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diff --git a/20.02/_splitter_end_to_end_test_impl_8hpp_source.xhtml b/20.02/_splitter_end_to_end_test_impl_8hpp_source.xhtml new file mode 100644 index 0000000000..8646b0bc1c --- /dev/null +++ b/20.02/_splitter_end_to_end_test_impl_8hpp_source.xhtml @@ -0,0 +1,128 @@ +<!-- 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/SplitterEndToEndTestImpl.hpp Source File</title> +<link href="tabs.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="jquery.js"></script> +<script type="text/javascript" src="dynsections.js"></script> +<link href="navtree.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="resize.js"></script> +<script type="text/javascript" src="navtreedata.js"></script> +<script type="text/javascript" src="navtree.js"></script> +<script type="text/javascript"> + $(document).ready(initResizable); +</script> +<link href="search/search.css" rel="stylesheet" type="text/css"/> +<script type="text/javascript" src="search/searchdata.js"></script> +<script type="text/javascript" src="search/search.js"></script> +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + extensions: ["tex2jax.js"], + jax: ["input/TeX","output/HTML-CSS"], +}); +</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script> +<link href="doxygen.css" rel="stylesheet" type="text/css" /> +<link href="stylesheet.css" rel="stylesheet" type="text/css"/> +</head> +<body> +<div id="top"><!-- do not remove this div, it is closed by doxygen! --> +<div id="titlearea"> +<table cellspacing="0" cellpadding="0"> + <tbody> + <tr style="height: 56px;"> + <img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 10rem; margin-top: .5rem; margin-left 10px"/> + <td style="padding-left: 0.5em;"> + <div id="projectname"> +  <span id="projectnumber">20.02</span> + </div> + </td> + </tr> + </tbody> +</table> +</div> +<!-- end header part --> +<!-- Generated by Doxygen 1.8.13 --> +<script type="text/javascript"> +var searchBox = new SearchBox("searchBox", "search",false,'Search'); +</script> +<script type="text/javascript" src="menudata.js"></script> +<script type="text/javascript" src="menu.js"></script> +<script type="text/javascript"> +$(function() { + initMenu('',true,false,'search.php','Search'); + $(document).ready(function() { init_search(); }); +}); +</script> +<div id="main-nav"></div> +</div><!-- top --> +<div id="side-nav" class="ui-resizable side-nav-resizable"> + <div id="nav-tree"> + <div id="nav-tree-contents"> + <div id="nav-sync" class="sync"></div> + </div> + </div> + <div id="splitbar" style="-moz-user-select:none;" + class="ui-resizable-handle"> + </div> +</div> +<script type="text/javascript"> +$(document).ready(function(){initNavTree('_splitter_end_to_end_test_impl_8hpp_source.xhtml','');}); +</script> +<div id="doc-content"> +<!-- window showing the filter options --> +<div id="MSearchSelectWindow" + onmouseover="return searchBox.OnSearchSelectShow()" + onmouseout="return searchBox.OnSearchSelectHide()" + onkeydown="return searchBox.OnSearchSelectKey(event)"> +</div> + +<!-- iframe showing the search results (closed by default) --> +<div id="MSearchResultsWindow"> +<iframe src="javascript:void(0)" frameborder="0" + name="MSearchResults" id="MSearchResults"> +</iframe> +</div> + +<div class="header"> + <div class="headertitle"> +<div class="title">SplitterEndToEndTestImpl.hpp</div> </div> +</div><!--header--> +<div class="contents"> +<a href="_splitter_end_to_end_test_impl_8hpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">//</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Copyright © 2017 Arm Ltd. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="preprocessor">#pragma once</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> </div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="preprocessor">#include <<a class="code" href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a>></span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> </div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_i_network_8hpp.xhtml">armnn/INetwork.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_common_test_utils_8hpp.xhtml">backendsCommon/test/CommonTestUtils.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> </div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <boost/test/unit_test.hpp></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <vector></span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> {</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> </div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="keyword">template</span><<span class="keyword">typename</span> armnn::DataType DataType></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> CreateSplitterNetwork(<span class="keyword">const</span> TensorShape& inputShape,</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  <span class="keyword">const</span> std::vector<TensorShape>& outputShapes,</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitAxis,</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numSplit,</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> qScale = 1.0f,</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="keyword">const</span> int32_t qOffset = 0)</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> {</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="comment">// Builds up the structure of the network.</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net(<a class="code" href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">INetwork::Create</a>());</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> </div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>, qScale, qOffset);</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  std::vector<unsigned int> splitterDimSizes(inputShape.GetNumDimensions());</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> </div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="comment">// Add current input shape to splitterDimSizes</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < inputShape.GetNumDimensions(); ++i)</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>  splitterDimSizes[i] = inputTensorInfo.GetShape()[i];</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  }</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> </div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keywordflow">if</span> (splitterDimSizes[splitAxis] % numSplit != 0)</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  {</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keywordflow">throw</span> <a class="code" href="classarmnn_1_1_parse_exception.xhtml">ParseException</a>(<span class="stringliteral">"Number of splits must evenly divide the dimension"</span>);</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  }</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  splitterDimSizes[splitAxis] /= numSplit;</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> </div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <a class="code" href="structarmnn_1_1_views_descriptor.xhtml">SplitterDescriptor</a> splitDesc(numSplit, inputShape.GetNumDimensions());</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> g = 0; g < numSplit; ++g)</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>  <span class="comment">// Set the size of the views.</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIdx = 0; dimIdx < splitterDimSizes.size(); ++dimIdx)</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>  splitDesc.<a class="code" href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">SetViewSize</a>(g, dimIdx, splitterDimSizes[dimIdx]);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  }</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  splitDesc.SetViewOriginCoord(g, splitAxis, splitterDimSizes[splitAxis] * g);</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> </div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* splitter = net->AddSplitterLayer(splitDesc, <span class="stringliteral">"splitter"</span>);</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* input = net->AddInputLayer(0, <span class="stringliteral">"input"</span>);</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(input, splitter, inputTensorInfo, 0, 0);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> </div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < outputShapes.size(); ++i)</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo(outputShapes[i], <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a>, qScale, qOffset);</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <a class="code" href="classarmnn_1_1_i_connectable_layer.xhtml">IConnectableLayer</a>* output = net->AddOutputLayer(boost::numeric_cast<LayerBindingId>(i));</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <a class="code" href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a>(splitter, output, outputTensorInfo, i, 0);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  }</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> </div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keywordflow">return</span> net;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> }</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> <span class="keyword">template</span><armnn::DataType ArmnnType></div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> <span class="keywordtype">void</span> Splitter1dEndToEnd(<span class="keyword">const</span> std::vector<BackendId>& backends)</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> {</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keyword">using</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">ResolveType<ArmnnType></a>;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitAxis = 0;</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numSplit = 2;</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape = { 4 };</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keyword">const</span> std::vector<TensorShape> outputShapes{{ 2 }, { 2 }};</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> </div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="comment">// Builds up the structure of the network</span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit);</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>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"create a network"</span>);</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> </div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  std::vector<T> inputData{ 1, 2, 3, 4 };</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> </div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  std::vector<T> expectedOutput0{ 1, 2 };</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  std::vector<T> expectedOutput1{ 3, 4 };</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>  std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } };</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput0 }, {1, expectedOutput1} };</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> </div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> }</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> </div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> <span class="keyword">template</span><armnn::DataType ArmnnType></div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> <span class="keywordtype">void</span> Splitter2dDim0EndToEnd(<span class="keyword">const</span> std::vector<BackendId>& backends)</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> {</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keyword">using</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">ResolveType<ArmnnType></a>;</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>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitAxis = 0;</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numSplit = 2;</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape = { 4, 3 };</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keyword">const</span> std::vector<TensorShape> outputShapes{{ 2, 3 }, { 2, 3 }};</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> </div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="comment">// Builds up the structure of the network</span></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit);</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> </div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"create a network"</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="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  std::vector<T> inputData{</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  1, 2,</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  3, 4,</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  5, 6,</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  7, 8,</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  9, 10,</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  11, 12</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> </div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  std::vector<T> expectedOutput0{ 1, 2, 3, 4, 5, 6 };</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  std::vector<T> expectedOutput1{ 7, 8, 9, 10, 11, 12 };</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> </div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } };</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput0 }, {1, expectedOutput1} };</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>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> }</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> </div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> <span class="keyword">template</span><armnn::DataType ArmnnType></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> <span class="keywordtype">void</span> Splitter2dDim1EndToEnd(<span class="keyword">const</span> std::vector<BackendId>& backends)</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="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keyword">using</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">ResolveType<ArmnnType></a>;</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> </div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitAxis = 1;</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numSplit = 3;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape = { 4, 3 };</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keyword">const</span> std::vector<TensorShape> outputShapes{{ 4, 1 }, { 4, 1 }, { 4, 1 }};</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> </div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <span class="comment">// Builds up the structure of the network</span></div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit);</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> </div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"create a network"</span>);</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> </div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  std::vector<T> inputData{</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  1, 2,</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  3, 4,</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  5, 6,</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  7, 8,</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  9, 10,</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  11, 12</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  };</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> </div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  std::vector<T> expectedOutput0{ 1, 4, 7, 10 };</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  std::vector<T> expectedOutput1{ 2, 5, 8, 11 };</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  std::vector<T> expectedOutput2{ 3, 6, 9, 12 };</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>  std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } };</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput0 },</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  { 1, expectedOutput1 },</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  { 2, expectedOutput2 } };</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> </div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);</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> </div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> <span class="keyword">template</span><armnn::DataType ArmnnType></div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> <span class="keywordtype">void</span> Splitter3dDim0EndToEnd(<span class="keyword">const</span> std::vector<BackendId>& backends)</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> {</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keyword">using</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">ResolveType<ArmnnType></a>;</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> </div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitAxis = 0;</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numSplit = 2;</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape = { 2, 4, 3 };</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keyword">const</span> std::vector<TensorShape> outputShapes{{ 1, 4, 3 }, { 1, 4, 3 }};</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span> </div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="comment">// Builds up the structure of the network</span></div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit);</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>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"create a network"</span>);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> </div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  std::vector<T> inputData{</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  1, 2, 3,</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  4, 5, 6,</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  7, 8, 9,</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  10, 11, 12,</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  13, 14, 15,</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  16, 17, 18,</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  19, 20, 21,</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  22, 23, 24</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  };</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> </div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  std::vector<T> expectedOutput0{</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  1, 2, 3,</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  4, 5, 6,</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  7, 8, 9,</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  10, 11, 12</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>  std::vector<T> expectedOutput1{</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  13, 14, 15,</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  16, 17, 18,</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  19, 20, 21,</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  22, 23, 24</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</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>  std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } };</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput0 },</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  { 1, expectedOutput1 } };</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> </div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span> }</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span> </div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span> <span class="keyword">template</span><armnn::DataType ArmnnType></div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span> <span class="keywordtype">void</span> Splitter3dDim1EndToEnd(<span class="keyword">const</span> std::vector<BackendId>& backends)</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> {</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keyword">using</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">ResolveType<ArmnnType></a>;</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitAxis = 1;</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numSplit = 2;</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape = { 2, 4, 3 };</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keyword">const</span> std::vector<TensorShape> outputShapes{{ 2, 2, 3 }, { 2, 2, 3 }};</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">// Builds up the structure of the network</span></div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit);</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>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"create a network"</span>);</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> </div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  std::vector<T> inputData{</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  1, 2, 3,</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  4, 5, 6,</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  7, 8, 9,</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  10, 11, 12,</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  13, 14, 15,</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  16, 17, 18,</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  19, 20, 21,</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  22, 23, 24</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  };</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span> </div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  std::vector<T> expectedOutput0{</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  1, 2, 3,</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  4, 5, 6,</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  13, 14, 15,</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  16, 17, 18</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  };</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  std::vector<T> expectedOutput1{</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  7, 8, 9,</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  10, 11, 12,</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  19, 20, 21,</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  22, 23, 24</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> </div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } };</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput0 },</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  { 1, expectedOutput1 } };</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span> </div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span> }</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> </div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> <span class="keyword">template</span><armnn::DataType ArmnnType></div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span> <span class="keywordtype">void</span> Splitter3dDim2EndToEnd(<span class="keyword">const</span> std::vector<BackendId>& backends)</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span> {</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="keyword">using</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">ResolveType<ArmnnType></a>;</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>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitAxis = 2;</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numSplit = 3;</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape = { 2, 4, 3 };</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keyword">const</span> std::vector<TensorShape> outputShapes{{ 2, 4, 1 }, { 2, 4, 1 }, { 2, 4, 1 }};</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>  <span class="comment">// Builds up the structure of the network</span></div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit);</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> </div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"create a network"</span>);</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> </div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  std::vector<T> inputData{</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  1, 2, 3,</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  4, 5, 6,</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  7, 8, 9,</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  10, 11, 12,</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  13, 14, 15,</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  16, 17, 18,</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  19, 20, 21,</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  22, 23, 24</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  };</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span> </div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  std::vector<T> expectedOutput0{ 1, 4, 7, 10, 13, 16, 19, 22 };</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  std::vector<T> expectedOutput1{ 2, 5, 8, 11, 14, 17, 20, 23 };</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  std::vector<T> expectedOutput2{ 3, 6, 9, 12, 15, 18, 21, 24 };</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span> </div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } };</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput0 },</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  { 1, expectedOutput1 },</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  { 2, expectedOutput2 } };</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span> </div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span> }</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span> </div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span> <span class="keyword">template</span><armnn::DataType ArmnnType></div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span> <span class="keywordtype">void</span> Splitter4dDim0EndToEnd(<span class="keyword">const</span> std::vector<BackendId>& backends)</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span> {</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="keyword">using</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">ResolveType<ArmnnType></a>;</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span> </div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitAxis = 0;</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numSplit = 2;</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape = { 4, 3, 2, 2 };</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <span class="keyword">const</span> std::vector<TensorShape> outputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }};</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span> </div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="comment">// Builds up the structure of the network</span></div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit);</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>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"create a network"</span>);</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span> </div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  std::vector<T> inputData{</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  1, 2,</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  3, 4,</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  5, 6,</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  7, 8,</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  9, 10,</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  11, 12,</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  13, 14,</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  15, 16,</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  17, 18,</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  19, 20,</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  21, 22,</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  23, 24,</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  25, 26,</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  27, 28,</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  29, 30,</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  31, 32,</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  33, 34,</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  35, 36,</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  37, 38,</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  39, 40,</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  41, 42,</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  43, 44,</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  45, 46,</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  47, 48</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  };</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span> </div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  std::vector<T> expectedOutput0{</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  1, 2,</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  3, 4,</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  5, 6,</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  7, 8,</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  9, 10,</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  11, 12,</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  13, 14,</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  15, 16,</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  17, 18,</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  19, 20,</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  21, 22,</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  23, 24</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  };</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span> </div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  std::vector<T> expectedOutput1{</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  25, 26,</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  27, 28,</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  29, 30,</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  31, 32,</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  33, 34,</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  35, 36,</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  37, 38,</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  39, 40,</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  41, 42,</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  43, 44,</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  45, 46,</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  47, 48</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  };</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span> </div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }};</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  std::map<int, std::vector<T>> expectedOutputData = {{ 0, expectedOutput0 }, { 1, expectedOutput1 }};</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>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span> }</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span> </div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span> <span class="keyword">template</span><armnn::DataType ArmnnType></div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span> <span class="keywordtype">void</span> Splitter4dDim1EndToEnd(<span class="keyword">const</span> std::vector<BackendId>& backends)</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span> {</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  <span class="keyword">using</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">ResolveType<ArmnnType></a>;</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span> </div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitAxis = 1;</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numSplit = 2;</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape = { 2, 6, 2, 2 };</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <span class="keyword">const</span> std::vector<TensorShape> outputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }};</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span> </div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="comment">// Builds up the structure of the network</span></div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit);</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span> </div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"create a network"</span>);</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span> </div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  std::vector<T> inputData{</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  1, 2,</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  3, 4,</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  5, 6,</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  7, 8,</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  9, 10,</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  11, 12,</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  13, 14,</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  15, 16,</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  17, 18,</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  19, 20,</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  21, 22,</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  23, 24,</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  25, 26,</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  27, 28,</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  29, 30,</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  31, 32,</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  33, 34,</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  35, 36,</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  37, 38,</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  39, 40,</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  41, 42,</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  43, 44,</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  45, 46,</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  47, 48</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  };</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span> </div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  std::vector<T> expectedOutput0{</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  1, 2,</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  3, 4,</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  5, 6,</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  7, 8,</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  9, 10,</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  11, 12,</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  25, 26,</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  27, 28,</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  29, 30,</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  31, 32,</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  33, 34,</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  35, 36</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  };</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span> </div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  std::vector<T> expectedOutput1{</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  13, 14,</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  15, 16,</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  17, 18,</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  19, 20,</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  21, 22,</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  23, 24,</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  37, 38,</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  39, 40,</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  41, 42,</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  43, 44,</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  45, 46,</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  47, 48</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> </div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }};</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  std::map<int, std::vector<T>> expectedOutputData = {{ 0, expectedOutput0 }, { 1, expectedOutput1 }};</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span> </div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span> }</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> <span class="keyword">template</span><armnn::DataType ArmnnType></div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span> <span class="keywordtype">void</span> Splitter4dDim2EndToEnd(<span class="keyword">const</span> std::vector<BackendId>& backends)</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span> {</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <span class="keyword">using</span> T = <a class="code" href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">ResolveType<ArmnnType></a>;</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span> </div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitAxis = 2;</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numSplit = 2;</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>& inputShape = { 2, 3, 4, 2 };</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <span class="keyword">const</span> std::vector<TensorShape> outputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }};</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span> </div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  <span class="comment">// Builds up the structure of the network</span></div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit);</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span> </div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"create a network"</span>);</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="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  std::vector<T> inputData{</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  1, 2,</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  3, 4,</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  5, 6,</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  7, 8,</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  9, 10,</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  11, 12,</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  13, 14,</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  15, 16,</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  17, 18,</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  19, 20,</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  21, 22,</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  23, 24,</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  25, 26,</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  27, 28,</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  29, 30,</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  31, 32,</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  33, 34,</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  35, 36,</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  37, 38,</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  39, 40,</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  41, 42,</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  43, 44,</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  45, 46,</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  47, 48</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  };</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span> </div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  std::vector<T> expectedOutput0{</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  1, 2,</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  3, 4,</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  9, 10,</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  11, 12,</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  17, 18,</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  19, 20,</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  25, 26,</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  27, 28,</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  33, 34,</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  35, 36,</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  41, 42,</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  43, 44</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  };</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span> </div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  std::vector<T> expectedOutput1{</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  5, 6,</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  7, 8,</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  13, 14,</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  15, 16,</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  21, 22,</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  23, 24,</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  29, 30,</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  31, 32,</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  37, 38,</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  39, 40,</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  45, 46,</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  47, 48</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  };</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span> </div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }};</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  std::map<int, std::vector<T>> expectedOutputData = {{ 0, expectedOutput0 }, { 1, expectedOutput1 }};</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span> </div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span> }</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span> </div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span> <span class="keyword">template</span><armnn::DataType ArmnnType, <span class="keyword">typename</span> T = armnn::ResolveType<ArmnnType>></div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span> <span class="keywordtype">void</span> Splitter4dDim3EndToEnd(<span class="keyword">const</span> std::vector<BackendId>& backends)</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span> {</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span> </div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> splitAxis = 3;</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numSplit = 2;</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_tensor_shape.xhtml">TensorShape</a>& inputShape = { 2, 3, 4, 2 };</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <span class="keyword">const</span> std::vector<TensorShape> outputShapes{{ 2, 3, 4, 1 }, { 2, 3, 4, 1 }};</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span> </div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <span class="comment">// Builds up the structure of the network</span></div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  <a class="code" href="namespacearmnn.xhtml#ace74f6f9feb95a964a49d79458232703">INetworkPtr</a> net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit);</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span> </div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  BOOST_TEST_CHECKPOINT(<span class="stringliteral">"create a network"</span>);</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span> </div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <span class="comment">// Creates structures for input & output.</span></div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  std::vector<T> inputData{</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  1, 2,</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  3, 4,</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  5, 6,</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  7, 8,</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  9, 10,</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  11, 12,</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  13, 14,</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  15, 16,</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  17, 18,</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  19, 20,</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  21, 22,</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  23, 24,</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  25, 26,</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  27, 28,</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  29, 30,</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  31, 32,</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  33, 34,</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  35, 36,</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  37, 38,</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  39, 40,</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  41, 42,</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  43, 44,</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  45, 46,</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  47, 48</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  };</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span> </div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  std::vector<T> expectedOutput0{</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  1, 3, 5, 7,</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  9, 11, 13, 15,</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  17, 19, 21, 23,</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  25, 27, 29, 31,</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  33, 35, 37, 39,</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  41, 43, 45, 47</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  };</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span> </div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  std::vector<T> expectedOutput1{</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  2, 4, 6, 8,</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  10, 12, 14, 16,</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  18, 20, 22, 24,</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  26, 28, 30, 32,</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  34, 36, 38, 40,</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  42, 44, 46, 48</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  };</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span> </div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }};</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  std::map<int, std::vector<T>> expectedOutputData = {{ 0, expectedOutput0 }, { 1, expectedOutput1 }};</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span> </div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends);</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span> }</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span> </div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span> } <span class="comment">// anonymous namespace</span></div><div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml">armnn::ViewsDescriptor</a></div><div class="ttdoc">A ViewsDescriptor for the SplitterLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00190">Descriptors.hpp:190</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_connectable_layer_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_connectable_layer.xhtml">armnn::IConnectableLayer</a></div><div class="ttdoc">Interface for a layer that is connectable to other layers via InputSlots and OutputSlots. </div><div class="ttdef"><b>Definition:</b> <a href="_i_network_8hpp_source.xhtml#l00061">INetwork.hpp:61</a></div></div> +<div class="ttc" id="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="namespacearmnn_xhtml_a0743ed5e860c316a20b68ca96301b411"><div class="ttname"><a href="namespacearmnn.xhtml#a0743ed5e860c316a20b68ca96301b411">armnn::ResolveType</a></div><div class="ttdeci">typename ResolveTypeImpl< DT >::Type ResolveType</div><div class="ttdef"><b>Definition:</b> <a href="_resolve_type_8hpp_source.xhtml#l00073">ResolveType.hpp:73</a></div></div> +<div class="ttc" id="_resolve_type_8hpp_xhtml"><div class="ttname"><a href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2020 ARM Limited. </div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00025">00_introduction.dox:25</a></div></div> +<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div> +<div class="ttc" id="_common_test_utils_8hpp_xhtml"><div class="ttname"><a href="_common_test_utils_8hpp.xhtml">CommonTestUtils.hpp</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00032">Types.hpp:32</a></div></div> +<div class="ttc" id="structarmnn_1_1_views_descriptor_xhtml_aae0893695f5803a3517985c7cb1ccb2e"><div class="ttname"><a href="structarmnn_1_1_views_descriptor.xhtml#aae0893695f5803a3517985c7cb1ccb2e">armnn::ViewsDescriptor::SetViewSize</a></div><div class="ttdeci">Status SetViewSize(uint32_t view, uint32_t coord, uint32_t value)</div><div class="ttdoc">Set the size of the views. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00307">Descriptors.cpp:307</a></div></div> +<div class="ttc" id="_i_network_8hpp_xhtml"><div class="ttname"><a href="_i_network_8hpp.xhtml">INetwork.hpp</a></div></div> +<div class="ttc" id="classarmnn_1_1_parse_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_parse_exception.xhtml">armnn::ParseException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00092">Exceptions.hpp:92</a></div></div> +<div class="ttc" id="_test_utils_8cpp_xhtml_a0b295acb179f15eb3fb862b32008f782"><div class="ttname"><a href="_test_utils_8cpp.xhtml#a0b295acb179f15eb3fb862b32008f782">Connect</a></div><div class="ttdeci">void Connect(armnn::IConnectableLayer *from, armnn::IConnectableLayer *to, const armnn::TensorInfo &tensorInfo, unsigned int fromIndex, unsigned int toIndex)</div><div class="ttdef"><b>Definition:</b> <a href="_test_utils_8cpp_source.xhtml#l00012">TestUtils.cpp:12</a></div></div> +<div class="ttc" id="namespacearmnn_xhtml_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#l00101">INetwork.hpp:101</a></div></div> +<div class="ttc" id="classarmnn_1_1_i_network_xhtml_a706f7345af3f18f4b16e226a672214c6"><div class="ttname"><a href="classarmnn_1_1_i_network.xhtml#a706f7345af3f18f4b16e226a672214c6">armnn::INetwork::Create</a></div><div class="ttdeci">static INetworkPtr Create()</div><div class="ttdef"><b>Definition:</b> <a href="_network_8cpp_source.xhtml#l00049">Network.cpp:49</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="_splitter_end_to_end_test_impl_8hpp.xhtml">SplitterEndToEndTestImpl.hpp</a></li> + <li class="footer">Generated on Fri Mar 13 2020 16:09:11 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> |