diff --git a/Documentation/_workload_data_validation_8cpp.xhtml b/Documentation/_workload_data_validation_8cpp.xhtml
new file mode 100644
index 0000000..d05694d
--- /dev/null
+++ b/Documentation/_workload_data_validation_8cpp.xhtml
@@ -0,0 +1,579 @@
+<!-- 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/WorkloadDataValidation.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">
+  $(document).ready(initResizable);
+</script>
+<link href="search/search.css" rel="stylesheet" type="text/css"/>
+<script type="text/javascript" src="search/searchdata.js"></script>
+<script type="text/javascript" src="search/search.js"></script>
+<script type="text/x-mathjax-config">
+  MathJax.Hub.Config({
+    extensions: ["tex2jax.js"],
+    jax: ["input/TeX","output/HTML-CSS"],
+});
+</script><script type="text/javascript" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script>
+<link href="doxygen.css" rel="stylesheet" type="text/css" />
+<link href="stylesheet.css" rel="stylesheet" type="text/css"/>
+</head>
+<body>
+<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
+<div id="titlearea">
+<table cellspacing="0" cellpadding="0">
+ <tbody>
+ <tr style="height: 56px;">
+  <img alt="ArmNN" src="Arm_NN_horizontal_blue.png" style="max-width: 10rem; margin-top: .5rem; margin-left 10px"/>
+  <td style="padding-left: 0.5em;">
+   <div id="projectname">
+   &#160;<span id="projectnumber">20.02</span>
+   </div>
+  </td>
+ </tr>
+ </tbody>
+</table>
+</div>
+<!-- end header part -->
+<!-- Generated by Doxygen 1.8.13 -->
+<script type="text/javascript">
+var searchBox = new SearchBox("searchBox", "search",false,'Search');
+</script>
+<script type="text/javascript" src="menudata.js"></script>
+<script type="text/javascript" src="menu.js"></script>
+<script type="text/javascript">
+$(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('_workload_data_validation_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">WorkloadDataValidation.cpp File Reference</div>  </div>
+</div><!--header-->
+<div class="contents">
+<div class="textblock"><code>#include &quot;<a class="el" href="_workload_test_utils_8hpp_source.xhtml">WorkloadTestUtils.hpp</a>&quot;</code><br />
+<code>#include &lt;<a class="el" href="_exceptions_8hpp_source.xhtml">armnn/Exceptions.hpp</a>&gt;</code><br />
+<code>#include &lt;<a class="el" href="_cpu_tensor_handle_8hpp_source.xhtml">backendsCommon/CpuTensorHandle.hpp</a>&gt;</code><br />
+<code>#include &lt;<a class="el" href="_workload_8hpp_source.xhtml">backendsCommon/Workload.hpp</a>&gt;</code><br />
+<code>#include &lt;<a class="el" href="_ref_workloads_8hpp_source.xhtml">reference/workloads/RefWorkloads.hpp</a>&gt;</code><br />
+<code>#include &lt;<a class="el" href="_ref_workload_factory_8hpp_source.xhtml">reference/RefWorkloadFactory.hpp</a>&gt;</code><br />
+<code>#include &lt;boost/test/unit_test.hpp&gt;</code><br />
+</div>
+<p><a href="_workload_data_validation_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:a5e734b459a947759e3ca5f76e2f278f2"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_workload_data_validation_8cpp.xhtml#a5e734b459a947759e3ca5f76e2f278f2">BOOST_AUTO_TEST_CASE</a> (BatchNormalizationQueueDescriptor_Validate_DifferentQuantizationData)</td></tr>
+<tr class="separator:a5e734b459a947759e3ca5f76e2f278f2"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a30f72f6909ab012842e16398984c77c0"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_workload_data_validation_8cpp.xhtml#a30f72f6909ab012842e16398984c77c0">BOOST_AUTO_TEST_CASE</a> (QueueDescriptor_Validate_WrongNumOfInputsOutputs)</td></tr>
+<tr class="separator:a30f72f6909ab012842e16398984c77c0"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a12def4fb1722495397eaf309cb8ef712"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_workload_data_validation_8cpp.xhtml#a12def4fb1722495397eaf309cb8ef712">BOOST_AUTO_TEST_CASE</a> (RefPooling2dFloat32Workload_Validate_WrongDimTensor)</td></tr>
+<tr class="separator:a12def4fb1722495397eaf309cb8ef712"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ae59870717f9ac828ab0bf3bc6804bfec"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_workload_data_validation_8cpp.xhtml#ae59870717f9ac828ab0bf3bc6804bfec">BOOST_AUTO_TEST_CASE</a> (SoftmaxQueueDescriptor_Validate_WrongInputHeight)</td></tr>
+<tr class="separator:ae59870717f9ac828ab0bf3bc6804bfec"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a1a76fe496d4e2ee0b0893a36736ae288"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_workload_data_validation_8cpp.xhtml#a1a76fe496d4e2ee0b0893a36736ae288">BOOST_AUTO_TEST_CASE</a> (FullyConnectedQueueDescriptor_Validate_RequiredDataMissing)</td></tr>
+<tr class="separator:a1a76fe496d4e2ee0b0893a36736ae288"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a5133fd5b53fab2340f3655d9cb27e6c1"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_workload_data_validation_8cpp.xhtml#a5133fd5b53fab2340f3655d9cb27e6c1">BOOST_AUTO_TEST_CASE</a> (NormalizationQueueDescriptor_Validate_WrongInputHeight)</td></tr>
+<tr class="separator:a5133fd5b53fab2340f3655d9cb27e6c1"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a454c029687438437ea707b94f88361e1"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_workload_data_validation_8cpp.xhtml#a454c029687438437ea707b94f88361e1">BOOST_AUTO_TEST_CASE</a> (SplitterQueueDescriptor_Validate_WrongWindow)</td></tr>
+<tr class="separator:a454c029687438437ea707b94f88361e1"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a198e5f41a13facee99cf14390282e186"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_workload_data_validation_8cpp.xhtml#a198e5f41a13facee99cf14390282e186">BOOST_AUTO_TEST_CASE</a> (ConcatQueueDescriptor_Validate_WrongWindow)</td></tr>
+<tr class="separator:a198e5f41a13facee99cf14390282e186"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a5f1870bd33bf34b70b985e24da85700f"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_workload_data_validation_8cpp.xhtml#a5f1870bd33bf34b70b985e24da85700f">BOOST_AUTO_TEST_CASE</a> (AdditionQueueDescriptor_Validate_InputNumbers)</td></tr>
+<tr class="separator:a5f1870bd33bf34b70b985e24da85700f"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a77921e834125a4a2d63142cb8ac115f6"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_workload_data_validation_8cpp.xhtml#a77921e834125a4a2d63142cb8ac115f6">BOOST_AUTO_TEST_CASE</a> (AdditionQueueDescriptor_Validate_InputShapes)</td></tr>
+<tr class="separator:a77921e834125a4a2d63142cb8ac115f6"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:abf20db1f5c57d502fe310198a9264b7c"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_workload_data_validation_8cpp.xhtml#abf20db1f5c57d502fe310198a9264b7c">BOOST_AUTO_TEST_CASE</a> (MultiplicationQueueDescriptor_Validate_InputTensorDimensionMismatch)</td></tr>
+<tr class="separator:abf20db1f5c57d502fe310198a9264b7c"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:a1877eca1aa5adb4c0e1302d04c96d013"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_workload_data_validation_8cpp.xhtml#a1877eca1aa5adb4c0e1302d04c96d013">BOOST_AUTO_TEST_CASE</a> (ReshapeQueueDescriptor_Validate_MismatchingNumElements)</td></tr>
+<tr class="separator:a1877eca1aa5adb4c0e1302d04c96d013"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:ad3a7db97268896c1bfa8df01f5834b3f"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_workload_data_validation_8cpp.xhtml#ad3a7db97268896c1bfa8df01f5834b3f">BOOST_AUTO_TEST_CASE</a> (LstmQueueDescriptor_Validate)</td></tr>
+<tr class="separator:ad3a7db97268896c1bfa8df01f5834b3f"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:adbdb7da153a5596e84d312e8abbd152d"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="_workload_data_validation_8cpp.xhtml#adbdb7da153a5596e84d312e8abbd152d">BOOST_AUTO_TEST_CASE</a> (BiasPerAxisQuantization_Validate)</td></tr>
+<tr class="separator:adbdb7da153a5596e84d312e8abbd152d"><td class="memSeparator" colspan="2">&#160;</td></tr>
+</table>
+<h2 class="groupheader">Function Documentation</h2>
+<a id="a5e734b459a947759e3ca5f76e2f278f2"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a5e734b459a947759e3ca5f76e2f278f2">&#9670;&nbsp;</a></span>BOOST_AUTO_TEST_CASE() <span class="overload">[1/14]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">BOOST_AUTO_TEST_CASE </td>
+          <td>(</td>
+          <td class="paramtype">BatchNormalizationQueueDescriptor_Validate_DifferentQuantizationData&#160;</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+<p class="definition">Definition at line <a class="el" href="_workload_data_validation_8cpp_source.xhtml#l00022">22</a> of file <a class="el" href="_workload_data_validation_8cpp_source.xhtml">WorkloadDataValidation.cpp</a>.</p>
+
+<p class="reference">References <a class="el" href="_workload_data_8hpp_source.xhtml#l00281">BatchNormalizationQueueDescriptor::m_Beta</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00282">BatchNormalizationQueueDescriptor::m_Gamma</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00279">BatchNormalizationQueueDescriptor::m_Mean</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00280">BatchNormalizationQueueDescriptor::m_Variance</a>, and <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::QAsymmU8</a>.</p>
+<div class="fragment"><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;{</div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape { 1, 3, 2, 2 };</div><div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape { 1, 3, 2, 2 };</div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;</div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>, .1f, 125);</div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>, .2f, 120);</div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;</div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;    <a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml">BatchNormalizationQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;    <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>                      invalidInfo;</div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;</div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> sameShape[] = { 10 };</div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> sameInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(1, sameShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>);</div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> sameTensor(sameInfo);</div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;</div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;</div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a40051a7aa82f25df43cc4244de04a7ec">m_Mean</a> = &amp;sameTensor;</div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a8cd8696bb773a02714d3fc095794ec54">m_Variance</a> = &amp;sameTensor;</div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#ad5f8f205ba69eb186688ca1c2aec207c">m_Beta</a>= &amp;sameTensor;</div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#afbe59e02a5464703b865ea1ccfff49fd">m_Gamma</a> = &amp;sameTensor;</div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;</div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;    BOOST_CHECK_NO_THROW(<a class="code" href="classarmnn_1_1_ref_batch_normalization_workload.xhtml">RefBatchNormalizationWorkload</a>(invalidData, invalidInfo));</div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml_afbe59e02a5464703b865ea1ccfff49fd"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#afbe59e02a5464703b865ea1ccfff49fd">armnn::BatchNormalizationQueueDescriptor::m_Gamma</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Gamma</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00282">WorkloadData.hpp:282</a></div></div>
+<div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml_ad5f8f205ba69eb186688ca1c2aec207c"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#ad5f8f205ba69eb186688ca1c2aec207c">armnn::BatchNormalizationQueueDescriptor::m_Beta</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Beta</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00281">WorkloadData.hpp:281</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="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml_a40051a7aa82f25df43cc4244de04a7ec"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a40051a7aa82f25df43cc4244de04a7ec">armnn::BatchNormalizationQueueDescriptor::m_Mean</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Mean</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00279">WorkloadData.hpp:279</a></div></div>
+<div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml_a8cd8696bb773a02714d3fc095794ec54"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a8cd8696bb773a02714d3fc095794ec54">armnn::BatchNormalizationQueueDescriptor::m_Variance</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Variance</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00280">WorkloadData.hpp:280</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="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div>
+<div class="ttc" id="classarmnn_1_1_ref_batch_normalization_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_ref_batch_normalization_workload.xhtml">armnn::RefBatchNormalizationWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_batch_normalization_workload_8hpp_source.xhtml#l00014">RefBatchNormalizationWorkload.hpp:14</a></div></div>
+<div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div>
+<div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml">armnn::BatchNormalizationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00269">WorkloadData.hpp:269</a></div></div>
+<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a id="a30f72f6909ab012842e16398984c77c0"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a30f72f6909ab012842e16398984c77c0">&#9670;&nbsp;</a></span>BOOST_AUTO_TEST_CASE() <span class="overload">[2/14]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">BOOST_AUTO_TEST_CASE </td>
+          <td>(</td>
+          <td class="paramtype">QueueDescriptor_Validate_WrongNumOfInputsOutputs&#160;</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+<p class="definition">Definition at line <a class="el" href="_workload_data_validation_8cpp_source.xhtml#l00048">48</a> of file <a class="el" href="_workload_data_validation_8cpp_source.xhtml">WorkloadDataValidation.cpp</a>.</p>
+<div class="fragment"><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;{</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;    <a class="code" href="structarmnn_1_1_mem_copy_queue_descriptor.xhtml">InputQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;    <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> invalidInfo;</div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;    <span class="comment">//Invalid argument exception is expected, because no inputs and no outputs were defined.</span></div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;    BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_workload_factory.xhtml">RefWorkloadFactory</a>().CreateInput(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_ref_workload_factory_xhtml"><div class="ttname"><a href="classarmnn_1_1_ref_workload_factory.xhtml">armnn::RefWorkloadFactory</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_workload_factory_8hpp_source.xhtml#l00030">RefWorkloadFactory.hpp:30</a></div></div>
+<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div>
+<div class="ttc" id="structarmnn_1_1_mem_copy_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_mem_copy_queue_descriptor.xhtml">armnn::MemCopyQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00058">WorkloadData.hpp:58</a></div></div>
+<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a id="a12def4fb1722495397eaf309cb8ef712"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a12def4fb1722495397eaf309cb8ef712">&#9670;&nbsp;</a></span>BOOST_AUTO_TEST_CASE() <span class="overload">[3/14]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">BOOST_AUTO_TEST_CASE </td>
+          <td>(</td>
+          <td class="paramtype">RefPooling2dFloat32Workload_Validate_WrongDimTensor&#160;</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+<p class="definition">Definition at line <a class="el" href="_workload_data_validation_8cpp_source.xhtml#l00056">56</a> of file <a class="el" href="_workload_data_validation_8cpp_source.xhtml">WorkloadDataValidation.cpp</a>.</p>
+
+<p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>.</p>
+<div class="fragment"><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;{</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[]  = {2, 3, 4}; <span class="comment">// &lt;- Invalid - input tensor has to be 4D.</span></div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = {2, 3, 4, 5};</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;    outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;    inputTensorInfo  = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(3, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;    <a class="code" href="structarmnn_1_1_pooling2d_queue_descriptor.xhtml">Pooling2dQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;    <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>           invalidInfo;</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;    <span class="comment">// Invalid argument exception is expected, input tensor has to be 4D.</span></div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;    BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_pooling2d_workload.xhtml">RefPooling2dWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
+<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
+<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
+<div class="ttc" id="structarmnn_1_1_pooling2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling2d_queue_descriptor.xhtml">armnn::Pooling2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00162">WorkloadData.hpp:162</a></div></div>
+<div class="ttc" id="classarmnn_1_1_ref_pooling2d_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_ref_pooling2d_workload.xhtml">armnn::RefPooling2dWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_pooling2d_workload_8hpp_source.xhtml#l00016">RefPooling2dWorkload.hpp:16</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a id="ae59870717f9ac828ab0bf3bc6804bfec"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ae59870717f9ac828ab0bf3bc6804bfec">&#9670;&nbsp;</a></span>BOOST_AUTO_TEST_CASE() <span class="overload">[4/14]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">BOOST_AUTO_TEST_CASE </td>
+          <td>(</td>
+          <td class="paramtype">SoftmaxQueueDescriptor_Validate_WrongInputHeight&#160;</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+<p class="definition">Definition at line <a class="el" href="_workload_data_validation_8cpp_source.xhtml#l00077">77</a> of file <a class="el" href="_workload_data_validation_8cpp_source.xhtml">WorkloadDataValidation.cpp</a>.</p>
+
+<p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>.</p>
+<div class="fragment"><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;{</div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 1;</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 1;</div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 4;</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 2;</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = inputChannels;</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = inputHeight + 1;    <span class="comment">//Makes data invalid - Softmax expects height and width to be 1.</span></div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = inputWidth;</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = inputNum;</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth };</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;    inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;    outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;    <a class="code" href="structarmnn_1_1_softmax_queue_descriptor.xhtml">SoftmaxQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;    <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>           invalidInfo;</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;    <span class="comment">//Invalid argument exception is expected, because height != 1.</span></div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;    BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_softmax_workload.xhtml">RefSoftmaxWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;}</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="structarmnn_1_1_softmax_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_softmax_queue_descriptor.xhtml">armnn::SoftmaxQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00077">WorkloadData.hpp:77</a></div></div>
+<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div>
+<div class="ttc" id="classarmnn_1_1_ref_softmax_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_ref_softmax_workload.xhtml">armnn::RefSoftmaxWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_softmax_workload_8hpp_source.xhtml#l00014">RefSoftmaxWorkload.hpp:14</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
+<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a id="a1a76fe496d4e2ee0b0893a36736ae288"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a1a76fe496d4e2ee0b0893a36736ae288">&#9670;&nbsp;</a></span>BOOST_AUTO_TEST_CASE() <span class="overload">[5/14]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">BOOST_AUTO_TEST_CASE </td>
+          <td>(</td>
+          <td class="paramtype">FullyConnectedQueueDescriptor_Validate_RequiredDataMissing&#160;</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+<p class="definition">Definition at line <a class="el" href="_workload_data_validation_8cpp_source.xhtml#l00108">108</a> of file <a class="el" href="_workload_data_validation_8cpp_source.xhtml">WorkloadDataValidation.cpp</a>.</p>
+
+<p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00150">FullyConnectedQueueDescriptor::m_Bias</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00386">FullyConnectedDescriptor::m_BiasEnabled</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00049">QueueDescriptorWithParameters&lt; LayerDescriptor &gt;::m_Parameters</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00388">FullyConnectedDescriptor::m_TransposeWeightMatrix</a>, and <a class="el" href="_workload_data_8hpp_source.xhtml#l00149">FullyConnectedQueueDescriptor::m_Weight</a>.</p>
+<div class="fragment"><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;{</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 1;</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 1;</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 5;</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 2;</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = 1;</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = 1;</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 3;</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = 2;</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;    <span class="comment">// Define the tensor descriptors.</span></div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsDesc;</div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesDesc;</div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;</div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth };</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsShape[] = { 1, 1, inputChannels, outputChannels };</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasShape[] = { 1, outputChannels, outputHeight, outputWidth };</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;    inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;    outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;    weightsDesc = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, weightsShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;    biasesDesc = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, biasShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;    <a class="code" href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml">FullyConnectedQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;    <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>                  invalidInfo;</div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> weightTensor(weightsDesc);</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> biasTensor(biasesDesc);</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &amp;weightTensor;</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &amp;biasTensor;</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">m_TransposeWeightMatrix</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;    <span class="comment">//Invalid argument exception is expected, because not all required fields have been provided.</span></div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;    <span class="comment">//In particular inputsData[0], outputsData[0] and weightsData can not be null.</span></div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;    BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_fully_connected_workload.xhtml">RefFullyConnectedWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_ref_fully_connected_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_ref_fully_connected_workload.xhtml">armnn::RefFullyConnectedWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_fully_connected_workload_8hpp_source.xhtml#l00018">RefFullyConnectedWorkload.hpp:18</a></div></div>
+<div class="ttc" id="structarmnn_1_1_fully_connected_queue_descriptor_xhtml_a3369b66d9316a773a41711e3f590c041"><div class="ttname"><a href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">armnn::FullyConnectedQueueDescriptor::m_Weight</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Weight</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00149">WorkloadData.hpp:149</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="structarmnn_1_1_fully_connected_descriptor_xhtml_a281fcaec86e17c97f7b8402633f6b55a"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">armnn::FullyConnectedDescriptor::m_TransposeWeightMatrix</a></div><div class="ttdeci">bool m_TransposeWeightMatrix</div><div class="ttdoc">Enable/disable transpose weight matrix. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00388">Descriptors.hpp:388</a></div></div>
+<div class="ttc" id="structarmnn_1_1_fully_connected_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml">armnn::FullyConnectedQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00141">WorkloadData.hpp:141</a></div></div>
+<div class="ttc" id="structarmnn_1_1_queue_descriptor_with_parameters_xhtml_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00049">WorkloadData.hpp:49</a></div></div>
+<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::FullyConnectedDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00386">Descriptors.hpp:386</a></div></div>
+<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div>
+<div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
+<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
+<div class="ttc" id="structarmnn_1_1_fully_connected_queue_descriptor_xhtml_ab3437cee6b0687812104fc1b37cbe8b3"><div class="ttname"><a href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">armnn::FullyConnectedQueueDescriptor::m_Bias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Bias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00150">WorkloadData.hpp:150</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a id="a5133fd5b53fab2340f3655d9cb27e6c1"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a5133fd5b53fab2340f3655d9cb27e6c1">&#9670;&nbsp;</a></span>BOOST_AUTO_TEST_CASE() <span class="overload">[6/14]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">BOOST_AUTO_TEST_CASE </td>
+          <td>(</td>
+          <td class="paramtype">NormalizationQueueDescriptor_Validate_WrongInputHeight&#160;</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+<p class="definition">Definition at line <a class="el" href="_workload_data_validation_8cpp_source.xhtml#l00156">156</a> of file <a class="el" href="_workload_data_validation_8cpp_source.xhtml">WorkloadDataValidation.cpp</a>.</p>
+
+<p class="reference">References <a class="el" href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437a810f43f3996922151c39b76143faeecc">armnn::Across</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, <a class="el" href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d">armnn::LocalBrightness</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00581">NormalizationDescriptor::m_Alpha</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00583">NormalizationDescriptor::m_Beta</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00585">NormalizationDescriptor::m_K</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00575">NormalizationDescriptor::m_NormChannelType</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00577">NormalizationDescriptor::m_NormMethodType</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00579">NormalizationDescriptor::m_NormSize</a>, and <a class="el" href="_workload_data_8hpp_source.xhtml#l00049">QueueDescriptorWithParameters&lt; LayerDescriptor &gt;::m_Parameters</a>.</p>
+<div class="fragment"><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;{</div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 5;</div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight   = 32;</div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth    = 24;</div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 3;</div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = inputNum;</div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = inputChannels;</div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = inputHeight + 1; <span class="comment">//Makes data invalid - normalization requires.</span></div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;                                                           <span class="comment">//Input and output to have the same dimensions.</span></div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth  = inputWidth;</div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;</div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;</div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[]  = {inputNum, inputChannels, inputHeight, inputWidth};</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;    inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;    outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;</div><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;</div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9">armnn::NormalizationAlgorithmMethod</a> normMethod = <a class="code" href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d">armnn::NormalizationAlgorithmMethod::LocalBrightness</a>;</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;    <a class="code" href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437">armnn::NormalizationAlgorithmChannel</a> normChannel = <a class="code" href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437a810f43f3996922151c39b76143faeecc">armnn::NormalizationAlgorithmChannel::Across</a>;</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    <span class="keywordtype">float</span> alpha = 1.f;</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;    <span class="keywordtype">float</span> beta = 1.f;</div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;    <span class="keywordtype">float</span> kappa = 1.f;</div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;    uint32_t normSize = 5;</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;</div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;    <a class="code" href="structarmnn_1_1_normalization_queue_descriptor.xhtml">NormalizationQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;    <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>                 invalidInfo;</div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;</div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#afe1f0f09d49ad2befc01f8789187b7dd">m_NormChannelType</a> = normChannel;</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a05945f080edf694b631960728b87aadb">m_NormMethodType</a>  = normMethod;</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#aa70c05f1aad12fbd9d9ec43ea4557b03">m_NormSize</a>        = normSize;</div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a174279be57d7596eeb04c6b7f7510f99">m_Alpha</a>           = alpha;</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a>            = beta;</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a8526ea7cf860d8e7f8340e9f9354f9f0">m_K</a>               = kappa;</div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;</div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;    <span class="comment">//Invalid argument exception is expected, because input height != output height.</span></div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;    BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_normalization_workload.xhtml">RefNormalizationWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a8526ea7cf860d8e7f8340e9f9354f9f0"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a8526ea7cf860d8e7f8340e9f9354f9f0">armnn::NormalizationDescriptor::m_K</a></div><div class="ttdeci">float m_K</div><div class="ttdoc">Kappa value used for the across channel normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00585">Descriptors.hpp:585</a></div></div>
+<div class="ttc" id="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="structarmnn_1_1_normalization_descriptor_xhtml_a174279be57d7596eeb04c6b7f7510f99"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a174279be57d7596eeb04c6b7f7510f99">armnn::NormalizationDescriptor::m_Alpha</a></div><div class="ttdeci">float m_Alpha</div><div class="ttdoc">Alpha value for the normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00581">Descriptors.hpp:581</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_abe18a5033f2ab9c0de82c676b48f5437"><div class="ttname"><a href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437">armnn::NormalizationAlgorithmChannel</a></div><div class="ttdeci">NormalizationAlgorithmChannel</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00126">Types.hpp:126</a></div></div>
+<div class="ttc" id="structarmnn_1_1_queue_descriptor_with_parameters_xhtml_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00049">WorkloadData.hpp:49</a></div></div>
+<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a05945f080edf694b631960728b87aadb"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a05945f080edf694b631960728b87aadb">armnn::NormalizationDescriptor::m_NormMethodType</a></div><div class="ttdeci">NormalizationAlgorithmMethod m_NormMethodType</div><div class="ttdoc">Normalization method algorithm to use (LocalBrightness, LocalContrast). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00577">Descriptors.hpp:577</a></div></div>
+<div class="ttc" id="classarmnn_1_1_ref_normalization_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_ref_normalization_workload.xhtml">armnn::RefNormalizationWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_normalization_workload_8hpp_source.xhtml#l00014">RefNormalizationWorkload.hpp:14</a></div></div>
+<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div>
+<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_afe1f0f09d49ad2befc01f8789187b7dd"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#afe1f0f09d49ad2befc01f8789187b7dd">armnn::NormalizationDescriptor::m_NormChannelType</a></div><div class="ttdeci">NormalizationAlgorithmChannel m_NormChannelType</div><div class="ttdoc">Normalization channel algorithm to use (Across, Within). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00575">Descriptors.hpp:575</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
+<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d"><div class="ttname"><a href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d">armnn::NormalizationAlgorithmMethod::LocalBrightness</a></div><div class="ttdoc">Krichevsky 2012: Local Brightness Normalization. </div></div>
+<div class="ttc" id="namespacearmnn_xhtml_abe18a5033f2ab9c0de82c676b48f5437a810f43f3996922151c39b76143faeecc"><div class="ttname"><a href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437a810f43f3996922151c39b76143faeecc">armnn::NormalizationAlgorithmChannel::Across</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_ad605d1661fa0d8c7fea651d82fbe11c9"><div class="ttname"><a href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9">armnn::NormalizationAlgorithmMethod</a></div><div class="ttdeci">NormalizationAlgorithmMethod</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00132">Types.hpp:132</a></div></div>
+<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a8275d51ef9a584feb95726ea0522f6e5"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">armnn::NormalizationDescriptor::m_Beta</a></div><div class="ttdeci">float m_Beta</div><div class="ttdoc">Beta value for the normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00583">Descriptors.hpp:583</a></div></div>
+<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_aa70c05f1aad12fbd9d9ec43ea4557b03"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#aa70c05f1aad12fbd9d9ec43ea4557b03">armnn::NormalizationDescriptor::m_NormSize</a></div><div class="ttdeci">uint32_t m_NormSize</div><div class="ttdoc">Depth radius value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00579">Descriptors.hpp:579</a></div></div>
+<div class="ttc" id="structarmnn_1_1_normalization_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_normalization_queue_descriptor.xhtml">armnn::NormalizationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00210">WorkloadData.hpp:210</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a id="a454c029687438437ea707b94f88361e1"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a454c029687438437ea707b94f88361e1">&#9670;&nbsp;</a></span>BOOST_AUTO_TEST_CASE() <span class="overload">[7/14]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">BOOST_AUTO_TEST_CASE </td>
+          <td>(</td>
+          <td class="paramtype">SplitterQueueDescriptor_Validate_WrongWindow&#160;</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+<p class="definition">Definition at line <a class="el" href="_workload_data_validation_8cpp_source.xhtml#l00203">203</a> of file <a class="el" href="_workload_data_validation_8cpp_source.xhtml">WorkloadDataValidation.cpp</a>.</p>
+
+<p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, and <a class="el" href="_workload_data_8hpp_source.xhtml#l00096">SplitterQueueDescriptor::m_ViewOrigins</a>.</p>
+<div class="fragment"><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;{</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 1;</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight   = 32;</div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth    = 24;</div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 3;</div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;</div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = inputNum;</div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = inputChannels;</div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = 18;</div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth  = inputWidth;</div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[]  = {inputNum, inputChannels, inputHeight, inputWidth};</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};</div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;    inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;    outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;</div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;    <a class="code" href="structarmnn_1_1_splitter_queue_descriptor.xhtml">SplitterQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;    <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>            invalidInfo;</div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;</div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;    <span class="comment">// Invalid, since it has only 3 dimensions while the input tensor is 4d.</span></div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;    std::vector&lt;unsigned int&gt; wOrigin = {0, 0, 0};</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;    <a class="code" href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml">armnn::SplitterQueueDescriptor::ViewOrigin</a> window(wOrigin);</div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_splitter_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window);</div><div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;</div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;    BOOST_TEST_INFO(<span class="stringliteral">&quot;Invalid argument exception is expected, because split window dimensionality does not &quot;</span></div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;        <span class="stringliteral">&quot;match input.&quot;</span>);</div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;    BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_splitter_workload.xhtml">RefSplitterWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;</div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;    <span class="comment">// Invalid, since window extends past the boundary of input tensor.</span></div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;    std::vector&lt;unsigned int&gt; wOrigin3 = {0, 0, 15, 0};</div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;    <a class="code" href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml">armnn::SplitterQueueDescriptor::ViewOrigin</a> window3(wOrigin3);</div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_splitter_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>[0] = window3;</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;    BOOST_TEST_INFO(<span class="stringliteral">&quot;Invalid argument exception is expected (wOrigin3[2]+ outputHeight &gt; inputHeight&quot;</span>);</div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;    BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_splitter_workload.xhtml">RefSplitterWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;</div><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;    std::vector&lt;unsigned int&gt; wOrigin4 = {0, 0, 0, 0};</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;    <a class="code" href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml">armnn::SplitterQueueDescriptor::ViewOrigin</a> window4(wOrigin4);</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_splitter_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>[0] = window4;</div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;</div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;    std::vector&lt;unsigned int&gt; wOrigin5 = {1, 16, 20, 2};</div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;    <a class="code" href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml">armnn::SplitterQueueDescriptor::ViewOrigin</a> window5(wOrigin4);</div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_splitter_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window5);</div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;</div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;    BOOST_TEST_INFO(<span class="stringliteral">&quot;Invalid exception due to number of split windows not matching number of outputs.&quot;</span>);</div><div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;    BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_splitter_workload.xhtml">RefSplitterWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_splitter_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_splitter_queue_descriptor.xhtml">armnn::SplitterQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00083">WorkloadData.hpp:83</a></div></div>
+<div class="ttc" id="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="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin_xhtml"><div class="ttname"><a href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml">armnn::SplitterQueueDescriptor::ViewOrigin</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00085">WorkloadData.hpp:85</a></div></div>
+<div class="ttc" id="classarmnn_1_1_ref_splitter_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_ref_splitter_workload.xhtml">armnn::RefSplitterWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_splitter_workload_8hpp_source.xhtml#l00016">RefSplitterWorkload.hpp:16</a></div></div>
+<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div>
+<div class="ttc" id="structarmnn_1_1_splitter_queue_descriptor_xhtml_ab1794eb3e74c9700cd3d500fc06dc2e5"><div class="ttname"><a href="structarmnn_1_1_splitter_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">armnn::SplitterQueueDescriptor::m_ViewOrigins</a></div><div class="ttdeci">std::vector&lt; ViewOrigin &gt; m_ViewOrigins</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00096">WorkloadData.hpp:96</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
+<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a id="a198e5f41a13facee99cf14390282e186"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a198e5f41a13facee99cf14390282e186">&#9670;&nbsp;</a></span>BOOST_AUTO_TEST_CASE() <span class="overload">[8/14]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">BOOST_AUTO_TEST_CASE </td>
+          <td>(</td>
+          <td class="paramtype">ConcatQueueDescriptor_Validate_WrongWindow&#160;</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+<p class="definition">Definition at line <a class="el" href="_workload_data_validation_8cpp_source.xhtml#l00261">261</a> of file <a class="el" href="_workload_data_validation_8cpp_source.xhtml">WorkloadDataValidation.cpp</a>.</p>
+
+<p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, and <a class="el" href="_workload_data_8hpp_source.xhtml#l00115">ConcatQueueDescriptor::m_ViewOrigins</a>.</p>
+<div class="fragment"><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;{</div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 1;</div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 3;</div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight   = 32;</div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth    = 24;</div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;</div><div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = 1;</div><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 3;</div><div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = 32;</div><div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth  = 24;</div><div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;</div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;</div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;</div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[]  = {inputNum, inputChannels, inputHeight, inputWidth};</div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};</div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;</div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;    inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;    outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;</div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;    <a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml">ConcatQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;    <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>          invalidInfo;</div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;</div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;    <span class="comment">// Invalid, since it has only 3 dimensions while the input tensor is 4d.</span></div><div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;    std::vector&lt;unsigned int&gt; wOrigin = {0, 0, 0};</div><div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;    <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">armnn::ConcatQueueDescriptor::ViewOrigin</a> window(wOrigin);</div><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window);</div><div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;</div><div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;    BOOST_TEST_INFO(<span class="stringliteral">&quot;Invalid argument exception is expected, because merge window dimensionality does not &quot;</span></div><div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;        <span class="stringliteral">&quot;match input.&quot;</span>);</div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;    BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_concat_workload.xhtml">RefConcatWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;</div><div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;    <span class="comment">// Invalid, since window extends past the boundary of output tensor.</span></div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;    std::vector&lt;unsigned int&gt; wOrigin3 = {0, 0, 15, 0};</div><div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;    <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">armnn::ConcatQueueDescriptor::ViewOrigin</a> window3(wOrigin3);</div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>[0] = window3;</div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;    BOOST_TEST_INFO(<span class="stringliteral">&quot;Invalid argument exception is expected (wOrigin3[2]+ inputHeight &gt; outputHeight&quot;</span>);</div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;    BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_concat_workload.xhtml">RefConcatWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;</div><div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;</div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;    std::vector&lt;unsigned int&gt; wOrigin4 = {0, 0, 0, 0};</div><div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;    <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">armnn::ConcatQueueDescriptor::ViewOrigin</a> window4(wOrigin4);</div><div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>[0] = window4;</div><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;</div><div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;    std::vector&lt;unsigned int&gt; wOrigin5 = {1, 16, 20, 2};</div><div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;    <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">armnn::ConcatQueueDescriptor::ViewOrigin</a> window5(wOrigin4);</div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;    invalidData.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window5);</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;</div><div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;    BOOST_TEST_INFO(<span class="stringliteral">&quot;Invalid exception due to number of merge windows not matching number of inputs.&quot;</span>);</div><div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;    BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_concat_workload.xhtml">RefConcatWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin_xhtml"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">armnn::ConcatQueueDescriptor::ViewOrigin</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00104">WorkloadData.hpp:104</a></div></div>
+<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
+<div class="ttc" id="classarmnn_1_1_ref_concat_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_ref_concat_workload.xhtml">armnn::RefConcatWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_concat_workload_8hpp_source.xhtml#l00014">RefConcatWorkload.hpp:14</a></div></div>
+<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor.xhtml">armnn::ConcatQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00102">WorkloadData.hpp:102</a></div></div>
+<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_xhtml_ab1794eb3e74c9700cd3d500fc06dc2e5"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">armnn::ConcatQueueDescriptor::m_ViewOrigins</a></div><div class="ttdeci">std::vector&lt; ViewOrigin &gt; m_ViewOrigins</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00115">WorkloadData.hpp:115</a></div></div>
+<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
+<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a id="a5f1870bd33bf34b70b985e24da85700f"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a5f1870bd33bf34b70b985e24da85700f">&#9670;&nbsp;</a></span>BOOST_AUTO_TEST_CASE() <span class="overload">[9/14]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">BOOST_AUTO_TEST_CASE </td>
+          <td>(</td>
+          <td class="paramtype">AdditionQueueDescriptor_Validate_InputNumbers&#160;</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+<p class="definition">Definition at line <a class="el" href="_workload_data_validation_8cpp_source.xhtml#l00318">318</a> of file <a class="el" href="_workload_data_validation_8cpp_source.xhtml">WorkloadDataValidation.cpp</a>.</p>
+
+<p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>.</p>
+<div class="fragment"><div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;{</div><div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input1TensorInfo;</div><div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input2TensorInfo;</div><div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input3TensorInfo;</div><div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;</div><div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape[]  = {1, 1, 1, 1};</div><div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;</div><div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;    input1TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;    input2TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;    input3TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;    outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;</div><div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;    <a class="code" href="structarmnn_1_1_addition_queue_descriptor.xhtml">AdditionQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;    <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>            invalidInfo;</div><div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;</div><div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;    AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;</div><div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;    <span class="comment">// Too few inputs.</span></div><div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;    BOOST_CHECK_THROW(<a class="code" href="namespacearmnn.xhtml#a7a9d365fbb868d53e67c4cdfdbf9cf7e">RefAdditionWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;</div><div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;    AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;</div><div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;    <span class="comment">// Correct.</span></div><div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;    BOOST_CHECK_NO_THROW(<a class="code" href="namespacearmnn.xhtml#a7a9d365fbb868d53e67c4cdfdbf9cf7e">RefAdditionWorkload</a>(invalidData, invalidInfo));</div><div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;</div><div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;    AddInputToWorkload(invalidData, invalidInfo, input3TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;</div><div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;    <span class="comment">// Too many inputs.</span></div><div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;    BOOST_CHECK_THROW(<a class="code" href="namespacearmnn.xhtml#a7a9d365fbb868d53e67c4cdfdbf9cf7e">RefAdditionWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;}</div><div class="ttc" id="namespacearmnn_xhtml_a7a9d365fbb868d53e67c4cdfdbf9cf7e"><div class="ttname"><a href="namespacearmnn.xhtml#a7a9d365fbb868d53e67c4cdfdbf9cf7e">armnn::RefAdditionWorkload</a></div><div class="ttdeci">RefElementwiseWorkload&lt; std::plus&lt; float &gt;, AdditionQueueDescriptor, StringMapping::RefAdditionWorkload_Execute &gt; RefAdditionWorkload</div><div class="ttdef"><b>Definition:</b> <a href="_ref_elementwise_workload_8hpp_source.xhtml#l00041">RefElementwiseWorkload.hpp:41</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="structarmnn_1_1_addition_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_addition_queue_descriptor.xhtml">armnn::AdditionQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00216">WorkloadData.hpp:216</a></div></div>
+<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
+<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a id="a77921e834125a4a2d63142cb8ac115f6"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a77921e834125a4a2d63142cb8ac115f6">&#9670;&nbsp;</a></span>BOOST_AUTO_TEST_CASE() <span class="overload">[10/14]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">BOOST_AUTO_TEST_CASE </td>
+          <td>(</td>
+          <td class="paramtype">AdditionQueueDescriptor_Validate_InputShapes&#160;</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+<p class="definition">Definition at line <a class="el" href="_workload_data_validation_8cpp_source.xhtml#l00352">352</a> of file <a class="el" href="_workload_data_validation_8cpp_source.xhtml">WorkloadDataValidation.cpp</a>.</p>
+
+<p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>.</p>
+<div class="fragment"><div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;{</div><div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input1TensorInfo;</div><div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input2TensorInfo;</div><div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;</div><div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape1[] = {1, 1, 2, 1};</div><div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape2[] = {1, 1, 3, 2};</div><div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;</div><div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;    <span class="comment">// Incompatible shapes even with broadcasting.</span></div><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;    {</div><div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;        input1TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape1, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;        input2TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape2, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;        outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape1, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;</div><div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;        <a class="code" href="structarmnn_1_1_addition_queue_descriptor.xhtml">AdditionQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;        <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>            invalidInfo;</div><div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;</div><div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;        AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;        AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;        AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;</div><div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;        BOOST_CHECK_THROW(<a class="code" href="namespacearmnn.xhtml#a7a9d365fbb868d53e67c4cdfdbf9cf7e">RefAdditionWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;    }</div><div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;</div><div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;    <span class="comment">// Output size not compatible with input sizes.</span></div><div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;    {</div><div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;        input1TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape1, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;        input2TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape1, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;        outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape2, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;</div><div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;        <a class="code" href="structarmnn_1_1_addition_queue_descriptor.xhtml">AdditionQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;        <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>            invalidInfo;</div><div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;</div><div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;        AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;        AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;        AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;</div><div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;        <span class="comment">// Output differs.</span></div><div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;        BOOST_CHECK_THROW(<a class="code" href="namespacearmnn.xhtml#a7a9d365fbb868d53e67c4cdfdbf9cf7e">RefAdditionWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;    }</div><div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;}</div><div class="ttc" id="namespacearmnn_xhtml_a7a9d365fbb868d53e67c4cdfdbf9cf7e"><div class="ttname"><a href="namespacearmnn.xhtml#a7a9d365fbb868d53e67c4cdfdbf9cf7e">armnn::RefAdditionWorkload</a></div><div class="ttdeci">RefElementwiseWorkload&lt; std::plus&lt; float &gt;, AdditionQueueDescriptor, StringMapping::RefAdditionWorkload_Execute &gt; RefAdditionWorkload</div><div class="ttdef"><b>Definition:</b> <a href="_ref_elementwise_workload_8hpp_source.xhtml#l00041">RefElementwiseWorkload.hpp:41</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="structarmnn_1_1_addition_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_addition_queue_descriptor.xhtml">armnn::AdditionQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00216">WorkloadData.hpp:216</a></div></div>
+<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
+<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a id="abf20db1f5c57d502fe310198a9264b7c"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#abf20db1f5c57d502fe310198a9264b7c">&#9670;&nbsp;</a></span>BOOST_AUTO_TEST_CASE() <span class="overload">[11/14]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">BOOST_AUTO_TEST_CASE </td>
+          <td>(</td>
+          <td class="paramtype">MultiplicationQueueDescriptor_Validate_InputTensorDimensionMismatch&#160;</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+<p class="definition">Definition at line <a class="el" href="_workload_data_validation_8cpp_source.xhtml#l00395">395</a> of file <a class="el" href="_workload_data_validation_8cpp_source.xhtml">WorkloadDataValidation.cpp</a>.</p>
+
+<p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>.</p>
+<div class="fragment"><div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;{</div><div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input0TensorInfo;</div><div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input1TensorInfo;</div><div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;</div><div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input0Shape[] = { 2, 2, 4, 4 };</div><div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;    constexpr std::size_t dimensionCount = std::extent&lt;decltype(input0Shape)&gt;::value;</div><div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;</div><div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;    <span class="comment">// Checks dimension consistency for input tensors.</span></div><div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIndex = 0; dimIndex &lt; dimensionCount; ++dimIndex)</div><div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;    {</div><div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input1Shape[dimensionCount];</div><div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; dimensionCount; ++i)</div><div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;        {</div><div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;            input1Shape[i] = input0Shape[i];</div><div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;        }</div><div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;</div><div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;        ++input1Shape[dimIndex];</div><div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;</div><div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;        input0TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(dimensionCount, input0Shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;        input1TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(dimensionCount, input1Shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;        outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(dimensionCount, input0Shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;</div><div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;        <a class="code" href="structarmnn_1_1_multiplication_queue_descriptor.xhtml">MultiplicationQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;        <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>                  invalidInfo;</div><div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;</div><div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;        AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;        AddInputToWorkload(invalidData, invalidInfo, input0TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;        AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;</div><div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;        BOOST_CHECK_THROW(<a class="code" href="namespacearmnn.xhtml#aabff736a576814611f65ce1a14600a17">RefMultiplicationWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;    }</div><div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;</div><div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;    <span class="comment">// Checks dimension consistency for input and output tensors.</span></div><div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIndex = 0; dimIndex &lt; dimensionCount; ++dimIndex)</div><div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;    {</div><div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[dimensionCount];</div><div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; dimensionCount; ++i)</div><div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;        {</div><div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;            outputShape[i] = input0Shape[i];</div><div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;        }</div><div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;</div><div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;        ++outputShape[dimIndex];</div><div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;</div><div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;        input0TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(dimensionCount, input0Shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;        input1TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(dimensionCount, input0Shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;        outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(dimensionCount, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;</div><div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;        <a class="code" href="structarmnn_1_1_multiplication_queue_descriptor.xhtml">MultiplicationQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;        <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>                  invalidInfo;</div><div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;</div><div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;        AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;        AddInputToWorkload(invalidData, invalidInfo, input0TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;        AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;</div><div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;        BOOST_CHECK_THROW(<a class="code" href="namespacearmnn.xhtml#aabff736a576814611f65ce1a14600a17">RefMultiplicationWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;    }</div><div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_multiplication_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_multiplication_queue_descriptor.xhtml">armnn::MultiplicationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00222">WorkloadData.hpp:222</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_aabff736a576814611f65ce1a14600a17"><div class="ttname"><a href="namespacearmnn.xhtml#aabff736a576814611f65ce1a14600a17">armnn::RefMultiplicationWorkload</a></div><div class="ttdeci">RefElementwiseWorkload&lt; std::multiplies&lt; float &gt;, MultiplicationQueueDescriptor, StringMapping::RefMultiplicationWorkload_Execute &gt; RefMultiplicationWorkload</div><div class="ttdef"><b>Definition:</b> <a href="_ref_elementwise_workload_8hpp_source.xhtml#l00051">RefElementwiseWorkload.hpp:51</a></div></div>
+<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
+<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
+<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a id="a1877eca1aa5adb4c0e1302d04c96d013"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a1877eca1aa5adb4c0e1302d04c96d013">&#9670;&nbsp;</a></span>BOOST_AUTO_TEST_CASE() <span class="overload">[12/14]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">BOOST_AUTO_TEST_CASE </td>
+          <td>(</td>
+          <td class="paramtype">ReshapeQueueDescriptor_Validate_MismatchingNumElements&#160;</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+<p class="definition">Definition at line <a class="el" href="_workload_data_validation_8cpp_source.xhtml#l00455">455</a> of file <a class="el" href="_workload_data_validation_8cpp_source.xhtml">WorkloadDataValidation.cpp</a>.</p>
+
+<p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>.</p>
+<div class="fragment"><div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;{</div><div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;</div><div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;    <span class="comment">// The input and output shapes should have the same number of elements, but these don&#39;t.</span></div><div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { 1, 1, 2, 3 };</div><div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { 1, 1, 1, 2 };</div><div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;</div><div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;    inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;    outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;</div><div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;    <a class="code" href="structarmnn_1_1_reshape_queue_descriptor.xhtml">ReshapeQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;    <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>           invalidInfo;</div><div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;</div><div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;</div><div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;    <span class="comment">// InvalidArgumentException is expected, because the number of elements don&#39;t match.</span></div><div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;    BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_reshape_workload.xhtml">RefReshapeWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;}</div><div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
+<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
+<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
+<div class="ttc" id="classarmnn_1_1_ref_reshape_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_ref_reshape_workload.xhtml">armnn::RefReshapeWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_reshape_workload_8hpp_source.xhtml#l00014">RefReshapeWorkload.hpp:14</a></div></div>
+<div class="ttc" id="structarmnn_1_1_reshape_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_reshape_queue_descriptor.xhtml">armnn::ReshapeQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00338">WorkloadData.hpp:338</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a id="ad3a7db97268896c1bfa8df01f5834b3f"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#ad3a7db97268896c1bfa8df01f5834b3f">&#9670;&nbsp;</a></span>BOOST_AUTO_TEST_CASE() <span class="overload">[13/14]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">BOOST_AUTO_TEST_CASE </td>
+          <td>(</td>
+          <td class="paramtype">LstmQueueDescriptor_Validate&#160;</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+<p class="definition">Definition at line <a class="el" href="_workload_data_validation_8cpp_source.xhtml#l00478">478</a> of file <a class="el" href="_workload_data_validation_8cpp_source.xhtml">WorkloadDataValidation.cpp</a>.</p>
+
+<p class="reference">References <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::Float32</a>, and <a class="el" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::info</a>.</p>
+<div class="fragment"><div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;{</div><div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;    <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> dataType = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>;</div><div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;</div><div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;    <span class="keywordtype">float</span> qScale = 0.0f;</div><div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;    int32_t qOffset = 0;</div><div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;</div><div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 2;</div><div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 3;</div><div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 5;</div><div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;    <span class="keywordtype">unsigned</span> numUnits = 4;</div><div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;</div><div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({batchSize , inputSize}, dataType,  qScale, qOffset );</div><div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateInTensorInfo({batchSize , outputSize}, dataType, qScale, qOffset);</div><div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateInTensorInfo({batchSize , numUnits}, dataType, qScale, qOffset);</div><div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;</div><div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160;    <span class="comment">// Scratch buffer size with CIFG [batchSize, numUnits * 4]</span></div><div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> scratchBufferTensorInfo({batchSize, numUnits * 4}, dataType, qScale, qOffset);</div><div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateOutTensorInfo({batchSize, numUnits}, dataType, qScale, qOffset);</div><div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateOutTensorInfo({batchSize, outputSize}, dataType, qScale, qOffset);</div><div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({batchSize, outputSize}, dataType, qScale, qOffset);</div><div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;</div><div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo3({outputSize}, dataType, qScale, qOffset);</div><div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo4({numUnits}, dataType, qScale, qOffset);</div><div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo4x5({numUnits, inputSize}, dataType, qScale, qOffset);</div><div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo4x3({numUnits, outputSize}, dataType, qScale, qOffset);</div><div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo3x4({outputSize, numUnits}, dataType, qScale, qOffset);</div><div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;</div><div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;    <a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml">LstmQueueDescriptor</a> data;</div><div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;    <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>        <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>;</div><div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;</div><div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160;    AddInputToWorkload(data, info, inputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;    AddInputToWorkload(data, info, outputStateInTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;    AddInputToWorkload(data, info, cellStateInTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;</div><div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;    AddOutputToWorkload(data, info, scratchBufferTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;    AddOutputToWorkload(data, info, outputStateOutTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;    AddOutputToWorkload(data, info, cellStateOutTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;    <span class="comment">// AddOutputToWorkload(data, info, outputTensorInfo, nullptr); is left out</span></div><div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;</div><div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToInputWeightsTensor(tensorInfo4x5);</div><div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToForgetWeightsTensor(tensorInfo4x5);</div><div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToCellWeightsTensor(tensorInfo4x5);</div><div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToOutputWeightsTensor(tensorInfo4x5);</div><div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToForgetWeightsTensor(tensorInfo4x3);</div><div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToInputWeightsTensor(tensorInfo4x3);</div><div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToCellWeightsTensor(tensorInfo4x3);</div><div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToOutputWeightsTensor(tensorInfo4x3);</div><div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellToInputWeightsTensor(tensorInfo4);</div><div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputGateBiasTensor(tensorInfo4);</div><div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> forgetGateBiasTensor(tensorInfo4);</div><div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellBiasTensor(tensorInfo4);</div><div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> outputGateBiasTensor(tensorInfo4);</div><div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellToForgetWeightsTensor(tensorInfo4);</div><div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellToOutputWeightsTensor(tensorInfo4);</div><div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> projectionWeightsTensor(tensorInfo3x4);</div><div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> projectionBiasTensor(tensorInfo3);</div><div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputLayerNormWeightsTensor(tensorInfo4);</div><div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> forgetLayerNormWeightsTensor(tensorInfo4);</div><div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellLayerNormWeightsTensor(tensorInfo4);</div><div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> outputLayerNormWeightsTensor(tensorInfo4);</div><div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;</div><div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;    data.m_InputToInputWeights = &amp;inputToInputWeightsTensor;</div><div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160;    data.m_InputToForgetWeights = &amp;inputToForgetWeightsTensor;</div><div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;    data.m_InputToCellWeights = &amp;inputToCellWeightsTensor;</div><div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160;    data.m_InputToOutputWeights = &amp;inputToOutputWeightsTensor;</div><div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;    data.m_RecurrentToInputWeights = &amp;recurrentToInputWeightsTensor;</div><div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;    data.m_RecurrentToForgetWeights = &amp;recurrentToForgetWeightsTensor;</div><div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;    data.m_RecurrentToCellWeights = &amp;recurrentToCellWeightsTensor;</div><div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160;    data.m_RecurrentToOutputWeights = &amp;recurrentToOutputWeightsTensor;</div><div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160;    data.m_CellToInputWeights = &amp;cellToInputWeightsTensor;</div><div class="line"><a name="l00549"></a><span class="lineno">  549</span>&#160;    data.m_InputGateBias = &amp;inputGateBiasTensor;</div><div class="line"><a name="l00550"></a><span class="lineno">  550</span>&#160;    data.m_ForgetGateBias = &amp;forgetGateBiasTensor;</div><div class="line"><a name="l00551"></a><span class="lineno">  551</span>&#160;    data.m_CellBias = &amp;cellBiasTensor;</div><div class="line"><a name="l00552"></a><span class="lineno">  552</span>&#160;    data.m_OutputGateBias = &amp;outputGateBiasTensor;</div><div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;    data.m_CellToForgetWeights = &amp;cellToForgetWeightsTensor;</div><div class="line"><a name="l00554"></a><span class="lineno">  554</span>&#160;    data.m_CellToOutputWeights = &amp;cellToOutputWeightsTensor;</div><div class="line"><a name="l00555"></a><span class="lineno">  555</span>&#160;    data.m_ProjectionWeights = &amp;projectionWeightsTensor;</div><div class="line"><a name="l00556"></a><span class="lineno">  556</span>&#160;    data.m_ProjectionBias = &amp;projectionBiasTensor;</div><div class="line"><a name="l00557"></a><span class="lineno">  557</span>&#160;</div><div class="line"><a name="l00558"></a><span class="lineno">  558</span>&#160;    data.m_InputLayerNormWeights = &amp;inputLayerNormWeightsTensor;</div><div class="line"><a name="l00559"></a><span class="lineno">  559</span>&#160;    data.m_ForgetLayerNormWeights = &amp;forgetLayerNormWeightsTensor;</div><div class="line"><a name="l00560"></a><span class="lineno">  560</span>&#160;    data.m_CellLayerNormWeights = &amp;cellLayerNormWeightsTensor;</div><div class="line"><a name="l00561"></a><span class="lineno">  561</span>&#160;    data.m_OutputLayerNormWeights = &amp;outputLayerNormWeightsTensor;</div><div class="line"><a name="l00562"></a><span class="lineno">  562</span>&#160;</div><div class="line"><a name="l00563"></a><span class="lineno">  563</span>&#160;    <span class="comment">// Flags to set test configuration</span></div><div class="line"><a name="l00564"></a><span class="lineno">  564</span>&#160;    data.m_Parameters.m_ActivationFunc = 4;</div><div class="line"><a name="l00565"></a><span class="lineno">  565</span>&#160;    data.m_Parameters.m_CifgEnabled = <span class="keyword">false</span>;</div><div class="line"><a name="l00566"></a><span class="lineno">  566</span>&#160;    data.m_Parameters.m_PeepholeEnabled = <span class="keyword">true</span>;</div><div class="line"><a name="l00567"></a><span class="lineno">  567</span>&#160;    data.m_Parameters.m_ProjectionEnabled = <span class="keyword">true</span>;</div><div class="line"><a name="l00568"></a><span class="lineno">  568</span>&#160;    data.m_Parameters.m_LayerNormEnabled = <span class="keyword">true</span>;</div><div class="line"><a name="l00569"></a><span class="lineno">  569</span>&#160;</div><div class="line"><a name="l00570"></a><span class="lineno">  570</span>&#160;    <span class="comment">// check wrong number of outputs</span></div><div class="line"><a name="l00571"></a><span class="lineno">  571</span>&#160;    BOOST_CHECK_THROW(data.Validate(info), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00572"></a><span class="lineno">  572</span>&#160;    AddOutputToWorkload(data, info, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00573"></a><span class="lineno">  573</span>&#160;</div><div class="line"><a name="l00574"></a><span class="lineno">  574</span>&#160;    <span class="comment">// check wrong cifg parameter configuration</span></div><div class="line"><a name="l00575"></a><span class="lineno">  575</span>&#160;    data.m_Parameters.m_CifgEnabled = <span class="keyword">true</span>;</div><div class="line"><a name="l00576"></a><span class="lineno">  576</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> scratchBufferTensorInfo2({batchSize, numUnits * 3}, dataType, qScale, qOffset);</div><div class="line"><a name="l00577"></a><span class="lineno">  577</span>&#160;    SetWorkloadOutput(data, info, 0, scratchBufferTensorInfo2, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00578"></a><span class="lineno">  578</span>&#160;    BOOST_CHECK_THROW(data.Validate(info), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00579"></a><span class="lineno">  579</span>&#160;    data.m_Parameters.m_CifgEnabled = <span class="keyword">false</span>;</div><div class="line"><a name="l00580"></a><span class="lineno">  580</span>&#160;    SetWorkloadOutput(data, info, 0, scratchBufferTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00581"></a><span class="lineno">  581</span>&#160;</div><div class="line"><a name="l00582"></a><span class="lineno">  582</span>&#160;    <span class="comment">// check wrong inputGateBias configuration</span></div><div class="line"><a name="l00583"></a><span class="lineno">  583</span>&#160;    data.m_InputGateBias = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00584"></a><span class="lineno">  584</span>&#160;    BOOST_CHECK_THROW(data.Validate(info), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00585"></a><span class="lineno">  585</span>&#160;    data.m_InputGateBias = &amp;inputGateBiasTensor;</div><div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160;</div><div class="line"><a name="l00587"></a><span class="lineno">  587</span>&#160;    <span class="comment">// check inconsistant projection parameters</span></div><div class="line"><a name="l00588"></a><span class="lineno">  588</span>&#160;    data.m_Parameters.m_ProjectionEnabled = <span class="keyword">false</span>;</div><div class="line"><a name="l00589"></a><span class="lineno">  589</span>&#160;    BOOST_CHECK_THROW(data.Validate(info), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00590"></a><span class="lineno">  590</span>&#160;    data.m_Parameters.m_ProjectionEnabled = <span class="keyword">true</span>;</div><div class="line"><a name="l00591"></a><span class="lineno">  591</span>&#160;    data.m_ProjectionWeights = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00592"></a><span class="lineno">  592</span>&#160;    BOOST_CHECK_THROW(data.Validate(info), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00593"></a><span class="lineno">  593</span>&#160;    data.m_ProjectionWeights = &amp;projectionWeightsTensor;</div><div class="line"><a name="l00594"></a><span class="lineno">  594</span>&#160;</div><div class="line"><a name="l00595"></a><span class="lineno">  595</span>&#160;    <span class="comment">// check missing input layer normalisation weights</span></div><div class="line"><a name="l00596"></a><span class="lineno">  596</span>&#160;    data.m_InputLayerNormWeights = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00597"></a><span class="lineno">  597</span>&#160;    BOOST_CHECK_THROW(data.Validate(info), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00598"></a><span class="lineno">  598</span>&#160;    data.m_InputLayerNormWeights = &amp;inputLayerNormWeightsTensor;</div><div class="line"><a name="l00599"></a><span class="lineno">  599</span>&#160;</div><div class="line"><a name="l00600"></a><span class="lineno">  600</span>&#160;    <span class="comment">// layer norm disabled but normalisation weights are present</span></div><div class="line"><a name="l00601"></a><span class="lineno">  601</span>&#160;    data.m_Parameters.m_LayerNormEnabled = <span class="keyword">false</span>;</div><div class="line"><a name="l00602"></a><span class="lineno">  602</span>&#160;    BOOST_CHECK_THROW(data.Validate(info), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00603"></a><span class="lineno">  603</span>&#160;    data.m_Parameters.m_LayerNormEnabled = <span class="keyword">true</span>;</div><div class="line"><a name="l00604"></a><span class="lineno">  604</span>&#160;</div><div class="line"><a name="l00605"></a><span class="lineno">  605</span>&#160;    <span class="comment">// check invalid outputTensor shape</span></div><div class="line"><a name="l00606"></a><span class="lineno">  606</span>&#160;    <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> incorrectOutputTensorInfo({batchSize, outputSize + 1}, dataType, qScale, qOffset);</div><div class="line"><a name="l00607"></a><span class="lineno">  607</span>&#160;    SetWorkloadOutput(data, info, 3, incorrectOutputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00608"></a><span class="lineno">  608</span>&#160;    BOOST_CHECK_THROW(data.Validate(info), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00609"></a><span class="lineno">  609</span>&#160;    SetWorkloadOutput(data, info, 3, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00610"></a><span class="lineno">  610</span>&#160;</div><div class="line"><a name="l00611"></a><span class="lineno">  611</span>&#160;    <span class="comment">// check invalid cell clipping parameters</span></div><div class="line"><a name="l00612"></a><span class="lineno">  612</span>&#160;    data.m_Parameters.m_ClippingThresCell = -1.0f;</div><div class="line"><a name="l00613"></a><span class="lineno">  613</span>&#160;    BOOST_CHECK_THROW(data.Validate(info), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00614"></a><span class="lineno">  614</span>&#160;    data.m_Parameters.m_ClippingThresCell = 0.0f;</div><div class="line"><a name="l00615"></a><span class="lineno">  615</span>&#160;</div><div class="line"><a name="l00616"></a><span class="lineno">  616</span>&#160;    <span class="comment">// check invalid projection clipping parameters</span></div><div class="line"><a name="l00617"></a><span class="lineno">  617</span>&#160;    data.m_Parameters.m_ClippingThresProj = -1.0f;</div><div class="line"><a name="l00618"></a><span class="lineno">  618</span>&#160;    BOOST_CHECK_THROW(data.Validate(info), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00619"></a><span class="lineno">  619</span>&#160;    data.m_Parameters.m_ClippingThresProj = 0.0f;</div><div class="line"><a name="l00620"></a><span class="lineno">  620</span>&#160;</div><div class="line"><a name="l00621"></a><span class="lineno">  621</span>&#160;    <span class="comment">// check correct configuration</span></div><div class="line"><a name="l00622"></a><span class="lineno">  622</span>&#160;    BOOST_CHECK_NO_THROW(data.Validate(info));</div><div class="line"><a name="l00623"></a><span class="lineno">  623</span>&#160;}</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_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_lstm_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml">armnn::LstmQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00358">WorkloadData.hpp:358</a></div></div>
+<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div>
+<div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
+<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a></div></div>
+<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
+</div><!-- fragment -->
+</div>
+</div>
+<a id="adbdb7da153a5596e84d312e8abbd152d"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#adbdb7da153a5596e84d312e8abbd152d">&#9670;&nbsp;</a></span>BOOST_AUTO_TEST_CASE() <span class="overload">[14/14]</span></h2>
+
+<div class="memitem">
+<div class="memproto">
+      <table class="memname">
+        <tr>
+          <td class="memname">BOOST_AUTO_TEST_CASE </td>
+          <td>(</td>
+          <td class="paramtype">BiasPerAxisQuantization_Validate&#160;</td>
+          <td class="paramname"></td><td>)</td>
+          <td></td>
+        </tr>
+      </table>
+</div><div class="memdoc">
+
+<p class="definition">Definition at line <a class="el" href="_workload_data_validation_8cpp_source.xhtml#l00625">625</a> of file <a class="el" href="_workload_data_validation_8cpp_source.xhtml">WorkloadDataValidation.cpp</a>.</p>
+
+<p class="reference">References <a class="el" href="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END()</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00177">Convolution2dQueueDescriptor::m_Bias</a>, <a class="el" href="_descriptors_8hpp_source.xhtml#l00438">Convolution2dDescriptor::m_BiasEnabled</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00049">QueueDescriptorWithParameters&lt; LayerDescriptor &gt;::m_Parameters</a>, <a class="el" href="_workload_data_8hpp_source.xhtml#l00176">Convolution2dQueueDescriptor::m_Weight</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::QAsymmU8</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::QSymmS8</a>, <a class="el" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::Signed32</a>, and <a class="el" href="_workload_data_8cpp_source.xhtml#l01159">Convolution2dQueueDescriptor::Validate()</a>.</p>
+<div class="fragment"><div class="line"><a name="l00626"></a><span class="lineno">  626</span>&#160;{</div><div class="line"><a name="l00627"></a><span class="lineno">  627</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> nInput  = 1u;</div><div class="line"><a name="l00628"></a><span class="lineno">  628</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> cInput  = 3u;</div><div class="line"><a name="l00629"></a><span class="lineno">  629</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> hInput  = 3u;</div><div class="line"><a name="l00630"></a><span class="lineno">  630</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> wInput  = 3u;</div><div class="line"><a name="l00631"></a><span class="lineno">  631</span>&#160;</div><div class="line"><a name="l00632"></a><span class="lineno">  632</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> nOutput = nInput;</div><div class="line"><a name="l00633"></a><span class="lineno">  633</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> cOutput = cInput;</div><div class="line"><a name="l00634"></a><span class="lineno">  634</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> hOutput = 1u;</div><div class="line"><a name="l00635"></a><span class="lineno">  635</span>&#160;    constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> wOutput = 1u;</div><div class="line"><a name="l00636"></a><span class="lineno">  636</span>&#160;</div><div class="line"><a name="l00637"></a><span class="lineno">  637</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape { nInput,  cInput,  hInput,  wInput  };</div><div class="line"><a name="l00638"></a><span class="lineno">  638</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape{ nOutput, cOutput, hOutput, wOutput };</div><div class="line"><a name="l00639"></a><span class="lineno">  639</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> weightShape{ cOutput, cInput,  hInput,  wInput  };</div><div class="line"><a name="l00640"></a><span class="lineno">  640</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> biasShape  { cOutput                            };</div><div class="line"><a name="l00641"></a><span class="lineno">  641</span>&#160;</div><div class="line"><a name="l00642"></a><span class="lineno">  642</span>&#160;    constexpr <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> inputType  = DataType::QAsymmU8;</div><div class="line"><a name="l00643"></a><span class="lineno">  643</span>&#160;    constexpr <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> weightType = DataType::QSymmS8;</div><div class="line"><a name="l00644"></a><span class="lineno">  644</span>&#160;    constexpr <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> biasType   = DataType::Signed32;</div><div class="line"><a name="l00645"></a><span class="lineno">  645</span>&#160;</div><div class="line"><a name="l00646"></a><span class="lineno">  646</span>&#160;    constexpr <span class="keywordtype">float</span> perTensorScale = 1.5f;</div><div class="line"><a name="l00647"></a><span class="lineno">  647</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo (inputShape,  inputType, perTensorScale);</div><div class="line"><a name="l00648"></a><span class="lineno">  648</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(outputShape, inputType, perTensorScale);</div><div class="line"><a name="l00649"></a><span class="lineno">  649</span>&#160;</div><div class="line"><a name="l00650"></a><span class="lineno">  650</span>&#160;    <span class="keyword">const</span> std::vector&lt;float&gt; weightPerAxisScales = { 2.50f, 3.50f };</div><div class="line"><a name="l00651"></a><span class="lineno">  651</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> weightInfo(weightShape, weightType, weightPerAxisScales, 0);</div><div class="line"><a name="l00652"></a><span class="lineno">  652</span>&#160;</div><div class="line"><a name="l00653"></a><span class="lineno">  653</span>&#160;    <a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">Convolution2dQueueDescriptor</a> queueDescriptor;</div><div class="line"><a name="l00654"></a><span class="lineno">  654</span>&#160;    queueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00655"></a><span class="lineno">  655</span>&#160;</div><div class="line"><a name="l00656"></a><span class="lineno">  656</span>&#160;    <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l00657"></a><span class="lineno">  657</span>&#160;    AddInputToWorkload(queueDescriptor, workloadInfo, inputInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00658"></a><span class="lineno">  658</span>&#160;    AddOutputToWorkload(queueDescriptor, workloadInfo, outputInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00659"></a><span class="lineno">  659</span>&#160;</div><div class="line"><a name="l00660"></a><span class="lineno">  660</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> weightTensor(weightInfo);</div><div class="line"><a name="l00661"></a><span class="lineno">  661</span>&#160;    queueDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &amp;weightTensor;</div><div class="line"><a name="l00662"></a><span class="lineno">  662</span>&#160;</div><div class="line"><a name="l00663"></a><span class="lineno">  663</span>&#160;    <span class="comment">// Test 1: correct per-axis quantization values</span></div><div class="line"><a name="l00664"></a><span class="lineno">  664</span>&#160;    <span class="keyword">const</span> std::vector&lt;float&gt; biasPerAxisScales1  = { 3.75f, 5.25f };</div><div class="line"><a name="l00665"></a><span class="lineno">  665</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo1(biasShape, biasType, biasPerAxisScales1, 0);</div><div class="line"><a name="l00666"></a><span class="lineno">  666</span>&#160;</div><div class="line"><a name="l00667"></a><span class="lineno">  667</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> biasHandle1(biasInfo1);</div><div class="line"><a name="l00668"></a><span class="lineno">  668</span>&#160;    queueDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &amp;biasHandle1;</div><div class="line"><a name="l00669"></a><span class="lineno">  669</span>&#160;</div><div class="line"><a name="l00670"></a><span class="lineno">  670</span>&#160;    BOOST_CHECK_NO_THROW(queueDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">Validate</a>(workloadInfo));</div><div class="line"><a name="l00671"></a><span class="lineno">  671</span>&#160;</div><div class="line"><a name="l00672"></a><span class="lineno">  672</span>&#160;    <span class="comment">// Test 2: wrong per-axis quantization values</span></div><div class="line"><a name="l00673"></a><span class="lineno">  673</span>&#160;    <span class="keyword">const</span> std::vector&lt;float&gt; biasPerAxisScales2 = { 4.00f, 5.00f };</div><div class="line"><a name="l00674"></a><span class="lineno">  674</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo2(biasShape, biasType, biasPerAxisScales2, 0);</div><div class="line"><a name="l00675"></a><span class="lineno">  675</span>&#160;</div><div class="line"><a name="l00676"></a><span class="lineno">  676</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> biasHandle2(biasInfo2);</div><div class="line"><a name="l00677"></a><span class="lineno">  677</span>&#160;    queueDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &amp;biasHandle2;</div><div class="line"><a name="l00678"></a><span class="lineno">  678</span>&#160;</div><div class="line"><a name="l00679"></a><span class="lineno">  679</span>&#160;    BOOST_CHECK_THROW(queueDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">Validate</a>(workloadInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>);</div><div class="line"><a name="l00680"></a><span class="lineno">  680</span>&#160;</div><div class="line"><a name="l00681"></a><span class="lineno">  681</span>&#160;    <span class="comment">// Test 3: mismatched number of quantization scales</span></div><div class="line"><a name="l00682"></a><span class="lineno">  682</span>&#160;    <span class="keyword">const</span> std::vector&lt;float&gt; biasPerAxisScales3 = { 3.75f, 5.25f, 5.25f };</div><div class="line"><a name="l00683"></a><span class="lineno">  683</span>&#160;    <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo3(biasShape, biasType, biasPerAxisScales3, 0);</div><div class="line"><a name="l00684"></a><span class="lineno">  684</span>&#160;</div><div class="line"><a name="l00685"></a><span class="lineno">  685</span>&#160;    <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> biasHandle3(biasInfo3);</div><div class="line"><a name="l00686"></a><span class="lineno">  686</span>&#160;    queueDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &amp;biasHandle3;</div><div class="line"><a name="l00687"></a><span class="lineno">  687</span>&#160;</div><div class="line"><a name="l00688"></a><span class="lineno">  688</span>&#160;    BOOST_CHECK_THROW(queueDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">Validate</a>(workloadInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>);</div><div class="line"><a name="l00689"></a><span class="lineno">  689</span>&#160;}</div><div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::Convolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00438">Descriptors.hpp:438</a></div></div>
+<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml_ab3437cee6b0687812104fc1b37cbe8b3"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">armnn::Convolution2dQueueDescriptor::m_Bias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Bias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00177">WorkloadData.hpp:177</a></div></div>
+<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00053">Tensor.hpp:53</a></div></div>
+<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div>
+<div class="ttc" id="structarmnn_1_1_queue_descriptor_with_parameters_xhtml_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00049">WorkloadData.hpp:49</a></div></div>
+<div class="ttc" id="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_convolution2d_queue_descriptor_xhtml_a3369b66d9316a773a41711e3f590c041"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">armnn::Convolution2dQueueDescriptor::m_Weight</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Weight</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00176">WorkloadData.hpp:176</a></div></div>
+<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::Convolution2dQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &amp;workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01159">WorkloadData.cpp:1159</a></div></div>
+<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div>
+<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">armnn::Convolution2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00168">WorkloadData.hpp:168</a></div></div>
+<div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div>
+<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about inputs and outputs to a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
+</div><!-- 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_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="_workload_data_validation_8cpp.xhtml">WorkloadDataValidation.cpp</a></li>
+    <li class="footer">Generated on Fri Mar 13 2020 16:09:15 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>