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  {</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  output2Layer = network->AddOutputLayer(1);</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  }</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> </div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="comment">// Set layer information</span></div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  inputLayer -><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(inputInfo);</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  convLayer -><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">SetTensorInfo</a>(outputInfo);</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  batchNormLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> </div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="comment">// Connect layers</span></div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  inputLayer -><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(convLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  convLayer -><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(batchNormLayer->GetInputSlot(0));</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  batchNormLayer->GetOutputSlot(0).Connect(outputLayer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span> </div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="keywordflow">if</span> (preventFusing)</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>  convLayer -><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">GetOutputSlot</a>(0).<a class="code" href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">Connect</a>(output2Layer-><a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">GetInputSlot</a>(0));</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  }</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> </div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keywordflow">return</span> network;</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> }</div><div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00062">INetwork.hpp:62</a></div></div> +<div class="ttc" id="classarmnn_1_1_optional_xhtml"><div class="ttname"><a href="classarmnn_1_1_optional.xhtml">armnn::Optional</a></div><div class="ttdef"><b>Definition:</b> <a href="_optional_8hpp_source.xhtml#l00270">Optional.hpp:270</a></div></div> +<div class="ttc" id="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#l00152">Tensor.hpp:152</a></div></div> +<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml_a6089e1ca91914015777ea780a513131a"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml#a6089e1ca91914015777ea780a513131a">armnn::BatchNormalizationDescriptor::m_DataLayout</a></div><div class="ttdeci">DataLayout m_DataLayout</div><div class="ttdoc">The data layout to be used (NCHW, NHWC). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00641">Descriptors.hpp:641</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_a5ee4a6c9a2481245487b1b1a70d20fd0"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#a5ee4a6c9a2481245487b1b1a70d20fd0">armnn::IOutputSlot::SetTensorInfo</a></div><div class="ttdeci">virtual void SetTensorInfo(const TensorInfo &tensorInfo)=0</div></div> +<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00314">Tensor.hpp:314</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a6ec9e0eb66d7d6a01240492a0b18104c"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a6ec9e0eb66d7d6a01240492a0b18104c">armnn::IConnectableLayer::GetInputSlot</a></div><div class="ttdeci">virtual const IInputSlot & GetInputSlot(unsigned int index) const =0</div><div class="ttdoc">Get a const input slot handle by slot index. </div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml_a80ac4eda2e7f2757ec9dd96fc96dbd16"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml#a80ac4eda2e7f2757ec9dd96fc96dbd16">armnn::IConnectableLayer::GetOutputSlot</a></div><div class="ttdeci">virtual const IOutputSlot & GetOutputSlot(unsigned int index) const =0</div><div class="ttdoc">Get the const output slot handle by slot index. </div></div> +<div class="ttc" id="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00173">INetwork.hpp:173</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_output_slot_xhtml_ac1835f8756a9f03c02fcf9664e3a0fce"><div class="ttname"><a href="classarmnn_1_1_i_output_slot.xhtml#ac1835f8756a9f03c02fcf9664e3a0fce">armnn::IOutputSlot::Connect</a></div><div class="ttdeci">virtual int Connect(IInputSlot &destination)=0</div></div> +<div class="ttc" id="structarmnn_1_1_batch_normalization_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_descriptor.xhtml">armnn::BatchNormalizationDescriptor</a></div><div class="ttdoc">A BatchNormalizationDescriptor for the BatchNormalizationLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00626">Descriptors.hpp:626</a></div></div> +</div><!-- fragment --> +</div> +</div> +<a id="aed39023dc42b609fa4ab7de2a502ab25"></a> +<h2 class="memtitle"><span class="permalink"><a href="#aed39023dc42b609fa4ab7de2a502ab25">◆ </a></span>FuseBatchNormIntoConvTest()</h2> + +<div class="memitem"> +<div class="memproto"> + <table class="memname"> + <tr> + <td class="memname">void FuseBatchNormIntoConvTest </td> + <td>(</td> + <td class="paramtype">bool </td> + <td class="paramname"><em>depthwise</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype">float </td> + <td class="paramname"><em>tolerance</em>, </td> + </tr> + <tr> + <td class="paramkey"></td> + <td></td> + <td class="paramtype"><a class="el" href="namespacearmnn.xhtml#ae2f04a162585c0a5222a537efd5456ae">armnn::Compute</a> </td> + <td class="paramname"><em>backendId</em> </td> + </tr> + <tr> + <td></td> + <td>)</td> + <td></td><td></td> + </tr> + </table> +</div><div class="memdoc"> + +<p class="definition">Definition at line <a class="el" href="_fuse_batch_norm_tests_8cpp_source.xhtml#l00177">177</a> of file <a class="el" href="_fuse_batch_norm_tests_8cpp_source.xhtml">FuseBatchNormTests.cpp</a>.</p> +<div class="fragment"><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> {</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="comment">// FIRST NETWORK: Fused</span></div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="comment">// Construct ArmNN network</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> networkFused = CreatNetwork<Conv2dTest, ArmnnType>(depthwise, <span class="keyword">false</span>);</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> </div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="comment">// Create ArmNN runtime</span></div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> run = IRuntime::Create(<a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a>()); <span class="comment">// default options</span></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="comment">// Optimise ArmNN network</span></div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNetFused = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*networkFused, {backendId}, run->GetDeviceSpec());</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> </div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& graphFused = <a class="code" href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">GetGraphForTesting</a>(optNetFused.get());</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span> </div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keyword">auto</span> checkFusedConv2d = [ ](<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_layer.xhtml">armnn::Layer</a>* <span class="keyword">const</span> layer) -> <span class="keywordtype">bool</span></div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  {</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <span class="keywordflow">return</span> IsLayerOfType<ConvLayerType>(layer) &&</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  (layer->GetNameStr() == <span class="stringliteral">"fused-batchNorm-into-convolution"</span>);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  };</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> </div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  BOOST_CHECK(3 == graphFused.GetNumLayers());</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  BOOST_TEST(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graphFused.cbegin(),</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  graphFused.cend(),</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  &IsLayerOfType<InputLayer>,</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  checkFusedConv2d,</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  &IsLayerOfType<OutputLayer>));</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>  <span class="comment">// Load network into runtime</span></div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> networkIdentifier;</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  BOOST_TEST(run->LoadNetwork(networkIdentifier, std::move(optNetFused)) == Status::Success);</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span> </div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="comment">//Creates structures for inputs and outputs.</span></div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  std::vector<T> inputDataFused = GetVector<T>(48, 1.0f, 0.1f);</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> </div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  std::vector<T> outputDataFused(36);</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="keywordflow">if</span> (depthwise)</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>  outputDataFused.resize(108);</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  }</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> </div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensorsFused {</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  {0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(run->GetInputTensorInfo (networkIdentifier, 0), inputDataFused.data())}};</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensorsFused{</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  {0, <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(run->GetOutputTensorInfo(networkIdentifier, 0), outputDataFused.data())}};</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>  <span class="comment">// Execute network</span></div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  run->EnqueueWorkload(networkIdentifier, inputTensorsFused, outputTensorsFused);</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span> </div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="comment">// SECOND NETWORK: NotFused</span></div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="comment">// Construct ArmNN network</span></div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> networkNotFused = CreatNetwork<Conv2dTest, ArmnnType>(depthwise, <span class="keyword">true</span>);</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="comment">// Create ArmNN runtime</span></div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <a class="code" href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">IRuntimePtr</a> runNotFused = IRuntime::Create(<a class="code" href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">IRuntime::CreationOptions</a>()); <span class="comment">// default options</span></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>  <span class="comment">// Optimise ArmNN network</span></div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <a class="code" href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">IOptimizedNetworkPtr</a> optNetNotFused = <a class="code" href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">Optimize</a>(*networkNotFused, {backendId}, runNotFused->GetDeviceSpec());</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>  <a class="code" href="classarmnn_1_1_graph.xhtml">Graph</a>& graphNotFused = <a class="code" href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">GetGraphForTesting</a>(optNetNotFused.get());</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> </div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  BOOST_CHECK(5 == graphNotFused.GetNumLayers());</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  BOOST_TEST(<a class="code" href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a>(graphNotFused.cbegin(),</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  graphNotFused.cend(),</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  &IsLayerOfType<armnn::InputLayer>,</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  &IsLayerOfType<ConvLayerType>,</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  &IsLayerOfType<armnn::BatchNormalizationLayer>,</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  &IsLayerOfType<armnn::OutputLayer>,</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  &IsLayerOfType<armnn::OutputLayer>));</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span> </div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="comment">// Load network into runtime</span></div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <a class="code" href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">NetworkId</a> networkIdentifierNotFused;</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  BOOST_TEST(runNotFused->LoadNetwork(networkIdentifierNotFused, std::move(optNetNotFused)) == Status::Success);</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> </div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="comment">//Creates structures for inputs and outputs.</span></div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  std::vector<T> inputDataNotFused = GetVector<T>(48, 1.0f, 0.1f);</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>  std::vector<T> outputDataNotFused(36);</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  std::vector<T> outputData2NotFused(36);</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span> </div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="keywordflow">if</span> (depthwise)</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  {</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  outputDataNotFused.resize(108);</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  outputData2NotFused.resize(108);</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  }</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <a class="code" href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">InputTensors</a> inputTensorsNotFused{</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  {0, <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a>(runNotFused->GetInputTensorInfo(networkIdentifierNotFused, 0), inputDataNotFused.data())}};</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <a class="code" href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">OutputTensors</a> outputTensorsNotFused{</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  {0, <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(runNotFused->GetOutputTensorInfo(networkIdentifierNotFused, 0), outputDataNotFused.data())},</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  {1, <a class="code" href="classarmnn_1_1_tensor.xhtml">Tensor</a>(runNotFused->GetOutputTensorInfo(networkIdentifierNotFused, 1), outputData2NotFused.data())}};</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> </div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="comment">// Execute network</span></div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  runNotFused->EnqueueWorkload(networkIdentifierNotFused, inputTensorsNotFused, outputTensorsNotFused);</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> </div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="comment">// Check the output of the fused-convolution matches with the output of the batchNormm in the "NotFused" network</span></div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> n = 0; n < outputDataFused.size(); ++n)</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  {</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  BOOST_CHECK_CLOSE(outputDataFused[n], outputDataNotFused[n], T(tolerance));</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  }</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span> }</div><div class="ttc" id="namespacearmnn_xhtml_a150468a02bd7b2d2d061c4aaaee939f0"><div class="ttname"><a href="namespacearmnn.xhtml#a150468a02bd7b2d2d061c4aaaee939f0">armnn::IRuntimePtr</a></div><div class="ttdeci">std::unique_ptr< IRuntime, void(*)(IRuntime *runtime)> IRuntimePtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00026">IRuntime.hpp:26</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_aa01bce88f89975a5a031db4cc8861527"><div class="ttname"><a href="namespacearmnn.xhtml#aa01bce88f89975a5a031db4cc8861527">armnn::InputTensors</a></div><div class="ttdeci">std::vector< std::pair< LayerBindingId, class ConstTensor > > InputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00340">Tensor.hpp:340</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a83015160d8c67d5d77735eb0d4033d9a"><div class="ttname"><a href="namespacearmnn.xhtml#a83015160d8c67d5d77735eb0d4033d9a">armnn::NetworkId</a></div><div class="ttdeci">int NetworkId</div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00020">IRuntime.hpp:20</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor.xhtml">armnn::Tensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and a mutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00306">Tensor.hpp:306</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a82e98ef05fd67036d1195ba17174d685"><div class="ttname"><a href="namespacearmnn.xhtml#a82e98ef05fd67036d1195ba17174d685">armnn::Optimize</a></div><div class="ttdeci">IOptimizedNetworkPtr Optimize(const INetwork &network, const std::vector< BackendId > &backendPreferences, const IDeviceSpec &deviceSpec, const OptimizerOptions &options=OptimizerOptions(), Optional< std::vector< std::string > &> messages=EmptyOptional())</div><div class="ttdoc">Create an optimized version of the network. </div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l01502">Network.cpp:1502</a></div></div> +<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00314">Tensor.hpp:314</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a8f091a512915d1cb29a4ebf13dfc53ea"><div class="ttname"><a href="namespacearmnn.xhtml#a8f091a512915d1cb29a4ebf13dfc53ea">armnn::OutputTensors</a></div><div class="ttdeci">std::vector< std::pair< LayerBindingId, class Tensor > > OutputTensors</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00341">Tensor.hpp:341</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a674efcf6cbdb9e831d653ff0e821fb38"><div class="ttname"><a href="namespacearmnn.xhtml#a674efcf6cbdb9e831d653ff0e821fb38">armnn::IOptimizedNetworkPtr</a></div><div class="ttdeci">std::unique_ptr< IOptimizedNetwork, void(*)(IOptimizedNetwork *network)> IOptimizedNetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00174">INetwork.hpp:174</a></div></div> +<div class="ttc" id="classarmnn_1_1_graph_xhtml"><div class="ttname"><a href="classarmnn_1_1_graph.xhtml">armnn::Graph</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_8hpp_source.xhtml#l00029">Graph.hpp:29</a></div></div> +<div class="ttc" id="structarmnn_1_1_i_runtime_1_1_creation_options_xhtml"><div class="ttname"><a href="structarmnn_1_1_i_runtime_1_1_creation_options.xhtml">armnn::IRuntime::CreationOptions</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_runtime_8hpp_source.xhtml#l00043">IRuntime.hpp:43</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_a6a2659750d6161b693d0e51616791959"><div class="ttname"><a href="namespacearmnn.xhtml#a6a2659750d6161b693d0e51616791959">armnn::GetGraphForTesting</a></div><div class="ttdeci">Graph & GetGraphForTesting(IOptimizedNetwork *optNet)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8cpp_source.xhtml#l00025">TestUtils.cpp:25</a></div></div> +<div class="ttc" id="_test_utils_8hpp_xhtml_a0eedb278f57355b47fa983450d4e378c"><div class="ttname"><a href="_test_utils_8hpp.xhtml#a0eedb278f57355b47fa983450d4e378c">CheckSequence</a></div><div class="ttdeci">bool CheckSequence(const armnn::Graph::ConstIterator first, const armnn::Graph::ConstIterator last)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8hpp_source.xhtml#l00021">TestUtils.hpp:21</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ace74f6f9feb95a964a49d79458232703"><div class="ttname"><a href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">armnn::INetworkPtr</a></div><div class="ttdeci">std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr</div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00173">INetwork.hpp:173</a></div></div> +<div class="ttc" id="classarmnn_1_1_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_layer.xhtml">armnn::Layer</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_8hpp_source.xhtml#l00210">Layer.hpp:210</a></div></div> +</div><!-- fragment --> +</div> +</div> +</div><!-- contents --> +</div><!-- doc-content --> +<!-- start footer part --> +<div id="nav-path" class="navpath"><!-- id is needed for treeview function! --> + <ul> + <li class="navelem"><a class="el" href="dir_68267d1309a1af8e8297ef4c3efbcdba.xhtml">src</a></li><li class="navelem"><a class="el" href="dir_e0a84d05c80a2ef4231141dcbbeac5c8.xhtml">armnn</a></li><li class="navelem"><a class="el" href="dir_9d86fd1fbecbedf5bdb69c7e7235fe5f.xhtml">test</a></li><li class="navelem"><a class="el" href="dir_f1cd0e6da811a659c139424442adfb5f.xhtml">optimizations</a></li><li class="navelem"><a class="el" href="_fuse_batch_norm_tests_8cpp.xhtml">FuseBatchNormTests.cpp</a></li> + <li class="footer">Generated on Thu Feb 25 2021 17:27:54 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> |